I am trying to export a model which takes two inputs where both the inputs can be of different size. Also the output is of varying size. How should I provide dynamic axes value to the export function? This is what I tried: text = torch.LongTensor(1, 39).fill_(0).to(device) ima...
# model being run x, # model input (or a tuple for multiple inputs) "super_resolution.onnx", # where to save the model (can be a file or file-like object) export_params=True, # store the
onnx_path=f'./weights/yolop-{height}-{width}.onnx' inputs=torch.randn(1,3,height,width) print(f"Converting to{onnx_path}") torch.onnx.export(model,inputs,onnx_path, verbose=False,opset_version=12,input_names=['images'], output_names=['det_out','drive_area_seg','lane_line_se...
x, # model input (or a tuple for multiple inputs) "super_resolution.onnx", # where to save the model (can be a file or file-like object) export_params=True, # store the trained parameter weights inside the model file opset_version=10, # the ONNX version to export the model to d...
You can export a trained MATLAB deep learning network that includes multiple inputs and multiple outputs to the ONNX model format. To learn about a multiple-input and multiple-output deep learning network, seeMultiple-Input and Multiple-Output Networks. ...
inputs, outputs, bindings, stream = allocate_buffers(engine) # input, output: host # bindings # Do inference shape_of_output = (max_batch_size, 1000) # Load data to the buffer inputs[0].host = img_np_nchw.reshape(-1) # inputs[1].host = ... for multiple input ...
torch.onnx.export(model, input, 'model.onnx', input_names=input_name, output_names=output_name, verbose=True) 其中 #model = x.model.cuda() #若是不添加cuda() model = x.model 出现报错 RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the...
if graph_input not in arg_params and graph_input not in aux_params and graph_input != output_label] data_shapes = [] # Adding extra dimension of batch_size 1 if the batch_size is different for multiple inputs. for idx, input_name in enumerate(data_names): data_shapes.append((input...
torch.onnx.export(torch_model, # model being run x, # model input (or a tuple for multiple inputs)"super_resolution.onnx", # where to save the model (can be a file or file-like object)export_params=True, # store the trained parameter weights inside the model file opset...
# Input to the modelx = torch.randn(batch_size, 1, 224, 224, requires_grad=True)torch_out = torch_model(x)# Export the modeltorch.onnx.export(torch_model, # model being run x, # model input (or a tuple for multiple inputs) "super_resolution.onnx", # where to save the model...