adaptive cropping

[nanmehta]

thanks for the wonderful concept.
can u please let me know exactly where in the code is this strategy of adaptive cropping

[Zheng222]

@nan-rock

IMDN/test_IMDN_AS.py

Lines 30 to 60 in f1170cf

	def crop_forward(x, model, shave=32):
	b, c, h, w = x.size()
	h_half, w_half = h // 2, w // 2
	h_size, w_size = h_half + shave - (h_half + shave) % 4, w_half + shave - (w_half + shave) % 4
	inputlist = [
	x[:, :, 0:h_size, 0:w_size],
	x[:, :, 0:h_size, (w - w_size):w],
	x[:, :, (h - h_size):h, 0:w_size],
	x[:, :, (h - h_size):h, (w - w_size):w]]
	outputlist = []
	with torch.no_grad():
	input_batch = torch.cat(inputlist, dim=0)
	output_batch = model(input_batch)
	outputlist.extend(output_batch.chunk(4, dim=0))
	output = torch.zeros_like(x)
	output[:, :, 0:h_half, 0:w_half] \
	= outputlist[0][:, :, 0:h_half, 0:w_half]
	output[:, :, 0:h_half, w_half:w] \
	= outputlist[1][:, :, 0:h_half, (w_size - w + w_half):w_size]
	output[:, :, h_half:h, 0:w_half] \
	= outputlist[2][:, :, (h_size - h + h_half):h_size, 0:w_half]
	output[:, :, h_half:h, w_half:w] \
	= outputlist[3][:, :, (h_size - h + h_half):h_size, (w_size - w + w_half):w_size]
	return output

[nanmehta]

thanks for the quick response.
i just wanted to ask as you mentioned in issue #9 regarding adaptive cropping tha it allows us to output exactly the same size as the target size. But does it make a difference in the overall result. As in the example you gave the output using this strategy is (2801x 2001) instead of 2800 X2000. But even we ignore 1 pixel from the boundary it wont make much difference

[EvelyneCalista]

Hello, may I ask if I would like to use ACS, shall I modify the train code with the crop_forward too?
thank you.