Decoupling path dependence in notebooks (#84)

* Cleanup parameters in Notebooks * Add image requisition from URL utils * Decoupling path dependence in notebooks * Temporarily ignore PyTorch Nightly in CI * Add some notes
zhiqwang · Mar 29, 2021 · 2aaee95 · 2aaee95
1 parent 3087993
commit 2aaee95
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diff --git a/.github/workflows/ci_test.yml b/.github/workflows/ci_test.yml
@@ -16,17 +16,17 @@ jobs:
       matrix:
         python-version: [3.7]
         os: [ubuntu-latest]
-        torch: [1.7.1+cpu, 1.8.0+cpu, nightly]
+        torch: [1.7.1+cpu, 1.8.1+cpu]  # nightly
         include:
           - torch: 1.7.1+cpu
             pip_address: torch==1.7.1+cpu torchvision==0.8.2+cpu -f https://download.pytorch.org/whl/torch_stable.html
             unittest_type: --cov=test --cov-report=xml
-          - torch: 1.8.0+cpu
-            pip_address: torch==1.8.0+cpu torchvision==0.9.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
+          - torch: 1.8.1+cpu
+            pip_address: torch==1.8.1+cpu torchvision==0.9.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
             unittest_type: --cov=test --cov-report=xml
-          - torch: nightly
-            pip_address: --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
-            unittest_type: test
+          # - torch: nightly
+          #   pip_address: --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
+          #   unittest_type: test
 
     steps:
       - name: Clone repository
@@ -65,7 +65,7 @@ jobs:
           PYTORCH_TEST_WITH_SLOW=1 pytest ${{ matrix.unittest_type }}
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v1
-        if: matrix.torch == '1.8.0+cpu'
+        if: matrix.torch == '1.8.1+cpu'
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           file: ./coverage.xml
@@ -75,7 +75,7 @@ jobs:
           fail_ci_if_error: true
           verbose: true
       - name: Build TorchVision Cpp ${{ matrix.torch }}
-        if: matrix.torch == '1.8.0+cpu'
+        if: matrix.torch == '1.8.1+cpu'
         run: |
           export TORCH_PATH=$(dirname $(python -c "import torch; print(torch.__file__)"))
           cd ..
@@ -87,11 +87,11 @@ jobs:
           make -j4
           sudo make install
       - name: Export torchscript model
-        if: matrix.torch == '1.8.0+cpu'
+        if: matrix.torch == '1.8.1+cpu'
         run: |
           python -m test.tracing.trace_model
       - name: Test libtorch tracing
-        if: matrix.torch == '1.8.0+cpu'
+        if: matrix.torch == '1.8.1+cpu'
         run: |
           export TORCH_PATH=$(dirname $(python -c "import torch; print(torch.__file__)"))
           export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$TORCH_PATH/lib/

diff --git a/notebooks/export-onnx-inference-onnxruntime.ipynb b/notebooks/export-onnx-inference-onnxruntime.ipynb
@@ -14,7 +14,7 @@
     "\n",
     "from yolort.models import yolov5s\n",
     "\n",
-    "from yolort.utils.image_utils import read_image"
+    "from yolort.utils import get_image_from_url, read_image_to_tensor"
    ]
   },
   {
@@ -26,9 +26,9 @@
     "import os\n",
     "\n",
     "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"5\"\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
     "\n",
-    "device = torch.device('cuda')"
+    "device = torch.device('cpu')"
    ]
   },
   {
@@ -44,7 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model = yolov5s(upstream_version='v4.0', export_friendly=True, pretrained=True, score_thresh=0.45)\n",
+    "model = yolov5s(export_friendly=True, pretrained=True, score_thresh=0.45)\n",
     "\n",
     "model = model.eval()\n",
     "model = model.to(device)"
@@ -63,12 +63,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "img_one = cv2.imread('test/assets/bus.jpg')\n",
-    "img_one = read_image(img_one, is_half=False)\n",
+    "img_one = get_image_from_url(\"https://gitee.com/zhiqwang/yolov5-rt-stack/raw/master/test/assets/bus.jpg\")\n",
+    "img_one = read_image_to_tensor(img_one, is_half=False)\n",
     "img_one = img_one.to(device)\n",
     "\n",
-    "img_two = cv2.imread('test/assets/zidane.jpg')\n",
-    "img_two = read_image(img_two, is_half=False)\n",
+    "img_two = get_image_from_url(\"https://gitee.com/zhiqwang/yolov5-rt-stack/raw/master/test/assets/bus.jpg\")\n",
+    "img_two = read_image_to_tensor(img_two, is_half=False)\n",
     "img_two = img_two.to(device)\n",
     "\n",
     "images = [img_one, img_two]"
@@ -78,7 +78,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Inference in `pytorch` backend"
+    "### Inference on PyTorch backend"
    ]
   },
   {
@@ -100,8 +100,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 16 ms, sys: 0 ns, total: 16 ms\n",
-      "Wall time: 16.8 ms\n"
+      "CPU times: user 3.72 s, sys: 132 ms, total: 3.86 s\n",
+      "Wall time: 109 ms\n"
      ]
     }
    ],
@@ -119,10 +119,10 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[ 52.1687, 384.9377, 235.4150, 899.1040],\n",
-       "        [223.6789, 406.9857, 346.8747, 862.1425],\n",
-       "        [677.8205, 390.5674, 811.9033, 871.8314],\n",
-       "        [  9.4887, 227.6140, 799.6432, 766.6011]], device='cuda:0')"
+       "tensor([[ 52.1603, 384.9539, 235.4333, 899.1226],\n",
+       "        [223.7285, 407.0463, 346.9296, 862.0854],\n",
+       "        [  8.5867, 227.5113, 805.2753, 765.3226],\n",
+       "        [675.7438, 394.1103, 811.3925, 869.5128]])"
       ]
      },
      "execution_count": 7,
@@ -142,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "tensor([0.8995, 0.8665, 0.8193, 0.8094], device='cuda:0')"
+       "tensor([0.8993, 0.8671, 0.8034, 0.8005])"
       ]
      },
      "execution_count": 8,
@@ -162,7 +162,7 @@
     {
      "data": {
       "text/plain": [
-       "tensor([0, 0, 0, 5], device='cuda:0')"
+       "tensor([0, 0, 5, 0])"
       ]
      },
      "execution_count": 9,
@@ -178,35 +178,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Export to `ONNX` model"
+    "## Export the model to ONNX"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchvision.ops._register_onnx_ops import _onnx_opset_version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting ONNX export with onnx 1.8.1, onnxruntime 1.6.0...\n"
+      "We are using opset version: 11\n"
      ]
     }
    ],
    "source": [
-    "# TorchScript export\n",
-    "print(f'Starting ONNX export with onnx {onnx.__version__}, onnxruntime {onnxruntime.__version__}...')\n",
-    "export_onnx_name = 'checkpoints/yolov5/yolov5s.onnx'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from torchvision.ops._register_onnx_ops import _onnx_opset_version"
+    "export_onnx_name = 'yolov5s.onnx'  # path of the exported ONNX models\n",
+    "\n",
+    "print(f'We are using opset version: {_onnx_opset_version}')"
    ]
   },
   {
@@ -224,17 +224,17 @@
       "  'Automatically generated names will be applied to each dynamic axes of input {}'.format(key))\n",
       "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3123: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  dtype=torch.float32)).float())) for i in range(dim)]\n",
-      "/mnt/yolov5-rt-stack/yolort/models/anchor_utils.py:31: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
+      "/data/wangzq/yolov5-rt-stack/yolort/models/anchor_utils.py:31: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
       "  stride = torch.as_tensor([stride], dtype=dtype, device=device)\n",
-      "/mnt/yolov5-rt-stack/yolort/models/anchor_utils.py:50: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
+      "/data/wangzq/yolov5-rt-stack/yolort/models/anchor_utils.py:50: TracerWarning: torch.as_tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
       "  anchor_grid = torch.as_tensor(anchor_grid, dtype=dtype, device=device)\n",
-      "/mnt/yolov5-rt-stack/yolort/models/anchor_utils.py:77: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
+      "/data/wangzq/yolov5-rt-stack/yolort/models/anchor_utils.py:77: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
       "  shifts = shifts - torch.tensor(0.5, dtype=shifts.dtype, device=device)\n",
-      "/mnt/yolov5-rt-stack/yolort/models/box_head.py:363: TracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
+      "/data/wangzq/yolov5-rt-stack/yolort/models/box_head.py:363: TracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
       "  for idx in range(batch_size):  # image idx, image inference\n",
-      "/mnt/yolov5-rt-stack/yolort/models/transform.py:287: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
+      "/data/wangzq/yolov5-rt-stack/yolort/models/transform.py:287: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
       "  for s, s_orig in zip(new_size, original_size)\n",
-      "/mnt/yolov5-rt-stack/yolort/models/transform.py:287: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "/data/wangzq/yolov5-rt-stack/yolort/models/transform.py:287: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
       "  for s, s_orig in zip(new_size, original_size)\n",
       "/usr/local/lib/python3.6/dist-packages/torch/onnx/symbolic_opset9.py:2378: UserWarning: Exporting aten::index operator of advanced indexing in opset 11 is achieved by combination of multiple ONNX operators, including Reshape, Transpose, Concat, and Gather. If indices include negative values, the exported graph will produce incorrect results.\n",
       "  \"If indices include negative values, the exported graph will produce incorrect results.\")\n",
@@ -261,17 +261,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simplifier exported `ONNX` model"
+    "## Simplify the exported ONNX model (Optional)\n",
+    "\n",
+    "*ONNX* is great, but sometimes too complicated. And thanks to @daquexian for providing a powerful tool named [`onnxsim`](https://github.com/daquexian/onnx-simplifier/) to eliminate some redundant operators.\n",
+    "\n",
+    "First of all, let's install `onnx-simplifier` with following script."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Don't forget to install `onnx-simplifier`\n",
-    "\n",
-    "```bash\n",
-    "!pip -U install onnx-simplifier\n",
+    "```shell\n",
+    "pip install -U onnx-simplifier\n",
     "```"
    ]
   },
@@ -284,7 +286,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Starting simplifing with onnxsim 0.3.2\n"
+      "Starting simplifing with onnxsim 0.3.3\n"
      ]
     }
    ],
@@ -301,7 +303,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "onnx_simp_name = 'checkpoints/yolov5/yolov5s.simp.onnx'"
+    "onnx_simp_name = 'yolov5s.simp.onnx'  # path of the simplified ONNX models"
    ]
   },
   {
@@ -330,13 +332,41 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Inference on `ONNXRuntime` Backend"
+    "## Inference on ONNXRuntime Backend\n",
+    "\n",
+    "Now, We begin to verify whether the inference results are consistent with PyTorch's, similarly, install `onnxruntime` first."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```shell\n",
+    "pip install -U onnxruntime\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting with onnx 1.8.1, onnxruntime 1.7.0...\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Starting with onnx {onnx.__version__}, onnxruntime {onnxruntime.__version__}...')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [],
    "source": [
     "images, _ = torch.jit._flatten(images)\n",
@@ -345,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -368,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -378,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -389,15 +419,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2.33 s, sys: 0 ns, total: 2.33 s\n",
-      "Wall time: 77.9 ms\n"
+      "CPU times: user 2.3 s, sys: 0 ns, total: 2.3 s\n",
+      "Wall time: 62.3 ms\n"
      ]
     }
    ],
@@ -410,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -458,4 +488,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}