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42 lines
1.9 KiB
42 lines
1.9 KiB
import torch
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def get_tensor(x):
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if isinstance(x, torch.Tensor):
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return x
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if hasattr(x, 'parameters') and len(list(x.parameters())) > 0:
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return list(x.parameters())[0]
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if callable(x):
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try:
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out = x()
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if isinstance(out, torch.Tensor):
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return out
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except Exception:
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pass
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raise RuntimeError('Could not extract tensor')
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def compare_tensors(py_path, cpp_path, label):
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a = torch.load(py_path)
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b = torch.load(cpp_path, weights_only=False)
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b = get_tensor(b)
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print(f'--- {label} ---')
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print('py:', a.shape, a.dtype, a.min().item(), a.max().item(), a.mean().item())
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print('cpp:', b.shape, b.dtype, b.min().item(), b.max().item(), b.mean().item())
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print('diff:', (a-b).abs().max().item(), (a-b).abs().mean().item())
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compare_tensors('test/output_py/resnet_py/sample_0/conv1_output.pt', 'test/output/resnet/sample_0_conv1_output.pt', 'conv1_output')
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compare_tensors('test/output_py/resnet_py/sample_0/debug_resnet_conv1_output_for_bn1_input.pt', 'test/output/resnet/sample_0_debug_resnet_conv1_output_for_bn1_input.pt', 'debug_resnet_conv1_output_for_bn1_input')
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compare_tensors('test/output_py/resnet_py/sample_0/bn1_output.pt', 'test/output/resnet/sample_0_bn1_output.pt', 'bn1_output')
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# Print BN1 epsilon and momentum from Python model
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try:
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from ltr.models.backbone import resnet50
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model = resnet50(output_layers=['layer1', 'layer2', 'layer3', 'layer4'], pretrained=False)
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model.load_state_dict(torch.load('backbone_pure_tensors/state_dict.pt'))
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print('\nPython BN1 epsilon:', model.bn1.eps)
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print('Python BN1 momentum:', model.bn1.momentum)
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except Exception as e:
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print('Could not print Python BN1 eps/momentum:', e)
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# Print expected C++ BN1 epsilon and momentum (from code)
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print('C++ BN1 epsilon: 1e-5 (from C++ code)')
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print('C++ BN1 momentum: 0.1 (from C++ code)')
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