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26 lines
614 B
26 lines
614 B
import copy
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import torch
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import numpy as np
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import megengine as mge
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from nets import Model
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# Read Megengine parameters
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pretrained_dict = mge.load("models/crestereo_eth3d.mge")
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model = Model(max_disp=256, mixed_precision=False, test_mode=True)
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model.eval()
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state_dict = model.state_dict()
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for key, value in pretrained_dict['state_dict'].items():
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print(f"Converting {key}")
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# Fix shape mismatch
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if value.shape[0] == 1:
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value = np.squeeze(value)
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state_dict[key] = torch.tensor(value)
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output_path = "models/crestereo_eth3d.pth"
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torch.save(state_dict, output_path)
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print(f"\nModel saved to: {output_path}") |