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Remove unnecessary input image padding #139

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Remove unnecessary input image padding #139

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4 changes: 2 additions & 2 deletions main_test_swinir.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,8 +67,8 @@ def main():
with torch.no_grad():
# pad input image to be a multiple of window_size
_, _, h_old, w_old = img_lq.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
h_pad = (window_size - h_old % window_size) % window_size
w_pad = (window_size - w_old % window_size) % window_size
img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, :h_old + h_pad, :]
img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[:, :, :, :w_old + w_pad]
output = test(img_lq, model, args, window_size)
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4 changes: 2 additions & 2 deletions predict.py
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Original file line number Diff line number Diff line change
Expand Up @@ -129,8 +129,8 @@ def predict(self, image, task_type='Real-World Image Super-Resolution', jpeg=40,
with torch.no_grad():
# pad input image to be a multiple of window_size
_, _, h_old, w_old = img_lq.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
h_pad = (window_size - h_old % window_size) % window_size
w_pad = (window_size - w_old % window_size) % window_size
img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, :h_old + h_pad, :]
img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[:, :, :, :w_old + w_pad]
output = model(img_lq)
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