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Unverified Commit b572d5e6 authored by vfdev's avatar vfdev Committed by GitHub
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Fixed torch randint incoherent sampling (compatible to random.randint) (#2401)

parent 86b6c3e2
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Showing with 4 additions and 4 deletions
torchvision/transforms/transforms.py
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...@@ -510,8 +510,8 @@ class RandomCrop(torch.nn.Module): ...@@ -510,8 +510,8 @@ class RandomCrop(torch.nn.Module):
if w == tw and h == th: if w == tw and h == th:
return 0, 0, h, w return 0, 0, h, w
i = torch.randint(0, h - th, size=(1, )).item() i = torch.randint(0, h - th + 1, size=(1, )).item()
j = torch.randint(0, w - tw, size=(1, )).item() j = torch.randint(0, w - tw + 1, size=(1, )).item()
return i, j, th, tw return i, j, th, tw
def __init__(self, size, padding=None, pad_if_needed=False, fill=0, padding_mode="constant"): def __init__(self, size, padding=None, pad_if_needed=False, fill=0, padding_mode="constant"):
...@@ -1433,8 +1433,8 @@ class RandomErasing(torch.nn.Module): ...@@ -1433,8 +1433,8 @@ class RandomErasing(torch.nn.Module):
else: else:
v = torch.tensor(value)[:, None, None] v = torch.tensor(value)[:, None, None]
i = torch.randint(0, img_h - h, size=(1, )).item() i = torch.randint(0, img_h - h + 1, size=(1, )).item()
j = torch.randint(0, img_w - w, size=(1, )).item() j = torch.randint(0, img_w - w + 1, size=(1, )).item()
return i, j, h, w, v return i, j, h, w, v
# Return original image # Return original image
......
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