Add onnx tutorial

README.md

@@ -81,6 +81,7 @@ Congratulations! You are done! Now you can train your model with your favorite f
  - Training model for cars segmentation on CamVid dataset [here](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/cars%20segmentation%20(camvid).ipynb).
  - Training SMP model with [Catalyst](https://github.com/catalyst-team/catalyst) (high-level framework for PyTorch), [TTAch](https://github.com/qubvel/ttach) (TTA library for PyTorch) and [Albumentations](https://github.com/albu/albumentations) (fast image augmentation library) - [here](https://github.com/catalyst-team/catalyst/blob/v21.02rc0/examples/notebooks/segmentation-tutorial.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/catalyst-team/catalyst/blob/v21.02rc0/examples/notebooks/segmentation-tutorial.ipynb)
  - Training SMP model with [Pytorch-Lightning](https://pytorch-lightning.readthedocs.io) framework - [here](https://github.com/ternaus/cloths_segmentation) (clothes binary segmentation by [@ternaus](https://github.com/ternaus)).
+ - Export trained model to ONNX - [notebook](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/convert_to_onnx.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/convert_to_onnx.ipynb)
 
 ### 📦 Models <a name="models"></a>
 

examples/convert_to_onnx.ipynb

@@ -0,0 +1,223 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/convert_to_onnx.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to make onnx export work\n",
+    "!pip install onnx onnxruntime"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See complete tutorial in Pytorch docs:\n",
+    " - https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import onnx\n",
+    "import onnxruntime\n",
+    "import numpy as np\n",
+    "\n",
+    "import torch\n",
+    "import segmentation_models_pytorch as smp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create random model (or load your own model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = smp.Unet(\"resnet34\", encoder_weights=\"imagenet\", classes=1)\n",
+    "model = model.eval()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Export the model to ONNX"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dynamic_axes is used to specify the variable length axes. it can be just batch size\n",
+    "dynamic_axes = {0: \"batch_size\", 2: \"height\", 3: \"width\"}\n",
+    "\n",
+    "onnx_model_name = \"unet_resnet34.onnx\"\n",
+    "\n",
+    "onnx_model = torch.onnx.export(\n",
+    "    model,  # model being run\n",
+    "    torch.randn(1, 3, 224, 224),  # model input\n",
+    "    onnx_model_name,  # where to save the model (can be a file or file-like object)\n",
+    "    export_params=True,  # store the trained parameter weights inside the model file\n",
+    "    opset_version=17,  # the ONNX version to export\n",
+    "    do_constant_folding=True,  # whether to execute constant folding for optimization\n",
+    "    input_names=[\"input\"],  # the model's input names\n",
+    "    output_names=[\"output\"],  # the model's output names\n",
+    "    dynamic_axes={  # variable length axes\n",
+    "        \"input\": dynamic_axes,\n",
+    "        \"output\": dynamic_axes,\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# check with onnx first\n",
+    "onnx_model = onnx.load(onnx_model_name)\n",
+    "onnx.checker.check_model(onnx_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run with onnxruntime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[array([[[[-1.41701847e-01, -4.63768840e-03,  1.21411584e-01, ...,\n",
+       "            5.22197843e-01,  3.40217263e-01,  8.52423906e-02],\n",
+       "          [-2.29843616e-01,  2.19401851e-01,  3.53053480e-01, ...,\n",
+       "            2.79466838e-01,  3.20288718e-01, -2.22393833e-02],\n",
+       "          [-3.12503517e-01, -3.66358161e-02,  1.19251609e-02, ...,\n",
+       "           -5.48991561e-02,  3.71140465e-02, -1.82842150e-01],\n",
+       "          ...,\n",
+       "          [-3.02772015e-01, -4.22928065e-01, -1.49621412e-01, ...,\n",
+       "           -1.42241001e-01, -9.90390778e-02, -1.33311331e-01],\n",
+       "          [-1.08293816e-01, -1.28070369e-01, -5.43620177e-02, ...,\n",
+       "           -8.64556879e-02, -1.74177170e-01,  6.03154302e-03],\n",
+       "          [-1.29619062e-01, -2.96604559e-02, -2.86361389e-03, ...,\n",
+       "           -1.91345289e-01, -1.82653710e-01,  1.17175849e-02]]],\n",
+       " \n",
+       " \n",
+       "        [[[-6.16237633e-02,  1.12350248e-01,  1.59193069e-01, ...,\n",
+       "            4.03313845e-01,  2.26862252e-01,  7.33022243e-02],\n",
+       "          [-1.60109222e-01,  1.21696621e-01,  1.84655115e-01, ...,\n",
+       "            1.20978586e-01,  2.45723248e-01,  1.00066036e-01],\n",
+       "          [-2.11992145e-01,  1.71708465e-02, -1.57656223e-02, ...,\n",
+       "           -1.11918494e-01, -1.64519548e-01, -1.73958957e-01],\n",
+       "          ...,\n",
+       "          [-2.79706120e-01, -2.87421644e-01, -5.19880295e-01, ...,\n",
+       "           -8.30744207e-02, -3.48939300e-02,  1.26617640e-01],\n",
+       "          [-2.62198627e-01, -2.91804910e-01, -2.82318443e-01, ...,\n",
+       "            1.81179233e-02,  2.32534595e-02,  1.85002953e-01],\n",
+       "          [-9.28771719e-02, -5.16399741e-05, -9.53909755e-03, ...,\n",
+       "           -2.28582099e-02, -5.09671569e-02,  2.05268264e-02]]]],\n",
+       "       dtype=float32)]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create sample with different batch size, height and width\n",
+    "# from what we used in export above\n",
+    "sample = torch.randn(2, 3, 512, 512)\n",
+    "\n",
+    "ort_session = onnxruntime.InferenceSession(\n",
+    "    onnx_model_name, providers=[\"CPUExecutionProvider\"]\n",
+    ")\n",
+    "\n",
+    "# compute ONNX Runtime output prediction\n",
+    "ort_inputs = {\"input\": sample.numpy()}\n",
+    "ort_outputs = ort_session.run(output_names=None, input_feed=ort_inputs)\n",
+    "ort_outputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Verify it's the same as for pytorch model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exported model has been tested with ONNXRuntime, and the result looks good!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# compute PyTorch output prediction\n",
+    "with torch.no_grad():\n",
+    "    torch_out = model(sample)\n",
+    "\n",
+    "# compare ONNX Runtime and PyTorch results\n",
+    "np.testing.assert_allclose(torch_out.numpy(), ort_outputs[0], rtol=1e-03, atol=1e-05)\n",
+    "\n",
+    "print(\"Exported model has been tested with ONNXRuntime, and the result looks good!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

segmentation_models_pytorch/__init__.py

@@ -26,7 +26,9 @@
 
 # Suppress the specific SyntaxWarning for `pretrainedmodels`
 warnings.filterwarnings("ignore", message="is with a literal", category=SyntaxWarning)
-warnings.filterwarnings("ignore", message=r'"is" with \'str\' literal.*', category=SyntaxWarning) # for python >= 3.12
+warnings.filterwarnings(
+    "ignore", message=r'"is" with \'str\' literal.*', category=SyntaxWarning
+)  # for python >= 3.12
 
 
 def create_model(

segmentation_models_pytorch/base/model.py

@@ -33,7 +33,8 @@ def check_input_shape(self, x):
     def forward(self, x):
         """Sequentially pass `x` trough model`s encoder, decoder and heads"""
 
-        self.check_input_shape(x)
+        if not torch.jit.is_tracing():
+            self.check_input_shape(x)
 
         features = self.encoder(x)
         decoder_output = self.decoder(*features)