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PyTorchTraining.py

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+import torch.onnx 
+
+#Function to Convert to ONNX 
+def Convert_ONNX(): 
+
+    # set the model to inference mode 
+    model.eval() 
+
+    # Let's create a dummy input tensor  
+    dummy_input = torch.randn(1, input_size, requires_grad=True)  
+
+    # Export the model   
+    torch.onnx.export(model,         # model being run 
+         dummy_input,       # model input (or a tuple for multiple inputs) 
+         "ImageClassifier.onnx",       # where to save the model  
+         export_params=True,  # store the trained parameter weights inside the model file 
+         opset_version=10,    # the ONNX version to export the model to 
+         do_constant_folding=True,  # whether to execute constant folding for optimization 
+         input_names = ['modelInput'],   # the model's input names 
+         output_names = ['modelOutput'], # the model's output names 
+         dynamic_axes={'modelInput' : {0 : 'batch_size'},    # variable length axes 
+                                'modelOutput' : {0 : 'batch_size'}}) 
+    print(" ") 
+    print('Model has been converted to ONNX')

pytorch_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:89c70e808d1783b6c07911306e106aaf0d4f7f3da8c61078b99ff7f8929a26f4
+size 116861841