Normalization in VGG preprocess

[QiuJueqin]

Hi, as stated in torchvision page, the input to torchvision's pretrained VGG should be RGB format and normalized by mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].

However in your code, RGB image is converted to BGR format and normalized by mean=[103.939, 116.779, 123.680] and std=[1.0, 1.0, 1.0]:

HEP/utils.py

Lines 219 to 229 in c0188bb

	def vgg_preprocess(batch):
	tensor_type = type(batch.data)
	(r, g, b) = torch.chunk(batch, 3, dim=1)
	batch = torch.cat((b, g, r), dim=1) # convert RGB to BGR
	batch = batch * 255 # * 0.5 [-1, 1] -> [0, 255]
	mean = tensor_type(batch.data.size()).cuda()
	mean[:, 0, :, :] = 103.939
	mean[:, 1, :, :] = 116.779
	mean[:, 2, :, :] = 123.680
	batch = batch.sub(Variable(mean)) # subtract mean
	return batch

Should this be fixed?

[fengzhang427]

It‘s just an empirical trick, you can try other normalization methods.