Upload SWIN_ONNX.ipynb
Browse files- SWIN_ONNX.ipynb +1701 -0
SWIN_ONNX.ipynb
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|
| 1 |
+
{
|
| 2 |
+
"nbformat": 4,
|
| 3 |
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"nbformat_minor": 0,
|
| 4 |
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"metadata": {
|
| 5 |
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"colab": {
|
| 6 |
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"provenance": [],
|
| 7 |
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"gpuType": "T4"
|
| 8 |
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},
|
| 9 |
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"kernelspec": {
|
| 10 |
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"name": "python3",
|
| 11 |
+
"display_name": "Python 3"
|
| 12 |
+
},
|
| 13 |
+
"language_info": {
|
| 14 |
+
"name": "python"
|
| 15 |
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},
|
| 16 |
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"accelerator": "GPU",
|
| 17 |
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"widgets": {
|
| 18 |
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"application/vnd.jupyter.widget-state+json": {
|
| 19 |
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"58533d8fb59449359914fe9f384d7623": {
|
| 20 |
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"model_module": "@jupyter-widgets/controls",
|
| 21 |
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"model_name": "HBoxModel",
|
| 22 |
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"model_module_version": "1.5.0",
|
| 23 |
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"state": {
|
| 24 |
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"_dom_classes": [],
|
| 25 |
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"_model_module": "@jupyter-widgets/controls",
|
| 26 |
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"_model_module_version": "1.5.0",
|
| 27 |
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"_model_name": "HBoxModel",
|
| 28 |
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"_view_count": null,
|
| 29 |
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"_view_module": "@jupyter-widgets/controls",
|
| 30 |
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"_view_module_version": "1.5.0",
|
| 31 |
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"_view_name": "HBoxView",
|
| 32 |
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"box_style": "",
|
| 33 |
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"children": [
|
| 34 |
+
"IPY_MODEL_c0d3b5a444614ce5a2a53132cd38ecf7",
|
| 35 |
+
"IPY_MODEL_7080ae74b8314eecb4bca56a6d874074",
|
| 36 |
+
"IPY_MODEL_4b93501f67a54b088ed1ba2fb05af3f5"
|
| 37 |
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],
|
| 38 |
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"layout": "IPY_MODEL_a9a1867e5ec2418da340ca72165e8ccf"
|
| 39 |
+
}
|
| 40 |
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},
|
| 41 |
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"c0d3b5a444614ce5a2a53132cd38ecf7": {
|
| 42 |
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"model_module": "@jupyter-widgets/controls",
|
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"Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.15.2+cu118)\n",
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"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->torchvision) (4.6.3)\n",
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"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->torchvision) (1.11.1)\n",
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"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->torchvision) (3.1)\n",
|
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"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->torchvision) (3.1.2)\n",
|
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"Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->torchvision) (2.0.0)\n",
|
| 1075 |
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"Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1->torchvision) (3.25.2)\n",
|
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|
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|
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.5.7)\n",
|
| 1079 |
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"Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.12)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.0.1->torchvision) (2.1.3)\n",
|
| 1082 |
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"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.0.1->torchvision) (1.3.0)\n"
|
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"extractor = AutoFeatureExtractor.from_pretrained(\"Nekshay/SWIN_Angle_Detection_Car\")\n",
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"e90ac08d902a470da7ddf97d287fb8d9",
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"87e0466a20fe4e00a5e57360775a69c0",
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"bd277feb5f014d3d9a34a36edb411837",
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"2ba4dad0f68445a89f8c9c005487470b",
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"2d065b4d97b9488a88846bc7d6dc80c6",
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"84105259880b454ba243808f163db44f",
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"14f27f5f8de84e69a1d9d875aaa4bc3f",
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"e7d65876cb524ecdb5be37c49c2896f6"
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"id": "TxykjJjfNQjP",
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"text": [
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"/usr/local/lib/python3.10/dist-packages/transformers/models/vit/feature_extraction_vit.py:28: FutureWarning: The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use ViTImageProcessor instead.\n",
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{
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"cell_type": "code",
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"source": [
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"model.save_pretrained('AngleDetection_carvana_SWIN')"
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],
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"metadata": {
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"id": "4aUJNXYBNhYa"
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"outputs": []
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{
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"cell_type": "code",
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"source": [
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"!wget https://huggingface.co/Nekshay/Car_VS_Rest/blob/main/model.onnx"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"id": "gFcHopsoOln_",
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"execution_count": 10,
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"outputs": [
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"--2023-07-04 09:27:29-- https://huggingface.co/Nekshay/Car_VS_Rest/blob/main/model.onnx\n",
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"Resolving huggingface.co (huggingface.co)... 65.9.86.71, 65.9.86.79, 65.9.86.62, ...\n",
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"Connecting to huggingface.co (huggingface.co)|65.9.86.71|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 51774 (51K) [text/html]\n",
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"Saving to: ‘model.onnx’\n",
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"\n",
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"\rmodel.onnx 0%[ ] 0 --.-KB/s \rmodel.onnx 100%[===================>] 50.56K --.-KB/s in 0.004s \n",
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"\n",
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"source": [],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 356
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},
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"id": "GIL02eRuMa00",
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"outputId": "cf3ef75e-de43-4951-f860-613b21e92de0"
|
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+
},
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"execution_count": 14,
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"outputs": [
|
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{
|
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"output_type": "error",
|
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+
"ename": "TypeError",
|
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+
"evalue": "ignored",
|
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+
"traceback": [
|
| 1409 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 1410 |
+
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
| 1411 |
+
"\u001b[0;32m<ipython-input-14-73aae8c4254a>\u001b[0m in \u001b[0;36m<cell line: 24>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;31m# Create an instance of your custom SWIN model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCustomSwinModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 25\u001b[0m \u001b[0;31m# Step 2: Load the custom SWIN model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;31m#model = YourCustomSwinModel() # Replace with your custom SWIN model implementation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1412 |
+
"\u001b[0;32m<ipython-input-14-73aae8c4254a>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstride\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mswin_block\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m \u001b[0;31m# Custom SWIN block(s)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1413 |
+
"\u001b[0;31mTypeError\u001b[0m: Linear.__init__() missing 1 required positional argument: 'out_features'"
|
| 1414 |
+
]
|
| 1415 |
+
}
|
| 1416 |
+
]
|
| 1417 |
+
},
|
| 1418 |
+
{
|
| 1419 |
+
"cell_type": "code",
|
| 1420 |
+
"source": [],
|
| 1421 |
+
"metadata": {
|
| 1422 |
+
"colab": {
|
| 1423 |
+
"base_uri": "https://localhost:8080/",
|
| 1424 |
+
"height": 435
|
| 1425 |
+
},
|
| 1426 |
+
"id": "gsBdI8hgOsIo",
|
| 1427 |
+
"outputId": "a2219f1f-3c88-4498-e6ac-28e495827fe6"
|
| 1428 |
+
},
|
| 1429 |
+
"execution_count": 13,
|
| 1430 |
+
"outputs": [
|
| 1431 |
+
{
|
| 1432 |
+
"output_type": "stream",
|
| 1433 |
+
"name": "stderr",
|
| 1434 |
+
"text": [
|
| 1435 |
+
"/usr/local/lib/python3.10/dist-packages/onnx/__init__.py:143: RuntimeWarning: Unexpected end-group tag: Not all data was converted\n",
|
| 1436 |
+
" decoded = typing.cast(Optional[int], proto.ParseFromString(s))\n"
|
| 1437 |
+
]
|
| 1438 |
+
},
|
| 1439 |
+
{
|
| 1440 |
+
"output_type": "error",
|
| 1441 |
+
"ename": "DecodeError",
|
| 1442 |
+
"evalue": "ignored",
|
| 1443 |
+
"traceback": [
|
| 1444 |
+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
| 1445 |
+
"\u001b[0;31mDecodeError\u001b[0m Traceback (most recent call last)",
|
| 1446 |
+
"\u001b[0;32m<ipython-input-13-62496424bd0b>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# Step 2: Load the ONNX model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0monnx_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0monnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/model.onnx\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Replace with the path to your ONNX model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m# Step 3: Create an ONNX Runtime session\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1447 |
+
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/onnx/__init__.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(f, format, load_external_data)\u001b[0m\n\u001b[1;32m 168\u001b[0m \"\"\"\n\u001b[1;32m 169\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_load_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model_from_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 171\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mload_external_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1448 |
+
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/onnx/__init__.py\u001b[0m in \u001b[0;36mload_model_from_string\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 210\u001b[0m \"\"\"\n\u001b[1;32m 211\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;31m# Unused\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_deserialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mModelProto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1449 |
+
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/onnx/__init__.py\u001b[0m in \u001b[0;36m_deserialize\u001b[0;34m(s, proto)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0mdecoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParseFromString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdecoded\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mdecoded\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m raise google.protobuf.message.DecodeError(\n\u001b[0m\u001b[1;32m 146\u001b[0m \u001b[0;34mf\"Protobuf decoding consumed too few bytes: {decoded} out of {len(s)}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 147\u001b[0m )\n",
|
| 1450 |
+
"\u001b[0;31mDecodeError\u001b[0m: Protobuf decoding consumed too few bytes: 1 out of 51774"
|
| 1451 |
+
]
|
| 1452 |
+
}
|
| 1453 |
+
]
|
| 1454 |
+
},
|
| 1455 |
+
{
|
| 1456 |
+
"cell_type": "code",
|
| 1457 |
+
"source": [
|
| 1458 |
+
"import torch\n",
|
| 1459 |
+
"from transformers import SwinForImageClassification\n",
|
| 1460 |
+
"import onnx\n",
|
| 1461 |
+
"import onnxruntime\n",
|
| 1462 |
+
"import numpy as np\n",
|
| 1463 |
+
"\n",
|
| 1464 |
+
"# Step 1: Install necessary dependencies\n",
|
| 1465 |
+
"# Ensure Transformers, ONNX, and ONNX Runtime are installed\n",
|
| 1466 |
+
"\n",
|
| 1467 |
+
"# Step 2: Load the pre-trained SWIN base model\n",
|
| 1468 |
+
"model = SwinForImageClassification.from_pretrained(\"Nekshay/SWIN_Angle_Detection_Car\") # Load pre-trained model\n",
|
| 1469 |
+
"\n",
|
| 1470 |
+
"# Step 3: Convert the model to ONNX format\n",
|
| 1471 |
+
"input_size = (3, 224, 224) # Example input size, adjust according to your model\n",
|
| 1472 |
+
"dummy_input = torch.randn(1, *input_size) # Create a dummy input tensor\n",
|
| 1473 |
+
"onnx_filename = \"swin_model.onnx\" # Output ONNX filename\n",
|
| 1474 |
+
"\n",
|
| 1475 |
+
"torch.onnx.export(model, dummy_input, onnx_filename, opset_version=11)\n",
|
| 1476 |
+
"\n",
|
| 1477 |
+
"# Step 4: Create an ONNX Runtime session\n",
|
| 1478 |
+
"session = onnxruntime.InferenceSession(onnx_filename)\n",
|
| 1479 |
+
"\n",
|
| 1480 |
+
"# Step 5: Prepare the input data\n",
|
| 1481 |
+
"input_name = session.get_inputs()[0].name\n",
|
| 1482 |
+
"output_name = session.get_outputs()[0].name\n",
|
| 1483 |
+
"dummy_input = np.random.randn(1, *input_size).astype(np.float32) # Create a dummy input\n",
|
| 1484 |
+
"\n",
|
| 1485 |
+
"# Step 6: Perform inference\n",
|
| 1486 |
+
"output = session.run([output_name], {input_name: dummy_input})\n",
|
| 1487 |
+
"\n",
|
| 1488 |
+
"# Process the output as required\n"
|
| 1489 |
+
],
|
| 1490 |
+
"metadata": {
|
| 1491 |
+
"colab": {
|
| 1492 |
+
"base_uri": "https://localhost:8080/"
|
| 1493 |
+
},
|
| 1494 |
+
"id": "RvrKYmjEO1HI",
|
| 1495 |
+
"outputId": "2aa9bfc2-51ac-4075-ed09-a1f8e1af673c"
|
| 1496 |
+
},
|
| 1497 |
+
"execution_count": 17,
|
| 1498 |
+
"outputs": [
|
| 1499 |
+
{
|
| 1500 |
+
"output_type": "stream",
|
| 1501 |
+
"name": "stderr",
|
| 1502 |
+
"text": [
|
| 1503 |
+
"/usr/local/lib/python3.10/dist-packages/transformers/models/swin/modeling_swin.py:314: TracerWarning: Converting a tensor to a Python boolean 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",
|
| 1504 |
+
" if num_channels != self.num_channels:\n",
|
| 1505 |
+
"/usr/local/lib/python3.10/dist-packages/transformers/models/swin/modeling_swin.py:304: TracerWarning: Converting a tensor to a Python boolean 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",
|
| 1506 |
+
" if width % self.patch_size[1] != 0:\n",
|
| 1507 |
+
"/usr/local/lib/python3.10/dist-packages/transformers/models/swin/modeling_swin.py:307: TracerWarning: Converting a tensor to a Python boolean 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",
|
| 1508 |
+
" if height % self.patch_size[0] != 0:\n",
|
| 1509 |
+
"/usr/local/lib/python3.10/dist-packages/transformers/models/swin/modeling_swin.py:611: TracerWarning: Converting a tensor to a Python boolean 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",
|
| 1510 |
+
" if min(input_resolution) <= self.window_size:\n",
|
| 1511 |
+
"/usr/local/lib/python3.10/dist-packages/transformers/models/swin/modeling_swin.py:703: TracerWarning: Converting a tensor to a Python boolean 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",
|
| 1512 |
+
" was_padded = pad_values[3] > 0 or pad_values[5] > 0\n",
|
| 1513 |
+
"/usr/local/lib/python3.10/dist-packages/transformers/models/swin/modeling_swin.py:704: TracerWarning: Converting a tensor to a Python boolean 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",
|
| 1514 |
+
" if was_padded:\n",
|
| 1515 |
+
"/usr/local/lib/python3.10/dist-packages/transformers/models/swin/modeling_swin.py:349: TracerWarning: Converting a tensor to a Python boolean 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",
|
| 1516 |
+
" should_pad = (height % 2 == 1) or (width % 2 == 1)\n",
|
| 1517 |
+
"/usr/local/lib/python3.10/dist-packages/transformers/models/swin/modeling_swin.py:350: TracerWarning: Converting a tensor to a Python boolean 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",
|
| 1518 |
+
" if should_pad:\n",
|
| 1519 |
+
"/usr/local/lib/python3.10/dist-packages/transformers/models/swin/modeling_swin.py:614: TracerWarning: Converting a tensor to a Python boolean 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",
|
| 1520 |
+
" self.window_size = min(input_resolution)\n"
|
| 1521 |
+
]
|
| 1522 |
+
},
|
| 1523 |
+
{
|
| 1524 |
+
"output_type": "stream",
|
| 1525 |
+
"name": "stdout",
|
| 1526 |
+
"text": [
|
| 1527 |
+
"============= Diagnostic Run torch.onnx.export version 2.0.1+cu118 =============\n",
|
| 1528 |
+
"verbose: False, log level: Level.ERROR\n",
|
| 1529 |
+
"======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n",
|
| 1530 |
+
"\n"
|
| 1531 |
+
]
|
| 1532 |
+
}
|
| 1533 |
+
]
|
| 1534 |
+
},
|
| 1535 |
+
{
|
| 1536 |
+
"cell_type": "code",
|
| 1537 |
+
"source": [],
|
| 1538 |
+
"metadata": {
|
| 1539 |
+
"colab": {
|
| 1540 |
+
"base_uri": "https://localhost:8080/"
|
| 1541 |
+
},
|
| 1542 |
+
"id": "MQjA6FRBQpMn",
|
| 1543 |
+
"outputId": "aac305e3-423d-4675-9123-c53e2a2f2a59"
|
| 1544 |
+
},
|
| 1545 |
+
"execution_count": 18,
|
| 1546 |
+
"outputs": [
|
| 1547 |
+
{
|
| 1548 |
+
"output_type": "execute_result",
|
| 1549 |
+
"data": {
|
| 1550 |
+
"text/plain": [
|
| 1551 |
+
"[array([[ 0.15227151, 0.21316442, 0.07631967, 0.28868374, 0.01127107,\n",
|
| 1552 |
+
" 0.18012685, 0.27240598, -0.13246158, -0.14007984, -0.00418442,\n",
|
| 1553 |
+
" 0.35363495, -0.14376894, 0.21728903, 0.07130641, -0.22561494,\n",
|
| 1554 |
+
" -0.2501627 ]], dtype=float32)]"
|
| 1555 |
+
]
|
| 1556 |
+
},
|
| 1557 |
+
"metadata": {},
|
| 1558 |
+
"execution_count": 18
|
| 1559 |
+
}
|
| 1560 |
+
]
|
| 1561 |
+
},
|
| 1562 |
+
{
|
| 1563 |
+
"cell_type": "code",
|
| 1564 |
+
"source": [
|
| 1565 |
+
"pip install pillow\n"
|
| 1566 |
+
],
|
| 1567 |
+
"metadata": {
|
| 1568 |
+
"colab": {
|
| 1569 |
+
"base_uri": "https://localhost:8080/"
|
| 1570 |
+
},
|
| 1571 |
+
"id": "2KgyPskmVtuP",
|
| 1572 |
+
"outputId": "174695e2-d69b-4fba-d44f-fcfcbf360238"
|
| 1573 |
+
},
|
| 1574 |
+
"execution_count": 21,
|
| 1575 |
+
"outputs": [
|
| 1576 |
+
{
|
| 1577 |
+
"output_type": "stream",
|
| 1578 |
+
"name": "stdout",
|
| 1579 |
+
"text": [
|
| 1580 |
+
"Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (8.4.0)\n"
|
| 1581 |
+
]
|
| 1582 |
+
}
|
| 1583 |
+
]
|
| 1584 |
+
},
|
| 1585 |
+
{
|
| 1586 |
+
"cell_type": "code",
|
| 1587 |
+
"source": [
|
| 1588 |
+
"from PIL import Image"
|
| 1589 |
+
],
|
| 1590 |
+
"metadata": {
|
| 1591 |
+
"id": "Y4XRBNZtV8KM"
|
| 1592 |
+
},
|
| 1593 |
+
"execution_count": 22,
|
| 1594 |
+
"outputs": []
|
| 1595 |
+
},
|
| 1596 |
+
{
|
| 1597 |
+
"cell_type": "code",
|
| 1598 |
+
"source": [
|
| 1599 |
+
"input_size = (3, 224, 224) # Example input size, adjust according to your model\n",
|
| 1600 |
+
"image_path = \"t.jpg\" # Replace with the path to your image\n",
|
| 1601 |
+
"image = Image.open(image_path).convert(\"RGB\") # Open and convert the image to RGB\n",
|
| 1602 |
+
"image = image.resize((input_size[2], input_size[1])) # Resize the image\n",
|
| 1603 |
+
"image = np.array(image) # Convert the image to a NumPy array\n",
|
| 1604 |
+
"image = image.transpose((2, 0, 1)) # Transpose the image dimensions to match the model's input\n",
|
| 1605 |
+
"image = image / 255.0 # Normalize the pixel values to [0, 1]\n",
|
| 1606 |
+
"image = np.expand_dims(image, axis=0).astype(np.float32) # Add batch dimension and convert to float32\n",
|
| 1607 |
+
"\n",
|
| 1608 |
+
"# Step 4: Create an ONNX Runtime session\n",
|
| 1609 |
+
"onnx_filename = \"swin_model.onnx\" # Path to the converted ONNX model\n",
|
| 1610 |
+
"session = onnxruntime.InferenceSession(onnx_filename)\n",
|
| 1611 |
+
"\n",
|
| 1612 |
+
"# Step 5: Perform inference\n",
|
| 1613 |
+
"input_name = session.get_inputs()[0].name\n",
|
| 1614 |
+
"output_name = session.get_outputs()[0].name\n",
|
| 1615 |
+
"output = session.run([output_name], {input_name: image})"
|
| 1616 |
+
],
|
| 1617 |
+
"metadata": {
|
| 1618 |
+
"id": "AE0Ul1wnU12t"
|
| 1619 |
+
},
|
| 1620 |
+
"execution_count": 24,
|
| 1621 |
+
"outputs": []
|
| 1622 |
+
},
|
| 1623 |
+
{
|
| 1624 |
+
"cell_type": "code",
|
| 1625 |
+
"source": [
|
| 1626 |
+
"predicted_label_index = np.argmax(output[0])\n",
|
| 1627 |
+
"label_mapping = {\n",
|
| 1628 |
+
" \"0\": \"Angle1\",\n",
|
| 1629 |
+
" \"1\": \"Angle10\",\n",
|
| 1630 |
+
" \"2\": \"Angle11\",\n",
|
| 1631 |
+
" \"3\": \"Angle12\",\n",
|
| 1632 |
+
" \"4\": \"Angle13\",\n",
|
| 1633 |
+
" \"5\": \"Angle14\",\n",
|
| 1634 |
+
" \"6\": \"Angle15\",\n",
|
| 1635 |
+
" \"7\": \"Angle16\",\n",
|
| 1636 |
+
" \"8\": \"Angle2\",\n",
|
| 1637 |
+
" \"9\": \"Angle3\",\n",
|
| 1638 |
+
" \"10\": \"Angle4\",\n",
|
| 1639 |
+
" \"11\": \"Angle5\",\n",
|
| 1640 |
+
" \"12\": \"Angle6\",\n",
|
| 1641 |
+
" \"13\": \"Angle7\",\n",
|
| 1642 |
+
" \"14\": \"Angle8\",\n",
|
| 1643 |
+
" \"15\": \"Angle9\"\n",
|
| 1644 |
+
" }\n",
|
| 1645 |
+
"predicted_label = label_mapping[str(predicted_label_index)]\n",
|
| 1646 |
+
"\n",
|
| 1647 |
+
"print(\"Predicted label:\", predicted_label)"
|
| 1648 |
+
],
|
| 1649 |
+
"metadata": {
|
| 1650 |
+
"colab": {
|
| 1651 |
+
"base_uri": "https://localhost:8080/"
|
| 1652 |
+
},
|
| 1653 |
+
"id": "om6AZP1LWFeX",
|
| 1654 |
+
"outputId": "1921ae88-b6fe-4962-ad46-17fa449bdfc2"
|
| 1655 |
+
},
|
| 1656 |
+
"execution_count": 28,
|
| 1657 |
+
"outputs": [
|
| 1658 |
+
{
|
| 1659 |
+
"output_type": "stream",
|
| 1660 |
+
"name": "stdout",
|
| 1661 |
+
"text": [
|
| 1662 |
+
"Predicted label: Angle15\n"
|
| 1663 |
+
]
|
| 1664 |
+
}
|
| 1665 |
+
]
|
| 1666 |
+
},
|
| 1667 |
+
{
|
| 1668 |
+
"cell_type": "code",
|
| 1669 |
+
"source": [],
|
| 1670 |
+
"metadata": {
|
| 1671 |
+
"colab": {
|
| 1672 |
+
"base_uri": "https://localhost:8080/"
|
| 1673 |
+
},
|
| 1674 |
+
"id": "e_43UAm9V_fT",
|
| 1675 |
+
"outputId": "44271324-7ff0-46be-fc73-5989e07cc1d7"
|
| 1676 |
+
},
|
| 1677 |
+
"execution_count": 27,
|
| 1678 |
+
"outputs": [
|
| 1679 |
+
{
|
| 1680 |
+
"output_type": "execute_result",
|
| 1681 |
+
"data": {
|
| 1682 |
+
"text/plain": [
|
| 1683 |
+
"6"
|
| 1684 |
+
]
|
| 1685 |
+
},
|
| 1686 |
+
"metadata": {},
|
| 1687 |
+
"execution_count": 27
|
| 1688 |
+
}
|
| 1689 |
+
]
|
| 1690 |
+
},
|
| 1691 |
+
{
|
| 1692 |
+
"cell_type": "code",
|
| 1693 |
+
"source": [],
|
| 1694 |
+
"metadata": {
|
| 1695 |
+
"id": "MhM9BJ9xZR9O"
|
| 1696 |
+
},
|
| 1697 |
+
"execution_count": null,
|
| 1698 |
+
"outputs": []
|
| 1699 |
+
}
|
| 1700 |
+
]
|
| 1701 |
+
}
|