gauthamk commited on
Commit
bbb36a7
1 Parent(s): 9208f94

Minor Changes

Browse files
app.py CHANGED
@@ -7,7 +7,7 @@ title = "Birds Classification - ResNet34 PyTorch"
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  examples = [os.path.join(examples_dir, i) for i in os.listdir('examples')]
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  interface = gr.Interface(fn=predict, inputs=gr.Image(type= 'numpy', shape=(224, 224)).style(height= 256),
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- outputs= gr.Label(num_top_classes= 5),
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  examples= examples, title= title, css= '.gr-box {background-color: rgb(230 230 230);}')
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  interface.launch()
 
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  examples = [os.path.join(examples_dir, i) for i in os.listdir('examples')]
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  interface = gr.Interface(fn=predict, inputs=gr.Image(type= 'numpy', shape=(224, 224)).style(height= 256),
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+ outputs= gr.Label(num_top_classes= 5), cache_examples= False,
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  examples= examples, title= title, css= '.gr-box {background-color: rgb(230 230 230);}')
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  interface.launch()
notebooks/onnx-testing.ipynb CHANGED
@@ -29,7 +29,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 6,
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  "metadata": {},
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  "outputs": [],
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  "source": [
@@ -48,16 +48,16 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 7,
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  "metadata": {},
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  "outputs": [
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  {
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  "data": {
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  "text/plain": [
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- "<matplotlib.image.AxesImage at 0x7f09fcc27e20>"
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  ]
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  },
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- "execution_count": 7,
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  "metadata": {},
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  "output_type": "execute_result"
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  },
@@ -78,7 +78,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 8,
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  "metadata": {},
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  "outputs": [
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  {
@@ -91,10 +91,10 @@
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  {
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  "data": {
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  "text/plain": [
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- "<matplotlib.image.AxesImage at 0x7f09fccb62c0>"
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  ]
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  },
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- "execution_count": 8,
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  "metadata": {},
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  "output_type": "execute_result"
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  },
@@ -133,7 +133,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 9,
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  "metadata": {},
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  "outputs": [
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  {
 
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  {
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  "cell_type": "code",
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+ "execution_count": 2,
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  "metadata": {},
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  "outputs": [],
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  "source": [
 
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  },
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  {
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  "cell_type": "code",
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+ "execution_count": 3,
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  "metadata": {},
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  "outputs": [
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  {
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  "data": {
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  "text/plain": [
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+ "<matplotlib.image.AxesImage at 0x7f3e0e31b4f0>"
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  ]
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  },
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+ "execution_count": 3,
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  "metadata": {},
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  "output_type": "execute_result"
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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": 4,
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  "metadata": {},
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  "outputs": [
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  {
 
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  {
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  "data": {
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  "text/plain": [
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+ "<matplotlib.image.AxesImage at 0x7f3e0c245fc0>"
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  ]
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  },
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+ "execution_count": 4,
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  "metadata": {},
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  "output_type": "execute_result"
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  },
 
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  {
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  "cell_type": "code",
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+ "execution_count": 5,
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  "metadata": {},
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  "outputs": [
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  {
notebooks/torch-to-onnx.ipynb CHANGED
@@ -232,97 +232,97 @@
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  " %/layer0/layer0.0/conv1/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.0/conv1/Conv\"](%/conv1/conv1.3/Relu_output_0, %onnx::Conv_396, %onnx::Conv_397), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.0/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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  " %/layer0/layer0.0/relu/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.0/relu/Relu\"](%/layer0/layer0.0/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer0/layer0.0/conv2/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.0/conv2/Conv\"](%/layer0/layer0.0/relu/Relu_output_0, %onnx::Conv_399, %onnx::Conv_400), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.0/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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- " %/layer0/layer0.0/Add_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer0/layer0.0/Add\"](%/layer0/layer0.0/conv2/Conv_output_0, %/conv1/conv1.3/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.0 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
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  " %/layer0/layer0.0/relu_1/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.0/relu_1/Relu\"](%/layer0/layer0.0/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer0/layer0.1/conv1/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.1/conv1/Conv\"](%/layer0/layer0.0/relu_1/Relu_output_0, %onnx::Conv_402, %onnx::Conv_403), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.1/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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  " %/layer0/layer0.1/relu/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.1/relu/Relu\"](%/layer0/layer0.1/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer0/layer0.1/conv2/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.1/conv2/Conv\"](%/layer0/layer0.1/relu/Relu_output_0, %onnx::Conv_405, %onnx::Conv_406), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.1/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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- " %/layer0/layer0.1/Add_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer0/layer0.1/Add\"](%/layer0/layer0.1/conv2/Conv_output_0, %/layer0/layer0.0/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.1 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
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  " %/layer0/layer0.1/relu_1/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.1/relu_1/Relu\"](%/layer0/layer0.1/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer0/layer0.2/conv1/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.2/conv1/Conv\"](%/layer0/layer0.1/relu_1/Relu_output_0, %onnx::Conv_408, %onnx::Conv_409), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.2/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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  " %/layer0/layer0.2/relu/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.2/relu/Relu\"](%/layer0/layer0.2/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer0/layer0.2/conv2/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.2/conv2/Conv\"](%/layer0/layer0.2/relu/Relu_output_0, %onnx::Conv_411, %onnx::Conv_412), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.2/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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- " %/layer0/layer0.2/Add_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer0/layer0.2/Add\"](%/layer0/layer0.2/conv2/Conv_output_0, %/layer0/layer0.1/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.2 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
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  " %/layer0/layer0.2/relu_1/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.2/relu_1/Relu\"](%/layer0/layer0.2/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer1/layer1.0/conv1/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2], onnx_name=\"/layer1/layer1.0/conv1/Conv\"](%/layer0/layer0.2/relu_1/Relu_output_0, %onnx::Conv_414, %onnx::Conv_415), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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  " %/layer1/layer1.0/relu/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.0/relu/Relu\"](%/layer1/layer1.0/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer1/layer1.0/conv2/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.0/conv2/Conv\"](%/layer1/layer1.0/relu/Relu_output_0, %onnx::Conv_417, %onnx::Conv_418), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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  " %/layer1/layer1.0/downsample/downsample.0/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2], onnx_name=\"/layer1/layer1.0/downsample/downsample.0/Conv\"](%/layer0/layer0.2/relu_1/Relu_output_0, %onnx::Conv_420, %onnx::Conv_421), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0/torch.nn.modules.container.Sequential::downsample/torch.nn.modules.conv.Conv2d::downsample.0 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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- " %/layer1/layer1.0/Add_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer1/layer1.0/Add\"](%/layer1/layer1.0/conv2/Conv_output_0, %/layer1/layer1.0/downsample/downsample.0/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
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  " %/layer1/layer1.0/relu_1/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.0/relu_1/Relu\"](%/layer1/layer1.0/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer1/layer1.1/conv1/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.1/conv1/Conv\"](%/layer1/layer1.0/relu_1/Relu_output_0, %onnx::Conv_423, %onnx::Conv_424), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.1/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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  " %/layer1/layer1.1/relu/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.1/relu/Relu\"](%/layer1/layer1.1/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer1/layer1.1/conv2/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.1/conv2/Conv\"](%/layer1/layer1.1/relu/Relu_output_0, %onnx::Conv_426, %onnx::Conv_427), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.1/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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- " %/layer1/layer1.1/Add_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer1/layer1.1/Add\"](%/layer1/layer1.1/conv2/Conv_output_0, %/layer1/layer1.0/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.1 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
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  " %/layer1/layer1.1/relu_1/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.1/relu_1/Relu\"](%/layer1/layer1.1/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer1/layer1.2/conv1/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.2/conv1/Conv\"](%/layer1/layer1.1/relu_1/Relu_output_0, %onnx::Conv_429, %onnx::Conv_430), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.2/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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  " %/layer1/layer1.2/relu/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.2/relu/Relu\"](%/layer1/layer1.2/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
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  " %/layer1/layer1.2/conv2/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.2/conv2/Conv\"](%/layer1/layer1.2/relu/Relu_output_0, %onnx::Conv_432, %onnx::Conv_433), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.2/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
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- " %/layer1/layer1.2/Add_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer1/layer1.2/Add\"](%/layer1/layer1.2/conv2/Conv_output_0, %/layer1/layer1.1/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.2 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
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  " %/layer1/layer1.2/relu_1/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.2/relu_1/Relu\"](%/layer1/layer1.2/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
263
  " %/layer1/layer1.3/conv1/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.3/conv1/Conv\"](%/layer1/layer1.2/relu_1/Relu_output_0, %onnx::Conv_435, %onnx::Conv_436), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.3/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
264
  " %/layer1/layer1.3/relu/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.3/relu/Relu\"](%/layer1/layer1.3/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.3/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
265
  " %/layer1/layer1.3/conv2/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.3/conv2/Conv\"](%/layer1/layer1.3/relu/Relu_output_0, %onnx::Conv_438, %onnx::Conv_439), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.3/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
266
- " %/layer1/layer1.3/Add_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer1/layer1.3/Add\"](%/layer1/layer1.3/conv2/Conv_output_0, %/layer1/layer1.2/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.3 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
267
  " %/layer1/layer1.3/relu_1/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.3/relu_1/Relu\"](%/layer1/layer1.3/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.3/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
268
  " %/layer2/layer2.0/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2], onnx_name=\"/layer2/layer2.0/conv1/Conv\"](%/layer1/layer1.3/relu_1/Relu_output_0, %onnx::Conv_441, %onnx::Conv_442), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
269
  " %/layer2/layer2.0/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.0/relu/Relu\"](%/layer2/layer2.0/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
270
  " %/layer2/layer2.0/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.0/conv2/Conv\"](%/layer2/layer2.0/relu/Relu_output_0, %onnx::Conv_444, %onnx::Conv_445), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
271
  " %/layer2/layer2.0/downsample/downsample.0/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2], onnx_name=\"/layer2/layer2.0/downsample/downsample.0/Conv\"](%/layer1/layer1.3/relu_1/Relu_output_0, %onnx::Conv_447, %onnx::Conv_448), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0/torch.nn.modules.container.Sequential::downsample/torch.nn.modules.conv.Conv2d::downsample.0 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
272
- " %/layer2/layer2.0/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.0/Add\"](%/layer2/layer2.0/conv2/Conv_output_0, %/layer2/layer2.0/downsample/downsample.0/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
273
  " %/layer2/layer2.0/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.0/relu_1/Relu\"](%/layer2/layer2.0/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
274
  " %/layer2/layer2.1/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.1/conv1/Conv\"](%/layer2/layer2.0/relu_1/Relu_output_0, %onnx::Conv_450, %onnx::Conv_451), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.1/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
275
  " %/layer2/layer2.1/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.1/relu/Relu\"](%/layer2/layer2.1/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
276
  " %/layer2/layer2.1/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.1/conv2/Conv\"](%/layer2/layer2.1/relu/Relu_output_0, %onnx::Conv_453, %onnx::Conv_454), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.1/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
277
- " %/layer2/layer2.1/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.1/Add\"](%/layer2/layer2.1/conv2/Conv_output_0, %/layer2/layer2.0/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.1 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
278
  " %/layer2/layer2.1/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.1/relu_1/Relu\"](%/layer2/layer2.1/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
279
  " %/layer2/layer2.2/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.2/conv1/Conv\"](%/layer2/layer2.1/relu_1/Relu_output_0, %onnx::Conv_456, %onnx::Conv_457), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.2/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
280
  " %/layer2/layer2.2/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.2/relu/Relu\"](%/layer2/layer2.2/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
281
  " %/layer2/layer2.2/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.2/conv2/Conv\"](%/layer2/layer2.2/relu/Relu_output_0, %onnx::Conv_459, %onnx::Conv_460), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.2/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
282
- " %/layer2/layer2.2/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.2/Add\"](%/layer2/layer2.2/conv2/Conv_output_0, %/layer2/layer2.1/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.2 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
283
  " %/layer2/layer2.2/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.2/relu_1/Relu\"](%/layer2/layer2.2/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
284
  " %/layer2/layer2.3/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.3/conv1/Conv\"](%/layer2/layer2.2/relu_1/Relu_output_0, %onnx::Conv_462, %onnx::Conv_463), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.3/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
285
  " %/layer2/layer2.3/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.3/relu/Relu\"](%/layer2/layer2.3/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.3/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
286
  " %/layer2/layer2.3/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.3/conv2/Conv\"](%/layer2/layer2.3/relu/Relu_output_0, %onnx::Conv_465, %onnx::Conv_466), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.3/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
287
- " %/layer2/layer2.3/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.3/Add\"](%/layer2/layer2.3/conv2/Conv_output_0, %/layer2/layer2.2/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.3 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
288
  " %/layer2/layer2.3/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.3/relu_1/Relu\"](%/layer2/layer2.3/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.3/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
289
  " %/layer2/layer2.4/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.4/conv1/Conv\"](%/layer2/layer2.3/relu_1/Relu_output_0, %onnx::Conv_468, %onnx::Conv_469), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.4/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
290
  " %/layer2/layer2.4/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.4/relu/Relu\"](%/layer2/layer2.4/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.4/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
291
  " %/layer2/layer2.4/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.4/conv2/Conv\"](%/layer2/layer2.4/relu/Relu_output_0, %onnx::Conv_471, %onnx::Conv_472), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.4/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
292
- " %/layer2/layer2.4/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.4/Add\"](%/layer2/layer2.4/conv2/Conv_output_0, %/layer2/layer2.3/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.4 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
293
  " %/layer2/layer2.4/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.4/relu_1/Relu\"](%/layer2/layer2.4/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.4/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
294
  " %/layer2/layer2.5/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.5/conv1/Conv\"](%/layer2/layer2.4/relu_1/Relu_output_0, %onnx::Conv_474, %onnx::Conv_475), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.5/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
295
  " %/layer2/layer2.5/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.5/relu/Relu\"](%/layer2/layer2.5/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.5/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
296
  " %/layer2/layer2.5/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.5/conv2/Conv\"](%/layer2/layer2.5/relu/Relu_output_0, %onnx::Conv_477, %onnx::Conv_478), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.5/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
297
- " %/layer2/layer2.5/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.5/Add\"](%/layer2/layer2.5/conv2/Conv_output_0, %/layer2/layer2.4/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.5 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
298
  " %/layer2/layer2.5/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.5/relu_1/Relu\"](%/layer2/layer2.5/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.5/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
299
  " %/layer3/layer3.0/conv1/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2], onnx_name=\"/layer3/layer3.0/conv1/Conv\"](%/layer2/layer2.5/relu_1/Relu_output_0, %onnx::Conv_480, %onnx::Conv_481), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
300
  " %/layer3/layer3.0/relu/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.0/relu/Relu\"](%/layer3/layer3.0/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
301
  " %/layer3/layer3.0/conv2/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer3/layer3.0/conv2/Conv\"](%/layer3/layer3.0/relu/Relu_output_0, %onnx::Conv_483, %onnx::Conv_484), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
302
  " %/layer3/layer3.0/downsample/downsample.0/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2], onnx_name=\"/layer3/layer3.0/downsample/downsample.0/Conv\"](%/layer2/layer2.5/relu_1/Relu_output_0, %onnx::Conv_486, %onnx::Conv_487), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0/torch.nn.modules.container.Sequential::downsample/torch.nn.modules.conv.Conv2d::downsample.0 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
303
- " %/layer3/layer3.0/Add_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer3/layer3.0/Add\"](%/layer3/layer3.0/conv2/Conv_output_0, %/layer3/layer3.0/downsample/downsample.0/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
304
  " %/layer3/layer3.0/relu_1/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.0/relu_1/Relu\"](%/layer3/layer3.0/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
305
  " %/layer3/layer3.1/conv1/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer3/layer3.1/conv1/Conv\"](%/layer3/layer3.0/relu_1/Relu_output_0, %onnx::Conv_489, %onnx::Conv_490), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.1/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
306
  " %/layer3/layer3.1/relu/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.1/relu/Relu\"](%/layer3/layer3.1/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
307
  " %/layer3/layer3.1/conv2/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer3/layer3.1/conv2/Conv\"](%/layer3/layer3.1/relu/Relu_output_0, %onnx::Conv_492, %onnx::Conv_493), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.1/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
308
- " %/layer3/layer3.1/Add_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer3/layer3.1/Add\"](%/layer3/layer3.1/conv2/Conv_output_0, %/layer3/layer3.0/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.1 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
309
  " %/layer3/layer3.1/relu_1/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.1/relu_1/Relu\"](%/layer3/layer3.1/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
310
  " %/layer3/layer3.2/conv1/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer3/layer3.2/conv1/Conv\"](%/layer3/layer3.1/relu_1/Relu_output_0, %onnx::Conv_495, %onnx::Conv_496), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.2/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
311
  " %/layer3/layer3.2/relu/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.2/relu/Relu\"](%/layer3/layer3.2/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
312
  " %/layer3/layer3.2/conv2/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer3/layer3.2/conv2/Conv\"](%/layer3/layer3.2/relu/Relu_output_0, %onnx::Conv_498, %onnx::Conv_499), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.2/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
313
- " %/layer3/layer3.2/Add_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer3/layer3.2/Add\"](%/layer3/layer3.2/conv2/Conv_output_0, %/layer3/layer3.1/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.2 # /tmp/ipykernel_3991/1263192908.py:23:0\n",
314
  " %/layer3/layer3.2/relu_1/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.2/relu_1/Relu\"](%/layer3/layer3.2/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
315
  " %/avg_pool/Constant_output_0 : Long(8, strides=[1], device=cpu) = onnx::Constant[value= 0 0 0 0 0 0 0 0 [ CPULongType{8} ], onnx_name=\"/avg_pool/Constant\"](), scope: __main__.ResNet34::/torch.nn.modules.pooling.AvgPool2d::avg_pool # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/pooling.py:628:0\n",
316
  " %/avg_pool/Pad_output_0 : Float(*, 512, 7, 7, device=cpu) = onnx::Pad[mode=\"constant\", onnx_name=\"/avg_pool/Pad\"](%/layer3/layer3.2/relu_1/Relu_output_0, %/avg_pool/Constant_output_0), scope: __main__.ResNet34::/torch.nn.modules.pooling.AvgPool2d::avg_pool # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/pooling.py:628:0\n",
317
  " %/avg_pool/AveragePool_output_0 : Float(*, 512, 1, 1, strides=[512, 1, 1, 1], requires_grad=1, device=cpu) = onnx::AveragePool[ceil_mode=0, kernel_shape=[7, 7], pads=[0, 0, 0, 0], strides=[7, 7], onnx_name=\"/avg_pool/AveragePool\"](%/avg_pool/Pad_output_0), scope: __main__.ResNet34::/torch.nn.modules.pooling.AvgPool2d::avg_pool # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/pooling.py:628:0\n",
318
- " %/Shape_output_0 : Long(4, strides=[1], device=cpu) = onnx::Shape[onnx_name=\"/Shape\"](%/avg_pool/AveragePool_output_0), scope: __main__.ResNet34:: # /tmp/ipykernel_3991/1263192908.py:62:0\n",
319
- " %/Constant_output_0 : Long(device=cpu) = onnx::Constant[value={0}, onnx_name=\"/Constant\"](), scope: __main__.ResNet34:: # /tmp/ipykernel_3991/1263192908.py:62:0\n",
320
- " %/Gather_output_0 : Long(device=cpu) = onnx::Gather[axis=0, onnx_name=\"/Gather\"](%/Shape_output_0, %/Constant_output_0), scope: __main__.ResNet34:: # /tmp/ipykernel_3991/1263192908.py:62:0\n",
321
  " %onnx::Unsqueeze_384 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
322
  " %/Unsqueeze_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/Unsqueeze\"](%/Gather_output_0, %onnx::Unsqueeze_384), scope: __main__.ResNet34::\n",
323
  " %/Constant_1_output_0 : Long(1, strides=[1], requires_grad=0, device=cpu) = onnx::Constant[value={-1}, onnx_name=\"/Constant_1\"](), scope: __main__.ResNet34::\n",
324
- " %/Concat_output_0 : Long(2, strides=[1], device=cpu) = onnx::Concat[axis=0, onnx_name=\"/Concat\"](%/Unsqueeze_output_0, %/Constant_1_output_0), scope: __main__.ResNet34:: # /tmp/ipykernel_3991/1263192908.py:62:0\n",
325
- " %/Reshape_output_0 : Float(*, 512, strides=[512, 1], requires_grad=1, device=cpu) = onnx::Reshape[allowzero=0, onnx_name=\"/Reshape\"](%/avg_pool/AveragePool_output_0, %/Concat_output_0), scope: __main__.ResNet34:: # /tmp/ipykernel_3991/1263192908.py:62:0\n",
326
  " %/fc/Gemm_output_0 : Float(*, 450, strides=[450, 1], requires_grad=1, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1, onnx_name=\"/fc/Gemm\"](%/Reshape_output_0, %fc.weight, %fc.bias), scope: __main__.ResNet34::/torch.nn.modules.linear.Linear::fc # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/linear.py:114:0\n",
327
  " %output : Float(*, 450, strides=[450, 1], requires_grad=1, device=cpu) = onnx::LogSoftmax[axis=1, onnx_name=\"/LogSoftmax\"](%/fc/Gemm_output_0), scope: __main__.ResNet34:: # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1927:0\n",
328
  " return (%output)\n",
 
232
  " %/layer0/layer0.0/conv1/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.0/conv1/Conv\"](%/conv1/conv1.3/Relu_output_0, %onnx::Conv_396, %onnx::Conv_397), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.0/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
233
  " %/layer0/layer0.0/relu/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.0/relu/Relu\"](%/layer0/layer0.0/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
234
  " %/layer0/layer0.0/conv2/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.0/conv2/Conv\"](%/layer0/layer0.0/relu/Relu_output_0, %onnx::Conv_399, %onnx::Conv_400), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.0/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
235
+ " %/layer0/layer0.0/Add_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer0/layer0.0/Add\"](%/layer0/layer0.0/conv2/Conv_output_0, %/conv1/conv1.3/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.0 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
236
  " %/layer0/layer0.0/relu_1/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.0/relu_1/Relu\"](%/layer0/layer0.0/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
237
  " %/layer0/layer0.1/conv1/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.1/conv1/Conv\"](%/layer0/layer0.0/relu_1/Relu_output_0, %onnx::Conv_402, %onnx::Conv_403), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.1/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
238
  " %/layer0/layer0.1/relu/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.1/relu/Relu\"](%/layer0/layer0.1/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
239
  " %/layer0/layer0.1/conv2/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.1/conv2/Conv\"](%/layer0/layer0.1/relu/Relu_output_0, %onnx::Conv_405, %onnx::Conv_406), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.1/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
240
+ " %/layer0/layer0.1/Add_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer0/layer0.1/Add\"](%/layer0/layer0.1/conv2/Conv_output_0, %/layer0/layer0.0/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.1 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
241
  " %/layer0/layer0.1/relu_1/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.1/relu_1/Relu\"](%/layer0/layer0.1/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
242
  " %/layer0/layer0.2/conv1/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.2/conv1/Conv\"](%/layer0/layer0.1/relu_1/Relu_output_0, %onnx::Conv_408, %onnx::Conv_409), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.2/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
243
  " %/layer0/layer0.2/relu/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.2/relu/Relu\"](%/layer0/layer0.2/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
244
  " %/layer0/layer0.2/conv2/Conv_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer0/layer0.2/conv2/Conv\"](%/layer0/layer0.2/relu/Relu_output_0, %onnx::Conv_411, %onnx::Conv_412), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.2/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
245
+ " %/layer0/layer0.2/Add_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer0/layer0.2/Add\"](%/layer0/layer0.2/conv2/Conv_output_0, %/layer0/layer0.1/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.2 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
246
  " %/layer0/layer0.2/relu_1/Relu_output_0 : Float(*, 64, 56, 56, strides=[200704, 3136, 56, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer0/layer0.2/relu_1/Relu\"](%/layer0/layer0.2/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer0/__main__.BasicBlock::layer0.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
247
  " %/layer1/layer1.0/conv1/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2], onnx_name=\"/layer1/layer1.0/conv1/Conv\"](%/layer0/layer0.2/relu_1/Relu_output_0, %onnx::Conv_414, %onnx::Conv_415), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
248
  " %/layer1/layer1.0/relu/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.0/relu/Relu\"](%/layer1/layer1.0/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
249
  " %/layer1/layer1.0/conv2/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.0/conv2/Conv\"](%/layer1/layer1.0/relu/Relu_output_0, %onnx::Conv_417, %onnx::Conv_418), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
250
  " %/layer1/layer1.0/downsample/downsample.0/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2], onnx_name=\"/layer1/layer1.0/downsample/downsample.0/Conv\"](%/layer0/layer0.2/relu_1/Relu_output_0, %onnx::Conv_420, %onnx::Conv_421), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0/torch.nn.modules.container.Sequential::downsample/torch.nn.modules.conv.Conv2d::downsample.0 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
251
+ " %/layer1/layer1.0/Add_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer1/layer1.0/Add\"](%/layer1/layer1.0/conv2/Conv_output_0, %/layer1/layer1.0/downsample/downsample.0/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
252
  " %/layer1/layer1.0/relu_1/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.0/relu_1/Relu\"](%/layer1/layer1.0/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
253
  " %/layer1/layer1.1/conv1/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.1/conv1/Conv\"](%/layer1/layer1.0/relu_1/Relu_output_0, %onnx::Conv_423, %onnx::Conv_424), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.1/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
254
  " %/layer1/layer1.1/relu/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.1/relu/Relu\"](%/layer1/layer1.1/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
255
  " %/layer1/layer1.1/conv2/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.1/conv2/Conv\"](%/layer1/layer1.1/relu/Relu_output_0, %onnx::Conv_426, %onnx::Conv_427), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.1/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
256
+ " %/layer1/layer1.1/Add_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer1/layer1.1/Add\"](%/layer1/layer1.1/conv2/Conv_output_0, %/layer1/layer1.0/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.1 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
257
  " %/layer1/layer1.1/relu_1/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.1/relu_1/Relu\"](%/layer1/layer1.1/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
258
  " %/layer1/layer1.2/conv1/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.2/conv1/Conv\"](%/layer1/layer1.1/relu_1/Relu_output_0, %onnx::Conv_429, %onnx::Conv_430), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.2/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
259
  " %/layer1/layer1.2/relu/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.2/relu/Relu\"](%/layer1/layer1.2/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
260
  " %/layer1/layer1.2/conv2/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.2/conv2/Conv\"](%/layer1/layer1.2/relu/Relu_output_0, %onnx::Conv_432, %onnx::Conv_433), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.2/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
261
+ " %/layer1/layer1.2/Add_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer1/layer1.2/Add\"](%/layer1/layer1.2/conv2/Conv_output_0, %/layer1/layer1.1/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.2 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
262
  " %/layer1/layer1.2/relu_1/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.2/relu_1/Relu\"](%/layer1/layer1.2/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
263
  " %/layer1/layer1.3/conv1/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.3/conv1/Conv\"](%/layer1/layer1.2/relu_1/Relu_output_0, %onnx::Conv_435, %onnx::Conv_436), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.3/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
264
  " %/layer1/layer1.3/relu/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.3/relu/Relu\"](%/layer1/layer1.3/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.3/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
265
  " %/layer1/layer1.3/conv2/Conv_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer1/layer1.3/conv2/Conv\"](%/layer1/layer1.3/relu/Relu_output_0, %onnx::Conv_438, %onnx::Conv_439), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.3/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
266
+ " %/layer1/layer1.3/Add_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer1/layer1.3/Add\"](%/layer1/layer1.3/conv2/Conv_output_0, %/layer1/layer1.2/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.3 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
267
  " %/layer1/layer1.3/relu_1/Relu_output_0 : Float(*, 128, 28, 28, strides=[100352, 784, 28, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer1/layer1.3/relu_1/Relu\"](%/layer1/layer1.3/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer1/__main__.BasicBlock::layer1.3/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
268
  " %/layer2/layer2.0/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2], onnx_name=\"/layer2/layer2.0/conv1/Conv\"](%/layer1/layer1.3/relu_1/Relu_output_0, %onnx::Conv_441, %onnx::Conv_442), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
269
  " %/layer2/layer2.0/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.0/relu/Relu\"](%/layer2/layer2.0/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
270
  " %/layer2/layer2.0/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.0/conv2/Conv\"](%/layer2/layer2.0/relu/Relu_output_0, %onnx::Conv_444, %onnx::Conv_445), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
271
  " %/layer2/layer2.0/downsample/downsample.0/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2], onnx_name=\"/layer2/layer2.0/downsample/downsample.0/Conv\"](%/layer1/layer1.3/relu_1/Relu_output_0, %onnx::Conv_447, %onnx::Conv_448), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0/torch.nn.modules.container.Sequential::downsample/torch.nn.modules.conv.Conv2d::downsample.0 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
272
+ " %/layer2/layer2.0/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.0/Add\"](%/layer2/layer2.0/conv2/Conv_output_0, %/layer2/layer2.0/downsample/downsample.0/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
273
  " %/layer2/layer2.0/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.0/relu_1/Relu\"](%/layer2/layer2.0/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
274
  " %/layer2/layer2.1/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.1/conv1/Conv\"](%/layer2/layer2.0/relu_1/Relu_output_0, %onnx::Conv_450, %onnx::Conv_451), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.1/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
275
  " %/layer2/layer2.1/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.1/relu/Relu\"](%/layer2/layer2.1/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
276
  " %/layer2/layer2.1/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.1/conv2/Conv\"](%/layer2/layer2.1/relu/Relu_output_0, %onnx::Conv_453, %onnx::Conv_454), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.1/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
277
+ " %/layer2/layer2.1/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.1/Add\"](%/layer2/layer2.1/conv2/Conv_output_0, %/layer2/layer2.0/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.1 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
278
  " %/layer2/layer2.1/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.1/relu_1/Relu\"](%/layer2/layer2.1/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
279
  " %/layer2/layer2.2/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.2/conv1/Conv\"](%/layer2/layer2.1/relu_1/Relu_output_0, %onnx::Conv_456, %onnx::Conv_457), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.2/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
280
  " %/layer2/layer2.2/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.2/relu/Relu\"](%/layer2/layer2.2/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
281
  " %/layer2/layer2.2/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.2/conv2/Conv\"](%/layer2/layer2.2/relu/Relu_output_0, %onnx::Conv_459, %onnx::Conv_460), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.2/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
282
+ " %/layer2/layer2.2/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.2/Add\"](%/layer2/layer2.2/conv2/Conv_output_0, %/layer2/layer2.1/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.2 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
283
  " %/layer2/layer2.2/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.2/relu_1/Relu\"](%/layer2/layer2.2/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
284
  " %/layer2/layer2.3/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.3/conv1/Conv\"](%/layer2/layer2.2/relu_1/Relu_output_0, %onnx::Conv_462, %onnx::Conv_463), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.3/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
285
  " %/layer2/layer2.3/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.3/relu/Relu\"](%/layer2/layer2.3/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.3/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
286
  " %/layer2/layer2.3/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.3/conv2/Conv\"](%/layer2/layer2.3/relu/Relu_output_0, %onnx::Conv_465, %onnx::Conv_466), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.3/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
287
+ " %/layer2/layer2.3/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.3/Add\"](%/layer2/layer2.3/conv2/Conv_output_0, %/layer2/layer2.2/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.3 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
288
  " %/layer2/layer2.3/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.3/relu_1/Relu\"](%/layer2/layer2.3/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.3/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
289
  " %/layer2/layer2.4/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.4/conv1/Conv\"](%/layer2/layer2.3/relu_1/Relu_output_0, %onnx::Conv_468, %onnx::Conv_469), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.4/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
290
  " %/layer2/layer2.4/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.4/relu/Relu\"](%/layer2/layer2.4/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.4/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
291
  " %/layer2/layer2.4/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.4/conv2/Conv\"](%/layer2/layer2.4/relu/Relu_output_0, %onnx::Conv_471, %onnx::Conv_472), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.4/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
292
+ " %/layer2/layer2.4/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.4/Add\"](%/layer2/layer2.4/conv2/Conv_output_0, %/layer2/layer2.3/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.4 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
293
  " %/layer2/layer2.4/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.4/relu_1/Relu\"](%/layer2/layer2.4/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.4/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
294
  " %/layer2/layer2.5/conv1/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.5/conv1/Conv\"](%/layer2/layer2.4/relu_1/Relu_output_0, %onnx::Conv_474, %onnx::Conv_475), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.5/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
295
  " %/layer2/layer2.5/relu/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.5/relu/Relu\"](%/layer2/layer2.5/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.5/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
296
  " %/layer2/layer2.5/conv2/Conv_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer2/layer2.5/conv2/Conv\"](%/layer2/layer2.5/relu/Relu_output_0, %onnx::Conv_477, %onnx::Conv_478), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.5/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
297
+ " %/layer2/layer2.5/Add_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer2/layer2.5/Add\"](%/layer2/layer2.5/conv2/Conv_output_0, %/layer2/layer2.4/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.5 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
298
  " %/layer2/layer2.5/relu_1/Relu_output_0 : Float(*, 256, 14, 14, strides=[50176, 196, 14, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer2/layer2.5/relu_1/Relu\"](%/layer2/layer2.5/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer2/__main__.BasicBlock::layer2.5/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
299
  " %/layer3/layer3.0/conv1/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2], onnx_name=\"/layer3/layer3.0/conv1/Conv\"](%/layer2/layer2.5/relu_1/Relu_output_0, %onnx::Conv_480, %onnx::Conv_481), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
300
  " %/layer3/layer3.0/relu/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.0/relu/Relu\"](%/layer3/layer3.0/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
301
  " %/layer3/layer3.0/conv2/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer3/layer3.0/conv2/Conv\"](%/layer3/layer3.0/relu/Relu_output_0, %onnx::Conv_483, %onnx::Conv_484), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
302
  " %/layer3/layer3.0/downsample/downsample.0/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[2, 2], onnx_name=\"/layer3/layer3.0/downsample/downsample.0/Conv\"](%/layer2/layer2.5/relu_1/Relu_output_0, %onnx::Conv_486, %onnx::Conv_487), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0/torch.nn.modules.container.Sequential::downsample/torch.nn.modules.conv.Conv2d::downsample.0 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
303
+ " %/layer3/layer3.0/Add_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer3/layer3.0/Add\"](%/layer3/layer3.0/conv2/Conv_output_0, %/layer3/layer3.0/downsample/downsample.0/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
304
  " %/layer3/layer3.0/relu_1/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.0/relu_1/Relu\"](%/layer3/layer3.0/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.0/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
305
  " %/layer3/layer3.1/conv1/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer3/layer3.1/conv1/Conv\"](%/layer3/layer3.0/relu_1/Relu_output_0, %onnx::Conv_489, %onnx::Conv_490), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.1/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
306
  " %/layer3/layer3.1/relu/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.1/relu/Relu\"](%/layer3/layer3.1/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
307
  " %/layer3/layer3.1/conv2/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer3/layer3.1/conv2/Conv\"](%/layer3/layer3.1/relu/Relu_output_0, %onnx::Conv_492, %onnx::Conv_493), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.1/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
308
+ " %/layer3/layer3.1/Add_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer3/layer3.1/Add\"](%/layer3/layer3.1/conv2/Conv_output_0, %/layer3/layer3.0/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.1 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
309
  " %/layer3/layer3.1/relu_1/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.1/relu_1/Relu\"](%/layer3/layer3.1/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.1/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
310
  " %/layer3/layer3.2/conv1/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer3/layer3.2/conv1/Conv\"](%/layer3/layer3.1/relu_1/Relu_output_0, %onnx::Conv_495, %onnx::Conv_496), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.2/torch.nn.modules.conv.Conv2d::conv1 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
311
  " %/layer3/layer3.2/relu/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.2/relu/Relu\"](%/layer3/layer3.2/conv1/Conv_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
312
  " %/layer3/layer3.2/conv2/Conv_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1], onnx_name=\"/layer3/layer3.2/conv2/Conv\"](%/layer3/layer3.2/relu/Relu_output_0, %onnx::Conv_498, %onnx::Conv_499), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.2/torch.nn.modules.conv.Conv2d::conv2 # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/conv.py:458:0\n",
313
+ " %/layer3/layer3.2/Add_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Add[onnx_name=\"/layer3/layer3.2/Add\"](%/layer3/layer3.2/conv2/Conv_output_0, %/layer3/layer3.1/relu_1/Relu_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.2 # /tmp/ipykernel_34870/1263192908.py:23:0\n",
314
  " %/layer3/layer3.2/relu_1/Relu_output_0 : Float(*, 512, 7, 7, strides=[25088, 49, 7, 1], requires_grad=1, device=cpu) = onnx::Relu[onnx_name=\"/layer3/layer3.2/relu_1/Relu\"](%/layer3/layer3.2/Add_output_0), scope: __main__.ResNet34::/torch.nn.modules.container.Sequential::layer3/__main__.BasicBlock::layer3.2/torch.nn.modules.activation.ReLU::relu # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1453:0\n",
315
  " %/avg_pool/Constant_output_0 : Long(8, strides=[1], device=cpu) = onnx::Constant[value= 0 0 0 0 0 0 0 0 [ CPULongType{8} ], onnx_name=\"/avg_pool/Constant\"](), scope: __main__.ResNet34::/torch.nn.modules.pooling.AvgPool2d::avg_pool # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/pooling.py:628:0\n",
316
  " %/avg_pool/Pad_output_0 : Float(*, 512, 7, 7, device=cpu) = onnx::Pad[mode=\"constant\", onnx_name=\"/avg_pool/Pad\"](%/layer3/layer3.2/relu_1/Relu_output_0, %/avg_pool/Constant_output_0), scope: __main__.ResNet34::/torch.nn.modules.pooling.AvgPool2d::avg_pool # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/pooling.py:628:0\n",
317
  " %/avg_pool/AveragePool_output_0 : Float(*, 512, 1, 1, strides=[512, 1, 1, 1], requires_grad=1, device=cpu) = onnx::AveragePool[ceil_mode=0, kernel_shape=[7, 7], pads=[0, 0, 0, 0], strides=[7, 7], onnx_name=\"/avg_pool/AveragePool\"](%/avg_pool/Pad_output_0), scope: __main__.ResNet34::/torch.nn.modules.pooling.AvgPool2d::avg_pool # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/pooling.py:628:0\n",
318
+ " %/Shape_output_0 : Long(4, strides=[1], device=cpu) = onnx::Shape[onnx_name=\"/Shape\"](%/avg_pool/AveragePool_output_0), scope: __main__.ResNet34:: # /tmp/ipykernel_34870/1263192908.py:62:0\n",
319
+ " %/Constant_output_0 : Long(device=cpu) = onnx::Constant[value={0}, onnx_name=\"/Constant\"](), scope: __main__.ResNet34:: # /tmp/ipykernel_34870/1263192908.py:62:0\n",
320
+ " %/Gather_output_0 : Long(device=cpu) = onnx::Gather[axis=0, onnx_name=\"/Gather\"](%/Shape_output_0, %/Constant_output_0), scope: __main__.ResNet34:: # /tmp/ipykernel_34870/1263192908.py:62:0\n",
321
  " %onnx::Unsqueeze_384 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
322
  " %/Unsqueeze_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/Unsqueeze\"](%/Gather_output_0, %onnx::Unsqueeze_384), scope: __main__.ResNet34::\n",
323
  " %/Constant_1_output_0 : Long(1, strides=[1], requires_grad=0, device=cpu) = onnx::Constant[value={-1}, onnx_name=\"/Constant_1\"](), scope: __main__.ResNet34::\n",
324
+ " %/Concat_output_0 : Long(2, strides=[1], device=cpu) = onnx::Concat[axis=0, onnx_name=\"/Concat\"](%/Unsqueeze_output_0, %/Constant_1_output_0), scope: __main__.ResNet34:: # /tmp/ipykernel_34870/1263192908.py:62:0\n",
325
+ " %/Reshape_output_0 : Float(*, 512, strides=[512, 1], requires_grad=1, device=cpu) = onnx::Reshape[allowzero=0, onnx_name=\"/Reshape\"](%/avg_pool/AveragePool_output_0, %/Concat_output_0), scope: __main__.ResNet34:: # /tmp/ipykernel_34870/1263192908.py:62:0\n",
326
  " %/fc/Gemm_output_0 : Float(*, 450, strides=[450, 1], requires_grad=1, device=cpu) = onnx::Gemm[alpha=1., beta=1., transB=1, onnx_name=\"/fc/Gemm\"](%/Reshape_output_0, %fc.weight, %fc.bias), scope: __main__.ResNet34::/torch.nn.modules.linear.Linear::fc # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/modules/linear.py:114:0\n",
327
  " %output : Float(*, 450, strides=[450, 1], requires_grad=1, device=cpu) = onnx::LogSoftmax[axis=1, onnx_name=\"/LogSoftmax\"](%/fc/Gemm_output_0), scope: __main__.ResNet34:: # /home/gautham/.local/lib/python3.10/site-packages/torch/nn/functional.py:1927:0\n",
328
  " return (%output)\n",