Thank you for opening the source code. Can you provide the code of model inference? My

How to inference about pyramidflow HOT 2 CLOSED

xcaizewu commented on June 2, 2024

How to inference

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Comments (2)

gasharper commented on June 2, 2024

The inference process is the same as the evaluation process.
You can refer to the following code in trian.py:

PyramidFlow/train.py

Lines 59 to 80 in c463b1c

 # val for template  

 flow.eval() 

 feat_sum, cnt = [0 for _ in range(num_layer)], 0 

 for val_dict in val_loader: 

 image = val_dict['images'].to(device) 

 with torch.no_grad(): 

 pyramid2= flow(image) 

 cnt += 1 

 feat_sum = [p0+p for p0, p in zip(feat_sum, pyramid2)] 

 feat_mean = [p/cnt for p in feat_sum] 

 # test 

 flow.eval() 

 diff_list, labels_list = [], [] 

 for test_dict in test_loader: 

 image, labels = test_dict['images'].to(device), test_dict['labels'] 

 with torch.no_grad(): 

 pyramid2 = flow(image) 

 pyramid_diff = [(feat2 - template).abs() for feat2, template in zip(pyramid2, feat_mean)] 

 diff = flow.pyramid.compose_pyramid(pyramid_diff).mean(1, keepdim=True)# b,1,h,w 

 diff_list.append(diff.cpu()) 

 labels_list.append(labels.cpu()==1)# b,1,h,w

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pribadihcr commented on June 2, 2024

I think we need the inference demo, start from how to load the trained model and infer the test image.

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	# val for template
	flow.eval()
	feat_sum, cnt = [0 for _ in range(num_layer)], 0
	for val_dict in val_loader:
	image = val_dict['images'].to(device)
	with torch.no_grad():
	pyramid2= flow(image)
	cnt += 1
	feat_sum = [p0+p for p0, p in zip(feat_sum, pyramid2)]
	feat_mean = [p/cnt for p in feat_sum]

	# test
	flow.eval()
	diff_list, labels_list = [], []
	for test_dict in test_loader:
	image, labels = test_dict['images'].to(device), test_dict['labels']
	with torch.no_grad():
	pyramid2 = flow(image)
	pyramid_diff = [(feat2 - template).abs() for feat2, template in zip(pyramid2, feat_mean)]
	diff = flow.pyramid.compose_pyramid(pyramid_diff).mean(1, keepdim=True)# b,1,h,w
	diff_list.append(diff.cpu())
	labels_list.append(labels.cpu()==1)# b,1,h,w

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