import torch import torch.nn.functional as F import numpy as np import cv2 from imread_from_url import imread_from_url from nets import Model if __name__ == '__main__': model_path = "models/crestereo_eth3d.pth" model = Model(max_disp=256, mixed_precision=False, test_mode=True) model.load_state_dict(torch.load(model_path), strict=True) model.eval() in_h, in_w = (480, 640) t1_half = torch.rand(1, 3, in_h//2, in_w//2) t2_half = torch.rand(1, 3, in_h//2, in_w//2) t1 = torch.rand(1, 3, in_h, in_w) t2 = torch.rand(1, 3, in_h, in_w) flow_init = torch.rand(1, 2, in_h//2, in_w//2) # Export the model torch.onnx.export(model, (t1, t2, flow_init), "crestereo.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=12, # the ONNX version to export the model to do_constant_folding=True, # whether to execute constant folding for optimization input_names = ['left', 'right','flow_init'], # the model's input names output_names = ['output']) # # Export the model without init_flow (it takes a lot of time) # # !! Needs Pytorch nightly until next release (1.12). Ref: https://github.com/pytorch/pytorch/pull/73760 # torch.onnx.export(model, # (t1_half, t2_half), # "crestereo_without_flow.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=12, # the ONNX version to export the model to # do_constant_folding=True, # whether to execute constant folding for optimization # input_names = ['left', 'right'], # the model's input names # output_names = ['output'])