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import torch
import transforms as T
class DetectionPresetTrain:
def __init__(self, *, data_augmentation, hflip_prob=0.5, mean=(123.0, 117.0, 104.0)):
if data_augmentation == "hflip":
self.transforms = T.Compose(
[
T.RandomHorizontalFlip(p=hflip_prob),
T.PILToTensor(),
T.ConvertImageDtype(torch.float),
]
)
elif data_augmentation == "lsj":
self.transforms = T.Compose(
[
T.ScaleJitter(target_size=(1024, 1024)),
T.FixedSizeCrop(size=(1024, 1024), fill=mean),
T.RandomHorizontalFlip(p=hflip_prob),
T.PILToTensor(),
T.ConvertImageDtype(torch.float),
]
)
elif data_augmentation == "multiscale":
self.transforms = T.Compose(
[
T.RandomShortestSize(
min_size=(480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800), max_size=1333
),
T.RandomHorizontalFlip(p=hflip_prob),
T.PILToTensor(),
T.ConvertImageDtype(torch.float),
]
)
elif data_augmentation == "ssd":
self.transforms = T.Compose(
[
T.RandomPhotometricDistort(),
T.RandomZoomOut(fill=list(mean)),
T.RandomIoUCrop(),
T.RandomHorizontalFlip(p=hflip_prob),
T.PILToTensor(),
T.ConvertImageDtype(torch.float),
]
)
elif data_augmentation == "ssdlite":
self.transforms = T.Compose(
[
T.RandomIoUCrop(),
T.RandomHorizontalFlip(p=hflip_prob),
T.PILToTensor(),
T.ConvertImageDtype(torch.float),
]
)
else:
raise ValueError(f'Unknown data augmentation policy "{data_augmentation}"')
def __call__(self, img, target):
return self.transforms(img, target)
class DetectionPresetEval:
def __init__(self):
self.transforms = T.Compose(
[
T.PILToTensor(),
T.ConvertImageDtype(torch.float),
]
)
def __call__(self, img, target):
return self.transforms(img, target)
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