Merge pull request #1090 from botcs/citscapes-dataset

Add native CityScapes dataset
facebookresearch · Oct 17, 2019 · 523ae86 · 523ae86
2 parents b2a2a74 + b9547f4
commit 523ae86
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Showing 6 changed files with 407 additions and 15 deletions.
diff --git a/INSTALL.md b/INSTALL.md
@@ -39,6 +39,12 @@ git clone https://github.com/cocodataset/cocoapi.git
 cd cocoapi/PythonAPI
 python setup.py build_ext install
 
+# install cityscapesScripts
+cd $INSTALL_DIR
+git clone https://github.com/mcordts/cityscapesScripts.git
+cd cityscapesScripts/
+python setup.py build_ext install
+
 # install apex
 cd $INSTALL_DIR
 git clone https://github.com/NVIDIA/apex.git
@@ -62,10 +68,10 @@ unset INSTALL_DIR
 # or if you are on macOS
 # MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develop
 ```
-#### Windows 10 
+#### Windows 10
 ```bash
-open a cmd and change to desired installation directory 
-from now on will be refered as INSTALL_DIR 
+open a cmd and change to desired installation directory
+from now on will be refered as INSTALL_DIR
 conda create --name maskrcnn_benchmark
 conda activate maskrcnn_benchmark
 
@@ -77,13 +83,13 @@ pip install ninja yacs cython matplotlib tqdm opencv-python
 
 # follow PyTorch installation in https://pytorch.org/get-started/locally/
 # we give the instructions for CUDA 9.0
-## Important : check the cuda version installed on your computer by running the command in the cmd : 
-nvcc -- version 
+## Important : check the cuda version installed on your computer by running the command in the cmd :
+nvcc -- version
 conda install -c pytorch pytorch-nightly torchvision cudatoolkit=9.0
 
 git clone https://github.com/cocodataset/cocoapi.git
-    
-    #To prevent installation error do the following after commiting cocooapi : 
+
+    #To prevent installation error do the following after commiting cocooapi :
     #using file explorer  naviagate to cocoapi\PythonAPI\setup.py and change line 14 from:
     #extra_compile_args=['-Wno-cpp', '-Wno-unused-function', '-std=c99'],
     #to
@@ -95,14 +101,14 @@ python setup.py build_ext install
 
 # navigate back to INSTALL_DIR
 cd ..
-cd .. 
+cd ..
 # install apex
 
 git clone https://github.com/NVIDIA/apex.git
 cd apex
 python setup.py install --cuda_ext --cpp_ext
 # navigate back to INSTALL_DIR
-cd .. 
+cd ..
 # install PyTorch Detection
 
 git clone https://github.com/Idolized22/maskrcnn-benchmark.git
@@ -119,15 +125,15 @@ python setup.py build develop
 Build image with defaults (`CUDA=9.0`, `CUDNN=7`, `FORCE_CUDA=1`):
 
     nvidia-docker build -t maskrcnn-benchmark docker/
-    
+
 Build image with other CUDA and CUDNN versions:
 
     nvidia-docker build -t maskrcnn-benchmark --build-arg CUDA=9.2 --build-arg CUDNN=7 docker/
-    
+
 Build image with FORCE_CUDA disabled:
 
     nvidia-docker build -t maskrcnn-benchmark --build-arg FORCE_CUDA=0 docker/
-    
+
 Build and run image with built-in jupyter notebook(note that the password is used to log in jupyter notebook):
 
     nvidia-docker build -t maskrcnn-benchmark-jupyter docker/docker-jupyter/

diff --git a/configs/cityscapes/e2e_mask_rcnn_R_50_FPN_1x_binarymask.yaml b/configs/cityscapes/e2e_mask_rcnn_R_50_FPN_1x_binarymask.yaml
@@ -0,0 +1,44 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+    NUM_CLASSES: 9
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("cityscapes_mask_instance_train",)
+  TEST: ("cityscapes_mask_instance_val",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (18000,)
+  MAX_ITER: 24000
+OUTPUT_DIR:
+  "runs/cityscapes_mask"
diff --git a/configs/cityscapes/e2e_mask_rcnn_R_50_FPN_1x_poly.yaml b/configs/cityscapes/e2e_mask_rcnn_R_50_FPN_1x_poly.yaml
@@ -0,0 +1,50 @@
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+    NUM_CLASSES: 11
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+INPUT:
+  MIN_SIZE_TRAIN: (800, 832, 864, 896, 928, 960, 992, 1024, 1024)
+  MAX_SIZE_TRAIN: 2048
+  MIN_SIZE_TEST: 1024
+  MAX_SIZE_TEST: 2048
+DATASETS:
+  TRAIN: ("cityscapes_poly_instance_train",)
+  TEST: ("cityscapes_poly_instance_val",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  IMS_PER_BATCH: 8
+  BASE_LR: 0.01
+  WEIGHT_DECAY: 0.0001
+  STEPS: (18000,)
+  MAX_ITER: 24000
+OUTPUT_DIR:
+  "runs/cityscapes_poly"
diff --git a/maskrcnn_benchmark/config/paths_catalog.py b/maskrcnn_benchmark/config/paths_catalog.py
@@ -2,7 +2,7 @@
 """Centralized catalog of paths."""
 
 import os
-
+from copy import deepcopy
 
 class DatasetCatalog(object):
     DATA_DIR = "datasets"
@@ -92,6 +92,9 @@ class DatasetCatalog(object):
             "split": "test"
             # PASCAL VOC2012 doesn't made the test annotations available, so there's no json annotation
         },
+
+        ##############################################
+        # These ones are deprecated, should be removed
         "cityscapes_fine_instanceonly_seg_train_cocostyle": {
             "img_dir": "cityscapes/images",
             "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_train.json"
@@ -103,7 +106,47 @@ class DatasetCatalog(object):
         "cityscapes_fine_instanceonly_seg_test_cocostyle": {
             "img_dir": "cityscapes/images",
             "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_test.json"
-        }
+        },
+        ##############################################
+
+        "cityscapes_poly_instance_train": {
+            "img_dir": "cityscapes/leftImg8bit/",
+            "ann_dir": "cityscapes/gtFine/",
+            "split": "train",
+            "mode": "poly",
+        },
+        "cityscapes_poly_instance_val": {
+            "img_dir": "cityscapes/leftImg8bit",
+            "ann_dir": "cityscapes/gtFine",
+            "split": "val",
+            "mode": "poly",
+        },
+        "cityscapes_poly_instance_minival": {
+            "img_dir": "cityscapes/leftImg8bit",
+            "ann_dir": "cityscapes/gtFine",
+            "split": "val",
+            "mode": "poly",
+            "mini": 10,
+        },
+        "cityscapes_mask_instance_train": {
+            "img_dir": "cityscapes/leftImg8bit/",
+            "ann_dir": "cityscapes/gtFine/",
+            "split": "train",
+            "mode": "mask",
+        },
+        "cityscapes_mask_instance_val": {
+            "img_dir": "cityscapes/leftImg8bit",
+            "ann_dir": "cityscapes/gtFine",
+            "split": "val",
+            "mode": "mask",
+        },
+        "cityscapes_mask_instance_minival": {
+            "img_dir": "cityscapes/leftImg8bit",
+            "ann_dir": "cityscapes/gtFine",
+            "split": "val",
+            "mode": "mask",
+            "mini": 10,
+        },
     }
 
     @staticmethod
@@ -130,6 +173,12 @@ def get(name):
                 factory="PascalVOCDataset",
                 args=args,
             )
+        elif "cityscapes" in name:
+            data_dir = DatasetCatalog.DATA_DIR
+            attrs = deepcopy(DatasetCatalog.DATASETS[name])
+            attrs["img_dir"] = os.path.join(data_dir, attrs["img_dir"])
+            attrs["ann_dir"] = os.path.join(data_dir, attrs["ann_dir"])
+            return dict(factory="CityScapesDataset", args=attrs)
         raise RuntimeError("Dataset not available: {}".format(name))
 
 

diff --git a/maskrcnn_benchmark/data/datasets/__init__.py b/maskrcnn_benchmark/data/datasets/__init__.py
@@ -4,5 +4,12 @@
 from .voc import PascalVOCDataset
 from .concat_dataset import ConcatDataset
 from .abstract import AbstractDataset
+from .cityscapes import CityScapesDataset
 
-__all__ = ["COCODataset", "ConcatDataset", "PascalVOCDataset", "AbstractDataset"]
+__all__ = [
+    "COCODataset",
+    "ConcatDataset",
+    "PascalVOCDataset",
+    "AbstractDataset",
+    "CityScapesDataset",
+]