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47 lines
1.9 KiB
47 lines
1.9 KiB
import torch
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import torch.nn.functional as F
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import numpy as np
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import cv2
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from imread_from_url import imread_from_url
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from nets import Model
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if __name__ == '__main__':
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model_path = "models/crestereo_eth3d.pth"
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model = Model(max_disp=256, mixed_precision=False, test_mode=True)
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model.load_state_dict(torch.load(model_path), strict=True)
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model.eval()
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in_h, in_w = (480, 640)
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t1_half = torch.rand(1, 3, in_h//2, in_w//2)
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t2_half = torch.rand(1, 3, in_h//2, in_w//2)
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t1 = torch.rand(1, 3, in_h, in_w)
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t2 = torch.rand(1, 3, in_h, in_w)
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flow_init = torch.rand(1, 2, in_h//2, in_w)
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# Export the model
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# !! Needs Pytorch nightly until next release (1.12). Ref: https://github.com/pytorch/pytorch/pull/73760
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torch.onnx.export(model,
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(t1_half, t2_half),
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"crestereo_without_flow.onnx", # where to save the model (can be a file or file-like object)
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export_params=True, # store the trained parameter weights inside the model file
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opset_version=12, # the ONNX version to export the model to
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do_constant_folding=True, # whether to execute constant folding for optimization
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input_names = ['left', 'right'], # the model's input names
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output_names = ['output'])
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# Export the model
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torch.onnx.export(model,
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(t1, t2, flow_init),
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"crestereo.onnx", # where to save the model (can be a file or file-like object)
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export_params=True, # store the trained parameter weights inside the model file
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opset_version=12, # the ONNX version to export the model to
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do_constant_folding=True, # whether to execute constant folding for optimization
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input_names = ['left', 'right','flow_init'], # the model's input names
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output_names = ['output'])
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