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Original file line number | Diff line number | Diff line change |
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@@ -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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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)] | ||
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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()] | ||
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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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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) | ||
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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}") | ||
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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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