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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') |