diff --git a/posts/2023-July-12-superres.ipynb b/posts/2023-July-12-superres.ipynb index 48b7dbf..ed28499 100644 --- a/posts/2023-July-12-superres.ipynb +++ b/posts/2023-July-12-superres.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "f41ca63d", "metadata": {}, "outputs": [], @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "284b338f", "metadata": {}, "outputs": [], @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "id": "aa12081e", "metadata": {}, "outputs": [], @@ -69,16 +69,18 @@ " plt.imshow(img.permute(1, 2, 0).squeeze(), cmap='gray')\n", " plt.axis('off')\n", "\n", - "# Displaying a batch of images\n", + "# Displaying a batch of images in 1 row and n columns\n", "def show_batch(batch):\n", - " grid = torch.cat([batch[i] for i in range(batch.shape[0])], dim=1)\n", - " show_image(grid)\n", + " fig, ax = plt.subplots(1, len(batch), figsize=(20, 20))\n", + " for i, img in enumerate(batch):\n", + " ax[i].imshow(img.permute(1, 2, 0).squeeze(), cmap='gray')\n", + " ax[i].axis('off')\n", " " ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "id": "64e67431", "metadata": {}, "outputs": [ @@ -105,21 +107,21 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "id": "4bda2fbe", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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+ "text/plain": [ + "
" ] }, "metadata": { "image/png": { - "height": 231, - "width": 36 + "height": 108, + "width": 1130 }, "needs_background": "light" }, @@ -235,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 14, "id": "19d69079", "metadata": {}, "outputs": [ @@ -245,7 +256,7 @@ "(torch.Size([10000, 1, 7, 7]), torch.Size([60000, 28, 28]))" ] }, - "execution_count": 65, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -256,7 +267,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 15, "id": "0030794c", "metadata": {}, "outputs": [ @@ -357,9 +368,17 @@ "print(f\"Output shape: {output.shape}\")\n" ] }, + { + "cell_type": "markdown", + "id": "27b9f1ce", + "metadata": {}, + "source": [ + "### Drawing the model (using ONNX and Netron)" + ] + }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 20, "id": "fa52f10d", "metadata": {}, "outputs": [ @@ -367,85 +386,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Exported graph: graph(%input.1 : Float(1, 1, 7, 7, strides=[49, 49, 7, 1], requires_grad=0, device=cpu),\n", - " %encoder.0.weight : Float(16, 1, 3, 3, strides=[9, 9, 3, 1], requires_grad=1, device=cpu),\n", - " %encoder.0.bias : Float(16, strides=[1], requires_grad=1, device=cpu),\n", - " %encoder.2.weight : Float(32, 16, 3, 3, strides=[144, 9, 3, 1], requires_grad=1, device=cpu),\n", - " %encoder.2.bias : Float(32, strides=[1], requires_grad=1, device=cpu),\n", - " %bottleneck.0.weight : Float(64, 32, 3, 3, strides=[288, 9, 3, 1], requires_grad=1, device=cpu),\n", - " %bottleneck.0.bias : Float(64, strides=[1], requires_grad=1, device=cpu),\n", - " %decoder.0.weight : Float(64, 32, 4, 4, strides=[512, 16, 4, 1], requires_grad=1, device=cpu),\n", - " %decoder.0.bias : Float(32, strides=[1], requires_grad=1, device=cpu),\n", - " %decoder.2.weight : Float(32, 16, 3, 3, strides=[144, 9, 3, 1], requires_grad=1, device=cpu),\n", - " %decoder.2.bias : Float(16, strides=[1], requires_grad=1, device=cpu),\n", - " %decoder.4.weight : Float(16, 1, 4, 4, strides=[16, 16, 4, 1], requires_grad=1, device=cpu),\n", - " %decoder.4.bias : Float(1, strides=[1], requires_grad=1, device=cpu)):\n", - " %/encoder/encoder.0/Conv_output_0 : Float(1, 16, 7, 7, strides=[784, 49, 7, 1], requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/encoder/encoder.0/Conv\"](%input.1, %encoder.0.weight, %encoder.0.bias), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.conv.Conv2d::encoder.0 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/conv.py:459:0\n", - " %/encoder/encoder.1/Constant_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}, onnx_name=\"/encoder/encoder.1/Constant\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1/Div_output_0 : Float(1, 16, 7, 7, strides=[784, 49, 7, 1], device=cpu) = onnx::Div[onnx_name=\"/encoder/encoder.1/Div\"](%/encoder/encoder.0/Conv_output_0, %/encoder/encoder.1/Constant_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1/Erf_output_0 : Float(1, 16, 7, 7, strides=[784, 49, 7, 1], device=cpu) = onnx::Erf[onnx_name=\"/encoder/encoder.1/Erf\"](%/encoder/encoder.1/Div_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1/Constant_1_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}, onnx_name=\"/encoder/encoder.1/Constant_1\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1/Add_output_0 : Float(1, 16, 7, 7, strides=[784, 49, 7, 1], device=cpu) = onnx::Add[onnx_name=\"/encoder/encoder.1/Add\"](%/encoder/encoder.1/Erf_output_0, %/encoder/encoder.1/Constant_1_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1/Mul_output_0 : Float(1, 16, 7, 7, strides=[784, 49, 7, 1], device=cpu) = onnx::Mul[onnx_name=\"/encoder/encoder.1/Mul\"](%/encoder/encoder.0/Conv_output_0, %/encoder/encoder.1/Add_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1/Constant_2_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.5}, onnx_name=\"/encoder/encoder.1/Constant_2\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1/Mul_1_output_0 : Float(1, 16, 7, 7, strides=[784, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Mul[onnx_name=\"/encoder/encoder.1/Mul_1\"](%/encoder/encoder.1/Mul_output_0, %/encoder/encoder.1/Constant_2_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.2/Conv_output_0 : Float(1, 32, 7, 7, strides=[1568, 49, 7, 1], requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/encoder/encoder.2/Conv\"](%/encoder/encoder.1/Mul_1_output_0, %encoder.2.weight, %encoder.2.bias), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.conv.Conv2d::encoder.2 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/conv.py:459:0\n", - " %/encoder/encoder.1_1/Constant_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}, onnx_name=\"/encoder/encoder.1_1/Constant\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1_1/Div_output_0 : Float(1, 32, 7, 7, strides=[1568, 49, 7, 1], device=cpu) = onnx::Div[onnx_name=\"/encoder/encoder.1_1/Div\"](%/encoder/encoder.2/Conv_output_0, %/encoder/encoder.1_1/Constant_output_0), scope: __main__.UNet::/torch.nn.modules.cont============= Diagnostic Run torch.onnx.export version 2.0.0+cu118 =============ainer.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1_1/Erf_output_0 : Float(1, 32, 7, 7, strides=[1568, 49, 7, 1], device=cpu) = onnx::Erf[onnx_name=\"/encoder/encoder.1_1/Erf\"](%/encoder/encoder.1_1/Div_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1_1/Constant_1_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}, onnx_name=\"/encoder/encoder.1_1/Constant_1\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1_1/Add_output_0 : Float(1, 32, 7, 7, strides=[1568, 4\n", - "9, 7, 1], device=cpu) = onnx::Add[onnx_name=\"/encoder/encoder.1_1/Add\"](%/encoder/encoder.1_1/Erf_output_0, %/encoder/encoder.1_1/Constant_1_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1_1/Mul_output_0 : Float(1, 32, 7, 7, strides=[1568, 49, 7, 1], device=cpu) = onnx::Mul[onnx_name=\"/encoder/encoder.1_1/Mul\"](%/encoder/encoder.2/Conv_output_0, %/encoder/encoder.1_1/Add_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1_1/Constant_2_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.5}, onnx_name=\"/encoder/encoder.1_1/Constant_2\"](), scope: __main__.UNet::/torch.nn.modules.container.Sverbose: False, log level: Level.ERROR\n", + "============= Diagnostic Run torch.onnx.export version 2.0.0+cu118 =============\n", + "verbose: False, log level: Level.ERROR\n", "======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================\n", "\n", - "equential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.1_1/Mul_1_output_0 : Float(1, 32, 7, 7, strides=[1568, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Mul[onnx_name=\"/encoder/encoder.1_1/Mul_1\"](%/encoder/encoder.1_1/Mul_output_0, %/encoder/encoder.1_1/Constant_2_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/encoder/encoder.4/MaxPool_output_0 : Float(1, 32, 3, 3, strides=[288, 9, 3, 1], requires_grad=1, device=cpu) = onnx::MaxPool[ceil_mode=0, kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2], onnx_name=\"/encoder/encoder.4/MaxPool\"](%/encoder/encoder.1_1/Mul_1_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::encoder/torch.nn.modules.pooling.MaxPool2d::encoder.4 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/functional.py:782:0\n", - " %/bottleneck/bottleneck.0/Conv_output_0 : Float(1, 64, 3, 3, strides=[576, 9, 3, 1], requires_grad=0, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/bottleneck/bottleneck.0/Conv\"](%/encoder/encoder.4/MaxPool_output_0, %bottleneck.0.weight, %bottleneck.0.bias), scope: __main__.UNet::/torch.nn.modules.container.Sequential::bottleneck/torch.nn.modules.conv.Conv2d::bottleneck.0 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/conv.py:459:0\n", - " %/bottleneck/encoder.1/Constant_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}, onnx_name=\"/bottleneck/encoder.1/Constant\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::bottleneck/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/bottleneck/encoder.1/Div_output_0 : Float(1, 64, 3, 3, strides=[576, 9, 3, 1], device=cpu) = onnx::Div[onnx_name=\"/bottleneck/encoder.1/Div\"](%/bottleneck/bottleneck.0/Conv_output_0, %/bottleneck/encoder.1/Constant_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::bottleneck/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/bottleneck/encoder.1/Erf_output_0 : Float(1, 64, 3, 3, strides=[576, 9, 3, 1], device=cpu) = onnx::Erf[onnx_name=\"/bottleneck/encoder.1/Erf\"](%/bottleneck/encoder.1/Div_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::bottleneck/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/bottleneck/encoder.1/Constant_1_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}, onnx_name=\"/bottleneck/encoder.1/Constant_1\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::bottleneck/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/bottleneck/encoder.1/Add_output_0 : Float(1, 64, 3, 3, strides=[576, 9, 3, 1], device=cpu) = onnx::Add[onnx_name=\"/bottleneck/encoder.1/Add\"](%/bottleneck/encoder.1/Erf_output_0, %/bottleneck/encoder.1/Constant_1_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::bottleneck/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/bottleneck/encoder.1/Mul_output_0 : Float(1, 64, 3, 3, strides=[576, 9, 3, 1], device=cpu) = onnx::Mul[onnx_name=\"/bottleneck/encoder.1/Mul\"](%/bottleneck/bottleneck.0/Conv_output_0, %/bottleneck/encoder.1/Add_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::bottleneck/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/bottleneck/encoder.1/Constant_2_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.5}, onnx_name=\"/bottleneck/encoder.1/Constant_2\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::bottleneck/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/bottleneck/encoder.1/Mul_1_output_0 : Float(1, 64, 3, 3, strides=[576, 9, 3, 1], requires_grad=1, device=cpu) = onnx::Mul[onnx_name=\"/bottleneck/encoder.1/Mul_1\"](%/bottleneck/encoder.1/Mul_output_0, %/bottleneck/encoder.1/Constant_2_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::bottleneck/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/decoder.0/ConvTranspose_output_0 : Float(1, 32, Model exported to ONNX successfully.\n", - "12, 12, strides=[4608, 144, 12, 1], requires_grad=0, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[4, 4], pads=[0, 0, 0, 0], strides=[4, 4], onnx_name=\"/decoder/decoder.0/ConvTranspose\"](%/bottleneck/encoder.1/Mul_1_output_0, %decoder.0.weight, %decoder.0.bias), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.conv.ConvTranspose2d::decoder.0 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/conv.py:956:0\n", - " %/decoder/encoder.1/Constant_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}, onnx_name=\"/decoder/encoder.1/Constant\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1/Div_output_0 : Float(1, 32, 12, 12, strides=[4608, 144, 12, 1], device=cpu) = onnx::Div[onnx_name=\"/decoder/encoder.1/Div\"](%/decoder/decoder.0/ConvTranspose_output_0, %/decoder/encoder.1/Constant_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1/Erf_output_0 : Float(1, 32, 12, 12, strides=[4608, 144, 12, 1], device=cpu) = onnx::Erf[onnx_name=\"/decoder/encoder.1/Erf\"](%/decoder/encoder.1/Div_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1/Constant_1_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}, onnx_name=\"/decoder/encoder.1/Constant_1\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1/Add_output_0 : Float(1, 32, 12, 12, strides=[4608, 144, 12, 1], device=cpu) = onnx::Add[onnx_name=\"/decoder/encoder.1/Add\"](%/decoder/encoder.1/Erf_output_0, %/decoder/encoder.1/Constant_1_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1/Mul_output_0 : Float(1, 32, 12, 12, strides=[4608, 144, 12, 1], device=cpu) = onnx::Mul[onnx_name=\"/decoder/encoder.1/Mul\"](%/decoder/decoder.0/ConvTranspose_output_0, %/decoder/encoder.1/Add_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1/Constant_2_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.5}, onnx_name=\"/decoder/encoder.1/Constant_2\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1/Mul_1_output_0 : Float(1, 32, 12, 12, strides=[4608, 144, 12, 1], requires_grad=1, device=cpu) = onnx::Mul[onnx_name=\"/decoder/encoder.1/Mul_1\"](%/decoder/encoder.1/Mul_output_0, %/decoder/encoder.1/Constant_2_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/decoder.2/ConvTranspose_output_0 : Float(1, 16, 14, 14, strides=[3136, 196, 14, 1], requires_grad=0, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[0, 0, 0, 0], strides=[1, 1], onnx_name=\"/decoder/decoder.2/ConvTranspose\"](%/decoder/encoder.1/Mul_1_output_0, %decoder.2.weight, %decoder.2.bias), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.conv.ConvTranspose2d::decoder.2 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/conv.py:956:0\n", - " %/decoder/encoder.1_1/Constant_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1.41421}, onnx_name=\"/decoder/encoder.1_1/Constant\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1_1/Div_output_0 : Float(1, 16, 14, 14, strides=[3136, 196, 14, 1], device=cpu) = onnx::Div[onnx_name=\"/decoder/encoder.1_1/Div\"](%/decoder/decoder.2/ConvTranspose_output_0, %/decoder/encoder.1_1/Constant_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1_1/Erf_output_0 : Float(1, 16, 14, 14, strides=[3136, 196, 14, 1], device=cpu) = onnx::Erf[onnx_name=\"/decoder/encoder.1_1/Erf\"](%/decoder/encoder.1_1/Div_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1_1/Constant_1_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={1}, onnx_name=\"/decoder/encoder.1_1/Constant_1\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1_1/Add_output_0 : Float(1, 16, 14, 14, strides=[3136, 196, 14, 1], device=cpu) = onnx::Add[onnx_name=\"/decoder/encoder.1_1/Add\"](%/decoder/encoder.1_1/Erf_output_0, %/decoder/encoder.1_1/Constant_1_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1_1/Mul_output_0 : Float(1, 16, 14, 14, strides=[3136, 196, 14, 1], device=cpu) = onnx::Mul[onnx_name=\"/decoder/encoder.1_1/Mul\"](%/decoder/decoder.2/ConvTranspose_output_0, %/decoder/encoder.1_1/Add_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1_1/Constant_2_output_0 : Float(requires_grad=0, device=cpu) = onnx::Constant[value={0.5}, onnx_name=\"/decoder/encoder.1_1/Constant_2\"](), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %/decoder/encoder.1_1/Mul_1_output_0 : Float(1, 16, 14, 14, strides=[3136, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Mul[onnx_name=\"/decoder/encoder.1_1/Mul_1\"](%/decoder/encoder.1_1/Mul_output_0, %/decoder/encoder.1_1/Constant_2_output_0), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.activation.GELU::encoder.1 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/activation.py:685:0\n", - " %59 : Float(1, 1, 28, 28, strides=[784, 784, 28, 1], requires_grad=1, device=cpu) = onnx::ConvTranspose[dilations=[1, 1], group=1, kernel_shape=[4, 4], pads=[1, 1, 1, 1], strides=[2, 2], onnx_name=\"/decoder/decoder.4/ConvTranspose\"](%/decoder/encoder.1_1/Mul_1_output_0, %decoder.4.weight, %decoder.4.bias), scope: __main__.UNet::/torch.nn.modules.container.Sequential::decoder/torch.nn.modules.conv.ConvTranspose2d::decoder.4 # /home/nipun.batra/miniforge3/lib/python3.9/site-packages/torch/nn/modules/conv.py:956:0\n", - " return (%59)\n", - "\n" + "Model exported to ONNX successfully.\n" ] } ], "source": [ - "# Provide an example input to the model\n", + "#Provide an example input to the model\n", "batch_size = 1\n", "input_size = (batch_size, 1, 7, 7)\n", "dummy_input = torch.randn(input_size)\n", "\n", "# Export the model to ONNX\n", "onnx_path = \"unet_model.onnx\"\n", - "torch.onnx.export(model, dummy_input, onnx_path, verbose=True)\n", + "torch.onnx.export(model, dummy_input, onnx_path, verbose=False)\n", "\n", "print(\"Model exported to ONNX successfully.\")" ] @@ -460,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 21, "id": "be475a00", "metadata": {}, "outputs": [], @@ -472,14 +429,12 @@ "# Create an instance of the modified UNet model\n", "\n", "# Output of the model is a batch of 1-channel 28x28 images\n", - "output_size = (batch_size, 1, 28, 28)\n", - "\n", - "\n" + "output_size = (batch_size, 1, 28, 28)\n" ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 22, "id": "e686c767", "metadata": {}, "outputs": [], @@ -496,12 +451,12 @@ "\n", "X_train.shape, Y_train.shape, X_test.shape, Y_test.shape\n", "\n", - "model = UNet(activation=nn.ReLU()).to(device)\n" + "model = UNet(activation=sin_activation).to(device)" ] }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 23, "id": "fc053976", "metadata": {}, "outputs": [ @@ -509,106 +464,57 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 1 loss: 0.11675256490707397\n", - "Epoch 51 loss: 0.06216473504900932\n", - "Epoch 101 loss: 0.04350370541214943\n", - "Epoch 151 loss: 0.0338316448032856\n", - "Epoch 201 loss: 0.030034823343157768\n", - "Epoch 251 loss: 0.02791508287191391\n", - "Epoch 301 loss: 0.026423178613185883\n", - "Epoch 351 loss: 0.025394216179847717\n", - "Epoch 401 loss: 0.024557942524552345\n", - "Epoch 451 loss: 0.023918969556689262\n", - "Epoch 501 loss: 0.02341558039188385\n", - "Epoch 551 loss: 0.02270268276333809\n", - "Epoch 601 loss: 0.022382188588380814\n", - "Epoch 651 loss: 0.021941369399428368\n", - "Epoch 701 loss: 0.02154976688325405\n", - "Epoch 751 loss: 0.021203456446528435\n", - "Epoch 801 loss: 0.020942917093634605\n", - "Epoch 851 loss: 0.020651856437325478\n", - "Epoch 901 loss: 0.020434202626347542\n", - "Epoch 951 loss: 0.020613599568605423\n", - "Epoch 1001 loss: 0.02005993388593197\n", - "Epoch 1051 loss: 0.019821004942059517\n", - "Epoch 1101 loss: 0.019648907706141472\n", - "Epoch 1151 loss: 0.019410282373428345\n", - "Epoch 1201 loss: 0.01924115978181362\n", - "Epoch 1251 loss: 0.019102536141872406\n", - "Epoch 1301 loss: 0.019002726301550865\n", - "Epoch 1351 loss: 0.018821245059370995\n", - "Epoch 1401 loss: 0.018730033189058304\n", - "Epoch 1451 loss: 0.01862199418246746\n", - "Epoch 1501 loss: 0.01850336417555809\n", - "Epoch 1551 loss: 0.018356366083025932\n", - "Epoch 1601 loss: 0.018290812149643898\n", - "Epoch 1651 loss: 0.01810508221387863\n", - "Epoch 1701 loss: 0.01807408779859543\n", - "Epoch 1751 loss: 0.018090611323714256\n", - "Epoch 1801 loss: 0.01804545894265175\n", - "Epoch 1851 loss: 0.017826281487941742\n", - "Epoch 1901 loss: 0.017982883378863335\n", - "Epoch 1951 loss: 0.01756354607641697\n", - "Epoch 2001 loss: 0.017589310184121132\n", - "Epoch 2051 loss: 0.017511853948235512\n", - "Epoch 2101 loss: 0.017362715676426888\n", - "Epoch 2151 loss: 0.017271511256694794\n", - "Epoch 2201 loss: 0.01725468412041664\n", - "Epoch 2251 loss: 0.017271995544433594\n", - "Epoch 2301 loss: 0.017192406579852104\n", - "Epoch 2351 loss: 0.017055923119187355\n", - "Epoch 2401 loss: 0.017075594514608383\n", - "Epoch 2451 loss: 0.017060816287994385\n", - "Epoch 2501 loss: 0.01689082384109497\n", - "Epoch 2551 loss: 0.016922233626246452\n", - "Epoch 2601 loss: 0.016747692599892616\n", - "Epoch 2651 loss: 0.016895895823836327\n", - "Epoch 2701 loss: 0.01675291918218136\n", - "Epoch 2751 loss: 0.016832010820508003\n", - "Epoch 2801 loss: 0.016572454944252968\n", - "Epoch 2851 loss: 0.016591450199484825\n", - "Epoch 2901 loss: 0.016644587740302086\n", - "Epoch 2951 loss: 0.01642812229692936\n", - "Epoch 3001 loss: 0.016392042860388756\n", - "Epoch 3051 loss: 0.016409950330853462\n", - "Epoch 3101 loss: 0.01646271161735058\n", - "Epoch 3151 loss: 0.016279829666018486\n", - "Epoch 3201 loss: 0.01633094809949398\n", - "Epoch 3251 loss: 0.0163764376193285\n", - "Epoch 3301 loss: 0.016387293115258217\n", - "Epoch 3351 loss: 0.016207262873649597\n", - "Epoch 3401 loss: 0.0162811279296875\n", - "Epoch 3451 loss: 0.01607055775821209\n", - "Epoch 3501 loss: 0.01602781005203724\n", - "Epoch 3551 loss: 0.0161014162003994\n", - "Epoch 3601 loss: 0.015988387167453766\n", - "Epoch 3651 loss: 0.015956243500113487\n", - "Epoch 3701 loss: 0.016116023063659668\n", - "Epoch 3751 loss: 0.015868166461586952\n", - "Epoch 3801 loss: 0.015873227268457413\n", - "Epoch 3851 loss: 0.016066253185272217\n", - "Epoch 3901 loss: 0.01580056920647621\n", - "Epoch 3951 loss: 0.015729645267128944\n", - "Epoch 4001 loss: 0.015741927549242973\n", - "Epoch 4051 loss: 0.015640201047062874\n", - "Epoch 4101 loss: 0.015609235502779484\n", - "Epoch 4151 loss: 0.015575672499835491\n", - "Epoch 4201 loss: 0.015578163787722588\n", - "Epoch 4251 loss: 0.015562615357339382\n", - "Epoch 4301 loss: 0.01568152941763401\n", - "Epoch 4351 loss: 0.015575124882161617\n", - "Epoch 4401 loss: 0.015435989014804363\n", - "Epoch 4451 loss: 0.015512256883084774\n", - "Epoch 4501 loss: 0.015420597046613693\n", - "Epoch 4551 loss: 0.015436469577252865\n", - "Epoch 4601 loss: 0.015379526652395725\n", - "Epoch 4651 loss: 0.015345440246164799\n", - "Epoch 4701 loss: 0.01537246908992529\n", - "Epoch 4751 loss: 0.015297477133572102\n", - "Epoch 4801 loss: 0.015267273411154747\n", - "Epoch 4851 loss: 0.015396918170154095\n", - "Epoch 4901 loss: 0.015253673307597637\n", - "Epoch 4951 loss: 0.01523192785680294\n" + "Epoch 1 loss: 0.11794766038656235\n", + "Epoch 101 loss: 0.05467110872268677\n", + "Epoch 201 loss: 0.04051697999238968\n", + "Epoch 301 loss: 0.035081446170806885\n", + "Epoch 401 loss: 0.03266175091266632\n", + "Epoch 501 loss: 0.03106730245053768\n", + "Epoch 601 loss: 0.0299631766974926\n", + "Epoch 701 loss: 0.029240472242236137\n", + "Epoch 801 loss: 0.028751315549016\n", + "Epoch 901 loss: 0.028380418196320534\n", + "Epoch 1001 loss: 0.02808808535337448\n", + "Epoch 1101 loss: 0.027867494150996208\n", + "Epoch 1201 loss: 0.02763254940509796\n", + "Epoch 1301 loss: 0.027589663863182068\n", + "Epoch 1401 loss: 0.027285786345601082\n", + "Epoch 1501 loss: 0.027152569964528084\n", + "Epoch 1601 loss: 0.02700323611497879\n", + "Epoch 1701 loss: 0.026871109381318092\n", + "Epoch 1801 loss: 0.026826491579413414\n", + "Epoch 1901 loss: 0.026641741394996643\n", + "Epoch 2001 loss: 0.02652570977807045\n", + "Epoch 2101 loss: 0.026443270966410637\n", + "Epoch 2201 loss: 0.026303958147764206\n", + "Epoch 2301 loss: 0.02622942440211773\n", + "Epoch 2401 loss: 0.026109065860509872\n", + "Epoch 2501 loss: 0.026039674878120422\n", + "Epoch 2601 loss: 0.025937331840395927\n", + "Epoch 2701 loss: 0.02582435868680477\n", + "Epoch 2801 loss: 0.02569013461470604\n", + "Epoch 2901 loss: 0.025580981746315956\n", + "Epoch 3001 loss: 0.025458581745624542\n", + "Epoch 3101 loss: 0.025304477661848068\n", + "Epoch 3201 loss: 0.025146884843707085\n", + "Epoch 3301 loss: 0.024986406788229942\n", + "Epoch 3401 loss: 0.024766957387328148\n", + "Epoch 3501 loss: 0.024587564170360565\n", + "Epoch 3601 loss: 0.02433406375348568\n", + "Epoch 3701 loss: 0.024068517610430717\n", + "Epoch 3801 loss: 0.023816896602511406\n", + "Epoch 3901 loss: 0.02363565005362034\n", + "Epoch 4001 loss: 0.023380018770694733\n", + "Epoch 4101 loss: 0.023191582411527634\n", + "Epoch 4201 loss: 0.02309882454574108\n", + "Epoch 4301 loss: 0.02286476083099842\n", + "Epoch 4401 loss: 0.022712064906954765\n", + "Epoch 4501 loss: 0.022565141320228577\n", + "Epoch 4601 loss: 0.02246268466114998\n", + "Epoch 4701 loss: 0.022299710661172867\n", + "Epoch 4801 loss: 0.022198667749762535\n", + "Epoch 4901 loss: 0.02206907607614994\n", + "Epoch 5001 loss: 0.021999521180987358\n" ] } ], @@ -617,12 +523,10 @@ "loss_fn = nn.MSELoss()\n", "\n", "# Define the optimizer\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n", - "\n", - "# Train on full 100 samples\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)\n", "\n", "# Number of epochs\n", - "n_epochs = 5000\n", + "n_epochs = 5001\n", "\n", "# List to store losses\n", "losses = []\n", @@ -636,7 +540,7 @@ " loss = loss_fn(Y_pred, Y_train)\n", "\n", " # Print loss\n", - " if epoch % 50 == 0:\n", + " if epoch % 100 == 0:\n", " print(f\"Epoch {epoch+1} loss: {loss.item()}\")\n", "\n", " # Store loss\n", @@ -654,23 +558,23 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 24, "id": "7ef4cd3c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 100, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -700,13 +604,13 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 25, "id": "6acc597f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -777,13 +681,13 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 26, "id": "37eb1ae2", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -808,14 +712,6 @@ "\n", "plot_images(X_test.cpu(), Y_test.cpu(), Y_hat.cpu())\n" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "13be9f12", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/posts/unet_model.onnx b/posts/unet_model.onnx index 5ae4dba..9ce650d 100644 Binary files a/posts/unet_model.onnx and b/posts/unet_model.onnx differ