From de2af162219ca5bebeff3816d46d447fba3b2a33 Mon Sep 17 00:00:00 2001 From: ckkelvinchan Date: Fri, 15 Oct 2021 22:28:57 +0800 Subject: [PATCH 1/3] translate --- demo/restorer_basic_tutorial.ipynb | 2 +- demo/restorer_basic_tutorial_zh-CN.ipynb | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 demo/restorer_basic_tutorial_zh-CN.ipynb diff --git a/demo/restorer_basic_tutorial.ipynb b/demo/restorer_basic_tutorial.ipynb index 2d0a212c50..9d308cb710 100644 --- a/demo/restorer_basic_tutorial.ipynb +++ b/demo/restorer_basic_tutorial.ipynb @@ -1 +1 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"name":"restorer_basic_tutorial.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3.7.7 64-bit ('pre-commit': conda)","name":"python377jvsc74a57bd04974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"},"language_info":{"name":"python","version":""},"metadata":{"interpreter":{"hash":"4974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"}}},"cells":[{"cell_type":"markdown","metadata":{"id":"T2WWQiheMF7q"},"source":["# MMEditing Basic Tutorial\n","\n","Welcome to MMEditing! This is the official Colab tutorial for MMEditing. In this tutorial you will learn how to train and test a restorer using the APIs provided in MMEditing. \n","\n","This is a quick guide for you to train and test existing models. If you want to develop you own models based on MMEditing and know more about the code structures, please refer to our comprehensive tutorial [here]().\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmedit/blob/main/demo/restorer_basic_tutorial.ipynb)\n","\n"]},{"cell_type":"markdown","metadata":{"id":"-kYw3WQ0MQry"},"source":["## Install MMEditing\n","\n","MMEditing can be installed in two steps:\n","\n","1. Install a compatible PyTorch version (You need to check you CUDA version by using `nvcc -V`).\n","2. Install pre-compiled MMCV\n","3. Clone and install MMEditing\n","\n","The steps are shown below:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uha_13idyl1b","executionInfo":{"status":"ok","timestamp":1625140540858,"user_tz":-480,"elapsed":321,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"87056561-5930-45b3-e1a8-f9e103d10b23"},"source":["# Check nvcc version\n","!nvcc -V\n","# Check GCC version (MMEditing needs gcc >= 5.0)\n","!gcc --version"],"execution_count":null,"outputs":[{"output_type":"stream","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Wed_Jul_22_19:09:09_PDT_2020\n","Cuda compilation tools, release 11.0, V11.0.221\n","Build cuda_11.0_bu.TC445_37.28845127_0\n","gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GIeIZEzZMfc0","executionInfo":{"status":"ok","timestamp":1625140820804,"user_tz":-480,"elapsed":279948,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"fe2e5ded-988d-4563-eb18-374344c316ef"},"source":["# Install dependencies: (use cu101 because colab has CUDA 11.0)\n","!pip install -U torch==1.7.0+cu110 torchvision==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html\n","\n","# Install mmcv-full thus we could use CUDA operators\n","!pip install mmcv-full==1.3.5 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html\n","\n","# Clone MMEditing\n","!rm -rf mmediting\n","!git clone https://github.com/open-mmlab/mmediting.git\n","%cd mmediting\n","\n","# Install MMEditing\n","!pip install -r requirements.txt\n","!pip install -v -e ."],"execution_count":null,"outputs":[{"output_type":"stream","text":["Looking in links: https://download.pytorch.org/whl/torch_stable.html\n","Collecting torch==1.7.0+cu110\n","\u001b[?25l Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl (1137.1MB)\n","\u001b[K 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yapf-0.31.0\n","Cloning into 'mmediting'...\n","remote: Enumerating objects: 7162, done.\u001b[K\n","remote: Counting objects: 100% (1367/1367), done.\u001b[K\n","remote: Compressing objects: 100% (793/793), done.\u001b[K\n","remote: Total 7162 (delta 827), reused 928 (delta 554), pack-reused 5795\u001b[K\n","Receiving objects: 100% (7162/7162), 5.02 MiB | 32.14 MiB/s, done.\n","Resolving deltas: 100% (4826/4826), done.\n","/content/mmediting\n","Requirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 1)) (0.99)\n","Requirement already satisfied: mmcv-full>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 2)) (1.3.5)\n","Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 3)) (0.16.2)\n","Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 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/tmp/pip-req-tracker-zk5q0q3z\n","Created requirements tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Created temporary directory: /tmp/pip-install-vr_vpseo\n","Obtaining file:///content/mmediting\n"," Added file:///content/mmediting to build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"," Running setup.py (path:/content/mmediting/setup.py) egg_info for package from file:///content/mmediting\n"," Running command python setup.py egg_info\n"," running egg_info\n"," creating mmedit.egg-info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 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already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.7.4.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (3.1.1)\n","Installing collected packages: mmedit\n"," Running setup.py develop for mmedit\n"," Running command /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmediting/setup.py'\"'\"'; __file__='\"'\"'/content/mmediting/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n"," running develop\n"," running egg_info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.7/dist-packages/mmedit.egg-link (link to .)\n"," Adding mmedit 0.8.0 to easy-install.pth file\n","\n"," Installed /content/mmediting\n","Successfully installed mmedit\n","Cleaning up...\n","Removed build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"QgX96Sc_3PcV"},"source":["## Download necessary material for this demo\n","We will need some data and configuration files in this demo. We will download it and put it in `./demo_files/`"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-K0zFSJ-3V42","executionInfo":{"status":"ok","timestamp":1625140825508,"user_tz":-480,"elapsed":4723,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"528a87f7-f78e-4219-84f3-dec19b88e88a"},"source":["!wget https://download.openmmlab.com/mmediting/demo_files.zip # download files\n","!unzip demo_files # unzip"],"execution_count":null,"outputs":[{"output_type":"stream","text":["--2021-07-01 11:59:48-- https://download.openmmlab.com/mmediting/demo_files.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19215781 (18M) [application/zip]\n","Saving to: ‘demo_files.zip’\n","\n","demo_files.zip 100%[===================>] 18.33M 6.00MB/s in 3.1s \n","\n","2021-07-01 11:59:52 (6.00 MB/s) - ‘demo_files.zip’ saved [19215781/19215781]\n","\n","Archive: demo_files.zip\n"," creating: demo_files/\n"," inflating: demo_files/demo_config_EDVR.py \n"," inflating: demo_files/demo_config_BasicVSR.py \n"," creating: demo_files/lq_sequences/\n"," creating: demo_files/lq_sequences/calendar/\n"," inflating: demo_files/lq_sequences/calendar/00000006.png \n"," inflating: demo_files/lq_sequences/calendar/00000007.png \n"," inflating: demo_files/lq_sequences/calendar/00000010.png \n"," inflating: demo_files/lq_sequences/calendar/00000004.png \n"," inflating: demo_files/lq_sequences/calendar/00000003.png \n"," inflating: demo_files/lq_sequences/calendar/00000001.png \n"," inflating: demo_files/lq_sequences/calendar/00000000.png \n"," inflating: demo_files/lq_sequences/calendar/00000009.png \n"," inflating: demo_files/lq_sequences/calendar/00000008.png \n"," inflating: demo_files/lq_sequences/calendar/00000002.png \n"," inflating: demo_files/lq_sequences/calendar/00000005.png \n"," creating: demo_files/lq_sequences/city/\n"," inflating: demo_files/lq_sequences/city/00000006.png \n"," inflating: demo_files/lq_sequences/city/00000007.png \n"," inflating: demo_files/lq_sequences/city/00000010.png \n"," inflating: demo_files/lq_sequences/city/00000004.png \n"," inflating: demo_files/lq_sequences/city/00000003.png \n"," inflating: demo_files/lq_sequences/city/00000001.png \n"," inflating: demo_files/lq_sequences/city/00000000.png \n"," inflating: demo_files/lq_sequences/city/00000009.png \n"," inflating: demo_files/lq_sequences/city/00000008.png \n"," inflating: demo_files/lq_sequences/city/00000002.png \n"," inflating: demo_files/lq_sequences/city/00000005.png \n"," creating: demo_files/lq_sequences/.ipynb_checkpoints/\n"," creating: demo_files/gt_images/\n"," inflating: demo_files/gt_images/bird.png \n"," inflating: demo_files/gt_images/woman.png \n"," inflating: demo_files/gt_images/head.png \n"," inflating: demo_files/gt_images/baby.png \n"," inflating: demo_files/gt_images/butterfly.png \n"," inflating: demo_files/demo_config_SRCNN.py \n"," creating: demo_files/lq_images/\n"," extracting: demo_files/lq_images/bird.png \n"," extracting: demo_files/lq_images/woman.png \n"," extracting: demo_files/lq_images/head.png \n"," extracting: demo_files/lq_images/baby.png \n"," extracting: demo_files/lq_images/butterfly.png \n"," creating: demo_files/gt_sequences/\n"," creating: demo_files/gt_sequences/calendar/\n"," inflating: demo_files/gt_sequences/calendar/00000006.png \n"," inflating: demo_files/gt_sequences/calendar/00000007.png \n"," inflating: demo_files/gt_sequences/calendar/00000010.png \n"," inflating: demo_files/gt_sequences/calendar/00000004.png \n"," inflating: demo_files/gt_sequences/calendar/00000003.png \n"," inflating: demo_files/gt_sequences/calendar/00000001.png \n"," inflating: demo_files/gt_sequences/calendar/00000000.png \n"," inflating: demo_files/gt_sequences/calendar/00000009.png \n"," inflating: demo_files/gt_sequences/calendar/00000008.png \n"," inflating: demo_files/gt_sequences/calendar/00000002.png \n"," inflating: demo_files/gt_sequences/calendar/00000005.png \n"," creating: demo_files/gt_sequences/city/\n"," inflating: demo_files/gt_sequences/city/00000006.png \n"," inflating: demo_files/gt_sequences/city/00000007.png \n"," inflating: demo_files/gt_sequences/city/00000010.png \n"," inflating: demo_files/gt_sequences/city/00000004.png \n"," inflating: demo_files/gt_sequences/city/00000003.png \n"," inflating: demo_files/gt_sequences/city/00000001.png \n"," inflating: demo_files/gt_sequences/city/00000000.png \n"," inflating: demo_files/gt_sequences/city/00000009.png \n"," inflating: demo_files/gt_sequences/city/00000008.png \n"," inflating: demo_files/gt_sequences/city/00000002.png \n"," inflating: demo_files/gt_sequences/city/00000005.png \n"," creating: demo_files/gt_sequences/.ipynb_checkpoints/\n"," creating: demo_files/.ipynb_checkpoints/\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"zXGurqGKOeNE"},"source":["## Inference with a pre-trained image restorer\n","You can easily perform inference on a single image with a pre-trained restorer by using `restoration_demo.py`. What you need are \n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer you want to use. It specifies the model you want to use. \n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model. \n","3. `IMAGE_FILE`: The path to the input image.\n","4. `SAVE_FILE`: The location where you want to store the output image.\n","5. `imshow`: Whether to show the image. (Optional)\n","6. `GPU_ID`: Which GPU you want to use. (Optional)\n","\n","Once you have all these details, you can directly use the following command:\n","\n","```\n","python demo/restoration_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} ${SAVE_FILE} [--imshow] [--device ${GPU_ID}]\n","```\n","\n","**Notes:** \n","1. Configuration files are located in `./configs`. \n","2. We support loading checkpoints from url. You can go to the corresponding page (e.g. [here](https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/esrgan)) to obtain the url of the pretrained model.\n","\n","---\n","\n","We will now use `SRCNN` and `ESRGAN` as examples.\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KiPvtvlqM1zb","executionInfo":{"status":"ok","timestamp":1625140884175,"user_tz":-480,"elapsed":58677,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"be7375a7-4632-4770-8383-2a8ce654b069"},"source":["# SRCNN\n","!python demo/restoration_demo.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth ./demo_files/lq_images/bird.png ./outputs/bird_SRCNN.png\n","\n","# ESRGAN\n","!python demo/restoration_demo.py ./configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth ./demo_files/lq_images/bird.png ./outputs/bird_ESRGAN.png\n","\n","# Check whether images are saved\n","!ls ./outputs"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\" to /root/.cache/torch/hub/checkpoints/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n","100% 83.9k/83.9k [00:00<00:00, 1.59MB/s]\n","2021-07-01 12:00:10,779 - mmedit - INFO - Use load_from_torchvision loader\n","Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n","100% 548M/548M [00:07<00:00, 76.0MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\" to /root/.cache/torch/hub/checkpoints/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n","100% 196M/196M [00:26<00:00, 7.61MB/s]\n","bird_ESRGAN.png bird_SRCNN.png\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"W1DfGHu3Xcfd"},"source":["## Inference with a pre-trained video restorer\n","\n","MMEditing also supports video super-resolution methods, and the procedure is similar. You can use `restoration_video_demo.py` with the following arguments:\n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer you want to use\n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model. \n","3. `INPUT_DIR`: The directory containing the video frames.\n","4. `OUTPUT_DIR`: The location where you want to store the output frames.\n","5. `WINDOW_SIZE`: Whether to show the image (Optional).\n","6. `GPU_ID`: Which GPU you want to use (Optional).\n","```\n","python demo/restoration_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} [--window_size=$WINDOW_SIZE] [--device ${GPU_ID}]\n","```\n","**Note:** There are two different frameworks in video super-resolution: ***sliding-window*** and ***recurrent*** frameworks. When you use the methods of the sliding-window framework, such as EDVR, you need to specify `window_size`. This value is dependent on the model you use.\n","\n","---\n","\n","We will now use `EDVR` and `BasicVSR` as examples.\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iaoE7UF5Xb2i","executionInfo":{"status":"ok","timestamp":1625140913405,"user_tz":-480,"elapsed":29263,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"a022e0bd-c47a-450e-f4e4-1bf9f92e4813"},"source":["# EDVR (Sliding-window framework)\n","!python demo/restoration_video_demo.py ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_EDVR --window_size=5\n","\n","# BasicVSR (Recurrent framework)\n","!python demo/restoration_video_demo.py ./configs/restorers/basicvsr/basicvsr_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_BasicVSR\n","\n","# Check whether video frames are saved\n","!ls ./outputs/calendar_BasicVSR"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\" to /root/.cache/torch/hub/checkpoints/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n","100% 11.5M/11.5M [00:01<00:00, 8.55MB/s]\n","2021-07-01 12:01:09,689 - mmedit - INFO - Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth\" to /root/.cache/torch/hub/checkpoints/spynet_20210409-c6c1bd09.pth\n","100% 5.50M/5.50M [00:00<00:00, 8.88MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\" to /root/.cache/torch/hub/checkpoints/basicvsr_reds4_20120409-0e599677.pth\n","100% 24.1M/24.1M [00:02<00:00, 8.97MB/s]\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Rf3LW57qMHXb"},"source":["## Test on a pre-defined dataset using the configuration file\n","\n","The above demos provide an easy way to perform inference on a single image or video sequence. If you want to perform inference on a set of images or sequences, you can make use of the configuration files located in `./configs`.\n"," \n","Existing configuration files allow you to perform inference on common datasets, such as `Set5` in image super-resolution and `REDS4` in video super-resolution. You can use the following command:\n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer and dataset you want to use\n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model.\n","3. `GPU_NUM`: Number of GPUs used for test. \n","4. `RESULT_FILE`: The path to the output result pickle file. (Optional)\n","5. `IMAGE_SAVE_PATH`: The location where you want to store the output image. (Optional)\n","\n","```\n","# single-gpu testing\n","python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","\n","# multi-gpu testing\n","./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","```\n","What you need to do is to modify the `lq_folder` and `gt_folder` in the configuration file:\n","```\n","test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/val_set5/Set5_bicLRx4',\n"," gt_folder='data/val_set5/Set5',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","```\n","\n","**Note**: Some dataset type (e.g. `SRREDSDataset`) requires an annotation file specifying the details of the dataset. Please refer to the corresponding file\n","in `./mmedit/dataset/` for more details. \n","\n","---\n","\n","The following is the command for SRCNN. For other models, you can simply change the paths to the configuration file and pretrained model. \n"]},{"cell_type":"code","metadata":{"id":"tClgIYgcbbVg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140927462,"user_tz":-480,"elapsed":14095,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"c37ec2de-e1c9-42ae-ed9b-31009d48ae64"},"source":["# single-gpu\n","!python tools/test.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/\n","\n","# multi-gpu testing\n","!./tools/dist_test.sh ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 1 --save-path ./outputs/"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"tools/test.py\", line 136, in \n"," main()\n"," File \"tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/test.py\", line 136, in \n"," main()\n"," File \"./tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/test.py', '--local_rank=0', './configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py', 'https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', '--launcher', 'pytorch', '--save-path', './outputs/']' returned non-zero exit status 1.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"KWKVyeEQelh3"},"source":["## Test on your own datasets\n","\n","When you want to test on your own datasets, you need to modify `test_dataset_type` in addition to the dataset paths. \n","\n","- For image super-resolution, you need to use `SRFolderDataset`\n","- For sliding-window framework in video super-resolution (e.g. EDVR, TDAN), you need to use `SRFolderVideoDataset`.\n","- For recurrent framework in video super-resolution (e.g. BasicVSR, IconVSR), you need to use `SRFolderMultipleGTDataset`.\n","\n","These dataset types assume that all images/sequences in the specified directory are used for test. The folder structures should be\n","```\n","| lq_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," | ...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","| gt_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," |...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","```\n","We will use **SRCNN**, **EDVR**, **BasicVSR** as examples. Please pay attention to the settings of `test_dataset_type` and `data['test']`. "]},{"cell_type":"markdown","metadata":{"id":"0p2rP8jV_dL1"},"source":["**SRCNN**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4kEev4wVIq_L","executionInfo":{"status":"ok","timestamp":1625140936180,"user_tz":-480,"elapsed":8729,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"8026ed73-f781-4eb2-bb80-d3446bd131df"},"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_SRCNN.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/testset_SRCNN\n","\n","# Check the output folder\n","!ls ./outputs/testset_SRCNN"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Use load_from_http loader\n","[>>] 5/5, 8.6 task/s, elapsed: 1s, ETA: 0s\n","Eval-PSNR: 28.433974369836108\n","Eval-SSIM: 0.8099053586583066\n","baby.png bird.png butterfly.png head.png woman.png\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"RONzjTTU_gem"},"source":["**EDVR**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vL8WOWXY0fNJ","executionInfo":{"status":"ok","timestamp":1625140955813,"user_tz":-480,"elapsed":19671,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"dc2a8f81-9bef-4ad4-c5b2-c6f124e6b113"},"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_EDVR.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth --save-path ./outputs/testset_EDVR\n","\n","# # Check the output folder\n","!ls ./outputs/testset_EDVR\n","!ls ./outputs/testset_EDVR/city"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Use load_from_http loader\n","[>>] 22/22, 2.0 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 23.89569862011228\n","Eval-SSIM: 0.7667098470108678\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5Tc7F-l5_i1e"},"source":["**BasicVSR**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jpW5GWC74Yvu","executionInfo":{"status":"ok","timestamp":1625140976026,"user_tz":-480,"elapsed":20220,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"7ba02a32-d4ec-40b2-8108-ef0729b62147"},"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_BasicVSR.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth --save-path ./outputs/testset_BasicVSR\n","\n","# # Check the output folder\n","!ls ./outputs/testset_BasicVSR\n","!ls ./outputs/testset_BasicVSR/calendar"],"execution_count":null,"outputs":[{"output_type":"stream","text":["2021-07-01 12:02:07,780 - mmedit - INFO - Use load_from_http loader\n","Use load_from_http loader\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","[>>] 2/2, 0.2 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 24.195768601433734\n","Eval-SSIM: 0.7828541339512978\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4DQxNL8BhI0y"},"source":["## Train a restorer on a pre-defined dataset\n","\n","MMEditing uses distributed training. The following command can be used for training. If you want to train on the pre-defined datasets specified in our configuration file, you can simply run the following command.\n","\n","```\n","./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n","```\n","\n","For more details about the optional arguments, please refer to `tools/train.py`.\n","\n","---\n","\n","Here is an example using EDVR.\n"]},{"cell_type":"code","metadata":{"id":"s-hOnSF6ItQM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140985357,"user_tz":-480,"elapsed":9337,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"e40e960a-29e1-43e8-b922-5e08c4e98afe"},"source":["!./tools/dist_train.sh ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py 1"],"execution_count":null,"outputs":[{"output_type":"stream","text":["2021-07-01 12:02:31,961 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:31,961 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:31,961 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:31,961 - mmedit - INFO - Config:\n","/content/mmediting/configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py\n","exp_name = 'edvrm_wotsa_x4_g8_600k_reds'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRREDSDataset'\n","val_dataset_type = 'SRREDSDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 600000\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50000, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=100,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 54, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 63, in load_annotations\n"," with open(self.ann_file, 'r') as fin:\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/train.py\", line 145, in \n"," main()\n"," File \"./tools/train.py\", line 111, in main\n"," datasets = [build_dataset(cfg.data.train)]\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 76, in build_dataset\n"," build_dataset(cfg['dataset'], default_args), cfg['times'])\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRREDSDataset: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/train.py', '--local_rank=0', './configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py', '--launcher', 'pytorch']' returned non-zero exit status 1.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"b0VfQkQQjg8N"},"source":["## Train a restorer on your own datasets\n","\n","Similar to the case when you want to test on your own datasets, you need to modify `train_dataset_type`. The dataset type you need is identical:\n","\n","- For image super-resolution, you need to use `SRFolderDataset`\n","- For sliding-window framework in video super-resolution (e.g. EDVR, TDAN), you need to use `SRFolderVideoDataset`.\n","- For recurrent framework in video super-resolution (e.g. BasicVSR, IconVSR), you need to use `SRFolderMultipleGTDataset`.\n","\n","After you modified the dataset type and the data path. You are all set to go."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"liGEKJpbIoXZ","executionInfo":{"status":"ok","timestamp":1625141113733,"user_tz":-480,"elapsed":128384,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"84e1502e-e2cb-458f-c7c5-e4b401e570b7"},"source":["# SRCNN (Single Image Super-Resolution)\n","!./tools/dist_train.sh ./demo_files/demo_config_SRCNN.py 1 "],"execution_count":null,"outputs":[{"output_type":"stream","text":["2021-07-01 12:02:41,185 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:41,185 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:41,185 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:41,185 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_SRCNN.py\n","exp_name = 'srcnn_demo'\n","\n","scale = 4\n","# model settings\n","model = dict(\n"," type='BasicRestorer',\n"," generator=dict(\n"," type='SRCNN',\n"," channels=(3, 64, 32, 3),\n"," kernel_sizes=(9, 1, 5),\n"," upscale_factor=scale),\n"," pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderDataset'\n","val_dataset_type = 'SRFolderDataset'\n","train_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=128),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","test_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=8,\n"," train_dataloader=dict(samples_per_gpu=16, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=train_pipeline,\n"," scale=scale)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[250000, 250000, 250000, 250000],\n"," restart_weights=[1, 1, 1, 1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","evaluation = dict(interval=50, save_image=True, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./experiments/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:02:41,192 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/experiments/srcnn_demo\n","2021-07-01 12:02:41,192 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:02:41.529307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:03:18,631 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.000e-04, eta: 0:57:01, time: 34.560, data_time: 34.446, memory: 586, loss_pix: 0.3999, loss: 0.3999\n","2021-07-01 12:03:18,712 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","INFO:mmedit:Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","2021-07-01 12:03:18,764 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","INFO:mmedit:Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","2021-07-01 12:03:18,840 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","INFO:mmedit:Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","2021-07-01 12:03:18,916 - mmedit - INFO - Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","INFO:mmedit:Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","2021-07-01 12:03:18,956 - mmedit - INFO - Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","INFO:mmedit:Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","2021-07-01 12:03:19,012 - mmedit - INFO - Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","INFO:mmedit:Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","2021-07-01 12:03:19,070 - mmedit - INFO - Iter [8/100]\tlr_generator: 2.000e-04, eta: 0:06:42, time: 0.057, data_time: 0.016, memory: 586, loss_pix: 0.3766, loss: 0.3766\n","INFO:mmedit:Iter [8/100]\tlr_generator: 2.000e-04, eta: 0:06:42, time: 0.057, data_time: 0.016, memory: 586, loss_pix: 0.3766, loss: 0.3766\n","2021-07-01 12:03:19,142 - mmedit - INFO - Iter [9/100]\tlr_generator: 2.000e-04, eta: 0:05:54, time: 0.064, data_time: 0.026, memory: 586, loss_pix: 0.3721, loss: 0.3721\n","INFO:mmedit:Iter [9/100]\tlr_generator: 2.000e-04, eta: 0:05:54, time: 0.064, data_time: 0.026, memory: 586, loss_pix: 0.3721, loss: 0.3721\n","2021-07-01 12:03:19,212 - mmedit - INFO - Iter [10/100]\tlr_generator: 2.000e-04, eta: 0:05:16, time: 0.079, data_time: 0.027, memory: 586, loss_pix: 0.3314, loss: 0.3314\n","INFO:mmedit:Iter [10/100]\tlr_generator: 2.000e-04, eta: 0:05:16, time: 0.079, data_time: 0.027, memory: 586, loss_pix: 0.3314, loss: 0.3314\n","2021-07-01 12:03:19,261 - mmedit - INFO - Iter [11/100]\tlr_generator: 2.000e-04, eta: 0:04:44, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.3249, loss: 0.3249\n","INFO:mmedit:Iter [11/100]\tlr_generator: 2.000e-04, eta: 0:04:44, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.3249, loss: 0.3249\n","2021-07-01 12:03:19,302 - 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mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:04:37,422 - mmedit - INFO - Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n","INFO:mmedit:Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"26uZ4Ak7qbC9","executionInfo":{"status":"ok","timestamp":1625141554036,"user_tz":-480,"elapsed":117937,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"2fb26527-eb9e-4b48-e03c-ba9a91c60db8"},"source":["# EDVR (Video Super-Resolution - Sliding Window)\n","!./tools/dist_train.sh ./demo_files/demo_config_EDVR.py 1 "],"execution_count":null,"outputs":[{"output_type":"stream","text":["2021-07-01 12:10:12,619 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:10:12,619 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:10:12,619 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:10:12,619 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_EDVR.py\n","exp_name = 'edvrm_demo'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderVideoDataset'\n","val_dataset_type = 'SRFolderVideoDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:10:12,701 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/edvrm_demo\n","2021-07-01 12:10:12,702 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:10:12.951771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:10:30,703 - mmedit - INFO - Iter [1/100]\tlr_generator: 4.000e-04, eta: 0:26:53, time: 16.295, data_time: 15.833, memory: 1341, loss_pix: 63917.2734, loss: 63917.2734\n","2021-07-01 12:10:31,046 - mmedit - INFO - Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","INFO:mmedit:Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","2021-07-01 12:10:31,386 - mmedit - INFO - Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","INFO:mmedit:Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","2021-07-01 12:10:31,731 - 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Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"b951b426-e06c-4f31-db01-449333eab333"},"source":["# BasicVSR (Video Super-Resolution - Recurrent)\n","!./tools/dist_train.sh ./demo_files/demo_config_BasicVSR.py 1 "],"execution_count":null,"outputs":[{"output_type":"stream","text":["2021-07-01 12:06:47,253 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:06:47,253 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:06:47,254 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:06:47,254 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_BasicVSR.py\n","exp_name = 'basicvsr_demo'\n","\n","# model settings\n","model = dict(\n"," type='BasicVSR',\n"," generator=dict(\n"," type='BasicVSRNet',\n"," mid_channels=64,\n"," num_blocks=30,\n"," spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n"," 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = dict(fix_iter=5000)\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderMultipleGTDataset'\n","val_dataset_type = 'SRFolderMultipleGTDataset'\n","\n","train_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=6,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True), # 2 gpus\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),\n","\n"," # train\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," # val\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," # test\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(\n"," generator=dict(\n"," type='Adam',\n"," lr=2e-4,\n"," betas=(0.9, 0.99),\n"," paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[300000],\n"," restart_weights=[1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," # dict(type='TensorboardLoggerHook'),\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","find_unused_parameters = True\n","\n","2021-07-01 12:06:47,291 - mmedit - INFO - Use load_from_http loader\n","2021-07-01 12:06:47,569 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/basicvsr_demo\n","2021-07-01 12:06:47,569 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:07:14,210 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.500e-05, eta: 0:42:52, time: 25.981, data_time: 24.045, memory: 3464, loss_pix: 0.0634, loss: 0.0634\n","2021-07-01 12:07:15,171 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.500e-05, eta: 0:22:00, time: 0.961, data_time: 0.011, memory: 3518, loss_pix: 0.0556, loss: 0.0556\n","2021-07-01 12:07:16,052 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.500e-05, eta: 0:14:59, time: 0.881, data_time: 0.003, memory: 3518, loss_pix: 0.0476, loss: 0.0476\n","2021-07-01 12:07:16,940 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.500e-05, eta: 0:11:29, time: 0.888, data_time: 0.003, memory: 3518, loss_pix: 0.0673, loss: 0.0673\n","2021-07-01 12:07:17,829 - 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mmedit - INFO - Iter(val) [100]\tPSNR: 21.4372, SSIM: 0.5687\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"QT0zwBFt7J13"},"source":["**This is the end of this tutorial. For more advanced usage, please see our comprehensive tutorial [here](). Enjoy coding with MMEditing!**"]}]} +{"nbformat":4,"nbformat_minor":2,"metadata":{"accelerator":"GPU","colab":{"name":"restorer_basic_tutorial.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3.7.7 64-bit ('pre-commit': conda)","name":"python377jvsc74a57bd04974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"},"language_info":{"name":"python","version":""},"metadata":{"interpreter":{"hash":"4974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"}}},"cells":[{"cell_type":"markdown","source":["# MMEditing Basic Tutorial\n","\n","Welcome to MMEditing! This is the official Colab tutorial for MMEditing. In this tutorial you will learn how to train and test a restorer using the APIs provided in MMEditing. \n","\n","This is a quick guide for you to train and test existing models. If you want to develop you own models based on MMEditing and know more about the code structures, please refer to our comprehensive tutorial [here]().\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmedit/blob/main/demo/restorer_basic_tutorial.ipynb)\n","\n"],"metadata":{"id":"T2WWQiheMF7q"}},{"cell_type":"markdown","source":["## Install MMEditing\n","\n","MMEditing can be installed in two steps:\n","\n","1. Install a compatible PyTorch version (You need to check you CUDA version by using `nvcc -V`).\n","2. Install pre-compiled MMCV\n","3. Clone and install MMEditing\n","\n","The steps are shown below:"],"metadata":{"id":"-kYw3WQ0MQry"}},{"cell_type":"code","execution_count":null,"source":["# Check nvcc version\n","!nvcc -V\n","# Check GCC version (MMEditing needs gcc >= 5.0)\n","!gcc --version"],"outputs":[{"output_type":"stream","name":"stdout","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Wed_Jul_22_19:09:09_PDT_2020\n","Cuda compilation tools, release 11.0, V11.0.221\n","Build cuda_11.0_bu.TC445_37.28845127_0\n","gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uha_13idyl1b","executionInfo":{"status":"ok","timestamp":1625140540858,"user_tz":-480,"elapsed":321,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"87056561-5930-45b3-e1a8-f9e103d10b23"}},{"cell_type":"code","execution_count":null,"source":["# Install dependencies: (use cu101 because colab has CUDA 11.0)\n","!pip install -U torch==1.7.0+cu110 torchvision==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html\n","\n","# Install mmcv-full thus we could use CUDA operators\n","!pip install mmcv-full==1.3.5 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html\n","\n","# Clone MMEditing\n","!rm -rf mmediting\n","!git clone https://github.com/open-mmlab/mmediting.git\n","%cd mmediting\n","\n","# Install MMEditing\n","!pip install -r requirements.txt\n","!pip install -v -e ."],"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in links: https://download.pytorch.org/whl/torch_stable.html\n","Collecting torch==1.7.0+cu110\n","\u001b[?25l Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl 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0x564553632853 0x5645536b4e36 0x5645537373e1 0x5645536976a9 0x564553602cc4 0x5645535c3559 0x5645536374f8 0x5645535c430a 0x5645536323b5 0x5645536317ad 0x5645535c43ea 0x5645536323b5 0x5645535c430a 0x5645536323b5\n","\u001b[K |████████████████████████████████| 1137.1MB 1.1MB/s eta 0:00:01tcmalloc: large alloc 1421369344 bytes == 0x564626ebc000 @ 0x7fce190c6615 0x5645535bfcdc 0x56455369f52a 0x5645535c2afd 0x5645536b3fed 0x564553636988 0x5645536314ae 0x5645535c43ea 0x56455363260e 0x5645536314ae 0x5645535c43ea 0x56455363260e 0x5645536314ae 0x5645535c43ea 0x56455363260e 0x5645536314ae 0x5645535c43ea 0x56455363260e 0x5645536314ae 0x5645535c43ea 0x56455363260e 0x5645535c430a 0x56455363260e 0x5645536314ae 0x5645535c43ea 0x56455363332a 0x5645536314ae 0x5645535c43ea 0x56455363332a 0x5645536314ae 0x5645535c4a81\n","\u001b[K |████████████████████████████████| 1137.1MB 16kB/s \n","\u001b[?25hCollecting torchvision==0.8.0\n","\u001b[?25l Downloading 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(7.1.2)\n","\u001b[31mERROR: torchtext 0.10.0 has requirement torch==1.9.0, but you'll have torch 1.7.0+cu110 which is incompatible.\u001b[0m\n","Installing collected packages: dataclasses, torch, torchvision\n"," Found existing installation: torch 1.9.0+cu102\n"," Uninstalling torch-1.9.0+cu102:\n"," Successfully uninstalled torch-1.9.0+cu102\n"," Found existing installation: torchvision 0.10.0+cu102\n"," Uninstalling torchvision-0.10.0+cu102:\n"," Successfully uninstalled torchvision-0.10.0+cu102\n","Successfully installed dataclasses-0.6 torch-1.7.0+cu110 torchvision-0.8.0\n","Looking in links: https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html\n","Collecting mmcv-full==1.3.5\n","\u001b[?25l Downloading https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/mmcv_full-1.3.5-cp37-cp37m-manylinux1_x86_64.whl (31.1MB)\n","\u001b[K |████████████████████████████████| 31.1MB 107kB/s \n","\u001b[?25hRequirement already satisfied: numpy in 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yapf-0.31.0\n","Cloning into 'mmediting'...\n","remote: Enumerating objects: 7162, done.\u001b[K\n","remote: Counting objects: 100% (1367/1367), done.\u001b[K\n","remote: Compressing objects: 100% (793/793), done.\u001b[K\n","remote: Total 7162 (delta 827), reused 928 (delta 554), pack-reused 5795\u001b[K\n","Receiving objects: 100% (7162/7162), 5.02 MiB | 32.14 MiB/s, done.\n","Resolving deltas: 100% (4826/4826), done.\n","/content/mmediting\n","Requirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 1)) (0.99)\n","Requirement already satisfied: mmcv-full>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 2)) (1.3.5)\n","Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 3)) (0.16.2)\n","Requirement already satisfied: tensorboard in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 4)) (2.5.0)\n","Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 5)) (0.31.0)\n","Collecting codecov\n"," Downloading https://files.pythonhosted.org/packages/93/9f/bbea5b6231308458963cb5c067bc5643da9949689702fa5a382714b59699/codecov-2.1.11-py2.py3-none-any.whl\n","Collecting flake8\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/fc/80/35a0716e5d5101e643404dabd20f07f5528a21f3ef4032d31a49c913237b/flake8-3.9.2-py2.py3-none-any.whl (73kB)\n","\u001b[K |████████████████████████████████| 81kB 9.7MB/s \n","\u001b[?25hCollecting interrogate\n"," Downloading https://files.pythonhosted.org/packages/cd/6d/ce3ac440b13c1b36b323a0eab191499a902adade3cc11b18078c07af3e6e/interrogate-1.4.0-py3-none-any.whl\n","Collecting isort==4.3.21\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/e5/b0/c121fd1fa3419ea9bfd55c7f9c4fedfec5143208d8c7ad3ce3db6c623c21/isort-4.3.21-py2.py3-none-any.whl 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requirements/runtime.txt (line 2)) (7.1.2)\n","Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (2.4.0)\n","Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (4.1.2.30)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (3.13)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.5.1)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.4.1)\n","Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) 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requirements/runtime.txt (line 4)) (1.8.0)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (2.23.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.4.4)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (57.0.0)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.12.0)\n","Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.12.4)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.3.4)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.31.0)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.0.1)\n","Requirement already satisfied: coverage in /usr/local/lib/python3.7/dist-packages (from codecov->-r requirements/tests.txt (line 1)) (3.7.1)\n","Collecting pyflakes<2.4.0,>=2.3.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6c/11/2a745612f1d3cbbd9c69ba14b1b43a35a2f5c3c81cd0124508c52c64307f/pyflakes-2.3.1-py2.py3-none-any.whl (68kB)\n","\u001b[K |████████████████████████████████| 71kB 9.8MB/s \n","\u001b[?25hCollecting pycodestyle<2.8.0,>=2.7.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/cc/227251b1471f129bc35e966bb0fceb005969023926d744139642d847b7ae/pycodestyle-2.7.0-py2.py3-none-any.whl (41kB)\n","\u001b[K |████████████████████████████████| 51kB 8.7MB/s \n","\u001b[?25hCollecting mccabe<0.7.0,>=0.6.0\n"," Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from flake8->-r requirements/tests.txt (line 2)) (4.5.0)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (0.8.9)\n","Collecting colorama\n"," Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (7.1.2)\n","Requirement already satisfied: toml in 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/tmp/pip-req-tracker-zk5q0q3z\n","Created requirements tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Created temporary directory: /tmp/pip-install-vr_vpseo\n","Obtaining file:///content/mmediting\n"," Added file:///content/mmediting to build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"," Running setup.py (path:/content/mmediting/setup.py) egg_info for package from file:///content/mmediting\n"," Running command python setup.py egg_info\n"," running egg_info\n"," creating mmedit.egg-info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," Source in /content/mmediting has version 0.8.0, which satisfies requirement mmedit==0.8.0 from file:///content/mmediting\n"," Removed mmedit==0.8.0 from file:///content/mmediting from build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Requirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (0.99)\n","Requirement already satisfied: mmcv-full>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (1.3.5)\n","Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (0.16.2)\n","Requirement already satisfied: tensorboard in 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already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.7.4.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (3.1.1)\n","Installing collected packages: mmedit\n"," Running setup.py develop for mmedit\n"," Running command /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmediting/setup.py'\"'\"'; __file__='\"'\"'/content/mmediting/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n"," running develop\n"," running egg_info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.7/dist-packages/mmedit.egg-link (link to .)\n"," Adding mmedit 0.8.0 to easy-install.pth file\n","\n"," Installed /content/mmediting\n","Successfully installed mmedit\n","Cleaning up...\n","Removed build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GIeIZEzZMfc0","executionInfo":{"status":"ok","timestamp":1625140820804,"user_tz":-480,"elapsed":279948,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"fe2e5ded-988d-4563-eb18-374344c316ef"}},{"cell_type":"markdown","source":["## Download necessary material for this demo\n","We will need some data and configuration files in this demo. We will download it and put it in `./demo_files/`"],"metadata":{"id":"QgX96Sc_3PcV"}},{"cell_type":"code","execution_count":null,"source":["!wget https://download.openmmlab.com/mmediting/demo_files.zip # download files\n","!unzip demo_files # unzip"],"outputs":[{"output_type":"stream","name":"stdout","text":["--2021-07-01 11:59:48-- https://download.openmmlab.com/mmediting/demo_files.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19215781 (18M) [application/zip]\n","Saving to: ‘demo_files.zip’\n","\n","demo_files.zip 100%[===================>] 18.33M 6.00MB/s in 3.1s \n","\n","2021-07-01 11:59:52 (6.00 MB/s) - ‘demo_files.zip’ saved [19215781/19215781]\n","\n","Archive: demo_files.zip\n"," creating: demo_files/\n"," inflating: demo_files/demo_config_EDVR.py \n"," inflating: demo_files/demo_config_BasicVSR.py \n"," creating: demo_files/lq_sequences/\n"," creating: demo_files/lq_sequences/calendar/\n"," inflating: demo_files/lq_sequences/calendar/00000006.png \n"," inflating: demo_files/lq_sequences/calendar/00000007.png \n"," inflating: demo_files/lq_sequences/calendar/00000010.png \n"," inflating: demo_files/lq_sequences/calendar/00000004.png \n"," inflating: demo_files/lq_sequences/calendar/00000003.png \n"," inflating: demo_files/lq_sequences/calendar/00000001.png \n"," inflating: demo_files/lq_sequences/calendar/00000000.png \n"," inflating: demo_files/lq_sequences/calendar/00000009.png \n"," inflating: demo_files/lq_sequences/calendar/00000008.png \n"," inflating: demo_files/lq_sequences/calendar/00000002.png \n"," inflating: demo_files/lq_sequences/calendar/00000005.png \n"," creating: demo_files/lq_sequences/city/\n"," inflating: demo_files/lq_sequences/city/00000006.png \n"," inflating: demo_files/lq_sequences/city/00000007.png \n"," inflating: demo_files/lq_sequences/city/00000010.png \n"," inflating: demo_files/lq_sequences/city/00000004.png \n"," inflating: demo_files/lq_sequences/city/00000003.png \n"," inflating: demo_files/lq_sequences/city/00000001.png \n"," inflating: demo_files/lq_sequences/city/00000000.png \n"," inflating: demo_files/lq_sequences/city/00000009.png \n"," inflating: demo_files/lq_sequences/city/00000008.png \n"," inflating: demo_files/lq_sequences/city/00000002.png \n"," inflating: demo_files/lq_sequences/city/00000005.png \n"," creating: demo_files/lq_sequences/.ipynb_checkpoints/\n"," creating: demo_files/gt_images/\n"," inflating: demo_files/gt_images/bird.png \n"," inflating: demo_files/gt_images/woman.png \n"," inflating: demo_files/gt_images/head.png \n"," inflating: demo_files/gt_images/baby.png \n"," inflating: demo_files/gt_images/butterfly.png \n"," inflating: demo_files/demo_config_SRCNN.py \n"," creating: demo_files/lq_images/\n"," extracting: demo_files/lq_images/bird.png \n"," extracting: demo_files/lq_images/woman.png \n"," extracting: demo_files/lq_images/head.png \n"," extracting: demo_files/lq_images/baby.png \n"," extracting: demo_files/lq_images/butterfly.png \n"," creating: demo_files/gt_sequences/\n"," creating: demo_files/gt_sequences/calendar/\n"," inflating: demo_files/gt_sequences/calendar/00000006.png \n"," inflating: demo_files/gt_sequences/calendar/00000007.png \n"," inflating: demo_files/gt_sequences/calendar/00000010.png \n"," inflating: demo_files/gt_sequences/calendar/00000004.png \n"," inflating: demo_files/gt_sequences/calendar/00000003.png \n"," inflating: demo_files/gt_sequences/calendar/00000001.png \n"," inflating: demo_files/gt_sequences/calendar/00000000.png \n"," inflating: demo_files/gt_sequences/calendar/00000009.png \n"," inflating: demo_files/gt_sequences/calendar/00000008.png \n"," inflating: demo_files/gt_sequences/calendar/00000002.png \n"," inflating: demo_files/gt_sequences/calendar/00000005.png \n"," creating: demo_files/gt_sequences/city/\n"," inflating: demo_files/gt_sequences/city/00000006.png \n"," inflating: demo_files/gt_sequences/city/00000007.png \n"," inflating: demo_files/gt_sequences/city/00000010.png \n"," inflating: demo_files/gt_sequences/city/00000004.png \n"," inflating: demo_files/gt_sequences/city/00000003.png \n"," inflating: demo_files/gt_sequences/city/00000001.png \n"," inflating: demo_files/gt_sequences/city/00000000.png \n"," inflating: demo_files/gt_sequences/city/00000009.png \n"," inflating: demo_files/gt_sequences/city/00000008.png \n"," inflating: demo_files/gt_sequences/city/00000002.png \n"," inflating: demo_files/gt_sequences/city/00000005.png \n"," creating: demo_files/gt_sequences/.ipynb_checkpoints/\n"," creating: demo_files/.ipynb_checkpoints/\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-K0zFSJ-3V42","executionInfo":{"status":"ok","timestamp":1625140825508,"user_tz":-480,"elapsed":4723,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"528a87f7-f78e-4219-84f3-dec19b88e88a"}},{"cell_type":"markdown","source":["## Inference with a pre-trained image restorer\n","You can easily perform inference on a single image with a pre-trained restorer by using `restoration_demo.py`. What you need are \n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer you want to use. It specifies the model you want to use. \n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model. \n","3. `IMAGE_FILE`: The path to the input image.\n","4. `SAVE_FILE`: The location where you want to store the output image.\n","5. `imshow`: Whether to show the image. (Optional)\n","6. `GPU_ID`: Which GPU you want to use. (Optional)\n","\n","Once you have all these details, you can directly use the following command:\n","\n","```\n","python demo/restoration_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} ${SAVE_FILE} [--imshow] [--device ${GPU_ID}]\n","```\n","\n","**Notes:** \n","1. Configuration files are located in `./configs`. \n","2. We support loading checkpoints from url. You can go to the corresponding page (e.g. [here](https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/esrgan)) to obtain the url of the pretrained model.\n","\n","---\n","\n","We will now use `SRCNN` and `ESRGAN` as examples.\n","\n"],"metadata":{"id":"zXGurqGKOeNE"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN\n","!python demo/restoration_demo.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth ./demo_files/lq_images/bird.png ./outputs/bird_SRCNN.png\n","\n","# ESRGAN\n","!python demo/restoration_demo.py ./configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth ./demo_files/lq_images/bird.png ./outputs/bird_ESRGAN.png\n","\n","# Check whether images are saved\n","!ls ./outputs"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\" to /root/.cache/torch/hub/checkpoints/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n","100% 83.9k/83.9k [00:00<00:00, 1.59MB/s]\n","2021-07-01 12:00:10,779 - mmedit - INFO - Use load_from_torchvision loader\n","Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n","100% 548M/548M [00:07<00:00, 76.0MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\" to /root/.cache/torch/hub/checkpoints/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n","100% 196M/196M [00:26<00:00, 7.61MB/s]\n","bird_ESRGAN.png bird_SRCNN.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KiPvtvlqM1zb","executionInfo":{"status":"ok","timestamp":1625140884175,"user_tz":-480,"elapsed":58677,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"be7375a7-4632-4770-8383-2a8ce654b069"}},{"cell_type":"markdown","source":["## Inference with a pre-trained video restorer\n","\n","MMEditing also supports video super-resolution methods, and the procedure is similar. You can use `restoration_video_demo.py` with the following arguments:\n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer you want to use\n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model. \n","3. `INPUT_DIR`: The directory containing the video frames.\n","4. `OUTPUT_DIR`: The location where you want to store the output frames.\n","5. `WINDOW_SIZE`: The window size if you are using sliding-window method (Optional).\n","6. `GPU_ID`: Which GPU you want to use (Optional).\n","```\n","python demo/restoration_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} [--window_size=$WINDOW_SIZE] [--device ${GPU_ID}]\n","```\n","**Note:** There are two different frameworks in video super-resolution: ***sliding-window*** and ***recurrent*** frameworks. When you use the methods of the sliding-window framework, such as EDVR, you need to specify `window_size`. This value is dependent on the model you use.\n","\n","---\n","\n","We will now use `EDVR` and `BasicVSR` as examples.\n","\n"],"metadata":{"id":"W1DfGHu3Xcfd"}},{"cell_type":"code","execution_count":null,"source":["# EDVR (Sliding-window framework)\n","!python demo/restoration_video_demo.py ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_EDVR --window_size=5\n","\n","# BasicVSR (Recurrent framework)\n","!python demo/restoration_video_demo.py ./configs/restorers/basicvsr/basicvsr_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_BasicVSR\n","\n","# Check whether video frames are saved\n","!ls ./outputs/calendar_BasicVSR"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\" to /root/.cache/torch/hub/checkpoints/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n","100% 11.5M/11.5M [00:01<00:00, 8.55MB/s]\n","2021-07-01 12:01:09,689 - mmedit - INFO - Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth\" to /root/.cache/torch/hub/checkpoints/spynet_20210409-c6c1bd09.pth\n","100% 5.50M/5.50M [00:00<00:00, 8.88MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\" to /root/.cache/torch/hub/checkpoints/basicvsr_reds4_20120409-0e599677.pth\n","100% 24.1M/24.1M [00:02<00:00, 8.97MB/s]\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iaoE7UF5Xb2i","executionInfo":{"status":"ok","timestamp":1625140913405,"user_tz":-480,"elapsed":29263,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"a022e0bd-c47a-450e-f4e4-1bf9f92e4813"}},{"cell_type":"markdown","source":["## Test on a pre-defined dataset using the configuration file\n","\n","The above demos provide an easy way to perform inference on a single image or video sequence. If you want to perform inference on a set of images or sequences, you can make use of the configuration files located in `./configs`.\n"," \n","Existing configuration files allow you to perform inference on common datasets, such as `Set5` in image super-resolution and `REDS4` in video super-resolution. You can use the following command:\n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer and dataset you want to use\n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model.\n","3. `GPU_NUM`: Number of GPUs used for test. \n","4. `RESULT_FILE`: The path to the output result pickle file. (Optional)\n","5. `IMAGE_SAVE_PATH`: The location where you want to store the output image. (Optional)\n","\n","```\n","# single-gpu testing\n","python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","\n","# multi-gpu testing\n","./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","```\n","What you need to do is to modify the `lq_folder` and `gt_folder` in the configuration file:\n","```\n","test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/val_set5/Set5_bicLRx4',\n"," gt_folder='data/val_set5/Set5',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","```\n","\n","**Note**: Some dataset type (e.g. `SRREDSDataset`) requires an annotation file specifying the details of the dataset. Please refer to the corresponding file\n","in `./mmedit/dataset/` for more details. \n","\n","---\n","\n","The following is the command for SRCNN. For other models, you can simply change the paths to the configuration file and pretrained model. \n"],"metadata":{"id":"Rf3LW57qMHXb"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu\n","!python tools/test.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/\n","\n","# multi-gpu testing\n","!./tools/dist_test.sh ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 1 --save-path ./outputs/"],"outputs":[{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"tools/test.py\", line 136, in \n"," main()\n"," File \"tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/test.py\", line 136, in \n"," main()\n"," File \"./tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/test.py', '--local_rank=0', './configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py', 'https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', '--launcher', 'pytorch', '--save-path', './outputs/']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"tClgIYgcbbVg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140927462,"user_tz":-480,"elapsed":14095,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"c37ec2de-e1c9-42ae-ed9b-31009d48ae64"}},{"cell_type":"markdown","source":["## Test on your own datasets\n","\n","When you want to test on your own datasets, you need to modify `test_dataset_type` in addition to the dataset paths. \n","\n","- For image super-resolution, you need to use `SRFolderDataset`\n","- For sliding-window framework in video super-resolution (e.g. EDVR, TDAN), you need to use `SRFolderVideoDataset`.\n","- For recurrent framework in video super-resolution (e.g. BasicVSR, IconVSR), you need to use `SRFolderMultipleGTDataset`.\n","\n","These dataset types assume that all images/sequences in the specified directory are used for test. The folder structures should be\n","```\n","| lq_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," | ...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","| gt_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," |...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","```\n","We will use **SRCNN**, **EDVR**, **BasicVSR** as examples. Please pay attention to the settings of `test_dataset_type` and `data['test']`. "],"metadata":{"id":"KWKVyeEQelh3"}},{"cell_type":"markdown","source":["**SRCNN**"],"metadata":{"id":"0p2rP8jV_dL1"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_SRCNN.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/testset_SRCNN\n","\n","# Check the output folder\n","!ls ./outputs/testset_SRCNN"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 5/5, 8.6 task/s, elapsed: 1s, ETA: 0s\n","Eval-PSNR: 28.433974369836108\n","Eval-SSIM: 0.8099053586583066\n","baby.png bird.png butterfly.png head.png woman.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4kEev4wVIq_L","executionInfo":{"status":"ok","timestamp":1625140936180,"user_tz":-480,"elapsed":8729,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"8026ed73-f781-4eb2-bb80-d3446bd131df"}},{"cell_type":"markdown","source":["**EDVR**"],"metadata":{"id":"RONzjTTU_gem"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_EDVR.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth --save-path ./outputs/testset_EDVR\n","\n","# # Check the output folder\n","!ls ./outputs/testset_EDVR\n","!ls ./outputs/testset_EDVR/city"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 22/22, 2.0 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 23.89569862011228\n","Eval-SSIM: 0.7667098470108678\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vL8WOWXY0fNJ","executionInfo":{"status":"ok","timestamp":1625140955813,"user_tz":-480,"elapsed":19671,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"dc2a8f81-9bef-4ad4-c5b2-c6f124e6b113"}},{"cell_type":"markdown","source":["**BasicVSR**"],"metadata":{"id":"5Tc7F-l5_i1e"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_BasicVSR.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth --save-path ./outputs/testset_BasicVSR\n","\n","# # Check the output folder\n","!ls ./outputs/testset_BasicVSR\n","!ls ./outputs/testset_BasicVSR/calendar"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:07,780 - mmedit - INFO - Use load_from_http loader\n","Use load_from_http loader\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","[>>] 2/2, 0.2 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 24.195768601433734\n","Eval-SSIM: 0.7828541339512978\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jpW5GWC74Yvu","executionInfo":{"status":"ok","timestamp":1625140976026,"user_tz":-480,"elapsed":20220,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"7ba02a32-d4ec-40b2-8108-ef0729b62147"}},{"cell_type":"markdown","source":["## Train a restorer on a pre-defined dataset\n","\n","MMEditing uses distributed training. The following command can be used for training. If you want to train on the pre-defined datasets specified in our configuration file, you can simply run the following command.\n","\n","```\n","./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n","```\n","\n","For more details about the optional arguments, please refer to `tools/train.py`.\n","\n","---\n","\n","Here is an example using EDVR.\n"],"metadata":{"id":"4DQxNL8BhI0y"}},{"cell_type":"code","execution_count":null,"source":["!./tools/dist_train.sh ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py 1"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:31,961 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:31,961 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:31,961 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:31,961 - mmedit - INFO - Config:\n","/content/mmediting/configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py\n","exp_name = 'edvrm_wotsa_x4_g8_600k_reds'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRREDSDataset'\n","val_dataset_type = 'SRREDSDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 600000\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50000, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=100,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 54, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 63, in load_annotations\n"," with open(self.ann_file, 'r') as fin:\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/train.py\", line 145, in \n"," main()\n"," File \"./tools/train.py\", line 111, in main\n"," datasets = [build_dataset(cfg.data.train)]\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 76, in build_dataset\n"," build_dataset(cfg['dataset'], default_args), cfg['times'])\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRREDSDataset: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/train.py', '--local_rank=0', './configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py', '--launcher', 'pytorch']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"s-hOnSF6ItQM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140985357,"user_tz":-480,"elapsed":9337,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"e40e960a-29e1-43e8-b922-5e08c4e98afe"}},{"cell_type":"markdown","source":["## Train a restorer on your own datasets\n","\n","Similar to the case when you want to test on your own datasets, you need to modify `train_dataset_type`. The dataset type you need is identical:\n","\n","- For image super-resolution, you need to use `SRFolderDataset`\n","- For sliding-window framework in video super-resolution (e.g. EDVR, TDAN), you need to use `SRFolderVideoDataset`.\n","- For recurrent framework in video super-resolution (e.g. BasicVSR, IconVSR), you need to use `SRFolderMultipleGTDataset`.\n","\n","After you modified the dataset type and the data path. You are all set to go."],"metadata":{"id":"b0VfQkQQjg8N"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN (Single Image Super-Resolution)\n","!./tools/dist_train.sh ./demo_files/demo_config_SRCNN.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:41,185 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:41,185 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:41,185 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:41,185 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_SRCNN.py\n","exp_name = 'srcnn_demo'\n","\n","scale = 4\n","# model settings\n","model = dict(\n"," type='BasicRestorer',\n"," generator=dict(\n"," type='SRCNN',\n"," channels=(3, 64, 32, 3),\n"," kernel_sizes=(9, 1, 5),\n"," upscale_factor=scale),\n"," pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderDataset'\n","val_dataset_type = 'SRFolderDataset'\n","train_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=128),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","test_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=8,\n"," train_dataloader=dict(samples_per_gpu=16, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=train_pipeline,\n"," scale=scale)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[250000, 250000, 250000, 250000],\n"," restart_weights=[1, 1, 1, 1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","evaluation = dict(interval=50, save_image=True, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./experiments/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:02:41,192 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/experiments/srcnn_demo\n","2021-07-01 12:02:41,192 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:02:41.529307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:03:18,631 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.000e-04, eta: 0:57:01, time: 34.560, data_time: 34.446, memory: 586, loss_pix: 0.3999, loss: 0.3999\n","2021-07-01 12:03:18,712 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","INFO:mmedit:Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","2021-07-01 12:03:18,764 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","INFO:mmedit:Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","2021-07-01 12:03:18,840 - 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mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:04:37,422 - mmedit - INFO - Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n","INFO:mmedit:Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"liGEKJpbIoXZ","executionInfo":{"status":"ok","timestamp":1625141113733,"user_tz":-480,"elapsed":128384,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"84e1502e-e2cb-458f-c7c5-e4b401e570b7"}},{"cell_type":"code","execution_count":null,"source":["# EDVR (Video Super-Resolution - Sliding Window)\n","!./tools/dist_train.sh ./demo_files/demo_config_EDVR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:10:12,619 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:10:12,619 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:10:12,619 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:10:12,619 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_EDVR.py\n","exp_name = 'edvrm_demo'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderVideoDataset'\n","val_dataset_type = 'SRFolderVideoDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:10:12,701 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/edvrm_demo\n","2021-07-01 12:10:12,702 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:10:12.951771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:10:30,703 - mmedit - INFO - Iter [1/100]\tlr_generator: 4.000e-04, eta: 0:26:53, time: 16.295, data_time: 15.833, memory: 1341, loss_pix: 63917.2734, loss: 63917.2734\n","2021-07-01 12:10:31,046 - mmedit - INFO - Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","INFO:mmedit:Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","2021-07-01 12:10:31,386 - mmedit - INFO - Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","INFO:mmedit:Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","2021-07-01 12:10:31,731 - mmedit - INFO - Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","INFO:mmedit:Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","2021-07-01 12:10:32,071 - mmedit - INFO - Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","INFO:mmedit:Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","2021-07-01 12:10:32,414 - mmedit - INFO - Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","INFO:mmedit:Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","2021-07-01 12:10:32,760 - 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Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"2fb26527-eb9e-4b48-e03c-ba9a91c60db8"}},{"cell_type":"code","execution_count":null,"source":["# BasicVSR (Video Super-Resolution - Recurrent)\n","!./tools/dist_train.sh ./demo_files/demo_config_BasicVSR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:06:47,253 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:06:47,253 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:06:47,254 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:06:47,254 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_BasicVSR.py\n","exp_name = 'basicvsr_demo'\n","\n","# model settings\n","model = dict(\n"," type='BasicVSR',\n"," generator=dict(\n"," type='BasicVSRNet',\n"," mid_channels=64,\n"," num_blocks=30,\n"," spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n"," 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = dict(fix_iter=5000)\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderMultipleGTDataset'\n","val_dataset_type = 'SRFolderMultipleGTDataset'\n","\n","train_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=6,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True), # 2 gpus\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),\n","\n"," # train\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," # val\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," # test\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(\n"," generator=dict(\n"," type='Adam',\n"," lr=2e-4,\n"," betas=(0.9, 0.99),\n"," paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[300000],\n"," restart_weights=[1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," # dict(type='TensorboardLoggerHook'),\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","find_unused_parameters = True\n","\n","2021-07-01 12:06:47,291 - mmedit - INFO - Use load_from_http loader\n","2021-07-01 12:06:47,569 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/basicvsr_demo\n","2021-07-01 12:06:47,569 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:07:14,210 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.500e-05, eta: 0:42:52, time: 25.981, data_time: 24.045, memory: 3464, loss_pix: 0.0634, loss: 0.0634\n","2021-07-01 12:07:15,171 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.500e-05, eta: 0:22:00, time: 0.961, data_time: 0.011, memory: 3518, loss_pix: 0.0556, loss: 0.0556\n","2021-07-01 12:07:16,052 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.500e-05, eta: 0:14:59, time: 0.881, data_time: 0.003, memory: 3518, loss_pix: 0.0476, loss: 0.0476\n","2021-07-01 12:07:16,940 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.500e-05, eta: 0:11:29, time: 0.888, data_time: 0.003, memory: 3518, loss_pix: 0.0673, loss: 0.0673\n","2021-07-01 12:07:17,829 - 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mmedit - INFO - Iter(val) [100]\tPSNR: 21.4372, SSIM: 0.5687\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_RdqmlT6qgt2","executionInfo":{"status":"ok","timestamp":1625141428032,"user_tz":-480,"elapsed":197033,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"b951b426-e06c-4f31-db01-449333eab333"}},{"cell_type":"markdown","source":["**This is the end of this tutorial. For more advanced usage, please see our comprehensive tutorial [here](). Enjoy coding with MMEditing!**"],"metadata":{"id":"QT0zwBFt7J13"}}]} diff --git a/demo/restorer_basic_tutorial_zh-CN.ipynb b/demo/restorer_basic_tutorial_zh-CN.ipynb new file mode 100644 index 0000000000..10dc2f6a76 --- /dev/null +++ b/demo/restorer_basic_tutorial_zh-CN.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":2,"metadata":{"accelerator":"GPU","colab":{"name":"restorer_basic_tutorial.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3.7.7 64-bit ('pre-commit': conda)","name":"python377jvsc74a57bd04974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"},"language_info":{"name":"python","version":""},"metadata":{"interpreter":{"hash":"4974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"}}},"cells":[{"cell_type":"markdown","source":["# MMEditing 基础教程\n","\n","欢迎来到MMEditing！ 这是 MMEditing 的官方 Colab 教程。 在本教程中，您将学习如何使用 MMEditing 中提供的 API 训练和测试恢复器。\n","\n","这是训练和测试现有模型的快速指南。 如果您想基于 MMEditing 开发自己的模型并了解有关代码结构的更多信息，请参阅我们的综合教程 [此处]()。\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmedit/blob/main/demo/restorer_basic_tutorial.ipynb)\n","\n"],"metadata":{"id":"T2WWQiheMF7q"}},{"cell_type":"markdown","source":["## 安装MMEditing\n","\n","MMEditing 可以分两步安装：\n","\n","1. 安装兼容的 PyTorch 版本（你需要使用 `nvcc -V` 检查你的 CUDA 版本）。\n","2. 安装预编译的MMCV\n","3. 克隆并安装MMEditing\n","\n","步骤如下所示："],"metadata":{"id":"-kYw3WQ0MQry"}},{"cell_type":"code","execution_count":null,"source":["# Check nvcc version\n","!nvcc -V\n","# Check GCC version (MMEditing needs gcc >= 5.0)\n","!gcc --version"],"outputs":[{"output_type":"stream","name":"stdout","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Wed_Jul_22_19:09:09_PDT_2020\n","Cuda compilation tools, release 11.0, V11.0.221\n","Build cuda_11.0_bu.TC445_37.28845127_0\n","gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uha_13idyl1b","executionInfo":{"status":"ok","timestamp":1625140540858,"user_tz":-480,"elapsed":321,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"87056561-5930-45b3-e1a8-f9e103d10b23"}},{"cell_type":"code","execution_count":null,"source":["# Install dependencies: (use cu101 because colab has CUDA 11.0)\n","!pip install -U torch==1.7.0+cu110 torchvision==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html\n","\n","# Install mmcv-full thus we could use CUDA operators\n","!pip install mmcv-full==1.3.5 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html\n","\n","# Clone MMEditing\n","!rm -rf mmediting\n","!git clone https://github.com/open-mmlab/mmediting.git\n","%cd mmediting\n","\n","# Install MMEditing\n","!pip install -r requirements.txt\n","!pip install -v -e ."],"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in links: https://download.pytorch.org/whl/torch_stable.html\n","Collecting torch==1.7.0+cu110\n","\u001b[?25l Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl 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4)) (2.5.0)\n","Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 5)) (0.31.0)\n","Collecting codecov\n"," Downloading https://files.pythonhosted.org/packages/93/9f/bbea5b6231308458963cb5c067bc5643da9949689702fa5a382714b59699/codecov-2.1.11-py2.py3-none-any.whl\n","Collecting flake8\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/fc/80/35a0716e5d5101e643404dabd20f07f5528a21f3ef4032d31a49c913237b/flake8-3.9.2-py2.py3-none-any.whl (73kB)\n","\u001b[K |████████████████████████████████| 81kB 9.7MB/s \n","\u001b[?25hCollecting interrogate\n"," Downloading https://files.pythonhosted.org/packages/cd/6d/ce3ac440b13c1b36b323a0eab191499a902adade3cc11b18078c07af3e6e/interrogate-1.4.0-py3-none-any.whl\n","Collecting isort==4.3.21\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/e5/b0/c121fd1fa3419ea9bfd55c7f9c4fedfec5143208d8c7ad3ce3db6c623c21/isort-4.3.21-py2.py3-none-any.whl (42kB)\n","\u001b[K |████████████████████████████████| 51kB 7.5MB/s \n","\u001b[?25hCollecting onnxruntime\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/f9/76/3d0f8bb2776961c7335693df06eccf8d099e48fa6fb552c7546867192603/onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5MB)\n","\u001b[K |████████████████████████████████| 4.5MB 37.4MB/s \n","\u001b[?25hRequirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 6)) (3.6.4)\n","Collecting pytest-runner\n"," Downloading https://files.pythonhosted.org/packages/f4/f5/6605d73bf3f4c198915872111b10c4b3c2dccd8485f47b7290ceef037190/pytest_runner-5.3.1-py3-none-any.whl\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (1.19.5)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (7.1.2)\n","Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (2.4.0)\n","Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (4.1.2.30)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (3.13)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.5.1)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.4.1)\n","Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) 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requirements/runtime.txt (line 4)) (1.8.0)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (2.23.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.4.4)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (57.0.0)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.12.0)\n","Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.12.4)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.3.4)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.31.0)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.0.1)\n","Requirement already satisfied: coverage in /usr/local/lib/python3.7/dist-packages (from codecov->-r requirements/tests.txt (line 1)) (3.7.1)\n","Collecting pyflakes<2.4.0,>=2.3.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6c/11/2a745612f1d3cbbd9c69ba14b1b43a35a2f5c3c81cd0124508c52c64307f/pyflakes-2.3.1-py2.py3-none-any.whl (68kB)\n","\u001b[K |████████████████████████████████| 71kB 9.8MB/s \n","\u001b[?25hCollecting pycodestyle<2.8.0,>=2.7.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/cc/227251b1471f129bc35e966bb0fceb005969023926d744139642d847b7ae/pycodestyle-2.7.0-py2.py3-none-any.whl (41kB)\n","\u001b[K |████████████████████████████████| 51kB 8.7MB/s \n","\u001b[?25hCollecting mccabe<0.7.0,>=0.6.0\n"," Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from flake8->-r requirements/tests.txt (line 2)) (4.5.0)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (0.8.9)\n","Collecting colorama\n"," Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (7.1.2)\n","Requirement already satisfied: toml in 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/tmp/pip-req-tracker-zk5q0q3z\n","Created requirements tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Created temporary directory: /tmp/pip-install-vr_vpseo\n","Obtaining file:///content/mmediting\n"," Added file:///content/mmediting to build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"," Running setup.py (path:/content/mmediting/setup.py) egg_info for package from file:///content/mmediting\n"," Running command python setup.py egg_info\n"," running egg_info\n"," creating mmedit.egg-info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," Source in /content/mmediting has version 0.8.0, which satisfies requirement mmedit==0.8.0 from file:///content/mmediting\n"," Removed mmedit==0.8.0 from file:///content/mmediting from build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Requirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (0.99)\n","Requirement already satisfied: mmcv-full>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (1.3.5)\n","Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (0.16.2)\n","Requirement already satisfied: tensorboard in 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chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (2021.5.30)\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->mmedit==0.8.0) (4.5.0)\n","Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (1.3.0)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->mmedit==0.8.0) (0.4.8)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.7.4.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (3.1.1)\n","Installing collected packages: mmedit\n"," Running setup.py develop for mmedit\n"," Running command /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmediting/setup.py'\"'\"'; __file__='\"'\"'/content/mmediting/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n"," running develop\n"," running egg_info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.7/dist-packages/mmedit.egg-link (link to .)\n"," Adding mmedit 0.8.0 to easy-install.pth file\n","\n"," Installed /content/mmediting\n","Successfully installed mmedit\n","Cleaning up...\n","Removed build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GIeIZEzZMfc0","executionInfo":{"status":"ok","timestamp":1625140820804,"user_tz":-480,"elapsed":279948,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"fe2e5ded-988d-4563-eb18-374344c316ef"}},{"cell_type":"markdown","source":["## 下载此演示所需的材料\n","在这个演示中，我们将需要一些数据和配置文件。我们将下载并放入 `./demo_files/`"],"metadata":{"id":"QgX96Sc_3PcV"}},{"cell_type":"code","execution_count":null,"source":["!wget https://download.openmmlab.com/mmediting/demo_files.zip # 下载文件\n","!unzip demo_files # 解压"],"outputs":[{"output_type":"stream","name":"stdout","text":["--2021-07-01 11:59:48-- https://download.openmmlab.com/mmediting/demo_files.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19215781 (18M) [application/zip]\n","Saving to: ‘demo_files.zip’\n","\n","demo_files.zip 100%[===================>] 18.33M 6.00MB/s in 3.1s \n","\n","2021-07-01 11:59:52 (6.00 MB/s) - ‘demo_files.zip’ saved [19215781/19215781]\n","\n","Archive: demo_files.zip\n"," creating: demo_files/\n"," inflating: demo_files/demo_config_EDVR.py \n"," inflating: demo_files/demo_config_BasicVSR.py \n"," creating: demo_files/lq_sequences/\n"," creating: demo_files/lq_sequences/calendar/\n"," inflating: demo_files/lq_sequences/calendar/00000006.png \n"," inflating: demo_files/lq_sequences/calendar/00000007.png \n"," inflating: demo_files/lq_sequences/calendar/00000010.png \n"," inflating: demo_files/lq_sequences/calendar/00000004.png \n"," inflating: demo_files/lq_sequences/calendar/00000003.png \n"," inflating: demo_files/lq_sequences/calendar/00000001.png \n"," inflating: demo_files/lq_sequences/calendar/00000000.png \n"," inflating: demo_files/lq_sequences/calendar/00000009.png \n"," inflating: demo_files/lq_sequences/calendar/00000008.png \n"," inflating: demo_files/lq_sequences/calendar/00000002.png \n"," inflating: demo_files/lq_sequences/calendar/00000005.png \n"," creating: demo_files/lq_sequences/city/\n"," inflating: demo_files/lq_sequences/city/00000006.png \n"," inflating: demo_files/lq_sequences/city/00000007.png \n"," inflating: demo_files/lq_sequences/city/00000010.png \n"," inflating: demo_files/lq_sequences/city/00000004.png \n"," inflating: demo_files/lq_sequences/city/00000003.png \n"," inflating: demo_files/lq_sequences/city/00000001.png \n"," inflating: demo_files/lq_sequences/city/00000000.png \n"," inflating: demo_files/lq_sequences/city/00000009.png \n"," inflating: demo_files/lq_sequences/city/00000008.png \n"," inflating: demo_files/lq_sequences/city/00000002.png \n"," inflating: demo_files/lq_sequences/city/00000005.png \n"," creating: demo_files/lq_sequences/.ipynb_checkpoints/\n"," creating: demo_files/gt_images/\n"," inflating: demo_files/gt_images/bird.png \n"," inflating: demo_files/gt_images/woman.png \n"," inflating: demo_files/gt_images/head.png \n"," inflating: demo_files/gt_images/baby.png \n"," inflating: demo_files/gt_images/butterfly.png \n"," inflating: demo_files/demo_config_SRCNN.py \n"," creating: demo_files/lq_images/\n"," extracting: demo_files/lq_images/bird.png \n"," extracting: demo_files/lq_images/woman.png \n"," extracting: demo_files/lq_images/head.png \n"," extracting: demo_files/lq_images/baby.png \n"," extracting: demo_files/lq_images/butterfly.png \n"," creating: demo_files/gt_sequences/\n"," creating: demo_files/gt_sequences/calendar/\n"," inflating: demo_files/gt_sequences/calendar/00000006.png \n"," inflating: demo_files/gt_sequences/calendar/00000007.png \n"," inflating: demo_files/gt_sequences/calendar/00000010.png \n"," inflating: demo_files/gt_sequences/calendar/00000004.png \n"," inflating: demo_files/gt_sequences/calendar/00000003.png \n"," inflating: demo_files/gt_sequences/calendar/00000001.png \n"," inflating: demo_files/gt_sequences/calendar/00000000.png \n"," inflating: demo_files/gt_sequences/calendar/00000009.png \n"," inflating: demo_files/gt_sequences/calendar/00000008.png \n"," inflating: demo_files/gt_sequences/calendar/00000002.png \n"," inflating: demo_files/gt_sequences/calendar/00000005.png \n"," creating: demo_files/gt_sequences/city/\n"," inflating: demo_files/gt_sequences/city/00000006.png \n"," inflating: demo_files/gt_sequences/city/00000007.png \n"," inflating: demo_files/gt_sequences/city/00000010.png \n"," inflating: demo_files/gt_sequences/city/00000004.png \n"," inflating: demo_files/gt_sequences/city/00000003.png \n"," inflating: demo_files/gt_sequences/city/00000001.png \n"," inflating: demo_files/gt_sequences/city/00000000.png \n"," inflating: demo_files/gt_sequences/city/00000009.png \n"," inflating: demo_files/gt_sequences/city/00000008.png \n"," inflating: demo_files/gt_sequences/city/00000002.png \n"," inflating: demo_files/gt_sequences/city/00000005.png \n"," creating: demo_files/gt_sequences/.ipynb_checkpoints/\n"," creating: demo_files/.ipynb_checkpoints/\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-K0zFSJ-3V42","executionInfo":{"status":"ok","timestamp":1625140825508,"user_tz":-480,"elapsed":4723,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"528a87f7-f78e-4219-84f3-dec19b88e88a"}},{"cell_type":"markdown","source":["## 使用预训练的图像恢复器进行推理\n","您可以使用 “restoration_demo.py” 轻松地使用预训练的恢复器对单个图像进行推理。您需要的是\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `IMAGE_FILE`：输入图像的路径。\n","4. `SAVE_FILE`：您要存储输出图像的位置。\n","5. `imshow`：是否显示图片。（可选的）\n","6. `GPU_ID`：您想使用哪个 GPU。（可选的）\n","\n","获得所有这些详细信息后，您可以直接使用以下命令：\n","\n","```\n","python demo/restoration_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} ${SAVE_FILE} [--imshow] [--device ${GPU_ID}]\n","```\n","\n","**注:** \n","1. 配置文件位于 `./configs`。\n","2. 我们支持从 url 加载权重文件。您可以到相应页面（例如[这里](https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/esrgan)）获取预训练模型的url。\n","\n","---\n","\n","我们现在将使用 `SRCNN` 和 `ESRGAN` 作为示例。\n","\n"],"metadata":{"id":"zXGurqGKOeNE"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN\n","!python demo/restoration_demo.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth ./demo_files/lq_images/bird.png ./outputs/bird_SRCNN.png\n","\n","# ESRGAN\n","!python demo/restoration_demo.py ./configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth ./demo_files/lq_images/bird.png ./outputs/bird_ESRGAN.png\n","\n","# 检查图像是否已保存\n","!ls ./outputs"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\" to /root/.cache/torch/hub/checkpoints/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n","100% 83.9k/83.9k [00:00<00:00, 1.59MB/s]\n","2021-07-01 12:00:10,779 - mmedit - INFO - Use load_from_torchvision loader\n","Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n","100% 548M/548M [00:07<00:00, 76.0MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\" to /root/.cache/torch/hub/checkpoints/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n","100% 196M/196M [00:26<00:00, 7.61MB/s]\n","bird_ESRGAN.png bird_SRCNN.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KiPvtvlqM1zb","executionInfo":{"status":"ok","timestamp":1625140884175,"user_tz":-480,"elapsed":58677,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"be7375a7-4632-4770-8383-2a8ce654b069"}},{"cell_type":"markdown","source":["## 使用预训练的视频复原器进行推理\n","\n","MMEditing 也支持视频超分辨率方法，过程类似。您可以使用带有以下参数的 `restoration_video_demo.py`：\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `INPUT_DIR`: 包含视频帧的目录。\n","4. `OUTPUT_DIR`: 要存储输出帧的位置。\n","5. `WINDOW_SIZE`: 使用滑动窗口方法时的窗口大小（可选）。\n","6. `GPU_ID`: 您想使用哪个 GPU（可选）。\n","\n","```\n","python demo/restoration_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} [--window_size=$WINDOW_SIZE] [--device ${GPU_ID}]\n","```\n","**注:** 视频超分辨率有两种不同的框架：***滑动窗口***和***循环***框架。使用 EDVR 等滑动窗口框架的方法时，需要指定 `window_size`。此值取决于您使用的模型。\n","\n","---\n","\n","我们现在将使用 `EDVR` 和 `BasicVSR` 作为示例。\n","\n"],"metadata":{"id":"W1DfGHu3Xcfd"}},{"cell_type":"code","execution_count":null,"source":["# EDVR（滑动窗口框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_EDVR --window_size=5\n","\n","# BasicVSR（循环框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/basicvsr/basicvsr_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_BasicVSR\n","\n","# 检查是否保存了视频帧\n","!ls ./outputs/calendar_BasicVSR"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\" to /root/.cache/torch/hub/checkpoints/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n","100% 11.5M/11.5M [00:01<00:00, 8.55MB/s]\n","2021-07-01 12:01:09,689 - mmedit - INFO - Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth\" to /root/.cache/torch/hub/checkpoints/spynet_20210409-c6c1bd09.pth\n","100% 5.50M/5.50M [00:00<00:00, 8.88MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\" to /root/.cache/torch/hub/checkpoints/basicvsr_reds4_20120409-0e599677.pth\n","100% 24.1M/24.1M [00:02<00:00, 8.97MB/s]\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iaoE7UF5Xb2i","executionInfo":{"status":"ok","timestamp":1625140913405,"user_tz":-480,"elapsed":29263,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"a022e0bd-c47a-450e-f4e4-1bf9f92e4813"}},{"cell_type":"markdown","source":["## 使用配置文件在预定义的数据集上进行测试\n","\n","上述演示提供了一种对单个图像或视频序列进行推理的简单方法。如果要对一组图像或序列进行推理，可以使用位于 `./configs` 中的配置文件。\n"," \n","现有的配置文件允许您对常见数据集进行推理，例如图像超分辨率中的 `Set5` 和视频超分辨率中的 `REDS4`。您可以使用以下命令：\n","\n","1. `CONFIG_FILE`: 你要使用的复原器和数据集对应的配置文件\n","2. `CHECKPOINT_FILE`: 预训练模型权重文件的路径。\n","3. `GPU_NUM`: 用于测试的 GPU 数量。\n","4. `RESULT_FILE`: 输出结果 pickle 文件的路径。（可选）\n","5. `IMAGE_SAVE_PATH`: 要存储输出图像的位置。（可选）\n","\n","```\n","# 单 GPU 测试\n","python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","\n","# 多 GPU 测试\n","./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","```\n","您需要做的是修改配置文件中的 `lq_folder` 和 `gt_folder`：\n","```\n","test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/val_set5/Set5_bicLRx4',\n"," gt_folder='data/val_set5/Set5',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","```\n","\n","**注**: 某些数据集类型（例如 `SRREDSDataset`）需要一个注释文件来指定数据集的详细信息。更多细节请参考 `./mmedit/dataset/` 中的相应文件。\n","\n","---\n","\n","以下是 SRCNN 的命令。对于其他模型，您可以简单地更改配置文件和预训练模型的路径。\n"],"metadata":{"id":"Rf3LW57qMHXb"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU\n","!python tools/test.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/\n","\n","# 多 GPU\n","!./tools/dist_test.sh ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 1 --save-path ./outputs/"],"outputs":[{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"tools/test.py\", line 136, in \n"," main()\n"," File \"tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/test.py\", line 136, in \n"," main()\n"," File \"./tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/test.py', '--local_rank=0', './configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py', 'https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', '--launcher', 'pytorch', '--save-path', './outputs/']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"tClgIYgcbbVg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140927462,"user_tz":-480,"elapsed":14095,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"c37ec2de-e1c9-42ae-ed9b-31009d48ae64"}},{"cell_type":"markdown","source":["## 在自定义数据集上进行测试\n","\n","当您想在自定义数据集上进行测试时，除了数据集路径之外，您还需要修改 `test_dataset_type`。 \n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","这些数据集类型假定指定目录中的所有图像/序列都用于测试。文件夹结构应该是\n","```\n","| lq_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," | ...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","| gt_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," |...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","```\n","我们将使用 **SRCNN**、**EDVR**、**BasicVSR** 作为示例。请注意 `test_dataset_type` 和 `data['test']` 的设置。"],"metadata":{"id":"KWKVyeEQelh3"}},{"cell_type":"markdown","source":["**SRCNN**"],"metadata":{"id":"0p2rP8jV_dL1"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_SRCNN.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/testset_SRCNN\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_SRCNN"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 5/5, 8.6 task/s, elapsed: 1s, ETA: 0s\n","Eval-PSNR: 28.433974369836108\n","Eval-SSIM: 0.8099053586583066\n","baby.png bird.png butterfly.png head.png woman.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4kEev4wVIq_L","executionInfo":{"status":"ok","timestamp":1625140936180,"user_tz":-480,"elapsed":8729,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"8026ed73-f781-4eb2-bb80-d3446bd131df"}},{"cell_type":"markdown","source":["**EDVR**"],"metadata":{"id":"RONzjTTU_gem"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_EDVR.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth --save-path ./outputs/testset_EDVR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_EDVR\n","!ls ./outputs/testset_EDVR/city"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 22/22, 2.0 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 23.89569862011228\n","Eval-SSIM: 0.7667098470108678\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vL8WOWXY0fNJ","executionInfo":{"status":"ok","timestamp":1625140955813,"user_tz":-480,"elapsed":19671,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"dc2a8f81-9bef-4ad4-c5b2-c6f124e6b113"}},{"cell_type":"markdown","source":["**BasicVSR**"],"metadata":{"id":"5Tc7F-l5_i1e"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_BasicVSR.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth --save-path ./outputs/testset_BasicVSR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_BasicVSR\n","!ls ./outputs/testset_BasicVSR/calendar"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:07,780 - mmedit - INFO - Use load_from_http loader\n","Use load_from_http loader\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","[>>] 2/2, 0.2 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 24.195768601433734\n","Eval-SSIM: 0.7828541339512978\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jpW5GWC74Yvu","executionInfo":{"status":"ok","timestamp":1625140976026,"user_tz":-480,"elapsed":20220,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"7ba02a32-d4ec-40b2-8108-ef0729b62147"}},{"cell_type":"markdown","source":["## 在预定义的数据集上训练恢复器\n","\n","MMEditing 使用分布式训练。以下命令可用于训练。如果要在我们的配置文件中指定的预定义数据集上进行训练，只需运行以下命令即可。\n","\n","```\n","./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n","```\n","\n","有关可选参数的更多详细信息，请参阅 `tools/train.py`。\n","\n","---\n","\n","这是一个使用 EDVR 的示例。\n"],"metadata":{"id":"4DQxNL8BhI0y"}},{"cell_type":"code","execution_count":null,"source":["!./tools/dist_train.sh ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py 1"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:31,961 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:31,961 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:31,961 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:31,961 - mmedit - INFO - Config:\n","/content/mmediting/configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py\n","exp_name = 'edvrm_wotsa_x4_g8_600k_reds'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRREDSDataset'\n","val_dataset_type = 'SRREDSDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 600000\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50000, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=100,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 54, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 63, in load_annotations\n"," with open(self.ann_file, 'r') as fin:\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/train.py\", line 145, in \n"," main()\n"," File \"./tools/train.py\", line 111, in main\n"," datasets = [build_dataset(cfg.data.train)]\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 76, in build_dataset\n"," build_dataset(cfg['dataset'], default_args), cfg['times'])\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRREDSDataset: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/train.py', '--local_rank=0', './configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py', '--launcher', 'pytorch']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"s-hOnSF6ItQM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140985357,"user_tz":-480,"elapsed":9337,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"e40e960a-29e1-43e8-b922-5e08c4e98afe"}},{"cell_type":"markdown","source":["## 在自定义数据集上训练复原器\n","\n","与您要在自己的数据集上进行测试的情况类似，您需要修改 `train_dataset_type`。 您需要的数据集类型是相同的：\n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","修改数据集类型和数据路径后。 一切都准备好了。"],"metadata":{"id":"b0VfQkQQjg8N"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN（图像超分辨率）\n","!./tools/dist_train.sh ./demo_files/demo_config_SRCNN.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:41,185 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:41,185 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:41,185 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:41,185 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_SRCNN.py\n","exp_name = 'srcnn_demo'\n","\n","scale = 4\n","# model settings\n","model = dict(\n"," type='BasicRestorer',\n"," generator=dict(\n"," type='SRCNN',\n"," channels=(3, 64, 32, 3),\n"," kernel_sizes=(9, 1, 5),\n"," upscale_factor=scale),\n"," pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderDataset'\n","val_dataset_type = 'SRFolderDataset'\n","train_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=128),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","test_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=8,\n"," train_dataloader=dict(samples_per_gpu=16, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=train_pipeline,\n"," scale=scale)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[250000, 250000, 250000, 250000],\n"," restart_weights=[1, 1, 1, 1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","evaluation = dict(interval=50, save_image=True, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./experiments/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:02:41,192 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/experiments/srcnn_demo\n","2021-07-01 12:02:41,192 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:02:41.529307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:03:18,631 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.000e-04, eta: 0:57:01, time: 34.560, data_time: 34.446, memory: 586, loss_pix: 0.3999, loss: 0.3999\n","2021-07-01 12:03:18,712 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","INFO:mmedit:Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","2021-07-01 12:03:18,764 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","INFO:mmedit:Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","2021-07-01 12:03:18,840 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","INFO:mmedit:Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","2021-07-01 12:03:18,916 - mmedit - INFO - Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","INFO:mmedit:Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","2021-07-01 12:03:18,956 - mmedit - INFO - Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","INFO:mmedit:Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","2021-07-01 12:03:19,012 - mmedit - INFO - Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","INFO:mmedit:Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","2021-07-01 12:03:19,070 - mmedit - INFO - Iter [8/100]\tlr_generator: 2.000e-04, eta: 0:06:42, time: 0.057, data_time: 0.016, memory: 586, loss_pix: 0.3766, loss: 0.3766\n","INFO:mmedit:Iter [8/100]\tlr_generator: 2.000e-04, eta: 0:06:42, time: 0.057, data_time: 0.016, memory: 586, loss_pix: 0.3766, loss: 0.3766\n","2021-07-01 12:03:19,142 - mmedit - INFO - Iter [9/100]\tlr_generator: 2.000e-04, eta: 0:05:54, time: 0.064, data_time: 0.026, memory: 586, loss_pix: 0.3721, loss: 0.3721\n","INFO:mmedit:Iter [9/100]\tlr_generator: 2.000e-04, eta: 0:05:54, time: 0.064, data_time: 0.026, memory: 586, loss_pix: 0.3721, loss: 0.3721\n","2021-07-01 12:03:19,212 - mmedit - INFO - Iter [10/100]\tlr_generator: 2.000e-04, eta: 0:05:16, time: 0.079, data_time: 0.027, memory: 586, loss_pix: 0.3314, loss: 0.3314\n","INFO:mmedit:Iter [10/100]\tlr_generator: 2.000e-04, eta: 0:05:16, time: 0.079, data_time: 0.027, memory: 586, loss_pix: 0.3314, loss: 0.3314\n","2021-07-01 12:03:19,261 - mmedit - INFO - Iter [11/100]\tlr_generator: 2.000e-04, eta: 0:04:44, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.3249, loss: 0.3249\n","INFO:mmedit:Iter [11/100]\tlr_generator: 2.000e-04, eta: 0:04:44, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.3249, loss: 0.3249\n","2021-07-01 12:03:19,302 - 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mmedit - INFO - Iter [48/100]\tlr_generator: 2.000e-04, eta: 0:00:40, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.0876, loss: 0.0876\n","INFO:mmedit:Iter [48/100]\tlr_generator: 2.000e-04, eta: 0:00:40, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.0876, loss: 0.0876\n","2021-07-01 12:03:21,743 - mmedit - INFO - Iter [49/100]\tlr_generator: 2.000e-04, eta: 0:00:39, time: 0.067, data_time: 0.011, memory: 586, loss_pix: 0.0923, loss: 0.0923\n","INFO:mmedit:Iter [49/100]\tlr_generator: 2.000e-04, eta: 0:00:39, time: 0.067, data_time: 0.011, memory: 586, loss_pix: 0.0923, loss: 0.0923\n","[>>] 5/5, 0.1 task/s, elapsed: 37s, ETA: 0s\n","\n","2021-07-01 12:03:59,996 - mmedit - INFO - Iter(val) [50]\tPSNR: 19.4410, SSIM: 0.5387\n","INFO:mmedit:Iter(val) [50]\tPSNR: 19.4410, SSIM: 0.5387\n","2021-07-01 12:04:00,047 - mmedit - INFO - Iter [51/100]\tlr_generator: 2.000e-04, eta: 0:01:12, time: 38.229, data_time: 38.203, memory: 586, loss_pix: 0.0909, loss: 0.0909\n","INFO:mmedit:Iter [51/100]\tlr_generator: 2.000e-04, eta: 0:01:12, time: 38.229, data_time: 38.203, memory: 586, loss_pix: 0.0909, loss: 0.0909\n","2021-07-01 12:04:00,114 - 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mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:04:37,422 - mmedit - INFO - Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n","INFO:mmedit:Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"liGEKJpbIoXZ","executionInfo":{"status":"ok","timestamp":1625141113733,"user_tz":-480,"elapsed":128384,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"84e1502e-e2cb-458f-c7c5-e4b401e570b7"}},{"cell_type":"code","execution_count":null,"source":["# EDVR（视频超分辨率-滑动窗口）\n","!./tools/dist_train.sh ./demo_files/demo_config_EDVR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:10:12,619 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:10:12,619 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:10:12,619 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:10:12,619 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_EDVR.py\n","exp_name = 'edvrm_demo'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderVideoDataset'\n","val_dataset_type = 'SRFolderVideoDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:10:12,701 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/edvrm_demo\n","2021-07-01 12:10:12,702 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:10:12.951771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:10:30,703 - mmedit - INFO - Iter [1/100]\tlr_generator: 4.000e-04, eta: 0:26:53, time: 16.295, data_time: 15.833, memory: 1341, loss_pix: 63917.2734, loss: 63917.2734\n","2021-07-01 12:10:31,046 - mmedit - INFO - Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","INFO:mmedit:Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","2021-07-01 12:10:31,386 - mmedit - INFO - Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","INFO:mmedit:Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","2021-07-01 12:10:31,731 - 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Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"2fb26527-eb9e-4b48-e03c-ba9a91c60db8"}},{"cell_type":"code","execution_count":null,"source":["# BasicVSR（视频超分辨率 - 循环）\n","!./tools/dist_train.sh ./demo_files/demo_config_BasicVSR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:06:47,253 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:06:47,253 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:06:47,254 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:06:47,254 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_BasicVSR.py\n","exp_name = 'basicvsr_demo'\n","\n","# model settings\n","model = dict(\n"," type='BasicVSR',\n"," generator=dict(\n"," type='BasicVSRNet',\n"," mid_channels=64,\n"," num_blocks=30,\n"," spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n"," 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = dict(fix_iter=5000)\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderMultipleGTDataset'\n","val_dataset_type = 'SRFolderMultipleGTDataset'\n","\n","train_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=6,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True), # 2 gpus\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),\n","\n"," # train\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," # val\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," # test\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(\n"," generator=dict(\n"," type='Adam',\n"," lr=2e-4,\n"," betas=(0.9, 0.99),\n"," paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[300000],\n"," restart_weights=[1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," # dict(type='TensorboardLoggerHook'),\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","find_unused_parameters = True\n","\n","2021-07-01 12:06:47,291 - mmedit - INFO - Use load_from_http loader\n","2021-07-01 12:06:47,569 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/basicvsr_demo\n","2021-07-01 12:06:47,569 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:07:14,210 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.500e-05, eta: 0:42:52, time: 25.981, data_time: 24.045, memory: 3464, loss_pix: 0.0634, loss: 0.0634\n","2021-07-01 12:07:15,171 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.500e-05, eta: 0:22:00, time: 0.961, data_time: 0.011, memory: 3518, loss_pix: 0.0556, loss: 0.0556\n","2021-07-01 12:07:16,052 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.500e-05, eta: 0:14:59, time: 0.881, data_time: 0.003, memory: 3518, loss_pix: 0.0476, loss: 0.0476\n","2021-07-01 12:07:16,940 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.500e-05, eta: 0:11:29, time: 0.888, data_time: 0.003, memory: 3518, loss_pix: 0.0673, loss: 0.0673\n","2021-07-01 12:07:17,829 - 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mmedit - INFO - Iter(val) [100]\tPSNR: 21.4372, SSIM: 0.5687\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_RdqmlT6qgt2","executionInfo":{"status":"ok","timestamp":1625141428032,"user_tz":-480,"elapsed":197033,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"b951b426-e06c-4f31-db01-449333eab333"}},{"cell_type":"markdown","source":["**本教程到此结束。 有关更高级的用法，请参阅我们的综合教程 [此处]()。 享受使用 MMEditing 的乐趣！**"],"metadata":{"id":"QT0zwBFt7J13"}}]} From 9beae7d915997bc7a2894ebfec5ba83164f6aca2 Mon Sep 17 00:00:00 2001 From: ckkelvinchan Date: Mon, 25 Oct 2021 15:36:47 +0800 Subject: [PATCH 2/3] update --- demo/restorer_basic_tutorial.ipynb | 2 +- demo/restorer_basic_tutorial_zh-CN.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/demo/restorer_basic_tutorial.ipynb b/demo/restorer_basic_tutorial.ipynb index 9d308cb710..bfde45ba61 100644 --- a/demo/restorer_basic_tutorial.ipynb +++ b/demo/restorer_basic_tutorial.ipynb @@ -1 +1 @@ -{"nbformat":4,"nbformat_minor":2,"metadata":{"accelerator":"GPU","colab":{"name":"restorer_basic_tutorial.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3.7.7 64-bit ('pre-commit': conda)","name":"python377jvsc74a57bd04974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"},"language_info":{"name":"python","version":""},"metadata":{"interpreter":{"hash":"4974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"}}},"cells":[{"cell_type":"markdown","source":["# MMEditing Basic Tutorial\n","\n","Welcome to MMEditing! This is the official Colab tutorial for MMEditing. In this tutorial you will learn how to train and test a restorer using the APIs provided in MMEditing. \n","\n","This is a quick guide for you to train and test existing models. If you want to develop you own models based on MMEditing and know more about the code structures, please refer to our comprehensive tutorial [here]().\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmedit/blob/main/demo/restorer_basic_tutorial.ipynb)\n","\n"],"metadata":{"id":"T2WWQiheMF7q"}},{"cell_type":"markdown","source":["## Install MMEditing\n","\n","MMEditing can be installed in two steps:\n","\n","1. Install a compatible PyTorch version (You need to check you CUDA version by using `nvcc -V`).\n","2. Install pre-compiled MMCV\n","3. Clone and install MMEditing\n","\n","The steps are shown below:"],"metadata":{"id":"-kYw3WQ0MQry"}},{"cell_type":"code","execution_count":null,"source":["# Check nvcc version\n","!nvcc -V\n","# Check GCC version (MMEditing needs gcc >= 5.0)\n","!gcc --version"],"outputs":[{"output_type":"stream","name":"stdout","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Wed_Jul_22_19:09:09_PDT_2020\n","Cuda compilation tools, release 11.0, V11.0.221\n","Build cuda_11.0_bu.TC445_37.28845127_0\n","gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uha_13idyl1b","executionInfo":{"status":"ok","timestamp":1625140540858,"user_tz":-480,"elapsed":321,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"87056561-5930-45b3-e1a8-f9e103d10b23"}},{"cell_type":"code","execution_count":null,"source":["# Install dependencies: (use cu101 because colab has CUDA 11.0)\n","!pip install -U torch==1.7.0+cu110 torchvision==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html\n","\n","# Install mmcv-full thus we could use CUDA operators\n","!pip install mmcv-full==1.3.5 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html\n","\n","# Clone MMEditing\n","!rm -rf mmediting\n","!git clone https://github.com/open-mmlab/mmediting.git\n","%cd mmediting\n","\n","# Install MMEditing\n","!pip install -r requirements.txt\n","!pip install -v -e ."],"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in links: https://download.pytorch.org/whl/torch_stable.html\n","Collecting torch==1.7.0+cu110\n","\u001b[?25l Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl 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4)) (2.5.0)\n","Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 5)) (0.31.0)\n","Collecting codecov\n"," Downloading https://files.pythonhosted.org/packages/93/9f/bbea5b6231308458963cb5c067bc5643da9949689702fa5a382714b59699/codecov-2.1.11-py2.py3-none-any.whl\n","Collecting flake8\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/fc/80/35a0716e5d5101e643404dabd20f07f5528a21f3ef4032d31a49c913237b/flake8-3.9.2-py2.py3-none-any.whl (73kB)\n","\u001b[K |████████████████████████████████| 81kB 9.7MB/s \n","\u001b[?25hCollecting interrogate\n"," Downloading https://files.pythonhosted.org/packages/cd/6d/ce3ac440b13c1b36b323a0eab191499a902adade3cc11b18078c07af3e6e/interrogate-1.4.0-py3-none-any.whl\n","Collecting isort==4.3.21\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/e5/b0/c121fd1fa3419ea9bfd55c7f9c4fedfec5143208d8c7ad3ce3db6c623c21/isort-4.3.21-py2.py3-none-any.whl (42kB)\n","\u001b[K |████████████████████████████████| 51kB 7.5MB/s \n","\u001b[?25hCollecting onnxruntime\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/f9/76/3d0f8bb2776961c7335693df06eccf8d099e48fa6fb552c7546867192603/onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5MB)\n","\u001b[K |████████████████████████████████| 4.5MB 37.4MB/s \n","\u001b[?25hRequirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 6)) (3.6.4)\n","Collecting pytest-runner\n"," Downloading https://files.pythonhosted.org/packages/f4/f5/6605d73bf3f4c198915872111b10c4b3c2dccd8485f47b7290ceef037190/pytest_runner-5.3.1-py3-none-any.whl\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (1.19.5)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (7.1.2)\n","Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (2.4.0)\n","Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (4.1.2.30)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (3.13)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.5.1)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.4.1)\n","Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) 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requirements/runtime.txt (line 4)) (1.8.0)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (2.23.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.4.4)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (57.0.0)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.12.0)\n","Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.12.4)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.3.4)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.31.0)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.0.1)\n","Requirement already satisfied: coverage in /usr/local/lib/python3.7/dist-packages (from codecov->-r requirements/tests.txt (line 1)) (3.7.1)\n","Collecting pyflakes<2.4.0,>=2.3.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6c/11/2a745612f1d3cbbd9c69ba14b1b43a35a2f5c3c81cd0124508c52c64307f/pyflakes-2.3.1-py2.py3-none-any.whl (68kB)\n","\u001b[K |████████████████████████████████| 71kB 9.8MB/s \n","\u001b[?25hCollecting pycodestyle<2.8.0,>=2.7.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/cc/227251b1471f129bc35e966bb0fceb005969023926d744139642d847b7ae/pycodestyle-2.7.0-py2.py3-none-any.whl (41kB)\n","\u001b[K |████████████████████████████████| 51kB 8.7MB/s \n","\u001b[?25hCollecting mccabe<0.7.0,>=0.6.0\n"," Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from flake8->-r requirements/tests.txt (line 2)) (4.5.0)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (0.8.9)\n","Collecting colorama\n"," Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (7.1.2)\n","Requirement already satisfied: toml in 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/tmp/pip-req-tracker-zk5q0q3z\n","Created requirements tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Created temporary directory: /tmp/pip-install-vr_vpseo\n","Obtaining file:///content/mmediting\n"," Added file:///content/mmediting to build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"," Running setup.py (path:/content/mmediting/setup.py) egg_info for package from file:///content/mmediting\n"," Running command python setup.py egg_info\n"," running egg_info\n"," creating mmedit.egg-info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," Source in /content/mmediting has version 0.8.0, which satisfies requirement mmedit==0.8.0 from file:///content/mmediting\n"," Removed mmedit==0.8.0 from file:///content/mmediting from build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Requirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (0.99)\n","Requirement already satisfied: mmcv-full>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (1.3.5)\n","Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (0.16.2)\n","Requirement already satisfied: tensorboard in 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chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (2021.5.30)\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->mmedit==0.8.0) (4.5.0)\n","Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (1.3.0)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->mmedit==0.8.0) (0.4.8)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.7.4.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (3.1.1)\n","Installing collected packages: mmedit\n"," Running setup.py develop for mmedit\n"," Running command /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmediting/setup.py'\"'\"'; __file__='\"'\"'/content/mmediting/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n"," running develop\n"," running egg_info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.7/dist-packages/mmedit.egg-link (link to .)\n"," Adding mmedit 0.8.0 to easy-install.pth file\n","\n"," Installed /content/mmediting\n","Successfully installed mmedit\n","Cleaning up...\n","Removed build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GIeIZEzZMfc0","executionInfo":{"status":"ok","timestamp":1625140820804,"user_tz":-480,"elapsed":279948,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"fe2e5ded-988d-4563-eb18-374344c316ef"}},{"cell_type":"markdown","source":["## Download necessary material for this demo\n","We will need some data and configuration files in this demo. We will download it and put it in `./demo_files/`"],"metadata":{"id":"QgX96Sc_3PcV"}},{"cell_type":"code","execution_count":null,"source":["!wget https://download.openmmlab.com/mmediting/demo_files.zip # download files\n","!unzip demo_files # unzip"],"outputs":[{"output_type":"stream","name":"stdout","text":["--2021-07-01 11:59:48-- https://download.openmmlab.com/mmediting/demo_files.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19215781 (18M) [application/zip]\n","Saving to: ‘demo_files.zip’\n","\n","demo_files.zip 100%[===================>] 18.33M 6.00MB/s in 3.1s \n","\n","2021-07-01 11:59:52 (6.00 MB/s) - ‘demo_files.zip’ saved [19215781/19215781]\n","\n","Archive: demo_files.zip\n"," creating: demo_files/\n"," inflating: demo_files/demo_config_EDVR.py \n"," inflating: demo_files/demo_config_BasicVSR.py \n"," creating: demo_files/lq_sequences/\n"," creating: demo_files/lq_sequences/calendar/\n"," inflating: demo_files/lq_sequences/calendar/00000006.png \n"," inflating: demo_files/lq_sequences/calendar/00000007.png \n"," inflating: demo_files/lq_sequences/calendar/00000010.png \n"," inflating: demo_files/lq_sequences/calendar/00000004.png \n"," inflating: demo_files/lq_sequences/calendar/00000003.png \n"," inflating: demo_files/lq_sequences/calendar/00000001.png \n"," inflating: demo_files/lq_sequences/calendar/00000000.png \n"," inflating: demo_files/lq_sequences/calendar/00000009.png \n"," inflating: demo_files/lq_sequences/calendar/00000008.png \n"," inflating: demo_files/lq_sequences/calendar/00000002.png \n"," inflating: demo_files/lq_sequences/calendar/00000005.png \n"," creating: demo_files/lq_sequences/city/\n"," inflating: demo_files/lq_sequences/city/00000006.png \n"," inflating: demo_files/lq_sequences/city/00000007.png \n"," inflating: demo_files/lq_sequences/city/00000010.png \n"," inflating: demo_files/lq_sequences/city/00000004.png \n"," inflating: demo_files/lq_sequences/city/00000003.png \n"," inflating: demo_files/lq_sequences/city/00000001.png \n"," inflating: demo_files/lq_sequences/city/00000000.png \n"," inflating: demo_files/lq_sequences/city/00000009.png \n"," inflating: demo_files/lq_sequences/city/00000008.png \n"," inflating: demo_files/lq_sequences/city/00000002.png \n"," inflating: demo_files/lq_sequences/city/00000005.png \n"," creating: demo_files/lq_sequences/.ipynb_checkpoints/\n"," creating: demo_files/gt_images/\n"," inflating: demo_files/gt_images/bird.png \n"," inflating: demo_files/gt_images/woman.png \n"," inflating: demo_files/gt_images/head.png \n"," inflating: demo_files/gt_images/baby.png \n"," inflating: demo_files/gt_images/butterfly.png \n"," inflating: demo_files/demo_config_SRCNN.py \n"," creating: demo_files/lq_images/\n"," extracting: demo_files/lq_images/bird.png \n"," extracting: demo_files/lq_images/woman.png \n"," extracting: demo_files/lq_images/head.png \n"," extracting: demo_files/lq_images/baby.png \n"," extracting: demo_files/lq_images/butterfly.png \n"," creating: demo_files/gt_sequences/\n"," creating: demo_files/gt_sequences/calendar/\n"," inflating: demo_files/gt_sequences/calendar/00000006.png \n"," inflating: demo_files/gt_sequences/calendar/00000007.png \n"," inflating: demo_files/gt_sequences/calendar/00000010.png \n"," inflating: demo_files/gt_sequences/calendar/00000004.png \n"," inflating: demo_files/gt_sequences/calendar/00000003.png \n"," inflating: demo_files/gt_sequences/calendar/00000001.png \n"," inflating: demo_files/gt_sequences/calendar/00000000.png \n"," inflating: demo_files/gt_sequences/calendar/00000009.png \n"," inflating: demo_files/gt_sequences/calendar/00000008.png \n"," inflating: demo_files/gt_sequences/calendar/00000002.png \n"," inflating: demo_files/gt_sequences/calendar/00000005.png \n"," creating: demo_files/gt_sequences/city/\n"," inflating: demo_files/gt_sequences/city/00000006.png \n"," inflating: demo_files/gt_sequences/city/00000007.png \n"," inflating: demo_files/gt_sequences/city/00000010.png \n"," inflating: demo_files/gt_sequences/city/00000004.png \n"," inflating: demo_files/gt_sequences/city/00000003.png \n"," inflating: demo_files/gt_sequences/city/00000001.png \n"," inflating: demo_files/gt_sequences/city/00000000.png \n"," inflating: demo_files/gt_sequences/city/00000009.png \n"," inflating: demo_files/gt_sequences/city/00000008.png \n"," inflating: demo_files/gt_sequences/city/00000002.png \n"," inflating: demo_files/gt_sequences/city/00000005.png \n"," creating: demo_files/gt_sequences/.ipynb_checkpoints/\n"," creating: demo_files/.ipynb_checkpoints/\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-K0zFSJ-3V42","executionInfo":{"status":"ok","timestamp":1625140825508,"user_tz":-480,"elapsed":4723,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"528a87f7-f78e-4219-84f3-dec19b88e88a"}},{"cell_type":"markdown","source":["## Inference with a pre-trained image restorer\n","You can easily perform inference on a single image with a pre-trained restorer by using `restoration_demo.py`. What you need are \n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer you want to use. It specifies the model you want to use. \n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model. \n","3. `IMAGE_FILE`: The path to the input image.\n","4. `SAVE_FILE`: The location where you want to store the output image.\n","5. `imshow`: Whether to show the image. (Optional)\n","6. `GPU_ID`: Which GPU you want to use. (Optional)\n","\n","Once you have all these details, you can directly use the following command:\n","\n","```\n","python demo/restoration_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} ${SAVE_FILE} [--imshow] [--device ${GPU_ID}]\n","```\n","\n","**Notes:** \n","1. Configuration files are located in `./configs`. \n","2. We support loading checkpoints from url. You can go to the corresponding page (e.g. [here](https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/esrgan)) to obtain the url of the pretrained model.\n","\n","---\n","\n","We will now use `SRCNN` and `ESRGAN` as examples.\n","\n"],"metadata":{"id":"zXGurqGKOeNE"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN\n","!python demo/restoration_demo.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth ./demo_files/lq_images/bird.png ./outputs/bird_SRCNN.png\n","\n","# ESRGAN\n","!python demo/restoration_demo.py ./configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth ./demo_files/lq_images/bird.png ./outputs/bird_ESRGAN.png\n","\n","# Check whether images are saved\n","!ls ./outputs"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\" to /root/.cache/torch/hub/checkpoints/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n","100% 83.9k/83.9k [00:00<00:00, 1.59MB/s]\n","2021-07-01 12:00:10,779 - mmedit - INFO - Use load_from_torchvision loader\n","Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n","100% 548M/548M [00:07<00:00, 76.0MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\" to /root/.cache/torch/hub/checkpoints/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n","100% 196M/196M [00:26<00:00, 7.61MB/s]\n","bird_ESRGAN.png bird_SRCNN.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KiPvtvlqM1zb","executionInfo":{"status":"ok","timestamp":1625140884175,"user_tz":-480,"elapsed":58677,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"be7375a7-4632-4770-8383-2a8ce654b069"}},{"cell_type":"markdown","source":["## Inference with a pre-trained video restorer\n","\n","MMEditing also supports video super-resolution methods, and the procedure is similar. You can use `restoration_video_demo.py` with the following arguments:\n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer you want to use\n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model. \n","3. `INPUT_DIR`: The directory containing the video frames.\n","4. `OUTPUT_DIR`: The location where you want to store the output frames.\n","5. `WINDOW_SIZE`: The window size if you are using sliding-window method (Optional).\n","6. `GPU_ID`: Which GPU you want to use (Optional).\n","```\n","python demo/restoration_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} [--window_size=$WINDOW_SIZE] [--device ${GPU_ID}]\n","```\n","**Note:** There are two different frameworks in video super-resolution: ***sliding-window*** and ***recurrent*** frameworks. When you use the methods of the sliding-window framework, such as EDVR, you need to specify `window_size`. This value is dependent on the model you use.\n","\n","---\n","\n","We will now use `EDVR` and `BasicVSR` as examples.\n","\n"],"metadata":{"id":"W1DfGHu3Xcfd"}},{"cell_type":"code","execution_count":null,"source":["# EDVR (Sliding-window framework)\n","!python demo/restoration_video_demo.py ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_EDVR --window_size=5\n","\n","# BasicVSR (Recurrent framework)\n","!python demo/restoration_video_demo.py ./configs/restorers/basicvsr/basicvsr_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_BasicVSR\n","\n","# Check whether video frames are saved\n","!ls ./outputs/calendar_BasicVSR"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\" to /root/.cache/torch/hub/checkpoints/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n","100% 11.5M/11.5M [00:01<00:00, 8.55MB/s]\n","2021-07-01 12:01:09,689 - mmedit - INFO - Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth\" to /root/.cache/torch/hub/checkpoints/spynet_20210409-c6c1bd09.pth\n","100% 5.50M/5.50M [00:00<00:00, 8.88MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\" to /root/.cache/torch/hub/checkpoints/basicvsr_reds4_20120409-0e599677.pth\n","100% 24.1M/24.1M [00:02<00:00, 8.97MB/s]\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iaoE7UF5Xb2i","executionInfo":{"status":"ok","timestamp":1625140913405,"user_tz":-480,"elapsed":29263,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"a022e0bd-c47a-450e-f4e4-1bf9f92e4813"}},{"cell_type":"markdown","source":["## Test on a pre-defined dataset using the configuration file\n","\n","The above demos provide an easy way to perform inference on a single image or video sequence. If you want to perform inference on a set of images or sequences, you can make use of the configuration files located in `./configs`.\n"," \n","Existing configuration files allow you to perform inference on common datasets, such as `Set5` in image super-resolution and `REDS4` in video super-resolution. You can use the following command:\n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer and dataset you want to use\n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model.\n","3. `GPU_NUM`: Number of GPUs used for test. \n","4. `RESULT_FILE`: The path to the output result pickle file. (Optional)\n","5. `IMAGE_SAVE_PATH`: The location where you want to store the output image. (Optional)\n","\n","```\n","# single-gpu testing\n","python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","\n","# multi-gpu testing\n","./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","```\n","What you need to do is to modify the `lq_folder` and `gt_folder` in the configuration file:\n","```\n","test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/val_set5/Set5_bicLRx4',\n"," gt_folder='data/val_set5/Set5',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","```\n","\n","**Note**: Some dataset type (e.g. `SRREDSDataset`) requires an annotation file specifying the details of the dataset. Please refer to the corresponding file\n","in `./mmedit/dataset/` for more details. \n","\n","---\n","\n","The following is the command for SRCNN. For other models, you can simply change the paths to the configuration file and pretrained model. \n"],"metadata":{"id":"Rf3LW57qMHXb"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu\n","!python tools/test.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/\n","\n","# multi-gpu testing\n","!./tools/dist_test.sh ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 1 --save-path ./outputs/"],"outputs":[{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"tools/test.py\", line 136, in \n"," main()\n"," File \"tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/test.py\", line 136, in \n"," main()\n"," File \"./tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/test.py', '--local_rank=0', './configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py', 'https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', '--launcher', 'pytorch', '--save-path', './outputs/']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"tClgIYgcbbVg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140927462,"user_tz":-480,"elapsed":14095,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"c37ec2de-e1c9-42ae-ed9b-31009d48ae64"}},{"cell_type":"markdown","source":["## Test on your own datasets\n","\n","When you want to test on your own datasets, you need to modify `test_dataset_type` in addition to the dataset paths. \n","\n","- For image super-resolution, you need to use `SRFolderDataset`\n","- For sliding-window framework in video super-resolution (e.g. EDVR, TDAN), you need to use `SRFolderVideoDataset`.\n","- For recurrent framework in video super-resolution (e.g. BasicVSR, IconVSR), you need to use `SRFolderMultipleGTDataset`.\n","\n","These dataset types assume that all images/sequences in the specified directory are used for test. The folder structures should be\n","```\n","| lq_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," | ...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","| gt_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," |...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","```\n","We will use **SRCNN**, **EDVR**, **BasicVSR** as examples. Please pay attention to the settings of `test_dataset_type` and `data['test']`. "],"metadata":{"id":"KWKVyeEQelh3"}},{"cell_type":"markdown","source":["**SRCNN**"],"metadata":{"id":"0p2rP8jV_dL1"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_SRCNN.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/testset_SRCNN\n","\n","# Check the output folder\n","!ls ./outputs/testset_SRCNN"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 5/5, 8.6 task/s, elapsed: 1s, ETA: 0s\n","Eval-PSNR: 28.433974369836108\n","Eval-SSIM: 0.8099053586583066\n","baby.png bird.png butterfly.png head.png woman.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4kEev4wVIq_L","executionInfo":{"status":"ok","timestamp":1625140936180,"user_tz":-480,"elapsed":8729,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"8026ed73-f781-4eb2-bb80-d3446bd131df"}},{"cell_type":"markdown","source":["**EDVR**"],"metadata":{"id":"RONzjTTU_gem"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_EDVR.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth --save-path ./outputs/testset_EDVR\n","\n","# # Check the output folder\n","!ls ./outputs/testset_EDVR\n","!ls ./outputs/testset_EDVR/city"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 22/22, 2.0 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 23.89569862011228\n","Eval-SSIM: 0.7667098470108678\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vL8WOWXY0fNJ","executionInfo":{"status":"ok","timestamp":1625140955813,"user_tz":-480,"elapsed":19671,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"dc2a8f81-9bef-4ad4-c5b2-c6f124e6b113"}},{"cell_type":"markdown","source":["**BasicVSR**"],"metadata":{"id":"5Tc7F-l5_i1e"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_BasicVSR.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth --save-path ./outputs/testset_BasicVSR\n","\n","# # Check the output folder\n","!ls ./outputs/testset_BasicVSR\n","!ls ./outputs/testset_BasicVSR/calendar"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:07,780 - mmedit - INFO - Use load_from_http loader\n","Use load_from_http loader\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","[>>] 2/2, 0.2 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 24.195768601433734\n","Eval-SSIM: 0.7828541339512978\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jpW5GWC74Yvu","executionInfo":{"status":"ok","timestamp":1625140976026,"user_tz":-480,"elapsed":20220,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"7ba02a32-d4ec-40b2-8108-ef0729b62147"}},{"cell_type":"markdown","source":["## Train a restorer on a pre-defined dataset\n","\n","MMEditing uses distributed training. The following command can be used for training. If you want to train on the pre-defined datasets specified in our configuration file, you can simply run the following command.\n","\n","```\n","./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n","```\n","\n","For more details about the optional arguments, please refer to `tools/train.py`.\n","\n","---\n","\n","Here is an example using EDVR.\n"],"metadata":{"id":"4DQxNL8BhI0y"}},{"cell_type":"code","execution_count":null,"source":["!./tools/dist_train.sh ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py 1"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:31,961 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:31,961 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:31,961 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:31,961 - mmedit - INFO - Config:\n","/content/mmediting/configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py\n","exp_name = 'edvrm_wotsa_x4_g8_600k_reds'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRREDSDataset'\n","val_dataset_type = 'SRREDSDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 600000\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50000, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=100,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 54, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 63, in load_annotations\n"," with open(self.ann_file, 'r') as fin:\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/train.py\", line 145, in \n"," main()\n"," File \"./tools/train.py\", line 111, in main\n"," datasets = [build_dataset(cfg.data.train)]\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 76, in build_dataset\n"," build_dataset(cfg['dataset'], default_args), cfg['times'])\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRREDSDataset: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/train.py', '--local_rank=0', './configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py', '--launcher', 'pytorch']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"s-hOnSF6ItQM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140985357,"user_tz":-480,"elapsed":9337,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"e40e960a-29e1-43e8-b922-5e08c4e98afe"}},{"cell_type":"markdown","source":["## Train a restorer on your own datasets\n","\n","Similar to the case when you want to test on your own datasets, you need to modify `train_dataset_type`. The dataset type you need is identical:\n","\n","- For image super-resolution, you need to use `SRFolderDataset`\n","- For sliding-window framework in video super-resolution (e.g. EDVR, TDAN), you need to use `SRFolderVideoDataset`.\n","- For recurrent framework in video super-resolution (e.g. BasicVSR, IconVSR), you need to use `SRFolderMultipleGTDataset`.\n","\n","After you modified the dataset type and the data path. You are all set to go."],"metadata":{"id":"b0VfQkQQjg8N"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN (Single Image Super-Resolution)\n","!./tools/dist_train.sh ./demo_files/demo_config_SRCNN.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:41,185 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:41,185 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:41,185 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:41,185 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_SRCNN.py\n","exp_name = 'srcnn_demo'\n","\n","scale = 4\n","# model settings\n","model = dict(\n"," type='BasicRestorer',\n"," generator=dict(\n"," type='SRCNN',\n"," channels=(3, 64, 32, 3),\n"," kernel_sizes=(9, 1, 5),\n"," upscale_factor=scale),\n"," pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderDataset'\n","val_dataset_type = 'SRFolderDataset'\n","train_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=128),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","test_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=8,\n"," train_dataloader=dict(samples_per_gpu=16, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=train_pipeline,\n"," scale=scale)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[250000, 250000, 250000, 250000],\n"," restart_weights=[1, 1, 1, 1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","evaluation = dict(interval=50, save_image=True, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./experiments/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:02:41,192 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/experiments/srcnn_demo\n","2021-07-01 12:02:41,192 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:02:41.529307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:03:18,631 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.000e-04, eta: 0:57:01, time: 34.560, data_time: 34.446, memory: 586, loss_pix: 0.3999, loss: 0.3999\n","2021-07-01 12:03:18,712 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","INFO:mmedit:Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","2021-07-01 12:03:18,764 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","INFO:mmedit:Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","2021-07-01 12:03:18,840 - 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mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:04:37,422 - mmedit - INFO - Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n","INFO:mmedit:Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"liGEKJpbIoXZ","executionInfo":{"status":"ok","timestamp":1625141113733,"user_tz":-480,"elapsed":128384,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"84e1502e-e2cb-458f-c7c5-e4b401e570b7"}},{"cell_type":"code","execution_count":null,"source":["# EDVR (Video Super-Resolution - Sliding Window)\n","!./tools/dist_train.sh ./demo_files/demo_config_EDVR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:10:12,619 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:10:12,619 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:10:12,619 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:10:12,619 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_EDVR.py\n","exp_name = 'edvrm_demo'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderVideoDataset'\n","val_dataset_type = 'SRFolderVideoDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:10:12,701 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/edvrm_demo\n","2021-07-01 12:10:12,702 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:10:12.951771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:10:30,703 - mmedit - INFO - Iter [1/100]\tlr_generator: 4.000e-04, eta: 0:26:53, time: 16.295, data_time: 15.833, memory: 1341, loss_pix: 63917.2734, loss: 63917.2734\n","2021-07-01 12:10:31,046 - mmedit - INFO - Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","INFO:mmedit:Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","2021-07-01 12:10:31,386 - mmedit - INFO - Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","INFO:mmedit:Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","2021-07-01 12:10:31,731 - mmedit - INFO - Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","INFO:mmedit:Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","2021-07-01 12:10:32,071 - mmedit - INFO - Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","INFO:mmedit:Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","2021-07-01 12:10:32,414 - mmedit - INFO - Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","INFO:mmedit:Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","2021-07-01 12:10:32,760 - 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Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"2fb26527-eb9e-4b48-e03c-ba9a91c60db8"}},{"cell_type":"code","execution_count":null,"source":["# BasicVSR (Video Super-Resolution - Recurrent)\n","!./tools/dist_train.sh ./demo_files/demo_config_BasicVSR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:06:47,253 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:06:47,253 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:06:47,254 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:06:47,254 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_BasicVSR.py\n","exp_name = 'basicvsr_demo'\n","\n","# model settings\n","model = dict(\n"," type='BasicVSR',\n"," generator=dict(\n"," type='BasicVSRNet',\n"," mid_channels=64,\n"," num_blocks=30,\n"," spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n"," 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = dict(fix_iter=5000)\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderMultipleGTDataset'\n","val_dataset_type = 'SRFolderMultipleGTDataset'\n","\n","train_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=6,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True), # 2 gpus\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),\n","\n"," # train\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," # val\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," # test\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(\n"," generator=dict(\n"," type='Adam',\n"," lr=2e-4,\n"," betas=(0.9, 0.99),\n"," paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[300000],\n"," restart_weights=[1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," # dict(type='TensorboardLoggerHook'),\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","find_unused_parameters = True\n","\n","2021-07-01 12:06:47,291 - mmedit - INFO - Use load_from_http loader\n","2021-07-01 12:06:47,569 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/basicvsr_demo\n","2021-07-01 12:06:47,569 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:07:14,210 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.500e-05, eta: 0:42:52, time: 25.981, data_time: 24.045, memory: 3464, loss_pix: 0.0634, loss: 0.0634\n","2021-07-01 12:07:15,171 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.500e-05, eta: 0:22:00, time: 0.961, data_time: 0.011, memory: 3518, loss_pix: 0.0556, loss: 0.0556\n","2021-07-01 12:07:16,052 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.500e-05, eta: 0:14:59, time: 0.881, data_time: 0.003, memory: 3518, loss_pix: 0.0476, loss: 0.0476\n","2021-07-01 12:07:16,940 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.500e-05, eta: 0:11:29, time: 0.888, data_time: 0.003, memory: 3518, loss_pix: 0.0673, loss: 0.0673\n","2021-07-01 12:07:17,829 - 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mmedit - INFO - Iter(val) [100]\tPSNR: 21.4372, SSIM: 0.5687\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_RdqmlT6qgt2","executionInfo":{"status":"ok","timestamp":1625141428032,"user_tz":-480,"elapsed":197033,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"b951b426-e06c-4f31-db01-449333eab333"}},{"cell_type":"markdown","source":["**This is the end of this tutorial. For more advanced usage, please see our comprehensive tutorial [here](). Enjoy coding with MMEditing!**"],"metadata":{"id":"QT0zwBFt7J13"}}]} +{"nbformat":4,"nbformat_minor":2,"metadata":{"accelerator":"GPU","colab":{"name":"restorer_basic_tutorial.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3.7.7 64-bit ('pre-commit': conda)","name":"python377jvsc74a57bd04974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"},"language_info":{"name":"python","version":""},"metadata":{"interpreter":{"hash":"4974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"}}},"cells":[{"cell_type":"markdown","source":["# MMEditing Basic Tutorial\n","\n","Welcome to MMEditing! This is the official Colab tutorial for MMEditing. In this tutorial you will learn how to train and test a restorer using the APIs provided in MMEditing. \n","\n","This is a quick guide for you to train and test existing models. If you want to develop you own models based on MMEditing and know more about the code structures, please refer to our comprehensive tutorial [here]().\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmedit/blob/main/demo/restorer_basic_tutorial.ipynb)\n","\n"],"metadata":{"id":"T2WWQiheMF7q"}},{"cell_type":"markdown","source":["## Install MMEditing\n","\n","MMEditing can be installed in three steps:\n","\n","1. Install a compatible PyTorch version (You need to check you CUDA version by using `nvcc -V`).\n","2. Install pre-compiled MMCV\n","3. Clone and install MMEditing\n","\n","The steps are shown below:"],"metadata":{"id":"-kYw3WQ0MQry"}},{"cell_type":"code","execution_count":null,"source":["# Check nvcc version\n","!nvcc -V\n","# Check GCC version (MMEditing needs gcc >= 5.0)\n","!gcc --version"],"outputs":[{"output_type":"stream","name":"stdout","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Wed_Jul_22_19:09:09_PDT_2020\n","Cuda compilation tools, release 11.0, V11.0.221\n","Build cuda_11.0_bu.TC445_37.28845127_0\n","gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uha_13idyl1b","executionInfo":{"status":"ok","timestamp":1625140540858,"user_tz":-480,"elapsed":321,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"87056561-5930-45b3-e1a8-f9e103d10b23"}},{"cell_type":"code","execution_count":null,"source":["# Install dependencies: (use cu101 because colab has CUDA 11.0)\n","!pip install -U torch==1.7.0+cu110 torchvision==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html\n","\n","# Install mmcv-full thus we could use CUDA operators\n","!pip install mmcv-full==1.3.5 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html\n","\n","# Clone MMEditing\n","!rm -rf mmediting\n","!git clone https://github.com/open-mmlab/mmediting.git\n","%cd mmediting\n","\n","# Install MMEditing\n","!pip install -r requirements.txt\n","!pip install -v -e ."],"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in links: https://download.pytorch.org/whl/torch_stable.html\n","Collecting torch==1.7.0+cu110\n","\u001b[?25l Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl 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requirements/runtime.txt (line 2)) (7.1.2)\n","Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (2.4.0)\n","Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (4.1.2.30)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (3.13)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.5.1)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.4.1)\n","Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) 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requirements/runtime.txt (line 4)) (1.8.0)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (2.23.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.4.4)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (57.0.0)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.12.0)\n","Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.12.4)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.3.4)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.31.0)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.0.1)\n","Requirement already satisfied: coverage in /usr/local/lib/python3.7/dist-packages (from codecov->-r requirements/tests.txt (line 1)) (3.7.1)\n","Collecting pyflakes<2.4.0,>=2.3.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6c/11/2a745612f1d3cbbd9c69ba14b1b43a35a2f5c3c81cd0124508c52c64307f/pyflakes-2.3.1-py2.py3-none-any.whl (68kB)\n","\u001b[K |████████████████████████████████| 71kB 9.8MB/s \n","\u001b[?25hCollecting pycodestyle<2.8.0,>=2.7.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/cc/227251b1471f129bc35e966bb0fceb005969023926d744139642d847b7ae/pycodestyle-2.7.0-py2.py3-none-any.whl (41kB)\n","\u001b[K |████████████████████████████████| 51kB 8.7MB/s \n","\u001b[?25hCollecting mccabe<0.7.0,>=0.6.0\n"," Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from flake8->-r requirements/tests.txt (line 2)) (4.5.0)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (0.8.9)\n","Collecting colorama\n"," Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (7.1.2)\n","Requirement already satisfied: toml in 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/tmp/pip-req-tracker-zk5q0q3z\n","Created requirements tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Created temporary directory: /tmp/pip-install-vr_vpseo\n","Obtaining file:///content/mmediting\n"," Added file:///content/mmediting to build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"," Running setup.py (path:/content/mmediting/setup.py) egg_info for package from file:///content/mmediting\n"," Running command python setup.py egg_info\n"," running egg_info\n"," creating mmedit.egg-info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 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already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.7.4.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (3.1.1)\n","Installing collected packages: mmedit\n"," Running setup.py develop for mmedit\n"," Running command /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmediting/setup.py'\"'\"'; __file__='\"'\"'/content/mmediting/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n"," running develop\n"," running egg_info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.7/dist-packages/mmedit.egg-link (link to .)\n"," Adding mmedit 0.8.0 to easy-install.pth file\n","\n"," Installed /content/mmediting\n","Successfully installed mmedit\n","Cleaning up...\n","Removed build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GIeIZEzZMfc0","executionInfo":{"status":"ok","timestamp":1625140820804,"user_tz":-480,"elapsed":279948,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"fe2e5ded-988d-4563-eb18-374344c316ef"}},{"cell_type":"markdown","source":["## Download necessary material for this demo\n","We will need some data and configuration files in this demo. We will download it and put it in `./demo_files/`"],"metadata":{"id":"QgX96Sc_3PcV"}},{"cell_type":"code","execution_count":null,"source":["!wget https://download.openmmlab.com/mmediting/demo_files.zip # download files\n","!unzip demo_files # unzip"],"outputs":[{"output_type":"stream","name":"stdout","text":["--2021-07-01 11:59:48-- https://download.openmmlab.com/mmediting/demo_files.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19215781 (18M) [application/zip]\n","Saving to: ‘demo_files.zip’\n","\n","demo_files.zip 100%[===================>] 18.33M 6.00MB/s in 3.1s \n","\n","2021-07-01 11:59:52 (6.00 MB/s) - ‘demo_files.zip’ saved [19215781/19215781]\n","\n","Archive: demo_files.zip\n"," creating: demo_files/\n"," inflating: demo_files/demo_config_EDVR.py \n"," inflating: demo_files/demo_config_BasicVSR.py \n"," creating: demo_files/lq_sequences/\n"," creating: demo_files/lq_sequences/calendar/\n"," inflating: demo_files/lq_sequences/calendar/00000006.png \n"," inflating: demo_files/lq_sequences/calendar/00000007.png \n"," inflating: demo_files/lq_sequences/calendar/00000010.png \n"," inflating: demo_files/lq_sequences/calendar/00000004.png \n"," inflating: demo_files/lq_sequences/calendar/00000003.png \n"," inflating: demo_files/lq_sequences/calendar/00000001.png \n"," inflating: demo_files/lq_sequences/calendar/00000000.png \n"," inflating: demo_files/lq_sequences/calendar/00000009.png \n"," inflating: demo_files/lq_sequences/calendar/00000008.png \n"," inflating: demo_files/lq_sequences/calendar/00000002.png \n"," inflating: demo_files/lq_sequences/calendar/00000005.png \n"," creating: demo_files/lq_sequences/city/\n"," inflating: demo_files/lq_sequences/city/00000006.png \n"," inflating: demo_files/lq_sequences/city/00000007.png \n"," inflating: demo_files/lq_sequences/city/00000010.png \n"," inflating: demo_files/lq_sequences/city/00000004.png \n"," inflating: demo_files/lq_sequences/city/00000003.png \n"," inflating: demo_files/lq_sequences/city/00000001.png \n"," inflating: demo_files/lq_sequences/city/00000000.png \n"," inflating: demo_files/lq_sequences/city/00000009.png \n"," inflating: demo_files/lq_sequences/city/00000008.png \n"," inflating: demo_files/lq_sequences/city/00000002.png \n"," inflating: demo_files/lq_sequences/city/00000005.png \n"," creating: demo_files/lq_sequences/.ipynb_checkpoints/\n"," creating: demo_files/gt_images/\n"," inflating: demo_files/gt_images/bird.png \n"," inflating: demo_files/gt_images/woman.png \n"," inflating: demo_files/gt_images/head.png \n"," inflating: demo_files/gt_images/baby.png \n"," inflating: demo_files/gt_images/butterfly.png \n"," inflating: demo_files/demo_config_SRCNN.py \n"," creating: demo_files/lq_images/\n"," extracting: demo_files/lq_images/bird.png \n"," extracting: demo_files/lq_images/woman.png \n"," extracting: demo_files/lq_images/head.png \n"," extracting: demo_files/lq_images/baby.png \n"," extracting: demo_files/lq_images/butterfly.png \n"," creating: demo_files/gt_sequences/\n"," creating: demo_files/gt_sequences/calendar/\n"," inflating: demo_files/gt_sequences/calendar/00000006.png \n"," inflating: demo_files/gt_sequences/calendar/00000007.png \n"," inflating: demo_files/gt_sequences/calendar/00000010.png \n"," inflating: demo_files/gt_sequences/calendar/00000004.png \n"," inflating: demo_files/gt_sequences/calendar/00000003.png \n"," inflating: demo_files/gt_sequences/calendar/00000001.png \n"," inflating: demo_files/gt_sequences/calendar/00000000.png \n"," inflating: demo_files/gt_sequences/calendar/00000009.png \n"," inflating: demo_files/gt_sequences/calendar/00000008.png \n"," inflating: demo_files/gt_sequences/calendar/00000002.png \n"," inflating: demo_files/gt_sequences/calendar/00000005.png \n"," creating: demo_files/gt_sequences/city/\n"," inflating: demo_files/gt_sequences/city/00000006.png \n"," inflating: demo_files/gt_sequences/city/00000007.png \n"," inflating: demo_files/gt_sequences/city/00000010.png \n"," inflating: demo_files/gt_sequences/city/00000004.png \n"," inflating: demo_files/gt_sequences/city/00000003.png \n"," inflating: demo_files/gt_sequences/city/00000001.png \n"," inflating: demo_files/gt_sequences/city/00000000.png \n"," inflating: demo_files/gt_sequences/city/00000009.png \n"," inflating: demo_files/gt_sequences/city/00000008.png \n"," inflating: demo_files/gt_sequences/city/00000002.png \n"," inflating: demo_files/gt_sequences/city/00000005.png \n"," creating: demo_files/gt_sequences/.ipynb_checkpoints/\n"," creating: demo_files/.ipynb_checkpoints/\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-K0zFSJ-3V42","executionInfo":{"status":"ok","timestamp":1625140825508,"user_tz":-480,"elapsed":4723,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"528a87f7-f78e-4219-84f3-dec19b88e88a"}},{"cell_type":"markdown","source":["## Inference with a pre-trained image restorer\n","You can easily perform inference on a single image with a pre-trained restorer by using `restoration_demo.py`. What you need are \n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer you want to use. It specifies the model you want to use. \n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model. \n","3. `IMAGE_FILE`: The path to the input image.\n","4. `SAVE_FILE`: The location where you want to store the output image.\n","5. `imshow`: Whether to show the image. (Optional)\n","6. `GPU_ID`: Which GPU you want to use. (Optional)\n","\n","Once you have all these details, you can directly use the following command:\n","\n","```\n","python demo/restoration_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} ${SAVE_FILE} [--imshow] [--device ${GPU_ID}]\n","```\n","\n","**Notes:** \n","1. Configuration files are located in `./configs`. \n","2. We support loading checkpoints from url. You can go to the corresponding page (e.g. [here](https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/esrgan)) to obtain the url of the pretrained model.\n","\n","---\n","\n","We will now use `SRCNN` and `ESRGAN` as examples.\n","\n"],"metadata":{"id":"zXGurqGKOeNE"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN\n","!python demo/restoration_demo.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth ./demo_files/lq_images/bird.png ./outputs/bird_SRCNN.png\n","\n","# ESRGAN\n","!python demo/restoration_demo.py ./configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth ./demo_files/lq_images/bird.png ./outputs/bird_ESRGAN.png\n","\n","# Check whether images are saved\n","!ls ./outputs"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\" to /root/.cache/torch/hub/checkpoints/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n","100% 83.9k/83.9k [00:00<00:00, 1.59MB/s]\n","2021-07-01 12:00:10,779 - mmedit - INFO - Use load_from_torchvision loader\n","Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n","100% 548M/548M [00:07<00:00, 76.0MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\" to /root/.cache/torch/hub/checkpoints/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n","100% 196M/196M [00:26<00:00, 7.61MB/s]\n","bird_ESRGAN.png bird_SRCNN.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KiPvtvlqM1zb","executionInfo":{"status":"ok","timestamp":1625140884175,"user_tz":-480,"elapsed":58677,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"be7375a7-4632-4770-8383-2a8ce654b069"}},{"cell_type":"markdown","source":["## Inference with a pre-trained video restorer\n","\n","MMEditing also supports video super-resolution methods, and the procedure is similar. You can use `restoration_video_demo.py` with the following arguments:\n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer you want to use\n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model. \n","3. `INPUT_DIR`: The directory containing the video frames.\n","4. `OUTPUT_DIR`: The location where you want to store the output frames.\n","5. `WINDOW_SIZE`: The window size if you are using sliding-window method (Optional).\n","6. `GPU_ID`: Which GPU you want to use (Optional).\n","```\n","python demo/restoration_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} [--window_size=$WINDOW_SIZE] [--device ${GPU_ID}]\n","```\n","**Note:** There are two different frameworks in video super-resolution: ***sliding-window*** and ***recurrent*** frameworks. When you use the methods of the sliding-window framework, such as EDVR, you need to specify `window_size`. This value is dependent on the model you use.\n","\n","---\n","\n","We will now use `EDVR` and `BasicVSR` as examples.\n","\n"],"metadata":{"id":"W1DfGHu3Xcfd"}},{"cell_type":"code","execution_count":null,"source":["# EDVR (Sliding-window framework)\n","!python demo/restoration_video_demo.py ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_EDVR --window_size=5\n","\n","# BasicVSR (Recurrent framework)\n","!python demo/restoration_video_demo.py ./configs/restorers/basicvsr/basicvsr_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_BasicVSR\n","\n","# Check whether video frames are saved\n","!ls ./outputs/calendar_BasicVSR"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\" to /root/.cache/torch/hub/checkpoints/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n","100% 11.5M/11.5M [00:01<00:00, 8.55MB/s]\n","2021-07-01 12:01:09,689 - mmedit - INFO - Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth\" to /root/.cache/torch/hub/checkpoints/spynet_20210409-c6c1bd09.pth\n","100% 5.50M/5.50M [00:00<00:00, 8.88MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\" to /root/.cache/torch/hub/checkpoints/basicvsr_reds4_20120409-0e599677.pth\n","100% 24.1M/24.1M [00:02<00:00, 8.97MB/s]\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iaoE7UF5Xb2i","executionInfo":{"status":"ok","timestamp":1625140913405,"user_tz":-480,"elapsed":29263,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"a022e0bd-c47a-450e-f4e4-1bf9f92e4813"}},{"cell_type":"markdown","source":["## Test on a pre-defined dataset using the configuration file\n","\n","The above demos provide an easy way to perform inference on a single image or video sequence. If you want to perform inference on a set of images or sequences, you can make use of the configuration files located in `./configs`.\n"," \n","Existing configuration files allow you to perform inference on common datasets, such as `Set5` in image super-resolution and `REDS4` in video super-resolution. You can use the following command:\n","\n","1. `CONFIG_FILE`: The configuration file corresponding to the restorer and dataset you want to use\n","2. `CHECKPOINT_FILE`: The path to the checkpoint of the pre-trained model.\n","3. `GPU_NUM`: Number of GPUs used for test. \n","4. `RESULT_FILE`: The path to the output result pickle file. (Optional)\n","5. `IMAGE_SAVE_PATH`: The location where you want to store the output image. (Optional)\n","\n","```\n","# single-gpu testing\n","python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","\n","# multi-gpu testing\n","./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","```\n","What you need to do is to modify the `lq_folder` and `gt_folder` in the configuration file:\n","```\n","test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/val_set5/Set5_bicLRx4',\n"," gt_folder='data/val_set5/Set5',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","```\n","\n","**Note**: Some dataset type (e.g. `SRREDSDataset`) requires an annotation file specifying the details of the dataset. Please refer to the corresponding file\n","in `./mmedit/dataset/` for more details. \n","\n","---\n","\n","The following is the command for SRCNN. For other models, you can simply change the paths to the configuration file and pretrained model. \n"],"metadata":{"id":"Rf3LW57qMHXb"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu\n","!python tools/test.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/\n","\n","# multi-gpu testing\n","!./tools/dist_test.sh ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 1 --save-path ./outputs/"],"outputs":[{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"tools/test.py\", line 136, in \n"," main()\n"," File \"tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/test.py\", line 136, in \n"," main()\n"," File \"./tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/test.py', '--local_rank=0', './configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py', 'https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', '--launcher', 'pytorch', '--save-path', './outputs/']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"tClgIYgcbbVg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140927462,"user_tz":-480,"elapsed":14095,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"c37ec2de-e1c9-42ae-ed9b-31009d48ae64"}},{"cell_type":"markdown","source":["## Test on your own datasets\n","\n","When you want to test on your own datasets, you need to modify `test_dataset_type` in addition to the dataset paths. \n","\n","- For image super-resolution, you need to use `SRFolderDataset`\n","- For sliding-window framework in video super-resolution (e.g. EDVR, TDAN), you need to use `SRFolderVideoDataset`.\n","- For recurrent framework in video super-resolution (e.g. BasicVSR, IconVSR), you need to use `SRFolderMultipleGTDataset`.\n","\n","These dataset types assume that all images/sequences in the specified directory are used for test. The folder structures should be\n","```\n","| lq_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," | ...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","| gt_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," |...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","```\n","We will use **SRCNN**, **EDVR**, **BasicVSR** as examples. Please pay attention to the settings of `test_dataset_type` and `data['test']`. "],"metadata":{"id":"KWKVyeEQelh3"}},{"cell_type":"markdown","source":["**SRCNN**"],"metadata":{"id":"0p2rP8jV_dL1"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_SRCNN.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/testset_SRCNN\n","\n","# Check the output folder\n","!ls ./outputs/testset_SRCNN"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 5/5, 8.6 task/s, elapsed: 1s, ETA: 0s\n","Eval-PSNR: 28.433974369836108\n","Eval-SSIM: 0.8099053586583066\n","baby.png bird.png butterfly.png head.png woman.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4kEev4wVIq_L","executionInfo":{"status":"ok","timestamp":1625140936180,"user_tz":-480,"elapsed":8729,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"8026ed73-f781-4eb2-bb80-d3446bd131df"}},{"cell_type":"markdown","source":["**EDVR**"],"metadata":{"id":"RONzjTTU_gem"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_EDVR.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth --save-path ./outputs/testset_EDVR\n","\n","# # Check the output folder\n","!ls ./outputs/testset_EDVR\n","!ls ./outputs/testset_EDVR/city"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 22/22, 2.0 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 23.89569862011228\n","Eval-SSIM: 0.7667098470108678\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vL8WOWXY0fNJ","executionInfo":{"status":"ok","timestamp":1625140955813,"user_tz":-480,"elapsed":19671,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"dc2a8f81-9bef-4ad4-c5b2-c6f124e6b113"}},{"cell_type":"markdown","source":["**BasicVSR**"],"metadata":{"id":"5Tc7F-l5_i1e"}},{"cell_type":"code","execution_count":null,"source":["# single-gpu (Colab has one GPU only)\n","!python tools/test.py ./demo_files/demo_config_BasicVSR.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth --save-path ./outputs/testset_BasicVSR\n","\n","# # Check the output folder\n","!ls ./outputs/testset_BasicVSR\n","!ls ./outputs/testset_BasicVSR/calendar"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:07,780 - mmedit - INFO - Use load_from_http loader\n","Use load_from_http loader\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","[>>] 2/2, 0.2 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 24.195768601433734\n","Eval-SSIM: 0.7828541339512978\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jpW5GWC74Yvu","executionInfo":{"status":"ok","timestamp":1625140976026,"user_tz":-480,"elapsed":20220,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"7ba02a32-d4ec-40b2-8108-ef0729b62147"}},{"cell_type":"markdown","source":["## Train a restorer on a pre-defined dataset\n","\n","MMEditing uses distributed training. The following command can be used for training. If you want to train on the pre-defined datasets specified in our configuration file, you can simply run the following command.\n","\n","```\n","./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n","```\n","\n","For more details about the optional arguments, please refer to `tools/train.py`.\n","\n","---\n","\n","Here is an example using EDVR.\n"],"metadata":{"id":"4DQxNL8BhI0y"}},{"cell_type":"code","execution_count":null,"source":["!./tools/dist_train.sh ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py 1"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:31,961 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:31,961 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:31,961 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:31,961 - mmedit - INFO - Config:\n","/content/mmediting/configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py\n","exp_name = 'edvrm_wotsa_x4_g8_600k_reds'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRREDSDataset'\n","val_dataset_type = 'SRREDSDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 600000\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50000, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=100,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 54, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 63, in load_annotations\n"," with open(self.ann_file, 'r') as fin:\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/train.py\", line 145, in \n"," main()\n"," File \"./tools/train.py\", line 111, in main\n"," datasets = [build_dataset(cfg.data.train)]\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 76, in build_dataset\n"," build_dataset(cfg['dataset'], default_args), cfg['times'])\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRREDSDataset: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/train.py', '--local_rank=0', './configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py', '--launcher', 'pytorch']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"s-hOnSF6ItQM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140985357,"user_tz":-480,"elapsed":9337,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"e40e960a-29e1-43e8-b922-5e08c4e98afe"}},{"cell_type":"markdown","source":["## Train a restorer on your own datasets\n","\n","Similar to the case when you want to test on your own datasets, you need to modify `train_dataset_type`. The dataset type you need is identical:\n","\n","- For image super-resolution, you need to use `SRFolderDataset`\n","- For sliding-window framework in video super-resolution (e.g. EDVR, TDAN), you need to use `SRFolderVideoDataset`.\n","- For recurrent framework in video super-resolution (e.g. BasicVSR, IconVSR), you need to use `SRFolderMultipleGTDataset`.\n","\n","After you modified the dataset type and the data path. You are all set to go."],"metadata":{"id":"b0VfQkQQjg8N"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN (Single Image Super-Resolution)\n","!./tools/dist_train.sh ./demo_files/demo_config_SRCNN.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:41,185 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:41,185 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:41,185 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:41,185 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_SRCNN.py\n","exp_name = 'srcnn_demo'\n","\n","scale = 4\n","# model settings\n","model = dict(\n"," type='BasicRestorer',\n"," generator=dict(\n"," type='SRCNN',\n"," channels=(3, 64, 32, 3),\n"," kernel_sizes=(9, 1, 5),\n"," upscale_factor=scale),\n"," pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderDataset'\n","val_dataset_type = 'SRFolderDataset'\n","train_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=128),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","test_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=8,\n"," train_dataloader=dict(samples_per_gpu=16, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=train_pipeline,\n"," scale=scale)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[250000, 250000, 250000, 250000],\n"," restart_weights=[1, 1, 1, 1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","evaluation = dict(interval=50, save_image=True, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./experiments/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:02:41,192 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/experiments/srcnn_demo\n","2021-07-01 12:02:41,192 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:02:41.529307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:03:18,631 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.000e-04, eta: 0:57:01, time: 34.560, data_time: 34.446, memory: 586, loss_pix: 0.3999, loss: 0.3999\n","2021-07-01 12:03:18,712 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","INFO:mmedit:Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","2021-07-01 12:03:18,764 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","INFO:mmedit:Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","2021-07-01 12:03:18,840 - 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mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:04:37,422 - mmedit - INFO - Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n","INFO:mmedit:Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"liGEKJpbIoXZ","executionInfo":{"status":"ok","timestamp":1625141113733,"user_tz":-480,"elapsed":128384,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"84e1502e-e2cb-458f-c7c5-e4b401e570b7"}},{"cell_type":"code","execution_count":null,"source":["# EDVR (Video Super-Resolution - Sliding Window)\n","!./tools/dist_train.sh ./demo_files/demo_config_EDVR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:10:12,619 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:10:12,619 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:10:12,619 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:10:12,619 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_EDVR.py\n","exp_name = 'edvrm_demo'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderVideoDataset'\n","val_dataset_type = 'SRFolderVideoDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:10:12,701 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/edvrm_demo\n","2021-07-01 12:10:12,702 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:10:12.951771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:10:30,703 - mmedit - INFO - Iter [1/100]\tlr_generator: 4.000e-04, eta: 0:26:53, time: 16.295, data_time: 15.833, memory: 1341, loss_pix: 63917.2734, loss: 63917.2734\n","2021-07-01 12:10:31,046 - mmedit - INFO - Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","INFO:mmedit:Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","2021-07-01 12:10:31,386 - mmedit - INFO - Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","INFO:mmedit:Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","2021-07-01 12:10:31,731 - mmedit - INFO - Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","INFO:mmedit:Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","2021-07-01 12:10:32,071 - mmedit - INFO - Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","INFO:mmedit:Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","2021-07-01 12:10:32,414 - mmedit - INFO - Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","INFO:mmedit:Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","2021-07-01 12:10:32,760 - 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Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"2fb26527-eb9e-4b48-e03c-ba9a91c60db8"}},{"cell_type":"code","execution_count":null,"source":["# BasicVSR (Video Super-Resolution - Recurrent)\n","!./tools/dist_train.sh ./demo_files/demo_config_BasicVSR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:06:47,253 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:06:47,253 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:06:47,254 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:06:47,254 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_BasicVSR.py\n","exp_name = 'basicvsr_demo'\n","\n","# model settings\n","model = dict(\n"," type='BasicVSR',\n"," generator=dict(\n"," type='BasicVSRNet',\n"," mid_channels=64,\n"," num_blocks=30,\n"," spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n"," 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = dict(fix_iter=5000)\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderMultipleGTDataset'\n","val_dataset_type = 'SRFolderMultipleGTDataset'\n","\n","train_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=6,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True), # 2 gpus\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),\n","\n"," # train\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," # val\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," # test\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(\n"," generator=dict(\n"," type='Adam',\n"," lr=2e-4,\n"," betas=(0.9, 0.99),\n"," paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[300000],\n"," restart_weights=[1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," # dict(type='TensorboardLoggerHook'),\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","find_unused_parameters = True\n","\n","2021-07-01 12:06:47,291 - mmedit - INFO - Use load_from_http loader\n","2021-07-01 12:06:47,569 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/basicvsr_demo\n","2021-07-01 12:06:47,569 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:07:14,210 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.500e-05, eta: 0:42:52, time: 25.981, data_time: 24.045, memory: 3464, loss_pix: 0.0634, loss: 0.0634\n","2021-07-01 12:07:15,171 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.500e-05, eta: 0:22:00, time: 0.961, data_time: 0.011, memory: 3518, loss_pix: 0.0556, loss: 0.0556\n","2021-07-01 12:07:16,052 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.500e-05, eta: 0:14:59, time: 0.881, data_time: 0.003, memory: 3518, loss_pix: 0.0476, loss: 0.0476\n","2021-07-01 12:07:16,940 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.500e-05, eta: 0:11:29, time: 0.888, data_time: 0.003, memory: 3518, loss_pix: 0.0673, loss: 0.0673\n","2021-07-01 12:07:17,829 - 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mmedit - INFO - Iter(val) [100]\tPSNR: 21.4372, SSIM: 0.5687\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_RdqmlT6qgt2","executionInfo":{"status":"ok","timestamp":1625141428032,"user_tz":-480,"elapsed":197033,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"b951b426-e06c-4f31-db01-449333eab333"}},{"cell_type":"markdown","source":["**This is the end of this tutorial. For more advanced usage, please see our comprehensive tutorial [here](). Enjoy coding with MMEditing!**"],"metadata":{"id":"QT0zwBFt7J13"}}]} diff --git a/demo/restorer_basic_tutorial_zh-CN.ipynb b/demo/restorer_basic_tutorial_zh-CN.ipynb index 10dc2f6a76..d62a54437a 100644 --- a/demo/restorer_basic_tutorial_zh-CN.ipynb +++ b/demo/restorer_basic_tutorial_zh-CN.ipynb @@ -1 +1 @@ -{"nbformat":4,"nbformat_minor":2,"metadata":{"accelerator":"GPU","colab":{"name":"restorer_basic_tutorial.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3.7.7 64-bit ('pre-commit': conda)","name":"python377jvsc74a57bd04974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"},"language_info":{"name":"python","version":""},"metadata":{"interpreter":{"hash":"4974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"}}},"cells":[{"cell_type":"markdown","source":["# MMEditing 基础教程\n","\n","欢迎来到MMEditing！ 这是 MMEditing 的官方 Colab 教程。 在本教程中，您将学习如何使用 MMEditing 中提供的 API 训练和测试恢复器。\n","\n","这是训练和测试现有模型的快速指南。 如果您想基于 MMEditing 开发自己的模型并了解有关代码结构的更多信息，请参阅我们的综合教程 [此处]()。\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmedit/blob/main/demo/restorer_basic_tutorial.ipynb)\n","\n"],"metadata":{"id":"T2WWQiheMF7q"}},{"cell_type":"markdown","source":["## 安装MMEditing\n","\n","MMEditing 可以分两步安装：\n","\n","1. 安装兼容的 PyTorch 版本（你需要使用 `nvcc -V` 检查你的 CUDA 版本）。\n","2. 安装预编译的MMCV\n","3. 克隆并安装MMEditing\n","\n","步骤如下所示："],"metadata":{"id":"-kYw3WQ0MQry"}},{"cell_type":"code","execution_count":null,"source":["# Check nvcc version\n","!nvcc -V\n","# Check GCC version (MMEditing needs gcc >= 5.0)\n","!gcc --version"],"outputs":[{"output_type":"stream","name":"stdout","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Wed_Jul_22_19:09:09_PDT_2020\n","Cuda compilation tools, release 11.0, V11.0.221\n","Build cuda_11.0_bu.TC445_37.28845127_0\n","gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uha_13idyl1b","executionInfo":{"status":"ok","timestamp":1625140540858,"user_tz":-480,"elapsed":321,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"87056561-5930-45b3-e1a8-f9e103d10b23"}},{"cell_type":"code","execution_count":null,"source":["# Install dependencies: (use cu101 because colab has CUDA 11.0)\n","!pip install -U torch==1.7.0+cu110 torchvision==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html\n","\n","# Install mmcv-full thus we could use CUDA operators\n","!pip install mmcv-full==1.3.5 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html\n","\n","# Clone MMEditing\n","!rm -rf mmediting\n","!git clone https://github.com/open-mmlab/mmediting.git\n","%cd mmediting\n","\n","# Install MMEditing\n","!pip install -r requirements.txt\n","!pip install -v -e ."],"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in links: https://download.pytorch.org/whl/torch_stable.html\n","Collecting torch==1.7.0+cu110\n","\u001b[?25l Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl 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4)) (2.5.0)\n","Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 5)) (0.31.0)\n","Collecting codecov\n"," Downloading https://files.pythonhosted.org/packages/93/9f/bbea5b6231308458963cb5c067bc5643da9949689702fa5a382714b59699/codecov-2.1.11-py2.py3-none-any.whl\n","Collecting flake8\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/fc/80/35a0716e5d5101e643404dabd20f07f5528a21f3ef4032d31a49c913237b/flake8-3.9.2-py2.py3-none-any.whl (73kB)\n","\u001b[K |████████████████████████████████| 81kB 9.7MB/s \n","\u001b[?25hCollecting interrogate\n"," Downloading https://files.pythonhosted.org/packages/cd/6d/ce3ac440b13c1b36b323a0eab191499a902adade3cc11b18078c07af3e6e/interrogate-1.4.0-py3-none-any.whl\n","Collecting isort==4.3.21\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/e5/b0/c121fd1fa3419ea9bfd55c7f9c4fedfec5143208d8c7ad3ce3db6c623c21/isort-4.3.21-py2.py3-none-any.whl (42kB)\n","\u001b[K |████████████████████████████████| 51kB 7.5MB/s \n","\u001b[?25hCollecting onnxruntime\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/f9/76/3d0f8bb2776961c7335693df06eccf8d099e48fa6fb552c7546867192603/onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5MB)\n","\u001b[K |████████████████████████████████| 4.5MB 37.4MB/s \n","\u001b[?25hRequirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 6)) (3.6.4)\n","Collecting pytest-runner\n"," Downloading https://files.pythonhosted.org/packages/f4/f5/6605d73bf3f4c198915872111b10c4b3c2dccd8485f47b7290ceef037190/pytest_runner-5.3.1-py3-none-any.whl\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (1.19.5)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (7.1.2)\n","Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (2.4.0)\n","Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (4.1.2.30)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (3.13)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.5.1)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.4.1)\n","Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) 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requirements/runtime.txt (line 4)) (1.8.0)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (2.23.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.4.4)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (57.0.0)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.12.0)\n","Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.12.4)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.3.4)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.31.0)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.0.1)\n","Requirement already satisfied: coverage in /usr/local/lib/python3.7/dist-packages (from codecov->-r requirements/tests.txt (line 1)) (3.7.1)\n","Collecting pyflakes<2.4.0,>=2.3.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6c/11/2a745612f1d3cbbd9c69ba14b1b43a35a2f5c3c81cd0124508c52c64307f/pyflakes-2.3.1-py2.py3-none-any.whl (68kB)\n","\u001b[K |████████████████████████████████| 71kB 9.8MB/s \n","\u001b[?25hCollecting pycodestyle<2.8.0,>=2.7.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/cc/227251b1471f129bc35e966bb0fceb005969023926d744139642d847b7ae/pycodestyle-2.7.0-py2.py3-none-any.whl (41kB)\n","\u001b[K |████████████████████████████████| 51kB 8.7MB/s \n","\u001b[?25hCollecting mccabe<0.7.0,>=0.6.0\n"," Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from flake8->-r requirements/tests.txt (line 2)) (4.5.0)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (0.8.9)\n","Collecting colorama\n"," Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (7.1.2)\n","Requirement already satisfied: toml in 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/tmp/pip-req-tracker-zk5q0q3z\n","Created requirements tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Created temporary directory: /tmp/pip-install-vr_vpseo\n","Obtaining file:///content/mmediting\n"," Added file:///content/mmediting to build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"," Running setup.py (path:/content/mmediting/setup.py) egg_info for package from file:///content/mmediting\n"," Running command python setup.py egg_info\n"," running egg_info\n"," creating mmedit.egg-info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," Source in /content/mmediting has version 0.8.0, which satisfies requirement mmedit==0.8.0 from file:///content/mmediting\n"," Removed mmedit==0.8.0 from file:///content/mmediting from build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Requirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (0.99)\n","Requirement already satisfied: mmcv-full>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (1.3.5)\n","Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (0.16.2)\n","Requirement already satisfied: tensorboard in 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chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (2021.5.30)\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->mmedit==0.8.0) (4.5.0)\n","Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (1.3.0)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->mmedit==0.8.0) (0.4.8)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.7.4.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (3.1.1)\n","Installing collected packages: mmedit\n"," Running setup.py develop for mmedit\n"," Running command /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmediting/setup.py'\"'\"'; __file__='\"'\"'/content/mmediting/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n"," running develop\n"," running egg_info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.7/dist-packages/mmedit.egg-link (link to .)\n"," Adding mmedit 0.8.0 to easy-install.pth file\n","\n"," Installed /content/mmediting\n","Successfully installed mmedit\n","Cleaning up...\n","Removed build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GIeIZEzZMfc0","executionInfo":{"status":"ok","timestamp":1625140820804,"user_tz":-480,"elapsed":279948,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"fe2e5ded-988d-4563-eb18-374344c316ef"}},{"cell_type":"markdown","source":["## 下载此演示所需的材料\n","在这个演示中，我们将需要一些数据和配置文件。我们将下载并放入 `./demo_files/`"],"metadata":{"id":"QgX96Sc_3PcV"}},{"cell_type":"code","execution_count":null,"source":["!wget https://download.openmmlab.com/mmediting/demo_files.zip # 下载文件\n","!unzip demo_files # 解压"],"outputs":[{"output_type":"stream","name":"stdout","text":["--2021-07-01 11:59:48-- https://download.openmmlab.com/mmediting/demo_files.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19215781 (18M) [application/zip]\n","Saving to: ‘demo_files.zip’\n","\n","demo_files.zip 100%[===================>] 18.33M 6.00MB/s in 3.1s \n","\n","2021-07-01 11:59:52 (6.00 MB/s) - ‘demo_files.zip’ saved [19215781/19215781]\n","\n","Archive: demo_files.zip\n"," creating: demo_files/\n"," inflating: demo_files/demo_config_EDVR.py \n"," inflating: demo_files/demo_config_BasicVSR.py \n"," creating: demo_files/lq_sequences/\n"," creating: demo_files/lq_sequences/calendar/\n"," inflating: demo_files/lq_sequences/calendar/00000006.png \n"," inflating: demo_files/lq_sequences/calendar/00000007.png \n"," inflating: demo_files/lq_sequences/calendar/00000010.png \n"," inflating: demo_files/lq_sequences/calendar/00000004.png \n"," inflating: demo_files/lq_sequences/calendar/00000003.png \n"," inflating: demo_files/lq_sequences/calendar/00000001.png \n"," inflating: demo_files/lq_sequences/calendar/00000000.png \n"," inflating: demo_files/lq_sequences/calendar/00000009.png \n"," inflating: demo_files/lq_sequences/calendar/00000008.png \n"," inflating: demo_files/lq_sequences/calendar/00000002.png \n"," inflating: demo_files/lq_sequences/calendar/00000005.png \n"," creating: demo_files/lq_sequences/city/\n"," inflating: demo_files/lq_sequences/city/00000006.png \n"," inflating: demo_files/lq_sequences/city/00000007.png \n"," inflating: demo_files/lq_sequences/city/00000010.png \n"," inflating: demo_files/lq_sequences/city/00000004.png \n"," inflating: demo_files/lq_sequences/city/00000003.png \n"," inflating: demo_files/lq_sequences/city/00000001.png \n"," inflating: demo_files/lq_sequences/city/00000000.png \n"," inflating: demo_files/lq_sequences/city/00000009.png \n"," inflating: demo_files/lq_sequences/city/00000008.png \n"," inflating: demo_files/lq_sequences/city/00000002.png \n"," inflating: demo_files/lq_sequences/city/00000005.png \n"," creating: demo_files/lq_sequences/.ipynb_checkpoints/\n"," creating: demo_files/gt_images/\n"," inflating: demo_files/gt_images/bird.png \n"," inflating: demo_files/gt_images/woman.png \n"," inflating: demo_files/gt_images/head.png \n"," inflating: demo_files/gt_images/baby.png \n"," inflating: demo_files/gt_images/butterfly.png \n"," inflating: demo_files/demo_config_SRCNN.py \n"," creating: demo_files/lq_images/\n"," extracting: demo_files/lq_images/bird.png \n"," extracting: demo_files/lq_images/woman.png \n"," extracting: demo_files/lq_images/head.png \n"," extracting: demo_files/lq_images/baby.png \n"," extracting: demo_files/lq_images/butterfly.png \n"," creating: demo_files/gt_sequences/\n"," creating: demo_files/gt_sequences/calendar/\n"," inflating: demo_files/gt_sequences/calendar/00000006.png \n"," inflating: demo_files/gt_sequences/calendar/00000007.png \n"," inflating: demo_files/gt_sequences/calendar/00000010.png \n"," inflating: demo_files/gt_sequences/calendar/00000004.png \n"," inflating: demo_files/gt_sequences/calendar/00000003.png \n"," inflating: demo_files/gt_sequences/calendar/00000001.png \n"," inflating: demo_files/gt_sequences/calendar/00000000.png \n"," inflating: demo_files/gt_sequences/calendar/00000009.png \n"," inflating: demo_files/gt_sequences/calendar/00000008.png \n"," inflating: demo_files/gt_sequences/calendar/00000002.png \n"," inflating: demo_files/gt_sequences/calendar/00000005.png \n"," creating: demo_files/gt_sequences/city/\n"," inflating: demo_files/gt_sequences/city/00000006.png \n"," inflating: demo_files/gt_sequences/city/00000007.png \n"," inflating: demo_files/gt_sequences/city/00000010.png \n"," inflating: demo_files/gt_sequences/city/00000004.png \n"," inflating: demo_files/gt_sequences/city/00000003.png \n"," inflating: demo_files/gt_sequences/city/00000001.png \n"," inflating: demo_files/gt_sequences/city/00000000.png \n"," inflating: demo_files/gt_sequences/city/00000009.png \n"," inflating: demo_files/gt_sequences/city/00000008.png \n"," inflating: demo_files/gt_sequences/city/00000002.png \n"," inflating: demo_files/gt_sequences/city/00000005.png \n"," creating: demo_files/gt_sequences/.ipynb_checkpoints/\n"," creating: demo_files/.ipynb_checkpoints/\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-K0zFSJ-3V42","executionInfo":{"status":"ok","timestamp":1625140825508,"user_tz":-480,"elapsed":4723,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"528a87f7-f78e-4219-84f3-dec19b88e88a"}},{"cell_type":"markdown","source":["## 使用预训练的图像恢复器进行推理\n","您可以使用 “restoration_demo.py” 轻松地使用预训练的恢复器对单个图像进行推理。您需要的是\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `IMAGE_FILE`：输入图像的路径。\n","4. `SAVE_FILE`：您要存储输出图像的位置。\n","5. `imshow`：是否显示图片。（可选的）\n","6. `GPU_ID`：您想使用哪个 GPU。（可选的）\n","\n","获得所有这些详细信息后，您可以直接使用以下命令：\n","\n","```\n","python demo/restoration_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} ${SAVE_FILE} [--imshow] [--device ${GPU_ID}]\n","```\n","\n","**注:** \n","1. 配置文件位于 `./configs`。\n","2. 我们支持从 url 加载权重文件。您可以到相应页面（例如[这里](https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/esrgan)）获取预训练模型的url。\n","\n","---\n","\n","我们现在将使用 `SRCNN` 和 `ESRGAN` 作为示例。\n","\n"],"metadata":{"id":"zXGurqGKOeNE"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN\n","!python demo/restoration_demo.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth ./demo_files/lq_images/bird.png ./outputs/bird_SRCNN.png\n","\n","# ESRGAN\n","!python demo/restoration_demo.py ./configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth ./demo_files/lq_images/bird.png ./outputs/bird_ESRGAN.png\n","\n","# 检查图像是否已保存\n","!ls ./outputs"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\" to /root/.cache/torch/hub/checkpoints/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n","100% 83.9k/83.9k [00:00<00:00, 1.59MB/s]\n","2021-07-01 12:00:10,779 - mmedit - INFO - Use load_from_torchvision loader\n","Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n","100% 548M/548M [00:07<00:00, 76.0MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\" to /root/.cache/torch/hub/checkpoints/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n","100% 196M/196M [00:26<00:00, 7.61MB/s]\n","bird_ESRGAN.png bird_SRCNN.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KiPvtvlqM1zb","executionInfo":{"status":"ok","timestamp":1625140884175,"user_tz":-480,"elapsed":58677,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"be7375a7-4632-4770-8383-2a8ce654b069"}},{"cell_type":"markdown","source":["## 使用预训练的视频复原器进行推理\n","\n","MMEditing 也支持视频超分辨率方法，过程类似。您可以使用带有以下参数的 `restoration_video_demo.py`：\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `INPUT_DIR`: 包含视频帧的目录。\n","4. `OUTPUT_DIR`: 要存储输出帧的位置。\n","5. `WINDOW_SIZE`: 使用滑动窗口方法时的窗口大小（可选）。\n","6. `GPU_ID`: 您想使用哪个 GPU（可选）。\n","\n","```\n","python demo/restoration_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} [--window_size=$WINDOW_SIZE] [--device ${GPU_ID}]\n","```\n","**注:** 视频超分辨率有两种不同的框架：***滑动窗口***和***循环***框架。使用 EDVR 等滑动窗口框架的方法时，需要指定 `window_size`。此值取决于您使用的模型。\n","\n","---\n","\n","我们现在将使用 `EDVR` 和 `BasicVSR` 作为示例。\n","\n"],"metadata":{"id":"W1DfGHu3Xcfd"}},{"cell_type":"code","execution_count":null,"source":["# EDVR（滑动窗口框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_EDVR --window_size=5\n","\n","# BasicVSR（循环框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/basicvsr/basicvsr_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_BasicVSR\n","\n","# 检查是否保存了视频帧\n","!ls ./outputs/calendar_BasicVSR"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\" to /root/.cache/torch/hub/checkpoints/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n","100% 11.5M/11.5M [00:01<00:00, 8.55MB/s]\n","2021-07-01 12:01:09,689 - mmedit - INFO - Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth\" to /root/.cache/torch/hub/checkpoints/spynet_20210409-c6c1bd09.pth\n","100% 5.50M/5.50M [00:00<00:00, 8.88MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\" to /root/.cache/torch/hub/checkpoints/basicvsr_reds4_20120409-0e599677.pth\n","100% 24.1M/24.1M [00:02<00:00, 8.97MB/s]\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iaoE7UF5Xb2i","executionInfo":{"status":"ok","timestamp":1625140913405,"user_tz":-480,"elapsed":29263,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"a022e0bd-c47a-450e-f4e4-1bf9f92e4813"}},{"cell_type":"markdown","source":["## 使用配置文件在预定义的数据集上进行测试\n","\n","上述演示提供了一种对单个图像或视频序列进行推理的简单方法。如果要对一组图像或序列进行推理，可以使用位于 `./configs` 中的配置文件。\n"," \n","现有的配置文件允许您对常见数据集进行推理，例如图像超分辨率中的 `Set5` 和视频超分辨率中的 `REDS4`。您可以使用以下命令：\n","\n","1. `CONFIG_FILE`: 你要使用的复原器和数据集对应的配置文件\n","2. `CHECKPOINT_FILE`: 预训练模型权重文件的路径。\n","3. `GPU_NUM`: 用于测试的 GPU 数量。\n","4. `RESULT_FILE`: 输出结果 pickle 文件的路径。（可选）\n","5. `IMAGE_SAVE_PATH`: 要存储输出图像的位置。（可选）\n","\n","```\n","# 单 GPU 测试\n","python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","\n","# 多 GPU 测试\n","./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","```\n","您需要做的是修改配置文件中的 `lq_folder` 和 `gt_folder`：\n","```\n","test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/val_set5/Set5_bicLRx4',\n"," gt_folder='data/val_set5/Set5',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","```\n","\n","**注**: 某些数据集类型（例如 `SRREDSDataset`）需要一个注释文件来指定数据集的详细信息。更多细节请参考 `./mmedit/dataset/` 中的相应文件。\n","\n","---\n","\n","以下是 SRCNN 的命令。对于其他模型，您可以简单地更改配置文件和预训练模型的路径。\n"],"metadata":{"id":"Rf3LW57qMHXb"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU\n","!python tools/test.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/\n","\n","# 多 GPU\n","!./tools/dist_test.sh ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 1 --save-path ./outputs/"],"outputs":[{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"tools/test.py\", line 136, in \n"," main()\n"," File \"tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/test.py\", line 136, in \n"," main()\n"," File \"./tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/test.py', '--local_rank=0', './configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py', 'https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', '--launcher', 'pytorch', '--save-path', './outputs/']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"tClgIYgcbbVg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140927462,"user_tz":-480,"elapsed":14095,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"c37ec2de-e1c9-42ae-ed9b-31009d48ae64"}},{"cell_type":"markdown","source":["## 在自定义数据集上进行测试\n","\n","当您想在自定义数据集上进行测试时，除了数据集路径之外，您还需要修改 `test_dataset_type`。 \n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","这些数据集类型假定指定目录中的所有图像/序列都用于测试。文件夹结构应该是\n","```\n","| lq_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," | ...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","| gt_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," |...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","```\n","我们将使用 **SRCNN**、**EDVR**、**BasicVSR** 作为示例。请注意 `test_dataset_type` 和 `data['test']` 的设置。"],"metadata":{"id":"KWKVyeEQelh3"}},{"cell_type":"markdown","source":["**SRCNN**"],"metadata":{"id":"0p2rP8jV_dL1"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_SRCNN.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/testset_SRCNN\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_SRCNN"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 5/5, 8.6 task/s, elapsed: 1s, ETA: 0s\n","Eval-PSNR: 28.433974369836108\n","Eval-SSIM: 0.8099053586583066\n","baby.png bird.png butterfly.png head.png woman.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4kEev4wVIq_L","executionInfo":{"status":"ok","timestamp":1625140936180,"user_tz":-480,"elapsed":8729,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"8026ed73-f781-4eb2-bb80-d3446bd131df"}},{"cell_type":"markdown","source":["**EDVR**"],"metadata":{"id":"RONzjTTU_gem"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_EDVR.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth --save-path ./outputs/testset_EDVR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_EDVR\n","!ls ./outputs/testset_EDVR/city"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 22/22, 2.0 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 23.89569862011228\n","Eval-SSIM: 0.7667098470108678\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vL8WOWXY0fNJ","executionInfo":{"status":"ok","timestamp":1625140955813,"user_tz":-480,"elapsed":19671,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"dc2a8f81-9bef-4ad4-c5b2-c6f124e6b113"}},{"cell_type":"markdown","source":["**BasicVSR**"],"metadata":{"id":"5Tc7F-l5_i1e"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_BasicVSR.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth --save-path ./outputs/testset_BasicVSR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_BasicVSR\n","!ls ./outputs/testset_BasicVSR/calendar"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:07,780 - mmedit - INFO - Use load_from_http loader\n","Use load_from_http loader\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","[>>] 2/2, 0.2 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 24.195768601433734\n","Eval-SSIM: 0.7828541339512978\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jpW5GWC74Yvu","executionInfo":{"status":"ok","timestamp":1625140976026,"user_tz":-480,"elapsed":20220,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"7ba02a32-d4ec-40b2-8108-ef0729b62147"}},{"cell_type":"markdown","source":["## 在预定义的数据集上训练恢复器\n","\n","MMEditing 使用分布式训练。以下命令可用于训练。如果要在我们的配置文件中指定的预定义数据集上进行训练，只需运行以下命令即可。\n","\n","```\n","./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n","```\n","\n","有关可选参数的更多详细信息，请参阅 `tools/train.py`。\n","\n","---\n","\n","这是一个使用 EDVR 的示例。\n"],"metadata":{"id":"4DQxNL8BhI0y"}},{"cell_type":"code","execution_count":null,"source":["!./tools/dist_train.sh ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py 1"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:31,961 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:31,961 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:31,961 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:31,961 - mmedit - INFO - Config:\n","/content/mmediting/configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py\n","exp_name = 'edvrm_wotsa_x4_g8_600k_reds'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRREDSDataset'\n","val_dataset_type = 'SRREDSDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 600000\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50000, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=100,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 54, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 63, in load_annotations\n"," with open(self.ann_file, 'r') as fin:\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/train.py\", line 145, in \n"," main()\n"," File \"./tools/train.py\", line 111, in main\n"," datasets = [build_dataset(cfg.data.train)]\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 76, in build_dataset\n"," build_dataset(cfg['dataset'], default_args), cfg['times'])\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRREDSDataset: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/train.py', '--local_rank=0', './configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py', '--launcher', 'pytorch']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"s-hOnSF6ItQM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140985357,"user_tz":-480,"elapsed":9337,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"e40e960a-29e1-43e8-b922-5e08c4e98afe"}},{"cell_type":"markdown","source":["## 在自定义数据集上训练复原器\n","\n","与您要在自己的数据集上进行测试的情况类似，您需要修改 `train_dataset_type`。 您需要的数据集类型是相同的：\n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","修改数据集类型和数据路径后。 一切都准备好了。"],"metadata":{"id":"b0VfQkQQjg8N"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN（图像超分辨率）\n","!./tools/dist_train.sh ./demo_files/demo_config_SRCNN.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:41,185 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:41,185 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:41,185 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:41,185 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_SRCNN.py\n","exp_name = 'srcnn_demo'\n","\n","scale = 4\n","# model settings\n","model = dict(\n"," type='BasicRestorer',\n"," generator=dict(\n"," type='SRCNN',\n"," channels=(3, 64, 32, 3),\n"," kernel_sizes=(9, 1, 5),\n"," upscale_factor=scale),\n"," pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderDataset'\n","val_dataset_type = 'SRFolderDataset'\n","train_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=128),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","test_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=8,\n"," train_dataloader=dict(samples_per_gpu=16, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=train_pipeline,\n"," scale=scale)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[250000, 250000, 250000, 250000],\n"," restart_weights=[1, 1, 1, 1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","evaluation = dict(interval=50, save_image=True, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./experiments/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:02:41,192 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/experiments/srcnn_demo\n","2021-07-01 12:02:41,192 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:02:41.529307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:03:18,631 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.000e-04, eta: 0:57:01, time: 34.560, data_time: 34.446, memory: 586, loss_pix: 0.3999, loss: 0.3999\n","2021-07-01 12:03:18,712 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","INFO:mmedit:Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","2021-07-01 12:03:18,764 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","INFO:mmedit:Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","2021-07-01 12:03:18,840 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","INFO:mmedit:Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","2021-07-01 12:03:18,916 - mmedit - INFO - Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","INFO:mmedit:Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","2021-07-01 12:03:18,956 - mmedit - INFO - Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","INFO:mmedit:Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","2021-07-01 12:03:19,012 - mmedit - INFO - Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","INFO:mmedit:Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","2021-07-01 12:03:19,070 - mmedit - INFO - Iter [8/100]\tlr_generator: 2.000e-04, eta: 0:06:42, time: 0.057, data_time: 0.016, memory: 586, loss_pix: 0.3766, loss: 0.3766\n","INFO:mmedit:Iter [8/100]\tlr_generator: 2.000e-04, eta: 0:06:42, time: 0.057, data_time: 0.016, memory: 586, loss_pix: 0.3766, loss: 0.3766\n","2021-07-01 12:03:19,142 - mmedit - INFO - Iter [9/100]\tlr_generator: 2.000e-04, eta: 0:05:54, time: 0.064, data_time: 0.026, memory: 586, loss_pix: 0.3721, loss: 0.3721\n","INFO:mmedit:Iter [9/100]\tlr_generator: 2.000e-04, eta: 0:05:54, time: 0.064, data_time: 0.026, memory: 586, loss_pix: 0.3721, loss: 0.3721\n","2021-07-01 12:03:19,212 - mmedit - INFO - Iter [10/100]\tlr_generator: 2.000e-04, eta: 0:05:16, time: 0.079, data_time: 0.027, memory: 586, loss_pix: 0.3314, loss: 0.3314\n","INFO:mmedit:Iter [10/100]\tlr_generator: 2.000e-04, eta: 0:05:16, time: 0.079, data_time: 0.027, memory: 586, loss_pix: 0.3314, loss: 0.3314\n","2021-07-01 12:03:19,261 - mmedit - INFO - Iter [11/100]\tlr_generator: 2.000e-04, eta: 0:04:44, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.3249, loss: 0.3249\n","INFO:mmedit:Iter [11/100]\tlr_generator: 2.000e-04, eta: 0:04:44, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.3249, loss: 0.3249\n","2021-07-01 12:03:19,302 - 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mmedit - INFO - Iter [48/100]\tlr_generator: 2.000e-04, eta: 0:00:40, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.0876, loss: 0.0876\n","INFO:mmedit:Iter [48/100]\tlr_generator: 2.000e-04, eta: 0:00:40, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.0876, loss: 0.0876\n","2021-07-01 12:03:21,743 - mmedit - INFO - Iter [49/100]\tlr_generator: 2.000e-04, eta: 0:00:39, time: 0.067, data_time: 0.011, memory: 586, loss_pix: 0.0923, loss: 0.0923\n","INFO:mmedit:Iter [49/100]\tlr_generator: 2.000e-04, eta: 0:00:39, time: 0.067, data_time: 0.011, memory: 586, loss_pix: 0.0923, loss: 0.0923\n","[>>] 5/5, 0.1 task/s, elapsed: 37s, ETA: 0s\n","\n","2021-07-01 12:03:59,996 - mmedit - INFO - Iter(val) [50]\tPSNR: 19.4410, SSIM: 0.5387\n","INFO:mmedit:Iter(val) [50]\tPSNR: 19.4410, SSIM: 0.5387\n","2021-07-01 12:04:00,047 - mmedit - INFO - Iter [51/100]\tlr_generator: 2.000e-04, eta: 0:01:12, time: 38.229, data_time: 38.203, memory: 586, loss_pix: 0.0909, loss: 0.0909\n","INFO:mmedit:Iter [51/100]\tlr_generator: 2.000e-04, eta: 0:01:12, time: 38.229, data_time: 38.203, memory: 586, loss_pix: 0.0909, loss: 0.0909\n","2021-07-01 12:04:00,114 - 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mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:04:37,422 - mmedit - INFO - Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n","INFO:mmedit:Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"liGEKJpbIoXZ","executionInfo":{"status":"ok","timestamp":1625141113733,"user_tz":-480,"elapsed":128384,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"84e1502e-e2cb-458f-c7c5-e4b401e570b7"}},{"cell_type":"code","execution_count":null,"source":["# EDVR（视频超分辨率-滑动窗口）\n","!./tools/dist_train.sh ./demo_files/demo_config_EDVR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:10:12,619 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:10:12,619 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:10:12,619 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:10:12,619 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_EDVR.py\n","exp_name = 'edvrm_demo'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderVideoDataset'\n","val_dataset_type = 'SRFolderVideoDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:10:12,701 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/edvrm_demo\n","2021-07-01 12:10:12,702 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:10:12.951771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:10:30,703 - mmedit - INFO - Iter [1/100]\tlr_generator: 4.000e-04, eta: 0:26:53, time: 16.295, data_time: 15.833, memory: 1341, loss_pix: 63917.2734, loss: 63917.2734\n","2021-07-01 12:10:31,046 - mmedit - INFO - Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","INFO:mmedit:Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","2021-07-01 12:10:31,386 - mmedit - INFO - Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","INFO:mmedit:Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","2021-07-01 12:10:31,731 - 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Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"2fb26527-eb9e-4b48-e03c-ba9a91c60db8"}},{"cell_type":"code","execution_count":null,"source":["# BasicVSR（视频超分辨率 - 循环）\n","!./tools/dist_train.sh ./demo_files/demo_config_BasicVSR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:06:47,253 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:06:47,253 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:06:47,254 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:06:47,254 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_BasicVSR.py\n","exp_name = 'basicvsr_demo'\n","\n","# model settings\n","model = dict(\n"," type='BasicVSR',\n"," generator=dict(\n"," type='BasicVSRNet',\n"," mid_channels=64,\n"," num_blocks=30,\n"," spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n"," 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = dict(fix_iter=5000)\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderMultipleGTDataset'\n","val_dataset_type = 'SRFolderMultipleGTDataset'\n","\n","train_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=6,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True), # 2 gpus\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),\n","\n"," # train\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," # val\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," # test\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(\n"," generator=dict(\n"," type='Adam',\n"," lr=2e-4,\n"," betas=(0.9, 0.99),\n"," paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[300000],\n"," restart_weights=[1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," # dict(type='TensorboardLoggerHook'),\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","find_unused_parameters = True\n","\n","2021-07-01 12:06:47,291 - mmedit - INFO - Use load_from_http loader\n","2021-07-01 12:06:47,569 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/basicvsr_demo\n","2021-07-01 12:06:47,569 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:07:14,210 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.500e-05, eta: 0:42:52, time: 25.981, data_time: 24.045, memory: 3464, loss_pix: 0.0634, loss: 0.0634\n","2021-07-01 12:07:15,171 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.500e-05, eta: 0:22:00, time: 0.961, data_time: 0.011, memory: 3518, loss_pix: 0.0556, loss: 0.0556\n","2021-07-01 12:07:16,052 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.500e-05, eta: 0:14:59, time: 0.881, data_time: 0.003, memory: 3518, loss_pix: 0.0476, loss: 0.0476\n","2021-07-01 12:07:16,940 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.500e-05, eta: 0:11:29, time: 0.888, data_time: 0.003, memory: 3518, loss_pix: 0.0673, loss: 0.0673\n","2021-07-01 12:07:17,829 - 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mmedit - INFO - Iter(val) [100]\tPSNR: 21.4372, SSIM: 0.5687\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_RdqmlT6qgt2","executionInfo":{"status":"ok","timestamp":1625141428032,"user_tz":-480,"elapsed":197033,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"b951b426-e06c-4f31-db01-449333eab333"}},{"cell_type":"markdown","source":["**本教程到此结束。 有关更高级的用法，请参阅我们的综合教程 [此处]()。 享受使用 MMEditing 的乐趣！**"],"metadata":{"id":"QT0zwBFt7J13"}}]} +{"nbformat":4,"nbformat_minor":2,"metadata":{"accelerator":"GPU","colab":{"name":"restorer_basic_tutorial.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3.7.7 64-bit ('pre-commit': conda)","name":"python377jvsc74a57bd04974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"},"language_info":{"name":"python","version":""},"metadata":{"interpreter":{"hash":"4974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"}}},"cells":[{"cell_type":"markdown","source":["# MMEditing 基础教程\n","\n","欢迎来到MMEditing！ 这是 MMEditing 的官方 Colab 教程。在本教程中，您将学习如何使用 MMEditing 中提供的 API 训练和测试恢复器。\n","\n","这是训练和测试现有模型的快速指南。如果您想基于 MMEditing 开发自己的模型并了解有关代码结构的更多信息，请参阅我们的综合教程 [此处]()。\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmedit/blob/main/demo/restorer_basic_tutorial.ipynb)\n","\n"],"metadata":{"id":"T2WWQiheMF7q"}},{"cell_type":"markdown","source":["## 安装MMEditing\n","\n","MMEditing 可以分三步安装：\n","\n","1. 安装兼容的 PyTorch 版本（你需要使用 `nvcc -V` 检查你的 CUDA 版本）。\n","2. 安装预编译的MMCV\n","3. 克隆并安装MMEditing\n","\n","步骤如下所示："],"metadata":{"id":"-kYw3WQ0MQry"}},{"cell_type":"code","execution_count":null,"source":["# Check nvcc version\n","!nvcc -V\n","# Check GCC version (MMEditing needs gcc >= 5.0)\n","!gcc --version"],"outputs":[{"output_type":"stream","name":"stdout","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Wed_Jul_22_19:09:09_PDT_2020\n","Cuda compilation tools, release 11.0, V11.0.221\n","Build cuda_11.0_bu.TC445_37.28845127_0\n","gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uha_13idyl1b","executionInfo":{"status":"ok","timestamp":1625140540858,"user_tz":-480,"elapsed":321,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"87056561-5930-45b3-e1a8-f9e103d10b23"}},{"cell_type":"code","execution_count":null,"source":["# 安装依赖：（使用 cu101 因为 colab 有 CUDA 11.0）\n","!pip install -U torch==1.7.0+cu110 torchvision==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html\n","\n","# 安装 mmcv-full 这样我们就可以使用 CUDA 运算符\n","!pip install mmcv-full==1.3.5 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html\n","\n","# 克隆 MMEditing \n","!rm -rf mmediting\n","!git clone https://github.com/open-mmlab/mmediting.git\n","%cd mmediting\n","\n","# 安装 MMEditing\n","!pip install -r requirements.txt\n","!pip install -v -e ."],"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in links: https://download.pytorch.org/whl/torch_stable.html\n","Collecting torch==1.7.0+cu110\n","\u001b[?25l Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl (1137.1MB)\n","\u001b[K |███████████████████████▌ 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requirements/runtime.txt (line 2)) (7.1.2)\n","Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (2.4.0)\n","Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (4.1.2.30)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (3.13)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.5.1)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.4.1)\n","Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) 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requirements/runtime.txt (line 4)) (1.8.0)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (2.23.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.4.4)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (57.0.0)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.12.0)\n","Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.12.4)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.3.4)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.31.0)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.0.1)\n","Requirement already satisfied: coverage in /usr/local/lib/python3.7/dist-packages (from codecov->-r requirements/tests.txt (line 1)) (3.7.1)\n","Collecting pyflakes<2.4.0,>=2.3.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6c/11/2a745612f1d3cbbd9c69ba14b1b43a35a2f5c3c81cd0124508c52c64307f/pyflakes-2.3.1-py2.py3-none-any.whl (68kB)\n","\u001b[K |████████████████████████████████| 71kB 9.8MB/s \n","\u001b[?25hCollecting pycodestyle<2.8.0,>=2.7.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/cc/227251b1471f129bc35e966bb0fceb005969023926d744139642d847b7ae/pycodestyle-2.7.0-py2.py3-none-any.whl (41kB)\n","\u001b[K |████████████████████████████████| 51kB 8.7MB/s \n","\u001b[?25hCollecting mccabe<0.7.0,>=0.6.0\n"," Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from flake8->-r requirements/tests.txt (line 2)) (4.5.0)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (0.8.9)\n","Collecting colorama\n"," Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (7.1.2)\n","Requirement already satisfied: toml in 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/tmp/pip-req-tracker-zk5q0q3z\n","Created requirements tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Created temporary directory: /tmp/pip-install-vr_vpseo\n","Obtaining file:///content/mmediting\n"," Added file:///content/mmediting to build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"," Running setup.py (path:/content/mmediting/setup.py) egg_info for package from file:///content/mmediting\n"," Running command python setup.py egg_info\n"," running egg_info\n"," creating mmedit.egg-info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 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already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.7.4.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (3.1.1)\n","Installing collected packages: mmedit\n"," Running setup.py develop for mmedit\n"," Running command /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmediting/setup.py'\"'\"'; __file__='\"'\"'/content/mmediting/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n"," running develop\n"," running egg_info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.7/dist-packages/mmedit.egg-link (link to .)\n"," Adding mmedit 0.8.0 to easy-install.pth file\n","\n"," Installed /content/mmediting\n","Successfully installed mmedit\n","Cleaning up...\n","Removed build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GIeIZEzZMfc0","executionInfo":{"status":"ok","timestamp":1625140820804,"user_tz":-480,"elapsed":279948,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"fe2e5ded-988d-4563-eb18-374344c316ef"}},{"cell_type":"markdown","source":["## 下载此演示所需的材料\n","在这个演示中，我们将需要一些数据和配置文件。我们将下载并放入 `./demo_files/`"],"metadata":{"id":"QgX96Sc_3PcV"}},{"cell_type":"code","execution_count":null,"source":["!wget https://download.openmmlab.com/mmediting/demo_files.zip # 下载文件\n","!unzip demo_files # 解压"],"outputs":[{"output_type":"stream","name":"stdout","text":["--2021-07-01 11:59:48-- https://download.openmmlab.com/mmediting/demo_files.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19215781 (18M) [application/zip]\n","Saving to: ‘demo_files.zip’\n","\n","demo_files.zip 100%[===================>] 18.33M 6.00MB/s in 3.1s \n","\n","2021-07-01 11:59:52 (6.00 MB/s) - ‘demo_files.zip’ saved [19215781/19215781]\n","\n","Archive: demo_files.zip\n"," creating: demo_files/\n"," inflating: demo_files/demo_config_EDVR.py \n"," inflating: demo_files/demo_config_BasicVSR.py \n"," creating: demo_files/lq_sequences/\n"," creating: demo_files/lq_sequences/calendar/\n"," inflating: demo_files/lq_sequences/calendar/00000006.png \n"," inflating: demo_files/lq_sequences/calendar/00000007.png \n"," inflating: demo_files/lq_sequences/calendar/00000010.png \n"," inflating: demo_files/lq_sequences/calendar/00000004.png \n"," inflating: demo_files/lq_sequences/calendar/00000003.png \n"," inflating: demo_files/lq_sequences/calendar/00000001.png \n"," inflating: demo_files/lq_sequences/calendar/00000000.png \n"," inflating: demo_files/lq_sequences/calendar/00000009.png \n"," inflating: demo_files/lq_sequences/calendar/00000008.png \n"," inflating: demo_files/lq_sequences/calendar/00000002.png \n"," inflating: demo_files/lq_sequences/calendar/00000005.png \n"," creating: demo_files/lq_sequences/city/\n"," inflating: demo_files/lq_sequences/city/00000006.png \n"," inflating: demo_files/lq_sequences/city/00000007.png \n"," inflating: demo_files/lq_sequences/city/00000010.png \n"," inflating: demo_files/lq_sequences/city/00000004.png \n"," inflating: demo_files/lq_sequences/city/00000003.png \n"," inflating: demo_files/lq_sequences/city/00000001.png \n"," inflating: demo_files/lq_sequences/city/00000000.png \n"," inflating: demo_files/lq_sequences/city/00000009.png \n"," inflating: demo_files/lq_sequences/city/00000008.png \n"," inflating: demo_files/lq_sequences/city/00000002.png \n"," inflating: demo_files/lq_sequences/city/00000005.png \n"," creating: demo_files/lq_sequences/.ipynb_checkpoints/\n"," creating: demo_files/gt_images/\n"," inflating: demo_files/gt_images/bird.png \n"," inflating: demo_files/gt_images/woman.png \n"," inflating: demo_files/gt_images/head.png \n"," inflating: demo_files/gt_images/baby.png \n"," inflating: demo_files/gt_images/butterfly.png \n"," inflating: demo_files/demo_config_SRCNN.py \n"," creating: demo_files/lq_images/\n"," extracting: demo_files/lq_images/bird.png \n"," extracting: demo_files/lq_images/woman.png \n"," extracting: demo_files/lq_images/head.png \n"," extracting: demo_files/lq_images/baby.png \n"," extracting: demo_files/lq_images/butterfly.png \n"," creating: demo_files/gt_sequences/\n"," creating: demo_files/gt_sequences/calendar/\n"," inflating: demo_files/gt_sequences/calendar/00000006.png \n"," inflating: demo_files/gt_sequences/calendar/00000007.png \n"," inflating: demo_files/gt_sequences/calendar/00000010.png \n"," inflating: demo_files/gt_sequences/calendar/00000004.png \n"," inflating: demo_files/gt_sequences/calendar/00000003.png \n"," inflating: demo_files/gt_sequences/calendar/00000001.png \n"," inflating: demo_files/gt_sequences/calendar/00000000.png \n"," inflating: demo_files/gt_sequences/calendar/00000009.png \n"," inflating: demo_files/gt_sequences/calendar/00000008.png \n"," inflating: demo_files/gt_sequences/calendar/00000002.png \n"," inflating: demo_files/gt_sequences/calendar/00000005.png \n"," creating: demo_files/gt_sequences/city/\n"," inflating: demo_files/gt_sequences/city/00000006.png \n"," inflating: demo_files/gt_sequences/city/00000007.png \n"," inflating: demo_files/gt_sequences/city/00000010.png \n"," inflating: demo_files/gt_sequences/city/00000004.png \n"," inflating: demo_files/gt_sequences/city/00000003.png \n"," inflating: demo_files/gt_sequences/city/00000001.png \n"," inflating: demo_files/gt_sequences/city/00000000.png \n"," inflating: demo_files/gt_sequences/city/00000009.png \n"," inflating: demo_files/gt_sequences/city/00000008.png \n"," inflating: demo_files/gt_sequences/city/00000002.png \n"," inflating: demo_files/gt_sequences/city/00000005.png \n"," creating: demo_files/gt_sequences/.ipynb_checkpoints/\n"," creating: demo_files/.ipynb_checkpoints/\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-K0zFSJ-3V42","executionInfo":{"status":"ok","timestamp":1625140825508,"user_tz":-480,"elapsed":4723,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"528a87f7-f78e-4219-84f3-dec19b88e88a"}},{"cell_type":"markdown","source":["## 使用预训练的图像恢复器进行推理\n","您可以使用 “restoration_demo.py” 轻松地使用预训练的恢复器对单个图像进行推理。您需要的是\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `IMAGE_FILE`：输入图像的路径。\n","4. `SAVE_FILE`：您要存储输出图像的位置。\n","5. `imshow`：是否显示图片。（可选的）\n","6. `GPU_ID`：您想使用哪个 GPU。（可选的）\n","\n","获得所有这些详细信息后，您可以直接使用以下命令：\n","\n","```\n","python demo/restoration_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} ${SAVE_FILE} [--imshow] [--device ${GPU_ID}]\n","```\n","\n","**注:** \n","1. 配置文件位于 `./configs`。\n","2. 我们支持从 url 加载权重文件。您可以到相应页面（例如[这里](https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/esrgan)）获取预训练模型的url。\n","\n","---\n","\n","我们现在将使用 `SRCNN` 和 `ESRGAN` 作为示例。\n","\n"],"metadata":{"id":"zXGurqGKOeNE"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN\n","!python demo/restoration_demo.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth ./demo_files/lq_images/bird.png ./outputs/bird_SRCNN.png\n","\n","# ESRGAN\n","!python demo/restoration_demo.py ./configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth ./demo_files/lq_images/bird.png ./outputs/bird_ESRGAN.png\n","\n","# 检查图像是否已保存\n","!ls ./outputs"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\" to /root/.cache/torch/hub/checkpoints/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n","100% 83.9k/83.9k [00:00<00:00, 1.59MB/s]\n","2021-07-01 12:00:10,779 - mmedit - INFO - Use load_from_torchvision loader\n","Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n","100% 548M/548M [00:07<00:00, 76.0MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\" to /root/.cache/torch/hub/checkpoints/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n","100% 196M/196M [00:26<00:00, 7.61MB/s]\n","bird_ESRGAN.png bird_SRCNN.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KiPvtvlqM1zb","executionInfo":{"status":"ok","timestamp":1625140884175,"user_tz":-480,"elapsed":58677,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"be7375a7-4632-4770-8383-2a8ce654b069"}},{"cell_type":"markdown","source":["## 使用预训练的视频复原器进行推理\n","\n","MMEditing 也支持视频超分辨率方法，过程类似。您可以使用带有以下参数的 `restoration_video_demo.py`：\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `INPUT_DIR`: 包含视频帧的目录。\n","4. `OUTPUT_DIR`: 要存储输出帧的位置。\n","5. `WINDOW_SIZE`: 使用滑动窗口方法时的窗口大小（可选）。\n","6. `GPU_ID`: 您想使用哪个 GPU（可选）。\n","\n","```\n","python demo/restoration_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} [--window_size=$WINDOW_SIZE] [--device ${GPU_ID}]\n","```\n","**注:** 视频超分辨率有两种不同的框架：***滑动窗口***和***循环***框架。使用 EDVR 等滑动窗口框架的方法时，需要指定 `window_size`。此值取决于您使用的模型。\n","\n","---\n","\n","我们现在将使用 `EDVR` 和 `BasicVSR` 作为示例。\n","\n"],"metadata":{"id":"W1DfGHu3Xcfd"}},{"cell_type":"code","execution_count":null,"source":["# EDVR（滑动窗口框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_EDVR --window_size=5\n","\n","# BasicVSR（循环框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/basicvsr/basicvsr_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_BasicVSR\n","\n","# 检查是否保存了视频帧\n","!ls ./outputs/calendar_BasicVSR"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\" to /root/.cache/torch/hub/checkpoints/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n","100% 11.5M/11.5M [00:01<00:00, 8.55MB/s]\n","2021-07-01 12:01:09,689 - mmedit - INFO - Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth\" to /root/.cache/torch/hub/checkpoints/spynet_20210409-c6c1bd09.pth\n","100% 5.50M/5.50M [00:00<00:00, 8.88MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\" to /root/.cache/torch/hub/checkpoints/basicvsr_reds4_20120409-0e599677.pth\n","100% 24.1M/24.1M [00:02<00:00, 8.97MB/s]\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iaoE7UF5Xb2i","executionInfo":{"status":"ok","timestamp":1625140913405,"user_tz":-480,"elapsed":29263,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"a022e0bd-c47a-450e-f4e4-1bf9f92e4813"}},{"cell_type":"markdown","source":["## 使用配置文件在预定义的数据集上进行测试\n","\n","上述演示提供了一种对单个图像或视频序列进行推理的简单方法。如果要对一组图像或序列进行推理，可以使用位于 `./configs` 中的配置文件。\n"," \n","现有的配置文件允许您对常见数据集进行推理，例如图像超分辨率中的 `Set5` 和视频超分辨率中的 `REDS4`。您可以使用以下命令：\n","\n","1. `CONFIG_FILE`: 你要使用的复原器和数据集对应的配置文件\n","2. `CHECKPOINT_FILE`: 预训练模型权重文件的路径。\n","3. `GPU_NUM`: 用于测试的 GPU 数量。\n","4. `RESULT_FILE`: 输出结果 pickle 文件的路径。（可选）\n","5. `IMAGE_SAVE_PATH`: 要存储输出图像的位置。（可选）\n","\n","```\n","# 单 GPU 测试\n","python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","\n","# 多 GPU 测试\n","./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","```\n","您需要做的是修改配置文件中的 `lq_folder` 和 `gt_folder`：\n","```\n","test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/val_set5/Set5_bicLRx4',\n"," gt_folder='data/val_set5/Set5',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","```\n","\n","**注**: 某些数据集类型（例如 `SRREDSDataset`）需要一个注释文件来指定数据集的详细信息。更多细节请参考 `./mmedit/dataset/` 中的相应文件。\n","\n","---\n","\n","以下是 SRCNN 的命令。对于其他模型，您可以简单地更改配置文件和预训练模型的路径。\n"],"metadata":{"id":"Rf3LW57qMHXb"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU\n","!python tools/test.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/\n","\n","# 多 GPU\n","!./tools/dist_test.sh ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 1 --save-path ./outputs/"],"outputs":[{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"tools/test.py\", line 136, in \n"," main()\n"," File \"tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/test.py\", line 136, in \n"," main()\n"," File \"./tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/test.py', '--local_rank=0', './configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py', 'https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', '--launcher', 'pytorch', '--save-path', './outputs/']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"tClgIYgcbbVg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140927462,"user_tz":-480,"elapsed":14095,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"c37ec2de-e1c9-42ae-ed9b-31009d48ae64"}},{"cell_type":"markdown","source":["## 在自定义数据集上进行测试\n","\n","当您想在自定义数据集上进行测试时，除了数据集路径之外，您还需要修改 `test_dataset_type`。 \n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","这些数据集类型假定指定目录中的所有图像/序列都用于测试。文件夹结构应该是\n","```\n","| lq_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," | ...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","| gt_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," |...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","```\n","我们将使用 **SRCNN**、**EDVR**、**BasicVSR** 作为示例。请注意 `test_dataset_type` 和 `data['test']` 的设置。"],"metadata":{"id":"KWKVyeEQelh3"}},{"cell_type":"markdown","source":["**SRCNN**"],"metadata":{"id":"0p2rP8jV_dL1"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_SRCNN.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/testset_SRCNN\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_SRCNN"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 5/5, 8.6 task/s, elapsed: 1s, ETA: 0s\n","Eval-PSNR: 28.433974369836108\n","Eval-SSIM: 0.8099053586583066\n","baby.png bird.png butterfly.png head.png woman.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4kEev4wVIq_L","executionInfo":{"status":"ok","timestamp":1625140936180,"user_tz":-480,"elapsed":8729,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"8026ed73-f781-4eb2-bb80-d3446bd131df"}},{"cell_type":"markdown","source":["**EDVR**"],"metadata":{"id":"RONzjTTU_gem"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_EDVR.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth --save-path ./outputs/testset_EDVR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_EDVR\n","!ls ./outputs/testset_EDVR/city"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 22/22, 2.0 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 23.89569862011228\n","Eval-SSIM: 0.7667098470108678\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vL8WOWXY0fNJ","executionInfo":{"status":"ok","timestamp":1625140955813,"user_tz":-480,"elapsed":19671,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"dc2a8f81-9bef-4ad4-c5b2-c6f124e6b113"}},{"cell_type":"markdown","source":["**BasicVSR**"],"metadata":{"id":"5Tc7F-l5_i1e"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_BasicVSR.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth --save-path ./outputs/testset_BasicVSR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_BasicVSR\n","!ls ./outputs/testset_BasicVSR/calendar"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:07,780 - mmedit - INFO - Use load_from_http loader\n","Use load_from_http loader\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","[>>] 2/2, 0.2 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 24.195768601433734\n","Eval-SSIM: 0.7828541339512978\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jpW5GWC74Yvu","executionInfo":{"status":"ok","timestamp":1625140976026,"user_tz":-480,"elapsed":20220,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"7ba02a32-d4ec-40b2-8108-ef0729b62147"}},{"cell_type":"markdown","source":["## 在预定义的数据集上训练恢复器\n","\n","MMEditing 使用分布式训练。以下命令可用于训练。如果要在我们的配置文件中指定的预定义数据集上进行训练，只需运行以下命令即可。\n","\n","```\n","./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n","```\n","\n","有关可选参数的更多详细信息，请参阅 `tools/train.py`。\n","\n","---\n","\n","这是一个使用 EDVR 的示例。\n"],"metadata":{"id":"4DQxNL8BhI0y"}},{"cell_type":"code","execution_count":null,"source":["!./tools/dist_train.sh ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py 1"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:31,961 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:31,961 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:31,961 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:31,961 - mmedit - INFO - Config:\n","/content/mmediting/configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py\n","exp_name = 'edvrm_wotsa_x4_g8_600k_reds'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRREDSDataset'\n","val_dataset_type = 'SRREDSDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 600000\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50000, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=100,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 54, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 63, in load_annotations\n"," with open(self.ann_file, 'r') as fin:\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/train.py\", line 145, in \n"," main()\n"," File \"./tools/train.py\", line 111, in main\n"," datasets = [build_dataset(cfg.data.train)]\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 76, in build_dataset\n"," build_dataset(cfg['dataset'], default_args), cfg['times'])\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRREDSDataset: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/train.py', '--local_rank=0', './configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py', '--launcher', 'pytorch']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"s-hOnSF6ItQM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140985357,"user_tz":-480,"elapsed":9337,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"e40e960a-29e1-43e8-b922-5e08c4e98afe"}},{"cell_type":"markdown","source":["## 在自定义数据集上训练复原器\n","\n","与您要在自己的数据集上进行测试的情况类似，您需要修改 `train_dataset_type`。您需要的数据集类型是相同的：\n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","修改数据集类型和数据路径后。一切都准备好了。"],"metadata":{"id":"b0VfQkQQjg8N"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN（图像超分辨率）\n","!./tools/dist_train.sh ./demo_files/demo_config_SRCNN.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:41,185 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:41,185 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:41,185 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:41,185 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_SRCNN.py\n","exp_name = 'srcnn_demo'\n","\n","scale = 4\n","# model settings\n","model = dict(\n"," type='BasicRestorer',\n"," generator=dict(\n"," type='SRCNN',\n"," channels=(3, 64, 32, 3),\n"," kernel_sizes=(9, 1, 5),\n"," upscale_factor=scale),\n"," pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderDataset'\n","val_dataset_type = 'SRFolderDataset'\n","train_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=128),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","test_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=8,\n"," train_dataloader=dict(samples_per_gpu=16, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=train_pipeline,\n"," scale=scale)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[250000, 250000, 250000, 250000],\n"," restart_weights=[1, 1, 1, 1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","evaluation = dict(interval=50, save_image=True, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./experiments/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:02:41,192 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/experiments/srcnn_demo\n","2021-07-01 12:02:41,192 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:02:41.529307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:03:18,631 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.000e-04, eta: 0:57:01, time: 34.560, data_time: 34.446, memory: 586, loss_pix: 0.3999, loss: 0.3999\n","2021-07-01 12:03:18,712 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","INFO:mmedit:Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","2021-07-01 12:03:18,764 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","INFO:mmedit:Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","2021-07-01 12:03:18,840 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","INFO:mmedit:Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","2021-07-01 12:03:18,916 - mmedit - INFO - Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","INFO:mmedit:Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","2021-07-01 12:03:18,956 - mmedit - INFO - Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","INFO:mmedit:Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","2021-07-01 12:03:19,012 - mmedit - INFO - Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","INFO:mmedit:Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","2021-07-01 12:03:19,070 - 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mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:04:37,422 - mmedit - INFO - Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n","INFO:mmedit:Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"liGEKJpbIoXZ","executionInfo":{"status":"ok","timestamp":1625141113733,"user_tz":-480,"elapsed":128384,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"84e1502e-e2cb-458f-c7c5-e4b401e570b7"}},{"cell_type":"code","execution_count":null,"source":["# EDVR（视频超分辨率-滑动窗口）\n","!./tools/dist_train.sh ./demo_files/demo_config_EDVR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:10:12,619 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:10:12,619 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:10:12,619 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:10:12,619 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_EDVR.py\n","exp_name = 'edvrm_demo'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderVideoDataset'\n","val_dataset_type = 'SRFolderVideoDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:10:12,701 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/edvrm_demo\n","2021-07-01 12:10:12,702 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:10:12.951771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:10:30,703 - mmedit - INFO - Iter [1/100]\tlr_generator: 4.000e-04, eta: 0:26:53, time: 16.295, data_time: 15.833, memory: 1341, loss_pix: 63917.2734, loss: 63917.2734\n","2021-07-01 12:10:31,046 - mmedit - INFO - Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","INFO:mmedit:Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","2021-07-01 12:10:31,386 - mmedit - INFO - Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","INFO:mmedit:Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","2021-07-01 12:10:31,731 - mmedit - INFO - Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","INFO:mmedit:Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","2021-07-01 12:10:32,071 - mmedit - INFO - Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","INFO:mmedit:Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","2021-07-01 12:10:32,414 - mmedit - INFO - Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","INFO:mmedit:Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","2021-07-01 12:10:32,760 - 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Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"2fb26527-eb9e-4b48-e03c-ba9a91c60db8"}},{"cell_type":"code","execution_count":null,"source":["# BasicVSR（视频超分辨率 - 循环）\n","!./tools/dist_train.sh ./demo_files/demo_config_BasicVSR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:06:47,253 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:06:47,253 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:06:47,254 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:06:47,254 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_BasicVSR.py\n","exp_name = 'basicvsr_demo'\n","\n","# model settings\n","model = dict(\n"," type='BasicVSR',\n"," generator=dict(\n"," type='BasicVSRNet',\n"," mid_channels=64,\n"," num_blocks=30,\n"," spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n"," 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = dict(fix_iter=5000)\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderMultipleGTDataset'\n","val_dataset_type = 'SRFolderMultipleGTDataset'\n","\n","train_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=6,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True), # 2 gpus\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),\n","\n"," # train\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," # val\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," # test\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(\n"," generator=dict(\n"," type='Adam',\n"," lr=2e-4,\n"," betas=(0.9, 0.99),\n"," paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[300000],\n"," restart_weights=[1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," # dict(type='TensorboardLoggerHook'),\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","find_unused_parameters = True\n","\n","2021-07-01 12:06:47,291 - mmedit - INFO - Use load_from_http loader\n","2021-07-01 12:06:47,569 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/basicvsr_demo\n","2021-07-01 12:06:47,569 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:07:14,210 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.500e-05, eta: 0:42:52, time: 25.981, data_time: 24.045, memory: 3464, loss_pix: 0.0634, loss: 0.0634\n","2021-07-01 12:07:15,171 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.500e-05, eta: 0:22:00, time: 0.961, data_time: 0.011, memory: 3518, loss_pix: 0.0556, loss: 0.0556\n","2021-07-01 12:07:16,052 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.500e-05, eta: 0:14:59, time: 0.881, data_time: 0.003, memory: 3518, loss_pix: 0.0476, loss: 0.0476\n","2021-07-01 12:07:16,940 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.500e-05, eta: 0:11:29, time: 0.888, data_time: 0.003, memory: 3518, loss_pix: 0.0673, loss: 0.0673\n","2021-07-01 12:07:17,829 - 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mmedit - INFO - Iter(val) [100]\tPSNR: 21.4372, SSIM: 0.5687\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_RdqmlT6qgt2","executionInfo":{"status":"ok","timestamp":1625141428032,"user_tz":-480,"elapsed":197033,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"b951b426-e06c-4f31-db01-449333eab333"}},{"cell_type":"markdown","source":["**本教程到此结束。 有关更高级的用法，请参阅我们的综合教程 [此处]()。 享受使用 MMEditing 的乐趣！**"],"metadata":{"id":"QT0zwBFt7J13"}}]} From 8c45986ebd8780f1e1586f4229d4bd708f84732d Mon Sep 17 00:00:00 2001 From: ckkelvinchan Date: Mon, 25 Oct 2021 15:39:12 +0800 Subject: [PATCH 3/3] update --- demo/restorer_basic_tutorial_zh-CN.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/demo/restorer_basic_tutorial_zh-CN.ipynb b/demo/restorer_basic_tutorial_zh-CN.ipynb index d62a54437a..b23f7b3a0b 100644 --- a/demo/restorer_basic_tutorial_zh-CN.ipynb +++ b/demo/restorer_basic_tutorial_zh-CN.ipynb @@ -1 +1 @@ -{"nbformat":4,"nbformat_minor":2,"metadata":{"accelerator":"GPU","colab":{"name":"restorer_basic_tutorial.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3.7.7 64-bit ('pre-commit': conda)","name":"python377jvsc74a57bd04974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"},"language_info":{"name":"python","version":""},"metadata":{"interpreter":{"hash":"4974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"}}},"cells":[{"cell_type":"markdown","source":["# MMEditing 基础教程\n","\n","欢迎来到MMEditing！ 这是 MMEditing 的官方 Colab 教程。在本教程中，您将学习如何使用 MMEditing 中提供的 API 训练和测试恢复器。\n","\n","这是训练和测试现有模型的快速指南。如果您想基于 MMEditing 开发自己的模型并了解有关代码结构的更多信息，请参阅我们的综合教程 [此处]()。\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmedit/blob/main/demo/restorer_basic_tutorial.ipynb)\n","\n"],"metadata":{"id":"T2WWQiheMF7q"}},{"cell_type":"markdown","source":["## 安装MMEditing\n","\n","MMEditing 可以分三步安装：\n","\n","1. 安装兼容的 PyTorch 版本（你需要使用 `nvcc -V` 检查你的 CUDA 版本）。\n","2. 安装预编译的MMCV\n","3. 克隆并安装MMEditing\n","\n","步骤如下所示："],"metadata":{"id":"-kYw3WQ0MQry"}},{"cell_type":"code","execution_count":null,"source":["# Check nvcc version\n","!nvcc -V\n","# Check GCC version (MMEditing needs gcc >= 5.0)\n","!gcc --version"],"outputs":[{"output_type":"stream","name":"stdout","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Wed_Jul_22_19:09:09_PDT_2020\n","Cuda compilation tools, release 11.0, V11.0.221\n","Build cuda_11.0_bu.TC445_37.28845127_0\n","gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uha_13idyl1b","executionInfo":{"status":"ok","timestamp":1625140540858,"user_tz":-480,"elapsed":321,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"87056561-5930-45b3-e1a8-f9e103d10b23"}},{"cell_type":"code","execution_count":null,"source":["# 安装依赖：（使用 cu101 因为 colab 有 CUDA 11.0）\n","!pip install -U torch==1.7.0+cu110 torchvision==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html\n","\n","# 安装 mmcv-full 这样我们就可以使用 CUDA 运算符\n","!pip install mmcv-full==1.3.5 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html\n","\n","# 克隆 MMEditing \n","!rm -rf mmediting\n","!git clone https://github.com/open-mmlab/mmediting.git\n","%cd mmediting\n","\n","# 安装 MMEditing\n","!pip install -r requirements.txt\n","!pip install -v -e ."],"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in links: https://download.pytorch.org/whl/torch_stable.html\n","Collecting torch==1.7.0+cu110\n","\u001b[?25l Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl (1137.1MB)\n","\u001b[K |███████████████████████▌ 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4)) (2.5.0)\n","Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from -r requirements/runtime.txt (line 5)) (0.31.0)\n","Collecting codecov\n"," Downloading https://files.pythonhosted.org/packages/93/9f/bbea5b6231308458963cb5c067bc5643da9949689702fa5a382714b59699/codecov-2.1.11-py2.py3-none-any.whl\n","Collecting flake8\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/fc/80/35a0716e5d5101e643404dabd20f07f5528a21f3ef4032d31a49c913237b/flake8-3.9.2-py2.py3-none-any.whl (73kB)\n","\u001b[K |████████████████████████████████| 81kB 9.7MB/s \n","\u001b[?25hCollecting interrogate\n"," Downloading https://files.pythonhosted.org/packages/cd/6d/ce3ac440b13c1b36b323a0eab191499a902adade3cc11b18078c07af3e6e/interrogate-1.4.0-py3-none-any.whl\n","Collecting isort==4.3.21\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/e5/b0/c121fd1fa3419ea9bfd55c7f9c4fedfec5143208d8c7ad3ce3db6c623c21/isort-4.3.21-py2.py3-none-any.whl (42kB)\n","\u001b[K |████████████████████████████████| 51kB 7.5MB/s \n","\u001b[?25hCollecting onnxruntime\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/f9/76/3d0f8bb2776961c7335693df06eccf8d099e48fa6fb552c7546867192603/onnxruntime-1.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.5MB)\n","\u001b[K |████████████████████████████████| 4.5MB 37.4MB/s \n","\u001b[?25hRequirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from -r requirements/tests.txt (line 6)) (3.6.4)\n","Collecting pytest-runner\n"," Downloading https://files.pythonhosted.org/packages/f4/f5/6605d73bf3f4c198915872111b10c4b3c2dccd8485f47b7290ceef037190/pytest_runner-5.3.1-py3-none-any.whl\n","Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (1.19.5)\n","Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (7.1.2)\n","Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (2.4.0)\n","Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (4.1.2.30)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (3.13)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.5.1)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.4.1)\n","Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) 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requirements/runtime.txt (line 4)) (1.8.0)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (2.23.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.4.4)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (57.0.0)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.12.0)\n","Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.12.4)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.3.4)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.31.0)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.0.1)\n","Requirement already satisfied: coverage in /usr/local/lib/python3.7/dist-packages (from codecov->-r requirements/tests.txt (line 1)) (3.7.1)\n","Collecting pyflakes<2.4.0,>=2.3.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6c/11/2a745612f1d3cbbd9c69ba14b1b43a35a2f5c3c81cd0124508c52c64307f/pyflakes-2.3.1-py2.py3-none-any.whl (68kB)\n","\u001b[K |████████████████████████████████| 71kB 9.8MB/s \n","\u001b[?25hCollecting pycodestyle<2.8.0,>=2.7.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/cc/227251b1471f129bc35e966bb0fceb005969023926d744139642d847b7ae/pycodestyle-2.7.0-py2.py3-none-any.whl (41kB)\n","\u001b[K |████████████████████████████████| 51kB 8.7MB/s \n","\u001b[?25hCollecting mccabe<0.7.0,>=0.6.0\n"," Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from flake8->-r requirements/tests.txt (line 2)) (4.5.0)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (0.8.9)\n","Collecting colorama\n"," Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (7.1.2)\n","Requirement already satisfied: toml in 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/tmp/pip-req-tracker-zk5q0q3z\n","Created requirements tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Created temporary directory: /tmp/pip-install-vr_vpseo\n","Obtaining file:///content/mmediting\n"," Added file:///content/mmediting to build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"," Running setup.py (path:/content/mmediting/setup.py) egg_info for package from file:///content/mmediting\n"," Running command python setup.py egg_info\n"," running egg_info\n"," creating mmedit.egg-info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," Source in /content/mmediting has version 0.8.0, which satisfies requirement mmedit==0.8.0 from file:///content/mmediting\n"," Removed mmedit==0.8.0 from file:///content/mmediting from build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Requirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (0.99)\n","Requirement already satisfied: mmcv-full>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (1.3.5)\n","Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from mmedit==0.8.0) (0.16.2)\n","Requirement already satisfied: tensorboard in 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chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->mmedit==0.8.0) (2021.5.30)\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->mmedit==0.8.0) (4.5.0)\n","Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (1.3.0)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->mmedit==0.8.0) (0.4.8)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.7.4.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (3.1.1)\n","Installing collected packages: mmedit\n"," Running setup.py develop for mmedit\n"," Running command /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmediting/setup.py'\"'\"'; __file__='\"'\"'/content/mmediting/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n"," running develop\n"," running egg_info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.7/dist-packages/mmedit.egg-link (link to .)\n"," Adding mmedit 0.8.0 to easy-install.pth file\n","\n"," Installed /content/mmediting\n","Successfully installed mmedit\n","Cleaning up...\n","Removed build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GIeIZEzZMfc0","executionInfo":{"status":"ok","timestamp":1625140820804,"user_tz":-480,"elapsed":279948,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"fe2e5ded-988d-4563-eb18-374344c316ef"}},{"cell_type":"markdown","source":["## 下载此演示所需的材料\n","在这个演示中，我们将需要一些数据和配置文件。我们将下载并放入 `./demo_files/`"],"metadata":{"id":"QgX96Sc_3PcV"}},{"cell_type":"code","execution_count":null,"source":["!wget https://download.openmmlab.com/mmediting/demo_files.zip # 下载文件\n","!unzip demo_files # 解压"],"outputs":[{"output_type":"stream","name":"stdout","text":["--2021-07-01 11:59:48-- https://download.openmmlab.com/mmediting/demo_files.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19215781 (18M) [application/zip]\n","Saving to: ‘demo_files.zip’\n","\n","demo_files.zip 100%[===================>] 18.33M 6.00MB/s in 3.1s \n","\n","2021-07-01 11:59:52 (6.00 MB/s) - ‘demo_files.zip’ saved [19215781/19215781]\n","\n","Archive: demo_files.zip\n"," creating: demo_files/\n"," inflating: demo_files/demo_config_EDVR.py \n"," inflating: demo_files/demo_config_BasicVSR.py \n"," creating: demo_files/lq_sequences/\n"," creating: demo_files/lq_sequences/calendar/\n"," inflating: demo_files/lq_sequences/calendar/00000006.png \n"," inflating: demo_files/lq_sequences/calendar/00000007.png \n"," inflating: demo_files/lq_sequences/calendar/00000010.png \n"," inflating: demo_files/lq_sequences/calendar/00000004.png \n"," inflating: demo_files/lq_sequences/calendar/00000003.png \n"," inflating: demo_files/lq_sequences/calendar/00000001.png \n"," inflating: demo_files/lq_sequences/calendar/00000000.png \n"," inflating: demo_files/lq_sequences/calendar/00000009.png \n"," inflating: demo_files/lq_sequences/calendar/00000008.png \n"," inflating: demo_files/lq_sequences/calendar/00000002.png \n"," inflating: demo_files/lq_sequences/calendar/00000005.png \n"," creating: demo_files/lq_sequences/city/\n"," inflating: demo_files/lq_sequences/city/00000006.png \n"," inflating: demo_files/lq_sequences/city/00000007.png \n"," inflating: demo_files/lq_sequences/city/00000010.png \n"," inflating: demo_files/lq_sequences/city/00000004.png \n"," inflating: demo_files/lq_sequences/city/00000003.png \n"," inflating: demo_files/lq_sequences/city/00000001.png \n"," inflating: demo_files/lq_sequences/city/00000000.png \n"," inflating: demo_files/lq_sequences/city/00000009.png \n"," inflating: demo_files/lq_sequences/city/00000008.png \n"," inflating: demo_files/lq_sequences/city/00000002.png \n"," inflating: demo_files/lq_sequences/city/00000005.png \n"," creating: demo_files/lq_sequences/.ipynb_checkpoints/\n"," creating: demo_files/gt_images/\n"," inflating: demo_files/gt_images/bird.png \n"," inflating: demo_files/gt_images/woman.png \n"," inflating: demo_files/gt_images/head.png \n"," inflating: demo_files/gt_images/baby.png \n"," inflating: demo_files/gt_images/butterfly.png \n"," inflating: demo_files/demo_config_SRCNN.py \n"," creating: demo_files/lq_images/\n"," extracting: demo_files/lq_images/bird.png \n"," extracting: demo_files/lq_images/woman.png \n"," extracting: demo_files/lq_images/head.png \n"," extracting: demo_files/lq_images/baby.png \n"," extracting: demo_files/lq_images/butterfly.png \n"," creating: demo_files/gt_sequences/\n"," creating: demo_files/gt_sequences/calendar/\n"," inflating: demo_files/gt_sequences/calendar/00000006.png \n"," inflating: demo_files/gt_sequences/calendar/00000007.png \n"," inflating: demo_files/gt_sequences/calendar/00000010.png \n"," inflating: demo_files/gt_sequences/calendar/00000004.png \n"," inflating: demo_files/gt_sequences/calendar/00000003.png \n"," inflating: demo_files/gt_sequences/calendar/00000001.png \n"," inflating: demo_files/gt_sequences/calendar/00000000.png \n"," inflating: demo_files/gt_sequences/calendar/00000009.png \n"," inflating: demo_files/gt_sequences/calendar/00000008.png \n"," inflating: demo_files/gt_sequences/calendar/00000002.png \n"," inflating: demo_files/gt_sequences/calendar/00000005.png \n"," creating: demo_files/gt_sequences/city/\n"," inflating: demo_files/gt_sequences/city/00000006.png \n"," inflating: demo_files/gt_sequences/city/00000007.png \n"," inflating: demo_files/gt_sequences/city/00000010.png \n"," inflating: demo_files/gt_sequences/city/00000004.png \n"," inflating: demo_files/gt_sequences/city/00000003.png \n"," inflating: demo_files/gt_sequences/city/00000001.png \n"," inflating: demo_files/gt_sequences/city/00000000.png \n"," inflating: demo_files/gt_sequences/city/00000009.png \n"," inflating: demo_files/gt_sequences/city/00000008.png \n"," inflating: demo_files/gt_sequences/city/00000002.png \n"," inflating: demo_files/gt_sequences/city/00000005.png \n"," creating: demo_files/gt_sequences/.ipynb_checkpoints/\n"," creating: demo_files/.ipynb_checkpoints/\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-K0zFSJ-3V42","executionInfo":{"status":"ok","timestamp":1625140825508,"user_tz":-480,"elapsed":4723,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"528a87f7-f78e-4219-84f3-dec19b88e88a"}},{"cell_type":"markdown","source":["## 使用预训练的图像恢复器进行推理\n","您可以使用 “restoration_demo.py” 轻松地使用预训练的恢复器对单个图像进行推理。您需要的是\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `IMAGE_FILE`：输入图像的路径。\n","4. `SAVE_FILE`：您要存储输出图像的位置。\n","5. `imshow`：是否显示图片。（可选的）\n","6. `GPU_ID`：您想使用哪个 GPU。（可选的）\n","\n","获得所有这些详细信息后，您可以直接使用以下命令：\n","\n","```\n","python demo/restoration_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} ${SAVE_FILE} [--imshow] [--device ${GPU_ID}]\n","```\n","\n","**注:** \n","1. 配置文件位于 `./configs`。\n","2. 我们支持从 url 加载权重文件。您可以到相应页面（例如[这里](https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/esrgan)）获取预训练模型的url。\n","\n","---\n","\n","我们现在将使用 `SRCNN` 和 `ESRGAN` 作为示例。\n","\n"],"metadata":{"id":"zXGurqGKOeNE"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN\n","!python demo/restoration_demo.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth ./demo_files/lq_images/bird.png ./outputs/bird_SRCNN.png\n","\n","# ESRGAN\n","!python demo/restoration_demo.py ./configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth ./demo_files/lq_images/bird.png ./outputs/bird_ESRGAN.png\n","\n","# 检查图像是否已保存\n","!ls ./outputs"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\" to /root/.cache/torch/hub/checkpoints/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n","100% 83.9k/83.9k [00:00<00:00, 1.59MB/s]\n","2021-07-01 12:00:10,779 - mmedit - INFO - Use load_from_torchvision loader\n","Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n","100% 548M/548M [00:07<00:00, 76.0MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\" to /root/.cache/torch/hub/checkpoints/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n","100% 196M/196M [00:26<00:00, 7.61MB/s]\n","bird_ESRGAN.png bird_SRCNN.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KiPvtvlqM1zb","executionInfo":{"status":"ok","timestamp":1625140884175,"user_tz":-480,"elapsed":58677,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"be7375a7-4632-4770-8383-2a8ce654b069"}},{"cell_type":"markdown","source":["## 使用预训练的视频复原器进行推理\n","\n","MMEditing 也支持视频超分辨率方法，过程类似。您可以使用带有以下参数的 `restoration_video_demo.py`：\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `INPUT_DIR`: 包含视频帧的目录。\n","4. `OUTPUT_DIR`: 要存储输出帧的位置。\n","5. `WINDOW_SIZE`: 使用滑动窗口方法时的窗口大小（可选）。\n","6. `GPU_ID`: 您想使用哪个 GPU（可选）。\n","\n","```\n","python demo/restoration_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} [--window_size=$WINDOW_SIZE] [--device ${GPU_ID}]\n","```\n","**注:** 视频超分辨率有两种不同的框架：***滑动窗口***和***循环***框架。使用 EDVR 等滑动窗口框架的方法时，需要指定 `window_size`。此值取决于您使用的模型。\n","\n","---\n","\n","我们现在将使用 `EDVR` 和 `BasicVSR` 作为示例。\n","\n"],"metadata":{"id":"W1DfGHu3Xcfd"}},{"cell_type":"code","execution_count":null,"source":["# EDVR（滑动窗口框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_EDVR --window_size=5\n","\n","# BasicVSR（循环框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/basicvsr/basicvsr_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_BasicVSR\n","\n","# 检查是否保存了视频帧\n","!ls ./outputs/calendar_BasicVSR"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\" to /root/.cache/torch/hub/checkpoints/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n","100% 11.5M/11.5M [00:01<00:00, 8.55MB/s]\n","2021-07-01 12:01:09,689 - mmedit - INFO - Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth\" to /root/.cache/torch/hub/checkpoints/spynet_20210409-c6c1bd09.pth\n","100% 5.50M/5.50M [00:00<00:00, 8.88MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\" to /root/.cache/torch/hub/checkpoints/basicvsr_reds4_20120409-0e599677.pth\n","100% 24.1M/24.1M [00:02<00:00, 8.97MB/s]\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iaoE7UF5Xb2i","executionInfo":{"status":"ok","timestamp":1625140913405,"user_tz":-480,"elapsed":29263,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"a022e0bd-c47a-450e-f4e4-1bf9f92e4813"}},{"cell_type":"markdown","source":["## 使用配置文件在预定义的数据集上进行测试\n","\n","上述演示提供了一种对单个图像或视频序列进行推理的简单方法。如果要对一组图像或序列进行推理，可以使用位于 `./configs` 中的配置文件。\n"," \n","现有的配置文件允许您对常见数据集进行推理，例如图像超分辨率中的 `Set5` 和视频超分辨率中的 `REDS4`。您可以使用以下命令：\n","\n","1. `CONFIG_FILE`: 你要使用的复原器和数据集对应的配置文件\n","2. `CHECKPOINT_FILE`: 预训练模型权重文件的路径。\n","3. `GPU_NUM`: 用于测试的 GPU 数量。\n","4. `RESULT_FILE`: 输出结果 pickle 文件的路径。（可选）\n","5. `IMAGE_SAVE_PATH`: 要存储输出图像的位置。（可选）\n","\n","```\n","# 单 GPU 测试\n","python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","\n","# 多 GPU 测试\n","./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","```\n","您需要做的是修改配置文件中的 `lq_folder` 和 `gt_folder`：\n","```\n","test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/val_set5/Set5_bicLRx4',\n"," gt_folder='data/val_set5/Set5',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","```\n","\n","**注**: 某些数据集类型（例如 `SRREDSDataset`）需要一个注释文件来指定数据集的详细信息。更多细节请参考 `./mmedit/dataset/` 中的相应文件。\n","\n","---\n","\n","以下是 SRCNN 的命令。对于其他模型，您可以简单地更改配置文件和预训练模型的路径。\n"],"metadata":{"id":"Rf3LW57qMHXb"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU\n","!python tools/test.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/\n","\n","# 多 GPU\n","!./tools/dist_test.sh ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 1 --save-path ./outputs/"],"outputs":[{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"tools/test.py\", line 136, in \n"," main()\n"," File \"tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/test.py\", line 136, in \n"," main()\n"," File \"./tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/test.py', '--local_rank=0', './configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py', 'https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', '--launcher', 'pytorch', '--save-path', './outputs/']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"tClgIYgcbbVg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140927462,"user_tz":-480,"elapsed":14095,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"c37ec2de-e1c9-42ae-ed9b-31009d48ae64"}},{"cell_type":"markdown","source":["## 在自定义数据集上进行测试\n","\n","当您想在自定义数据集上进行测试时，除了数据集路径之外，您还需要修改 `test_dataset_type`。 \n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","这些数据集类型假定指定目录中的所有图像/序列都用于测试。文件夹结构应该是\n","```\n","| lq_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," | ...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","| gt_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," |...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","```\n","我们将使用 **SRCNN**、**EDVR**、**BasicVSR** 作为示例。请注意 `test_dataset_type` 和 `data['test']` 的设置。"],"metadata":{"id":"KWKVyeEQelh3"}},{"cell_type":"markdown","source":["**SRCNN**"],"metadata":{"id":"0p2rP8jV_dL1"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_SRCNN.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/testset_SRCNN\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_SRCNN"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 5/5, 8.6 task/s, elapsed: 1s, ETA: 0s\n","Eval-PSNR: 28.433974369836108\n","Eval-SSIM: 0.8099053586583066\n","baby.png bird.png butterfly.png head.png woman.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4kEev4wVIq_L","executionInfo":{"status":"ok","timestamp":1625140936180,"user_tz":-480,"elapsed":8729,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"8026ed73-f781-4eb2-bb80-d3446bd131df"}},{"cell_type":"markdown","source":["**EDVR**"],"metadata":{"id":"RONzjTTU_gem"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_EDVR.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth --save-path ./outputs/testset_EDVR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_EDVR\n","!ls ./outputs/testset_EDVR/city"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 22/22, 2.0 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 23.89569862011228\n","Eval-SSIM: 0.7667098470108678\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vL8WOWXY0fNJ","executionInfo":{"status":"ok","timestamp":1625140955813,"user_tz":-480,"elapsed":19671,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"dc2a8f81-9bef-4ad4-c5b2-c6f124e6b113"}},{"cell_type":"markdown","source":["**BasicVSR**"],"metadata":{"id":"5Tc7F-l5_i1e"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_BasicVSR.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth --save-path ./outputs/testset_BasicVSR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_BasicVSR\n","!ls ./outputs/testset_BasicVSR/calendar"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:07,780 - mmedit - INFO - Use load_from_http loader\n","Use load_from_http loader\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","[>>] 2/2, 0.2 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 24.195768601433734\n","Eval-SSIM: 0.7828541339512978\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jpW5GWC74Yvu","executionInfo":{"status":"ok","timestamp":1625140976026,"user_tz":-480,"elapsed":20220,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"7ba02a32-d4ec-40b2-8108-ef0729b62147"}},{"cell_type":"markdown","source":["## 在预定义的数据集上训练恢复器\n","\n","MMEditing 使用分布式训练。以下命令可用于训练。如果要在我们的配置文件中指定的预定义数据集上进行训练，只需运行以下命令即可。\n","\n","```\n","./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n","```\n","\n","有关可选参数的更多详细信息，请参阅 `tools/train.py`。\n","\n","---\n","\n","这是一个使用 EDVR 的示例。\n"],"metadata":{"id":"4DQxNL8BhI0y"}},{"cell_type":"code","execution_count":null,"source":["!./tools/dist_train.sh ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py 1"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:31,961 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:31,961 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:31,961 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:31,961 - mmedit - INFO - Config:\n","/content/mmediting/configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py\n","exp_name = 'edvrm_wotsa_x4_g8_600k_reds'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRREDSDataset'\n","val_dataset_type = 'SRREDSDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 600000\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50000, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=100,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 54, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 63, in load_annotations\n"," with open(self.ann_file, 'r') as fin:\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/train.py\", line 145, in \n"," main()\n"," File \"./tools/train.py\", line 111, in main\n"," datasets = [build_dataset(cfg.data.train)]\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 76, in build_dataset\n"," build_dataset(cfg['dataset'], default_args), cfg['times'])\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRREDSDataset: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/train.py', '--local_rank=0', './configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py', '--launcher', 'pytorch']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"s-hOnSF6ItQM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140985357,"user_tz":-480,"elapsed":9337,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"e40e960a-29e1-43e8-b922-5e08c4e98afe"}},{"cell_type":"markdown","source":["## 在自定义数据集上训练复原器\n","\n","与您要在自己的数据集上进行测试的情况类似，您需要修改 `train_dataset_type`。您需要的数据集类型是相同的：\n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","修改数据集类型和数据路径后。一切都准备好了。"],"metadata":{"id":"b0VfQkQQjg8N"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN（图像超分辨率）\n","!./tools/dist_train.sh ./demo_files/demo_config_SRCNN.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:41,185 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:41,185 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:41,185 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:41,185 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_SRCNN.py\n","exp_name = 'srcnn_demo'\n","\n","scale = 4\n","# model settings\n","model = dict(\n"," type='BasicRestorer',\n"," generator=dict(\n"," type='SRCNN',\n"," channels=(3, 64, 32, 3),\n"," kernel_sizes=(9, 1, 5),\n"," upscale_factor=scale),\n"," pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderDataset'\n","val_dataset_type = 'SRFolderDataset'\n","train_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=128),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","test_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=8,\n"," train_dataloader=dict(samples_per_gpu=16, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=train_pipeline,\n"," scale=scale)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[250000, 250000, 250000, 250000],\n"," restart_weights=[1, 1, 1, 1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","evaluation = dict(interval=50, save_image=True, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./experiments/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:02:41,192 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/experiments/srcnn_demo\n","2021-07-01 12:02:41,192 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:02:41.529307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:03:18,631 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.000e-04, eta: 0:57:01, time: 34.560, data_time: 34.446, memory: 586, loss_pix: 0.3999, loss: 0.3999\n","2021-07-01 12:03:18,712 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","INFO:mmedit:Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","2021-07-01 12:03:18,764 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","INFO:mmedit:Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","2021-07-01 12:03:18,840 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","INFO:mmedit:Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","2021-07-01 12:03:18,916 - mmedit - INFO - Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","INFO:mmedit:Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","2021-07-01 12:03:18,956 - mmedit - INFO - Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","INFO:mmedit:Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","2021-07-01 12:03:19,012 - mmedit - INFO - Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","INFO:mmedit:Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","2021-07-01 12:03:19,070 - mmedit - INFO - Iter [8/100]\tlr_generator: 2.000e-04, eta: 0:06:42, time: 0.057, data_time: 0.016, memory: 586, loss_pix: 0.3766, loss: 0.3766\n","INFO:mmedit:Iter [8/100]\tlr_generator: 2.000e-04, eta: 0:06:42, time: 0.057, data_time: 0.016, memory: 586, loss_pix: 0.3766, loss: 0.3766\n","2021-07-01 12:03:19,142 - mmedit - INFO - Iter [9/100]\tlr_generator: 2.000e-04, eta: 0:05:54, time: 0.064, data_time: 0.026, memory: 586, loss_pix: 0.3721, loss: 0.3721\n","INFO:mmedit:Iter [9/100]\tlr_generator: 2.000e-04, eta: 0:05:54, time: 0.064, data_time: 0.026, memory: 586, loss_pix: 0.3721, loss: 0.3721\n","2021-07-01 12:03:19,212 - mmedit - INFO - Iter [10/100]\tlr_generator: 2.000e-04, eta: 0:05:16, time: 0.079, data_time: 0.027, memory: 586, loss_pix: 0.3314, loss: 0.3314\n","INFO:mmedit:Iter [10/100]\tlr_generator: 2.000e-04, eta: 0:05:16, time: 0.079, data_time: 0.027, memory: 586, loss_pix: 0.3314, loss: 0.3314\n","2021-07-01 12:03:19,261 - mmedit - INFO - Iter [11/100]\tlr_generator: 2.000e-04, eta: 0:04:44, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.3249, loss: 0.3249\n","INFO:mmedit:Iter [11/100]\tlr_generator: 2.000e-04, eta: 0:04:44, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.3249, loss: 0.3249\n","2021-07-01 12:03:19,302 - 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mmedit - INFO - Iter [48/100]\tlr_generator: 2.000e-04, eta: 0:00:40, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.0876, loss: 0.0876\n","INFO:mmedit:Iter [48/100]\tlr_generator: 2.000e-04, eta: 0:00:40, time: 0.054, data_time: 0.016, memory: 586, loss_pix: 0.0876, loss: 0.0876\n","2021-07-01 12:03:21,743 - mmedit - INFO - Iter [49/100]\tlr_generator: 2.000e-04, eta: 0:00:39, time: 0.067, data_time: 0.011, memory: 586, loss_pix: 0.0923, loss: 0.0923\n","INFO:mmedit:Iter [49/100]\tlr_generator: 2.000e-04, eta: 0:00:39, time: 0.067, data_time: 0.011, memory: 586, loss_pix: 0.0923, loss: 0.0923\n","[>>] 5/5, 0.1 task/s, elapsed: 37s, ETA: 0s\n","\n","2021-07-01 12:03:59,996 - mmedit - INFO - Iter(val) [50]\tPSNR: 19.4410, SSIM: 0.5387\n","INFO:mmedit:Iter(val) [50]\tPSNR: 19.4410, SSIM: 0.5387\n","2021-07-01 12:04:00,047 - mmedit - INFO - Iter [51/100]\tlr_generator: 2.000e-04, eta: 0:01:12, time: 38.229, data_time: 38.203, memory: 586, loss_pix: 0.0909, loss: 0.0909\n","INFO:mmedit:Iter [51/100]\tlr_generator: 2.000e-04, eta: 0:01:12, time: 38.229, data_time: 38.203, memory: 586, loss_pix: 0.0909, loss: 0.0909\n","2021-07-01 12:04:00,114 - 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mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:04:37,422 - mmedit - INFO - Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n","INFO:mmedit:Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"liGEKJpbIoXZ","executionInfo":{"status":"ok","timestamp":1625141113733,"user_tz":-480,"elapsed":128384,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"84e1502e-e2cb-458f-c7c5-e4b401e570b7"}},{"cell_type":"code","execution_count":null,"source":["# EDVR（视频超分辨率-滑动窗口）\n","!./tools/dist_train.sh ./demo_files/demo_config_EDVR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:10:12,619 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:10:12,619 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:10:12,619 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:10:12,619 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_EDVR.py\n","exp_name = 'edvrm_demo'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderVideoDataset'\n","val_dataset_type = 'SRFolderVideoDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:10:12,701 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/edvrm_demo\n","2021-07-01 12:10:12,702 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:10:12.951771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:10:30,703 - mmedit - INFO - Iter [1/100]\tlr_generator: 4.000e-04, eta: 0:26:53, time: 16.295, data_time: 15.833, memory: 1341, loss_pix: 63917.2734, loss: 63917.2734\n","2021-07-01 12:10:31,046 - mmedit - INFO - Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","INFO:mmedit:Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","2021-07-01 12:10:31,386 - mmedit - INFO - Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","INFO:mmedit:Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","2021-07-01 12:10:31,731 - 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Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"2fb26527-eb9e-4b48-e03c-ba9a91c60db8"}},{"cell_type":"code","execution_count":null,"source":["# BasicVSR（视频超分辨率 - 循环）\n","!./tools/dist_train.sh ./demo_files/demo_config_BasicVSR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:06:47,253 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:06:47,253 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:06:47,254 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:06:47,254 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_BasicVSR.py\n","exp_name = 'basicvsr_demo'\n","\n","# model settings\n","model = dict(\n"," type='BasicVSR',\n"," generator=dict(\n"," type='BasicVSRNet',\n"," mid_channels=64,\n"," num_blocks=30,\n"," spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n"," 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = dict(fix_iter=5000)\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderMultipleGTDataset'\n","val_dataset_type = 'SRFolderMultipleGTDataset'\n","\n","train_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=6,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True), # 2 gpus\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),\n","\n"," # train\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," # val\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," # test\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(\n"," generator=dict(\n"," type='Adam',\n"," lr=2e-4,\n"," betas=(0.9, 0.99),\n"," paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[300000],\n"," restart_weights=[1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," # dict(type='TensorboardLoggerHook'),\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","find_unused_parameters = True\n","\n","2021-07-01 12:06:47,291 - mmedit - INFO - Use load_from_http loader\n","2021-07-01 12:06:47,569 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/basicvsr_demo\n","2021-07-01 12:06:47,569 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:07:14,210 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.500e-05, eta: 0:42:52, time: 25.981, data_time: 24.045, memory: 3464, loss_pix: 0.0634, loss: 0.0634\n","2021-07-01 12:07:15,171 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.500e-05, eta: 0:22:00, time: 0.961, data_time: 0.011, memory: 3518, loss_pix: 0.0556, loss: 0.0556\n","2021-07-01 12:07:16,052 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.500e-05, eta: 0:14:59, time: 0.881, data_time: 0.003, memory: 3518, loss_pix: 0.0476, loss: 0.0476\n","2021-07-01 12:07:16,940 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.500e-05, eta: 0:11:29, time: 0.888, data_time: 0.003, memory: 3518, loss_pix: 0.0673, loss: 0.0673\n","2021-07-01 12:07:17,829 - 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mmedit - INFO - Iter(val) [100]\tPSNR: 21.4372, SSIM: 0.5687\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_RdqmlT6qgt2","executionInfo":{"status":"ok","timestamp":1625141428032,"user_tz":-480,"elapsed":197033,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"b951b426-e06c-4f31-db01-449333eab333"}},{"cell_type":"markdown","source":["**本教程到此结束。 有关更高级的用法，请参阅我们的综合教程 [此处]()。 享受使用 MMEditing 的乐趣！**"],"metadata":{"id":"QT0zwBFt7J13"}}]} +{"nbformat":4,"nbformat_minor":2,"metadata":{"accelerator":"GPU","colab":{"name":"restorer_basic_tutorial.ipynb","provenance":[],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"display_name":"Python 3.7.7 64-bit ('pre-commit': conda)","name":"python377jvsc74a57bd04974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"},"language_info":{"name":"python","version":""},"metadata":{"interpreter":{"hash":"4974e937eb671323d80c6d61c9a5d7e99245fcc571fd7f47cadafec83c412660"}}},"cells":[{"cell_type":"markdown","source":["# MMEditing 基础教程\n","\n","欢迎来到MMEditing！ 这是 MMEditing 的官方 Colab 教程。在本教程中，您将学习如何使用 MMEditing 中提供的 API 训练和测试恢复器。\n","\n","这是训练和测试现有模型的快速指南。如果您想基于 MMEditing 开发自己的模型并了解有关代码结构的更多信息，请参阅我们的[综合教程]()。\n","\n","[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-mmlab/mmedit/blob/main/demo/restorer_basic_tutorial.ipynb)\n","\n"],"metadata":{"id":"T2WWQiheMF7q"}},{"cell_type":"markdown","source":["## 安装MMEditing\n","\n","MMEditing 可以分三步安装：\n","\n","1. 安装兼容的 PyTorch 版本（你需要使用 `nvcc -V` 检查你的 CUDA 版本）。\n","2. 安装预编译的MMCV\n","3. 克隆并安装MMEditing\n","\n","步骤如下所示："],"metadata":{"id":"-kYw3WQ0MQry"}},{"cell_type":"code","execution_count":null,"source":["# Check nvcc version\n","!nvcc -V\n","# Check GCC version (MMEditing needs gcc >= 5.0)\n","!gcc --version"],"outputs":[{"output_type":"stream","name":"stdout","text":["nvcc: NVIDIA (R) Cuda compiler driver\n","Copyright (c) 2005-2020 NVIDIA Corporation\n","Built on Wed_Jul_22_19:09:09_PDT_2020\n","Cuda compilation tools, release 11.0, V11.0.221\n","Build cuda_11.0_bu.TC445_37.28845127_0\n","gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","Copyright (C) 2017 Free Software Foundation, Inc.\n","This is free software; see the source for copying conditions. There is NO\n","warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n","\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uha_13idyl1b","executionInfo":{"status":"ok","timestamp":1625140540858,"user_tz":-480,"elapsed":321,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"87056561-5930-45b3-e1a8-f9e103d10b23"}},{"cell_type":"code","execution_count":null,"source":["# 安装依赖：（使用 cu101 因为 colab 有 CUDA 11.0）\n","!pip install -U torch==1.7.0+cu110 torchvision==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html\n","\n","# 安装 mmcv-full 这样我们就可以使用 CUDA 运算符\n","!pip install mmcv-full==1.3.5 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html\n","\n","# 克隆 MMEditing \n","!rm -rf mmediting\n","!git clone https://github.com/open-mmlab/mmediting.git\n","%cd mmediting\n","\n","# 安装 MMEditing\n","!pip install -r requirements.txt\n","!pip install -v -e ."],"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in links: https://download.pytorch.org/whl/torch_stable.html\n","Collecting torch==1.7.0+cu110\n","\u001b[?25l Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp37-cp37m-linux_x86_64.whl (1137.1MB)\n","\u001b[K |███████████████████████▌ 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requirements/runtime.txt (line 2)) (7.1.2)\n","Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (2.4.0)\n","Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (4.1.2.30)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full>=1.2.0->-r requirements/runtime.txt (line 2)) (3.13)\n","Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.5.1)\n","Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) (2.4.1)\n","Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->-r requirements/runtime.txt (line 3)) 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requirements/runtime.txt (line 4)) (1.8.0)\n","Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (2.23.0)\n","Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.4.4)\n","Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (57.0.0)\n","Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (0.12.0)\n","Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.12.4)\n","Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (3.3.4)\n","Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.31.0)\n","Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->-r requirements/runtime.txt (line 4)) (1.0.1)\n","Requirement already satisfied: coverage in /usr/local/lib/python3.7/dist-packages (from codecov->-r requirements/tests.txt (line 1)) (3.7.1)\n","Collecting pyflakes<2.4.0,>=2.3.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/6c/11/2a745612f1d3cbbd9c69ba14b1b43a35a2f5c3c81cd0124508c52c64307f/pyflakes-2.3.1-py2.py3-none-any.whl (68kB)\n","\u001b[K |████████████████████████████████| 71kB 9.8MB/s \n","\u001b[?25hCollecting pycodestyle<2.8.0,>=2.7.0\n","\u001b[?25l Downloading https://files.pythonhosted.org/packages/de/cc/227251b1471f129bc35e966bb0fceb005969023926d744139642d847b7ae/pycodestyle-2.7.0-py2.py3-none-any.whl (41kB)\n","\u001b[K |████████████████████████████████| 51kB 8.7MB/s \n","\u001b[?25hCollecting mccabe<0.7.0,>=0.6.0\n"," Downloading https://files.pythonhosted.org/packages/87/89/479dc97e18549e21354893e4ee4ef36db1d237534982482c3681ee6e7b57/mccabe-0.6.1-py2.py3-none-any.whl\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from flake8->-r requirements/tests.txt (line 2)) (4.5.0)\n","Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (0.8.9)\n","Collecting colorama\n"," Downloading https://files.pythonhosted.org/packages/44/98/5b86278fbbf250d239ae0ecb724f8572af1c91f4a11edf4d36a206189440/colorama-0.4.4-py2.py3-none-any.whl\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from interrogate->-r requirements/tests.txt (line 3)) (7.1.2)\n","Requirement already satisfied: toml in 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/tmp/pip-req-tracker-zk5q0q3z\n","Created requirements tracker '/tmp/pip-req-tracker-zk5q0q3z'\n","Created temporary directory: /tmp/pip-install-vr_vpseo\n","Obtaining file:///content/mmediting\n"," Added file:///content/mmediting to build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"," Running setup.py (path:/content/mmediting/setup.py) egg_info for package from file:///content/mmediting\n"," Running command python setup.py egg_info\n"," running egg_info\n"," creating mmedit.egg-info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 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already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->mmedit==0.8.0) (3.7.4.3)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->mmedit==0.8.0) (3.1.1)\n","Installing collected packages: mmedit\n"," Running setup.py develop for mmedit\n"," Running command /usr/bin/python3 -c 'import sys, setuptools, tokenize; sys.argv[0] = '\"'\"'/content/mmediting/setup.py'\"'\"'; __file__='\"'\"'/content/mmediting/setup.py'\"'\"';f=getattr(tokenize, '\"'\"'open'\"'\"', open)(__file__);code=f.read().replace('\"'\"'\\r\\n'\"'\"', '\"'\"'\\n'\"'\"');f.close();exec(compile(code, __file__, '\"'\"'exec'\"'\"'))' develop --no-deps\n"," running develop\n"," running egg_info\n"," writing mmedit.egg-info/PKG-INFO\n"," writing dependency_links to mmedit.egg-info/dependency_links.txt\n"," writing requirements to mmedit.egg-info/requires.txt\n"," writing top-level names to mmedit.egg-info/top_level.txt\n"," reading manifest template 'MANIFEST.in'\n"," warning: no files found matching 'mmedit/VERSION'\n"," warning: no files found matching 'mmedit/model_zoo.yml'\n"," warning: no files found matching '*.py' under directory 'mmedit/configs'\n"," warning: no files found matching '*.yml' under directory 'mmedit/configs'\n"," warning: no files found matching '*.sh' under directory 'mmedit/tools'\n"," warning: no files found matching '*.py' under directory 'mmedit/tools'\n"," adding license file 'LICENSE'\n"," writing manifest file 'mmedit.egg-info/SOURCES.txt'\n"," running build_ext\n"," Creating /usr/local/lib/python3.7/dist-packages/mmedit.egg-link (link to .)\n"," Adding mmedit 0.8.0 to easy-install.pth file\n","\n"," Installed /content/mmediting\n","Successfully installed mmedit\n","Cleaning up...\n","Removed build tracker '/tmp/pip-req-tracker-zk5q0q3z'\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GIeIZEzZMfc0","executionInfo":{"status":"ok","timestamp":1625140820804,"user_tz":-480,"elapsed":279948,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"fe2e5ded-988d-4563-eb18-374344c316ef"}},{"cell_type":"markdown","source":["## 下载此演示所需的材料\n","在这个演示中，我们将需要一些数据和配置文件。我们将下载并放入 `./demo_files/`"],"metadata":{"id":"QgX96Sc_3PcV"}},{"cell_type":"code","execution_count":null,"source":["!wget https://download.openmmlab.com/mmediting/demo_files.zip # 下载文件\n","!unzip demo_files # 解压"],"outputs":[{"output_type":"stream","name":"stdout","text":["--2021-07-01 11:59:48-- https://download.openmmlab.com/mmediting/demo_files.zip\n","Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n","Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 19215781 (18M) [application/zip]\n","Saving to: ‘demo_files.zip’\n","\n","demo_files.zip 100%[===================>] 18.33M 6.00MB/s in 3.1s \n","\n","2021-07-01 11:59:52 (6.00 MB/s) - ‘demo_files.zip’ saved [19215781/19215781]\n","\n","Archive: demo_files.zip\n"," creating: demo_files/\n"," inflating: demo_files/demo_config_EDVR.py \n"," inflating: demo_files/demo_config_BasicVSR.py \n"," creating: demo_files/lq_sequences/\n"," creating: demo_files/lq_sequences/calendar/\n"," inflating: demo_files/lq_sequences/calendar/00000006.png \n"," inflating: demo_files/lq_sequences/calendar/00000007.png \n"," inflating: demo_files/lq_sequences/calendar/00000010.png \n"," inflating: demo_files/lq_sequences/calendar/00000004.png \n"," inflating: demo_files/lq_sequences/calendar/00000003.png \n"," inflating: demo_files/lq_sequences/calendar/00000001.png \n"," inflating: demo_files/lq_sequences/calendar/00000000.png \n"," inflating: demo_files/lq_sequences/calendar/00000009.png \n"," inflating: demo_files/lq_sequences/calendar/00000008.png \n"," inflating: demo_files/lq_sequences/calendar/00000002.png \n"," inflating: demo_files/lq_sequences/calendar/00000005.png \n"," creating: demo_files/lq_sequences/city/\n"," inflating: demo_files/lq_sequences/city/00000006.png \n"," inflating: demo_files/lq_sequences/city/00000007.png \n"," inflating: demo_files/lq_sequences/city/00000010.png \n"," inflating: demo_files/lq_sequences/city/00000004.png \n"," inflating: demo_files/lq_sequences/city/00000003.png \n"," inflating: demo_files/lq_sequences/city/00000001.png \n"," inflating: demo_files/lq_sequences/city/00000000.png \n"," inflating: demo_files/lq_sequences/city/00000009.png \n"," inflating: demo_files/lq_sequences/city/00000008.png \n"," inflating: demo_files/lq_sequences/city/00000002.png \n"," inflating: demo_files/lq_sequences/city/00000005.png \n"," creating: demo_files/lq_sequences/.ipynb_checkpoints/\n"," creating: demo_files/gt_images/\n"," inflating: demo_files/gt_images/bird.png \n"," inflating: demo_files/gt_images/woman.png \n"," inflating: demo_files/gt_images/head.png \n"," inflating: demo_files/gt_images/baby.png \n"," inflating: demo_files/gt_images/butterfly.png \n"," inflating: demo_files/demo_config_SRCNN.py \n"," creating: demo_files/lq_images/\n"," extracting: demo_files/lq_images/bird.png \n"," extracting: demo_files/lq_images/woman.png \n"," extracting: demo_files/lq_images/head.png \n"," extracting: demo_files/lq_images/baby.png \n"," extracting: demo_files/lq_images/butterfly.png \n"," creating: demo_files/gt_sequences/\n"," creating: demo_files/gt_sequences/calendar/\n"," inflating: demo_files/gt_sequences/calendar/00000006.png \n"," inflating: demo_files/gt_sequences/calendar/00000007.png \n"," inflating: demo_files/gt_sequences/calendar/00000010.png \n"," inflating: demo_files/gt_sequences/calendar/00000004.png \n"," inflating: demo_files/gt_sequences/calendar/00000003.png \n"," inflating: demo_files/gt_sequences/calendar/00000001.png \n"," inflating: demo_files/gt_sequences/calendar/00000000.png \n"," inflating: demo_files/gt_sequences/calendar/00000009.png \n"," inflating: demo_files/gt_sequences/calendar/00000008.png \n"," inflating: demo_files/gt_sequences/calendar/00000002.png \n"," inflating: demo_files/gt_sequences/calendar/00000005.png \n"," creating: demo_files/gt_sequences/city/\n"," inflating: demo_files/gt_sequences/city/00000006.png \n"," inflating: demo_files/gt_sequences/city/00000007.png \n"," inflating: demo_files/gt_sequences/city/00000010.png \n"," inflating: demo_files/gt_sequences/city/00000004.png \n"," inflating: demo_files/gt_sequences/city/00000003.png \n"," inflating: demo_files/gt_sequences/city/00000001.png \n"," inflating: demo_files/gt_sequences/city/00000000.png \n"," inflating: demo_files/gt_sequences/city/00000009.png \n"," inflating: demo_files/gt_sequences/city/00000008.png \n"," inflating: demo_files/gt_sequences/city/00000002.png \n"," inflating: demo_files/gt_sequences/city/00000005.png \n"," creating: demo_files/gt_sequences/.ipynb_checkpoints/\n"," creating: demo_files/.ipynb_checkpoints/\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-K0zFSJ-3V42","executionInfo":{"status":"ok","timestamp":1625140825508,"user_tz":-480,"elapsed":4723,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"528a87f7-f78e-4219-84f3-dec19b88e88a"}},{"cell_type":"markdown","source":["## 使用预训练的图像恢复器进行推理\n","您可以使用 “restoration_demo.py” 轻松地使用预训练的恢复器对单个图像进行推理。您需要的是\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `IMAGE_FILE`：输入图像的路径。\n","4. `SAVE_FILE`：您要存储输出图像的位置。\n","5. `imshow`：是否显示图片。（可选的）\n","6. `GPU_ID`：您想使用哪个 GPU。（可选的）\n","\n","获得所有这些详细信息后，您可以直接使用以下命令：\n","\n","```\n","python demo/restoration_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${IMAGE_FILE} ${SAVE_FILE} [--imshow] [--device ${GPU_ID}]\n","```\n","\n","**注:** \n","1. 配置文件位于 `./configs`。\n","2. 我们支持从 url 加载权重文件。您可以到相应页面（例如[这里](https://github.com/open-mmlab/mmediting/tree/master/configs/restorers/esrgan)）获取预训练模型的url。\n","\n","---\n","\n","我们现在将使用 `SRCNN` 和 `ESRGAN` 作为示例。\n","\n"],"metadata":{"id":"zXGurqGKOeNE"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN\n","!python demo/restoration_demo.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth ./demo_files/lq_images/bird.png ./outputs/bird_SRCNN.png\n","\n","# ESRGAN\n","!python demo/restoration_demo.py ./configs/restorers/esrgan/esrgan_x4c64b23g32_g1_400k_div2k.py https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth ./demo_files/lq_images/bird.png ./outputs/bird_ESRGAN.png\n","\n","# 检查图像是否已保存\n","!ls ./outputs"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\" to /root/.cache/torch/hub/checkpoints/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth\n","100% 83.9k/83.9k [00:00<00:00, 1.59MB/s]\n","2021-07-01 12:00:10,779 - mmedit - INFO - Use load_from_torchvision loader\n","Downloading: \"https://download.pytorch.org/models/vgg19-dcbb9e9d.pth\" to /root/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth\n","100% 548M/548M [00:07<00:00, 76.0MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\" to /root/.cache/torch/hub/checkpoints/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth\n","100% 196M/196M [00:26<00:00, 7.61MB/s]\n","bird_ESRGAN.png bird_SRCNN.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KiPvtvlqM1zb","executionInfo":{"status":"ok","timestamp":1625140884175,"user_tz":-480,"elapsed":58677,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"be7375a7-4632-4770-8383-2a8ce654b069"}},{"cell_type":"markdown","source":["## 使用预训练的视频复原器进行推理\n","\n","MMEditing 也支持视频超分辨率方法，过程类似。您可以使用带有以下参数的 `restoration_video_demo.py`：\n","\n","1. `CONFIG_FILE`：你要使用的 restorer 对应的配置文件。它指定您要使用的模型。\n","2. `CHECKPOINT_FILE`：预训练模型权重文件的路径。\n","3. `INPUT_DIR`: 包含视频帧的目录。\n","4. `OUTPUT_DIR`: 要存储输出帧的位置。\n","5. `WINDOW_SIZE`: 使用滑动窗口方法时的窗口大小（可选）。\n","6. `GPU_ID`: 您想使用哪个 GPU（可选）。\n","\n","```\n","python demo/restoration_video_demo.py ${CONFIG_FILE} ${CHECKPOINT_FILE} ${INPUT_DIR} ${OUTPUT_DIR} [--window_size=$WINDOW_SIZE] [--device ${GPU_ID}]\n","```\n","**注:** 视频超分辨率有两种不同的框架：***滑动窗口***和***循环***框架。使用 EDVR 等滑动窗口框架的方法时，需要指定 `window_size`。此值取决于您使用的模型。\n","\n","---\n","\n","我们现在将使用 `EDVR` 和 `BasicVSR` 作为示例。\n","\n"],"metadata":{"id":"W1DfGHu3Xcfd"}},{"cell_type":"code","execution_count":null,"source":["# EDVR（滑动窗口框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_EDVR --window_size=5\n","\n","# BasicVSR（循环框架）\n","!python demo/restoration_video_demo.py ./configs/restorers/basicvsr/basicvsr_reds4.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth demo_files/lq_sequences/calendar/ ./outputs/calendar_BasicVSR\n","\n","# 检查是否保存了视频帧\n","!ls ./outputs/calendar_BasicVSR"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\" to /root/.cache/torch/hub/checkpoints/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth\n","100% 11.5M/11.5M [00:01<00:00, 8.55MB/s]\n","2021-07-01 12:01:09,689 - mmedit - INFO - Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth\" to /root/.cache/torch/hub/checkpoints/spynet_20210409-c6c1bd09.pth\n","100% 5.50M/5.50M [00:00<00:00, 8.88MB/s]\n","Use load_from_http loader\n","Downloading: \"https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth\" to /root/.cache/torch/hub/checkpoints/basicvsr_reds4_20120409-0e599677.pth\n","100% 24.1M/24.1M [00:02<00:00, 8.97MB/s]\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iaoE7UF5Xb2i","executionInfo":{"status":"ok","timestamp":1625140913405,"user_tz":-480,"elapsed":29263,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"a022e0bd-c47a-450e-f4e4-1bf9f92e4813"}},{"cell_type":"markdown","source":["## 使用配置文件在预定义的数据集上进行测试\n","\n","上述演示提供了一种对单个图像或视频序列进行推理的简单方法。如果要对一组图像或序列进行推理，可以使用位于 `./configs` 中的配置文件。\n"," \n","现有的配置文件允许您对常见数据集进行推理，例如图像超分辨率中的 `Set5` 和视频超分辨率中的 `REDS4`。您可以使用以下命令：\n","\n","1. `CONFIG_FILE`: 你要使用的复原器和数据集对应的配置文件\n","2. `CHECKPOINT_FILE`: 预训练模型权重文件的路径。\n","3. `GPU_NUM`: 用于测试的 GPU 数量。\n","4. `RESULT_FILE`: 输出结果 pickle 文件的路径。（可选）\n","5. `IMAGE_SAVE_PATH`: 要存储输出图像的位置。（可选）\n","\n","```\n","# 单 GPU 测试\n","python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","\n","# 多 GPU 测试\n","./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]\n","```\n","您需要做的是修改配置文件中的 `lq_folder` 和 `gt_folder`：\n","```\n","test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/val_set5/Set5_bicLRx4',\n"," gt_folder='data/val_set5/Set5',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","```\n","\n","**注**: 某些数据集类型（例如 `SRREDSDataset`）需要一个注释文件来指定数据集的详细信息。更多细节请参考 `./mmedit/dataset/` 中的相应文件。\n","\n","---\n","\n","以下是 SRCNN 的命令。对于其他模型，您可以简单地更改配置文件和预训练模型的路径。\n"],"metadata":{"id":"Rf3LW57qMHXb"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU\n","!python tools/test.py ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/\n","\n","# 多 GPU\n","!./tools/dist_test.sh ./configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth 1 --save-path ./outputs/"],"outputs":[{"output_type":"stream","name":"stdout","text":["Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"tools/test.py\", line 136, in \n"," main()\n"," File \"tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 62, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_folder_dataset.py\", line 73, in load_annotations\n"," lq_paths = self.scan_folder(self.lq_folder)\n"," File \"/content/mmediting/mmedit/datasets/base_sr_dataset.py\", line 39, in scan_folder\n"," images = list(scandir(path, suffix=IMG_EXTENSIONS, recursive=True))\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/path.py\", line 63, in _scandir\n"," for entry in os.scandir(dir_path):\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/test.py\", line 136, in \n"," main()\n"," File \"./tools/test.py\", line 73, in main\n"," dataset = build_dataset(cfg.data.test)\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRFolderDataset: [Errno 2] No such file or directory: 'data/val_set5/Set5_bicLRx4'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/test.py', '--local_rank=0', './configs/restorers/srcnn/srcnn_x4k915_g1_1000k_div2k.py', 'https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth', '--launcher', 'pytorch', '--save-path', './outputs/']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"tClgIYgcbbVg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140927462,"user_tz":-480,"elapsed":14095,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"c37ec2de-e1c9-42ae-ed9b-31009d48ae64"}},{"cell_type":"markdown","source":["## 在自定义数据集上进行测试\n","\n","当您想在自定义数据集上进行测试时，除了数据集路径之外，您还需要修改 `test_dataset_type`。 \n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","这些数据集类型假定指定目录中的所有图像/序列都用于测试。文件夹结构应该是\n","```\n","| lq_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," | ...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","| gt_root\n"," | sequence_1\n"," | 000.png\n"," | 001.png\n"," |...\n"," | sequence_2\n"," | 000.png\n"," | ...\n"," | ...\n","```\n","我们将使用 **SRCNN**、**EDVR**、**BasicVSR** 作为示例。请注意 `test_dataset_type` 和 `data['test']` 的设置。"],"metadata":{"id":"KWKVyeEQelh3"}},{"cell_type":"markdown","source":["**SRCNN**"],"metadata":{"id":"0p2rP8jV_dL1"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_SRCNN.py https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth --save-path ./outputs/testset_SRCNN\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_SRCNN"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 5/5, 8.6 task/s, elapsed: 1s, ETA: 0s\n","Eval-PSNR: 28.433974369836108\n","Eval-SSIM: 0.8099053586583066\n","baby.png bird.png butterfly.png head.png woman.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4kEev4wVIq_L","executionInfo":{"status":"ok","timestamp":1625140936180,"user_tz":-480,"elapsed":8729,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"8026ed73-f781-4eb2-bb80-d3446bd131df"}},{"cell_type":"markdown","source":["**EDVR**"],"metadata":{"id":"RONzjTTU_gem"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_EDVR.py https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth --save-path ./outputs/testset_EDVR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_EDVR\n","!ls ./outputs/testset_EDVR/city"],"outputs":[{"output_type":"stream","name":"stdout","text":["Use load_from_http loader\n","[>>] 22/22, 2.0 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 23.89569862011228\n","Eval-SSIM: 0.7667098470108678\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vL8WOWXY0fNJ","executionInfo":{"status":"ok","timestamp":1625140955813,"user_tz":-480,"elapsed":19671,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"dc2a8f81-9bef-4ad4-c5b2-c6f124e6b113"}},{"cell_type":"markdown","source":["**BasicVSR**"],"metadata":{"id":"5Tc7F-l5_i1e"}},{"cell_type":"code","execution_count":null,"source":["# 单 GPU（Colab 只有一个 GPU）\n","!python tools/test.py ./demo_files/demo_config_BasicVSR.py https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth --save-path ./outputs/testset_BasicVSR\n","\n","# 检查输出文件夹\n","!ls ./outputs/testset_BasicVSR\n","!ls ./outputs/testset_BasicVSR/calendar"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:07,780 - mmedit - INFO - Use load_from_http loader\n","Use load_from_http loader\n","The model and loaded state dict do not match exactly\n","\n","missing keys in source state_dict: step_counter\n","\n","[>>] 2/2, 0.2 task/s, elapsed: 11s, ETA: 0s\n","Eval-PSNR: 24.195768601433734\n","Eval-SSIM: 0.7828541339512978\n","calendar city\n","00000000.png 00000003.png 00000006.png 00000009.png\n","00000001.png 00000004.png 00000007.png 00000010.png\n","00000002.png 00000005.png 00000008.png\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jpW5GWC74Yvu","executionInfo":{"status":"ok","timestamp":1625140976026,"user_tz":-480,"elapsed":20220,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"7ba02a32-d4ec-40b2-8108-ef0729b62147"}},{"cell_type":"markdown","source":["## 在预定义的数据集上训练恢复器\n","\n","MMEditing 使用分布式训练。以下命令可用于训练。如果要在我们的配置文件中指定的预定义数据集上进行训练，只需运行以下命令即可。\n","\n","```\n","./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]\n","```\n","\n","有关可选参数的更多详细信息，请参阅 `tools/train.py`。\n","\n","---\n","\n","这是一个使用 EDVR 的示例。\n"],"metadata":{"id":"4DQxNL8BhI0y"}},{"cell_type":"code","execution_count":null,"source":["!./tools/dist_train.sh ./configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py 1"],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:31,961 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:31,961 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:31,961 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:31,961 - mmedit - INFO - Config:\n","/content/mmediting/configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py\n","exp_name = 'edvrm_wotsa_x4_g8_600k_reds'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRREDSDataset'\n","val_dataset_type = 'SRREDSDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='data/REDS/train_sharp_bicubic/X4',\n"," gt_folder='data/REDS/train_sharp',\n"," ann_file='data/REDS/meta_info_REDS_GT.txt',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," val_partition='REDS4',\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 600000\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50000, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=100,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","Traceback (most recent call last):\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 51, in build_from_cfg\n"," return obj_cls(**args)\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 54, in __init__\n"," self.data_infos = self.load_annotations()\n"," File \"/content/mmediting/mmedit/datasets/sr_reds_dataset.py\", line 63, in load_annotations\n"," with open(self.ann_file, 'r') as fin:\n","FileNotFoundError: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","\n","During handling of the above exception, another exception occurred:\n","\n","Traceback (most recent call last):\n"," File \"./tools/train.py\", line 145, in \n"," main()\n"," File \"./tools/train.py\", line 111, in main\n"," datasets = [build_dataset(cfg.data.train)]\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 76, in build_dataset\n"," build_dataset(cfg['dataset'], default_args), cfg['times'])\n"," File \"/content/mmediting/mmedit/datasets/builder.py\", line 80, in build_dataset\n"," dataset = build_from_cfg(cfg, DATASETS, default_args)\n"," File \"/usr/local/lib/python3.7/dist-packages/mmcv/utils/registry.py\", line 54, in build_from_cfg\n"," raise type(e)(f'{obj_cls.__name__}: {e}')\n","FileNotFoundError: SRREDSDataset: [Errno 2] No such file or directory: 'data/REDS/meta_info_REDS_GT.txt'\n","Traceback (most recent call last):\n"," File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n"," \"__main__\", mod_spec)\n"," File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n"," exec(code, run_globals)\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 260, in \n"," main()\n"," File \"/usr/local/lib/python3.7/dist-packages/torch/distributed/launch.py\", line 256, in main\n"," cmd=cmd)\n","subprocess.CalledProcessError: Command '['/usr/bin/python3', '-u', './tools/train.py', '--local_rank=0', './configs/restorers/edvr/edvrm_wotsa_x4_g8_600k_reds.py', '--launcher', 'pytorch']' returned non-zero exit status 1.\n"]}],"metadata":{"id":"s-hOnSF6ItQM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1625140985357,"user_tz":-480,"elapsed":9337,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"e40e960a-29e1-43e8-b922-5e08c4e98afe"}},{"cell_type":"markdown","source":["## 在自定义数据集上训练复原器\n","\n","与您要在自己的数据集上进行测试的情况类似，您需要修改 `train_dataset_type`。您需要的数据集类型是相同的：\n","\n","- 对于图像超分辨率，需要使用 `SRFolderDataset`\n","- 对于视频超分辨率的滑动窗口框架（例如 EDVR、TDAN），需要使用 `SRFolderVideoDataset`。\n","- 对于视频超分辨率的循环框架（例如 BasicVSR、IconVSR），需要使用 `SRFolderMultipleGTDataset`。\n","\n","修改数据集类型和数据路径后。一切都准备好了。"],"metadata":{"id":"b0VfQkQQjg8N"}},{"cell_type":"code","execution_count":null,"source":["# SRCNN（图像超分辨率）\n","!./tools/dist_train.sh ./demo_files/demo_config_SRCNN.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:02:41,185 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:02:41,185 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:02:41,185 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:02:41,185 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_SRCNN.py\n","exp_name = 'srcnn_demo'\n","\n","scale = 4\n","# model settings\n","model = dict(\n"," type='BasicRestorer',\n"," generator=dict(\n"," type='SRCNN',\n"," channels=(3, 64, 32, 3),\n"," kernel_sizes=(9, 1, 5),\n"," upscale_factor=scale),\n"," pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderDataset'\n","val_dataset_type = 'SRFolderDataset'\n","train_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=128),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","test_pipeline = [\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFile',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'lq_path']),\n"," dict(type='ImageToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=8,\n"," train_dataloader=dict(samples_per_gpu=16, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=train_pipeline,\n"," scale=scale)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_images',\n"," gt_folder='./demo_files/gt_images',\n"," pipeline=test_pipeline,\n"," scale=scale,\n"," filename_tmpl='{}'))\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=2e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[250000, 250000, 250000, 250000],\n"," restart_weights=[1, 1, 1, 1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","evaluation = dict(interval=50, save_image=True, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./experiments/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:02:41,192 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/experiments/srcnn_demo\n","2021-07-01 12:02:41,192 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:02:41.529307: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:03:18,631 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.000e-04, eta: 0:57:01, time: 34.560, data_time: 34.446, memory: 586, loss_pix: 0.3999, loss: 0.3999\n","2021-07-01 12:03:18,712 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","INFO:mmedit:Iter [2/100]\tlr_generator: 2.000e-04, eta: 0:28:16, time: 0.068, data_time: 0.020, memory: 586, loss_pix: 0.4382, loss: 0.4382\n","2021-07-01 12:03:18,764 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","INFO:mmedit:Iter [3/100]\tlr_generator: 2.000e-04, eta: 0:18:41, time: 0.053, data_time: 0.027, memory: 586, loss_pix: 0.5043, loss: 0.5043\n","2021-07-01 12:03:18,840 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","INFO:mmedit:Iter [4/100]\tlr_generator: 2.000e-04, eta: 0:13:54, time: 0.076, data_time: 0.026, memory: 586, loss_pix: 0.4364, loss: 0.4364\n","2021-07-01 12:03:18,916 - mmedit - INFO - Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","INFO:mmedit:Iter [5/100]\tlr_generator: 2.000e-04, eta: 0:11:01, time: 0.085, data_time: 0.028, memory: 586, loss_pix: 0.3853, loss: 0.3853\n","2021-07-01 12:03:18,956 - mmedit - INFO - Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","INFO:mmedit:Iter [6/100]\tlr_generator: 2.000e-04, eta: 0:09:06, time: 0.041, data_time: 0.017, memory: 586, loss_pix: 0.4315, loss: 0.4315\n","2021-07-01 12:03:19,012 - mmedit - INFO - Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","INFO:mmedit:Iter [7/100]\tlr_generator: 2.000e-04, eta: 0:07:44, time: 0.056, data_time: 0.016, memory: 586, loss_pix: 0.4003, loss: 0.4003\n","2021-07-01 12:03:19,070 - 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mmedit - INFO - Saving checkpoint at 100 iterations\n","INFO:mmedit:Saving checkpoint at 100 iterations\n","2021-07-01 12:04:37,422 - mmedit - INFO - Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n","INFO:mmedit:Iter(val) [100]\tPSNR: 22.4152, SSIM: 0.6525\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"liGEKJpbIoXZ","executionInfo":{"status":"ok","timestamp":1625141113733,"user_tz":-480,"elapsed":128384,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"84e1502e-e2cb-458f-c7c5-e4b401e570b7"}},{"cell_type":"code","execution_count":null,"source":["# EDVR（视频超分辨率-滑动窗口）\n","!./tools/dist_train.sh ./demo_files/demo_config_EDVR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:10:12,619 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:10:12,619 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:10:12,619 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:10:12,619 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_EDVR.py\n","exp_name = 'edvrm_demo'\n","\n","# model settings\n","model = dict(\n"," type='EDVR',\n"," generator=dict(\n"," type='EDVRNet',\n"," in_channels=3,\n"," out_channels=3,\n"," mid_channels=64,\n"," num_frames=5,\n"," deform_groups=8,\n"," num_blocks_extraction=5,\n"," num_blocks_reconstruction=10,\n"," center_frame_idx=2,\n"," with_tsa=False),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'))\n","# model training and testing settings\n","train_cfg = None\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderVideoDataset'\n","val_dataset_type = 'SRFolderVideoDataset'\n","train_pipeline = [\n"," dict(type='GenerateFrameIndices', interval_list=[1], frames_per_clip=99),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," flag='unchanged'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," flag='unchanged'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(\n"," type='Normalize',\n"," keys=['lq', 'gt'],\n"," mean=[0, 0, 0],\n"," std=[1, 1, 1],\n"," to_rgb=True),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=4,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True),\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1),\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(generator=dict(type='Adam', lr=4e-4, betas=(0.9, 0.999)))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[150000, 150000, 150000, 150000],\n"," restart_weights=[1, 0.5, 0.5, 0.5],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," dict(type='TensorboardLoggerHook'),\n"," # dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit-sr'))\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","\n","2021-07-01 12:10:12,701 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/edvrm_demo\n","2021-07-01 12:10:12,702 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:10:12.951771: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n","2021-07-01 12:10:30,703 - mmedit - INFO - Iter [1/100]\tlr_generator: 4.000e-04, eta: 0:26:53, time: 16.295, data_time: 15.833, memory: 1341, loss_pix: 63917.2734, loss: 63917.2734\n","2021-07-01 12:10:31,046 - mmedit - INFO - Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","INFO:mmedit:Iter [2/100]\tlr_generator: 4.000e-04, eta: 0:13:35, time: 0.342, data_time: 0.003, memory: 1372, loss_pix: 54741.8516, loss: 54741.8516\n","2021-07-01 12:10:31,386 - mmedit - INFO - Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","INFO:mmedit:Iter [3/100]\tlr_generator: 4.000e-04, eta: 0:09:08, time: 0.341, data_time: 0.004, memory: 1372, loss_pix: 49077.6562, loss: 49077.6562\n","2021-07-01 12:10:31,731 - mmedit - INFO - Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","INFO:mmedit:Iter [4/100]\tlr_generator: 4.000e-04, eta: 0:06:55, time: 0.345, data_time: 0.004, memory: 1372, loss_pix: 45100.3984, loss: 45100.3984\n","2021-07-01 12:10:32,071 - mmedit - INFO - Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","INFO:mmedit:Iter [5/100]\tlr_generator: 4.000e-04, eta: 0:05:35, time: 0.340, data_time: 0.004, memory: 1372, loss_pix: 37305.7891, loss: 37305.7891\n","2021-07-01 12:10:32,414 - mmedit - INFO - Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","INFO:mmedit:Iter [6/100]\tlr_generator: 4.000e-04, eta: 0:04:42, time: 0.343, data_time: 0.003, memory: 1372, loss_pix: 53724.2422, loss: 53724.2422\n","2021-07-01 12:10:32,760 - 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Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"2fb26527-eb9e-4b48-e03c-ba9a91c60db8"}},{"cell_type":"code","execution_count":null,"source":["# BasicVSR（视频超分辨率 - 循环）\n","!./tools/dist_train.sh ./demo_files/demo_config_BasicVSR.py 1 "],"outputs":[{"output_type":"stream","name":"stdout","text":["2021-07-01 12:06:47,253 - mmedit - INFO - Environment info:\n","------------------------------------------------------------\n","sys.platform: linux\n","Python: 3.7.10 (default, May 3 2021, 02:48:31) [GCC 7.5.0]\n","CUDA available: True\n","GPU 0: Tesla T4\n","CUDA_HOME: /usr/local/cuda\n","NVCC: Build cuda_11.0_bu.TC445_37.28845127_0\n","GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n","PyTorch: 1.7.0+cu110\n","PyTorch compiling details: PyTorch built with:\n"," - GCC 7.3\n"," - C++ Version: 201402\n"," - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n"," - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\n"," - OpenMP 201511 (a.k.a. OpenMP 4.5)\n"," - NNPACK is enabled\n"," - CPU capability usage: AVX2\n"," - CUDA Runtime 11.0\n"," - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80\n"," - CuDNN 8.0.4\n"," - Magma 2.5.2\n"," - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n","\n","TorchVision: 0.8.0\n","OpenCV: 4.1.2\n","MMCV: 1.3.5\n","MMCV Compiler: GCC 7.3\n","MMCV CUDA Compiler: 11.0\n","MMEditing: 0.8.0+7f4fa79\n","------------------------------------------------------------\n","\n","2021-07-01 12:06:47,253 - mmedit - INFO - Distributed training: True\n","2021-07-01 12:06:47,254 - mmedit - INFO - mmedit Version: 0.8.0\n","2021-07-01 12:06:47,254 - mmedit - INFO - Config:\n","/content/mmediting/demo_files/demo_config_BasicVSR.py\n","exp_name = 'basicvsr_demo'\n","\n","# model settings\n","model = dict(\n"," type='BasicVSR',\n"," generator=dict(\n"," type='BasicVSRNet',\n"," mid_channels=64,\n"," num_blocks=30,\n"," spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'\n"," 'basicvsr/spynet_20210409-c6c1bd09.pth'),\n"," pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))\n","# model training and testing settings\n","train_cfg = dict(fix_iter=5000)\n","test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)\n","\n","# dataset settings\n","train_dataset_type = 'SRFolderMultipleGTDataset'\n","val_dataset_type = 'SRFolderMultipleGTDataset'\n","\n","train_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='PairedRandomCrop', gt_patch_size=256),\n"," dict(\n"," type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,\n"," direction='horizontal'),\n"," dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),\n"," dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])\n","]\n","\n","test_pipeline = [\n"," dict(type='GenerateSegmentIndices', interval_list=[1]),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='lq',\n"," channel_order='rgb'),\n"," dict(\n"," type='LoadImageFromFileList',\n"," io_backend='disk',\n"," key='gt',\n"," channel_order='rgb'),\n"," dict(type='RescaleToZeroOne', keys=['lq', 'gt']),\n"," dict(type='FramesToTensor', keys=['lq', 'gt']),\n"," dict(\n"," type='Collect',\n"," keys=['lq', 'gt'],\n"," meta_keys=['lq_path', 'gt_path', 'key'])\n","]\n","\n","data = dict(\n"," workers_per_gpu=6,\n"," train_dataloader=dict(samples_per_gpu=4, drop_last=True), # 2 gpus\n"," val_dataloader=dict(samples_per_gpu=1),\n"," test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),\n","\n"," # train\n"," train=dict(\n"," type='RepeatDataset',\n"," times=1000,\n"," dataset=dict(\n"," type=train_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," num_input_frames=5,\n"," pipeline=train_pipeline,\n"," scale=4,\n"," test_mode=False)),\n"," # val\n"," val=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n"," # test\n"," test=dict(\n"," type=val_dataset_type,\n"," lq_folder='./demo_files/lq_sequences',\n"," gt_folder='./demo_files/gt_sequences',\n"," pipeline=test_pipeline,\n"," scale=4,\n"," test_mode=True),\n",")\n","\n","# optimizer\n","optimizers = dict(\n"," generator=dict(\n"," type='Adam',\n"," lr=2e-4,\n"," betas=(0.9, 0.99),\n"," paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))\n","\n","# learning policy\n","total_iters = 100\n","lr_config = dict(\n"," policy='CosineRestart',\n"," by_epoch=False,\n"," periods=[300000],\n"," restart_weights=[1],\n"," min_lr=1e-7)\n","\n","checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False)\n","# remove gpu_collect=True in non distributed training\n","evaluation = dict(interval=50, save_image=False, gpu_collect=True)\n","log_config = dict(\n"," interval=1,\n"," hooks=[\n"," dict(type='TextLoggerHook', by_epoch=False),\n"," # dict(type='TensorboardLoggerHook'),\n"," ])\n","visual_config = None\n","\n","# runtime settings\n","dist_params = dict(backend='nccl')\n","log_level = 'INFO'\n","work_dir = f'./work_dirs/{exp_name}'\n","load_from = None\n","resume_from = None\n","workflow = [('train', 1)]\n","find_unused_parameters = True\n","\n","2021-07-01 12:06:47,291 - mmedit - INFO - Use load_from_http loader\n","2021-07-01 12:06:47,569 - mmedit - INFO - Start running, host: root@fce870c778f5, work_dir: /content/mmediting/work_dirs/basicvsr_demo\n","2021-07-01 12:06:47,569 - mmedit - INFO - workflow: [('train', 1)], max: 100 iters\n","2021-07-01 12:07:14,210 - mmedit - INFO - Iter [1/100]\tlr_generator: 2.500e-05, eta: 0:42:52, time: 25.981, data_time: 24.045, memory: 3464, loss_pix: 0.0634, loss: 0.0634\n","2021-07-01 12:07:15,171 - mmedit - INFO - Iter [2/100]\tlr_generator: 2.500e-05, eta: 0:22:00, time: 0.961, data_time: 0.011, memory: 3518, loss_pix: 0.0556, loss: 0.0556\n","2021-07-01 12:07:16,052 - mmedit - INFO - Iter [3/100]\tlr_generator: 2.500e-05, eta: 0:14:59, time: 0.881, data_time: 0.003, memory: 3518, loss_pix: 0.0476, loss: 0.0476\n","2021-07-01 12:07:16,940 - mmedit - INFO - Iter [4/100]\tlr_generator: 2.500e-05, eta: 0:11:29, time: 0.888, data_time: 0.003, memory: 3518, loss_pix: 0.0673, loss: 0.0673\n","2021-07-01 12:07:17,829 - 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mmedit - INFO - Iter(val) [100]\tPSNR: 21.4372, SSIM: 0.5687\n"]}],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_RdqmlT6qgt2","executionInfo":{"status":"ok","timestamp":1625141428032,"user_tz":-480,"elapsed":197033,"user":{"displayName":"Kelvin C.K. Chan","photoUrl":"","userId":"05911692378028575851"}},"outputId":"b951b426-e06c-4f31-db01-449333eab333"}},{"cell_type":"markdown","source":["**本教程到此结束。有关更高级的用法，请参阅我们的[综合教程]()。享受使用 MMEditing 的乐趣！**"],"metadata":{"id":"QT0zwBFt7J13"}}]}