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IainmonIainmon
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Fix sobel image example with style_transfer. Working on MPS functionality.
1 parent 96c9eaa commit 6a48d50

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Lines changed: 324 additions & 3 deletions

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‎demos/video/style-transfer/model.ipynb‎

Lines changed: 131 additions & 1 deletion
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@@ -139,10 +139,140 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 22,
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"id": "51739d61",
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"metadata": {},
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"outputs": [],
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"source": [
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"# sm = torch.jit.script(style_model.to(torch.float32))\n",
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"# sm.save(f\"models/{model_name}_float32.pt\")\n",
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"\n",
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"# sm = torch.jit.script(style_model.to(torch.float16))\n",
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"# sm.save(f\"models/{model_name}_float16.pt\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b0173e2e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# torch::Tensor sobel_dx = torch::tensor({{-1, 0, 1},\n",
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"# {-2, 0, 2},\n",
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"# {-1, 0, 1}}).to(torch::kFloat32);\n",
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"# torch::Tensor sobel_dy = torch::tensor({{-1, -2, -1},\n",
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"# {0, 0, 0},\n",
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"# {1, 2, 1}}).to(torch::kFloat32);\n",
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"\n",
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"# torch::Tensor sobel_kernel = torch::cat({sobel_dx, sobel_dy}, 0).unsqueeze(0).unsqueeze(0);\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"id": "c09f3a28",
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"metadata": {},
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"outputs": [
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{
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"ename": "RuntimeError",
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"evalue": "Given groups=1, weight of size [1, 1, 6, 3], expected input[1, 3, 1428, 1904] to have 1 channels, but got 3 channels instead",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[32], line 20\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msobel_cnn(x)\n\u001b[1;32m 19\u001b[0m sobel \u001b[38;5;241m=\u001b[39m Sobel()\u001b[38;5;241m.\u001b[39mto(torch\u001b[38;5;241m.\u001b[39mfloat16)\n\u001b[0;32m---> 20\u001b[0m \u001b[43msobel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1428\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1904\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat16\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m sm \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mscript(sobel)\n\u001b[1;32m 22\u001b[0m sm\u001b[38;5;241m.\u001b[39msave(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodels/sobel.pt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
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"File \u001b[0;32m~/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
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"Cell \u001b[0;32mIn[32], line 17\u001b[0m, in \u001b[0;36mSobel.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msobel_cnn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
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"File \u001b[0;32m~/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py:554\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 554\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/.venv/lib/python3.12/site-packages/torch/nn/modules/conv.py:549\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 538\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(\n\u001b[1;32m 539\u001b[0m F\u001b[38;5;241m.\u001b[39mpad(\n\u001b[1;32m 540\u001b[0m \u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 547\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups,\n\u001b[1;32m 548\u001b[0m )\n\u001b[0;32m--> 549\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 550\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\n\u001b[1;32m 551\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[0;31mRuntimeError\u001b[0m: Given groups=1, weight of size [1, 1, 6, 3], expected input[1, 3, 1428, 1904] to have 1 channels, but got 3 channels instead"
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]
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}
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],
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"source": [
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"class Sobel(torch.nn.Module):\n",
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" def __init__(self):\n",
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" super(Sobel, self).__init__()\n",
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" sobel_dx = torch.tensor([[-1, 0, 1],\n",
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" [-2, 0, 2],\n",
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" [-1, 0, 1]]).to(torch.float16)\n",
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" sobel_dy = torch.tensor([[-1, -2, -1],\n",
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" [0, 0, 0],\n",
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" [1, 2, 1]]).to(torch.float16)\n",
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" sobel_kernel = torch.cat((sobel_dx, sobel_dy), 0).unsqueeze(0).unsqueeze(0)\n",
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" sobel_kernel = sobel_kernel.to(torch.float16)\n",
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" self.sobel_kernel = torch.nn.Parameter(sobel_kernel, requires_grad=False)\n",
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" self.sobel_cnn = torch.nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)\n",
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" self.sobel_cnn.weight = torch.nn.Parameter(sobel_kernel, requires_grad=False)\n",
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"\n",
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" def forward(self, x):\n",
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" return self.sobel_cnn(x)\n",
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"\n",
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"sobel = Sobel().to(torch.float16)\n",
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"sobel(torch.randn(3, 1428, 1904).to(torch.float16))\n",
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"sm = torch.jit.script(sobel)\n",
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"sm.save(\"models/sobel.pt\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"id": "3507a1fb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch.Size([3, 3])\n",
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"torch.Size([3, 3])\n",
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"torch.Size([2, 3, 3])\n"
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]
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},
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{
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"ename": "RuntimeError",
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"evalue": "expected stride to be a single integer value or a list of 1 values to match the convolution dimensions, but got stride=[1, 1]",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[57], line 18\u001b[0m\n\u001b[1;32m 11\u001b[0m X \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrandn(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m1428\u001b[39m, \u001b[38;5;241m1904\u001b[39m)\u001b[38;5;241m.\u001b[39mto(torch\u001b[38;5;241m.\u001b[39mfloat16)\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# sobel_cnn = torch.nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)\u001b[39;00m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# sobel_cnn.weight = torch.nn.Parameter(sobel_kernel, requires_grad=False)\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# sobel_cnn = sobel_cnn.to(torch.float16)\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# sobel_cnn(X)\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m Y \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunctional\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msobel_kernel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstride\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpadding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
245+
"\u001b[0;31mRuntimeError\u001b[0m: expected stride to be a single integer value or a list of 1 values to match the convolution dimensions, but got stride=[1, 1]"
246+
]
247+
}
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],
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"source": [
250+
"sobel_dx = torch.tensor([[-1, 0, 1],\n",
251+
" [-2, 0, 2],\n",
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" [-1, 0, 1]]).to(torch.float16)\n",
253+
"print(sobel_dx.shape)\n",
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"sobel_dy = torch.tensor([[-1, -2, -1],\n",
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" [0, 0, 0],\n",
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" [1, 2, 1]]).to(torch.float16)\n",
257+
"print(sobel_dy.shape)\n",
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"sobel_kernel = torch.cat([sobel_dx.unsqueeze(0), sobel_dy.unsqueeze(0)], 0)\n",
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"print(sobel_kernel.shape)\n",
260+
"X = torch.randn(1,3, 1428, 1904).to(torch.float16)\n",
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"\n",
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"# sobel_cnn = torch.nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1, bias=False)\n",
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"# sobel_cnn.weight = torch.nn.Parameter(sobel_kernel, requires_grad=False)\n",
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"# sobel_cnn = sobel_cnn.to(torch.float16)\n",
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"# sobel_cnn(X)\n",
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"\n",
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"Y = torch.nn.functional.conv2d(X, sobel_kernel, stride=1, padding=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "59d85573",
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"metadata": {},
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"outputs": [],
146276
"source": []
147277
}
148278
],

‎demos/video/style-transfer/models/sobel.pt‎

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‎demos/video/style-transfer/models/sobel_float16.pt‎

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‎demos/video/style-transfer/models/sobel_float32.pt‎

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