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MoCo/SimCLR transforms update #1357

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merged 5 commits into from
May 23, 2023
Merged

MoCo/SimCLR transforms update #1357

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e7e648e
fix randomgrayscale device bug
isaaccorley May 22, 2023
3092ce0
add random brightness and contrast augs to moco
isaaccorley May 22, 2023
1b6e249
update simclr
isaaccorley May 22, 2023
70ddb79
update augmentation values
isaaccorley May 23, 2023
19a2611
kornia 0.6.9+ required for RandomBrightness
isaaccorley May 23, 2023
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8 changes: 8 additions & 0 deletions torchgeo/trainers/moco.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,6 +62,8 @@ def moco_augmentations(
T.RandomGrayscale(weights=weights, p=0.2),
# Not appropriate for multispectral imagery, seasonal contrast used instead
# K.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.4, p=1)
K.RandomBrightness(brightness=(0.8, 1.0), p=0.8),
K.RandomContrast(contrast=(0.8, 1.0), p=0.8),
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K.RandomHorizontalFlip(),
K.RandomVerticalFlip(), # added
data_keys=["input"],
Expand All @@ -74,6 +76,8 @@ def moco_augmentations(
# K.ColorJitter(
# brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1, p=0.8
# )
K.RandomBrightness(brightness=(0.8, 1.0), p=0.8),
K.RandomContrast(contrast=(0.8, 1.0), p=0.8),
T.RandomGrayscale(weights=weights, p=0.2),
K.RandomGaussianBlur(kernel_size=(ks, ks), sigma=(0.1, 2), p=0.5),
K.RandomHorizontalFlip(),
Expand All @@ -88,6 +92,8 @@ def moco_augmentations(
# K.ColorJitter(
# brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1, p=0.8
# )
K.RandomBrightness(brightness=(0.8, 1.0), p=0.8),
K.RandomContrast(contrast=(0.8, 1.0), p=0.8),
T.RandomGrayscale(weights=weights, p=0.2),
K.RandomGaussianBlur(kernel_size=(ks, ks), sigma=(0.1, 2), p=1),
K.RandomHorizontalFlip(),
Expand All @@ -100,6 +106,8 @@ def moco_augmentations(
# K.ColorJitter(
# brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1, p=0.8
# )
K.RandomBrightness(brightness=(0.8, 1.0), p=0.8),
K.RandomContrast(contrast=(0.8, 1.0), p=0.8),
T.RandomGrayscale(weights=weights, p=0.2),
K.RandomGaussianBlur(kernel_size=(ks, ks), sigma=(0.1, 2), p=0.1),
K.RandomSolarize(p=0.2),
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2 changes: 2 additions & 0 deletions torchgeo/trainers/simclr.py
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Expand Up @@ -49,6 +49,8 @@ def simclr_augmentations(size: int, weights: Tensor) -> nn.Module:
K.RandomVerticalFlip(), # added
# Not appropriate for multispectral imagery, seasonal contrast used instead
# K.ColorJitter(brightness=0.8, contrast=0.8, saturation=0.8, hue=0.2, p=0.8)
K.RandomBrightness(brightness=(0.8, 1.0), p=0.8),
K.RandomContrast(contrast=(0.8, 1.0), p=0.8),
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T.RandomGrayscale(weights=weights, p=0.2),
K.RandomGaussianBlur(kernel_size=(ks, ks), sigma=(0.1, 2)),
data_keys=["input"],
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2 changes: 1 addition & 1 deletion torchgeo/transforms/color.py
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Expand Up @@ -70,7 +70,7 @@ def apply_transform(
Returns:
The augmented input.
"""
weights = flags["weights"][..., :, None, None]
weights = flags["weights"][..., :, None, None].to(input.device)
out = input * weights
out = out.sum(dim=-3)
out = out.unsqueeze(-3).expand(input.shape)
Expand Down