CREStereo Repository for the 'Towards accurate and robust depth estimation' project
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CREStereo-pytorch-nxt/convert_to_onnx.py

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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'])