def get_transform(train, args):
if train:
return presets.DetectionPresetTrain(args.data_augmentation)
elif not args.weights:
return presets.DetectionPresetEval()
else:
weights = PM.get_weight(args.weights)
return weights.transforms()
def get_transform(train, args):
if train:
return presets.DetectionPresetTrain(data_augmentation=args.data_augmentation)
elif args.weights and args.test_only:
weights = torchvision.models.get_weight(args.weights)
trans = weights.transforms()
return lambda img, target: (trans(img), target)
else:
return presets.DetectionPresetEval()
def get_transform(train, args):
if train:
return presets.DetectionPresetTrain(args.data_augmentation)
elif not args.prototype:
return presets.DetectionPresetEval()
else:
if args.weights:
weights = prototype.models.get_weight(args.weights)
return weights.transforms()
else:
return prototype.transforms.CocoEval()
def get_transform(train):
return presets.DetectionPresetTrain(
) if train else presets.DetectionPresetEval()
def get_transform(train, data_augmentation):
return presets.DetectionPresetTrain(
data_augmentation) if train else presets.DetectionPresetEval()