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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,39 +1,106 @@ | ||
| from collections import defaultdict | ||
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| import torch | ||
| import transforms as T | ||
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| def get_modules(use_v2): | ||
| # We need a protected import to avoid the V2 warning in case just V1 is used | ||
| if use_v2: | ||
| import torchvision.datapoints | ||
| import torchvision.transforms.v2 | ||
| import v2_extras | ||
|
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| return torchvision.transforms.v2, torchvision.datapoints, v2_extras | ||
| else: | ||
| import transforms | ||
|
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| return transforms, None, None | ||
|
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|
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| class SegmentationPresetTrain: | ||
| def __init__(self, *, base_size, crop_size, hflip_prob=0.5, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): | ||
| min_size = int(0.5 * base_size) | ||
| max_size = int(2.0 * base_size) | ||
| def __init__( | ||
| self, | ||
| *, | ||
| base_size, | ||
| crop_size, | ||
| hflip_prob=0.5, | ||
| mean=(0.485, 0.456, 0.406), | ||
| std=(0.229, 0.224, 0.225), | ||
| backend="pil", | ||
| use_v2=False, | ||
| ): | ||
| T, datapoints, v2_extras = get_modules(use_v2) | ||
|
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||
| transforms = [] | ||
| backend = backend.lower() | ||
| if backend == "datapoint": | ||
| transforms.append(T.ToImageTensor()) | ||
| elif backend == "tensor": | ||
| transforms.append(T.PILToTensor()) | ||
| elif backend != "pil": | ||
| raise ValueError(f"backend can be 'datapoint', 'tensor' or 'pil', but got {backend}") | ||
|
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| transforms += [T.RandomResize(min_size=int(0.5 * base_size), max_size=int(2.0 * base_size))] | ||
|
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| trans = [T.RandomResize(min_size, max_size)] | ||
| if hflip_prob > 0: | ||
| trans.append(T.RandomHorizontalFlip(hflip_prob)) | ||
| trans.extend( | ||
| [ | ||
| T.RandomCrop(crop_size), | ||
| T.PILToTensor(), | ||
| T.ConvertImageDtype(torch.float), | ||
| T.Normalize(mean=mean, std=std), | ||
| transforms += [T.RandomHorizontalFlip(hflip_prob)] | ||
|
|
||
| if use_v2: | ||
| # We need a custom pad transform here, since the padding we want to perform here is fundamentally | ||
| # different from the padding in `RandomCrop` if `pad_if_needed=True`. | ||
| transforms += [v2_extras.PadIfSmaller(crop_size, fill=defaultdict(lambda: 0, {datapoints.Mask: 255}))] | ||
|
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| transforms += [T.RandomCrop(crop_size)] | ||
|
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| if backend == "pil": | ||
| transforms += [T.PILToTensor()] | ||
|
|
||
| if use_v2: | ||
| img_type = datapoints.Image if backend == "datapoint" else torch.Tensor | ||
| transforms += [ | ||
| T.ToDtype(dtype={img_type: torch.float32, datapoints.Mask: torch.int64, "others": None}, scale=True) | ||
| ] | ||
| ) | ||
| self.transforms = T.Compose(trans) | ||
| else: | ||
| # No need to explicitly convert masks as they're magically int64 already | ||
| transforms += [T.ConvertImageDtype(torch.float)] | ||
|
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| transforms += [T.Normalize(mean=mean, std=std)] | ||
|
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| self.transforms = T.Compose(transforms) | ||
|
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| def __call__(self, img, target): | ||
| return self.transforms(img, target) | ||
|
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|
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| class SegmentationPresetEval: | ||
| def __init__(self, *, base_size, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): | ||
| self.transforms = T.Compose( | ||
| [ | ||
| T.RandomResize(base_size, base_size), | ||
| T.PILToTensor(), | ||
| T.ConvertImageDtype(torch.float), | ||
| T.Normalize(mean=mean, std=std), | ||
| ] | ||
| ) | ||
| def __init__( | ||
| self, *, base_size, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), backend="pil", use_v2=False | ||
| ): | ||
| T, _, _ = get_modules(use_v2) | ||
|
|
||
| transforms = [] | ||
| backend = backend.lower() | ||
| if backend == "tensor": | ||
| transforms += [T.PILToTensor()] | ||
| elif backend == "datapoint": | ||
| transforms += [T.ToImageTensor()] | ||
| elif backend != "pil": | ||
| raise ValueError(f"backend can be 'datapoint', 'tensor' or 'pil', but got {backend}") | ||
|
|
||
| if use_v2: | ||
| transforms += [T.Resize(size=(base_size, base_size))] | ||
| else: | ||
| transforms += [T.RandomResize(min_size=base_size, max_size=base_size)] | ||
|
|
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| if backend == "pil": | ||
| # Note: we could just convert to pure tensors even in v2? | ||
| transforms += [T.ToImageTensor() if use_v2 else T.PILToTensor()] | ||
|
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||
| transforms += [ | ||
| T.ConvertImageDtype(torch.float), | ||
| T.Normalize(mean=mean, std=std), | ||
| ] | ||
| self.transforms = T.Compose(transforms) | ||
|
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||
| def __call__(self, img, target): | ||
| return self.transforms(img, target) |
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