def make_coco_transforms(image_set):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
#original_scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
#scales = [int(s * 0.5) for s in original_scales]
scales = list(range(640 // 5, 640, 32))
if image_set == 'train':
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomSelect(
T.RandomResize(scales, max_size=640), # Originally: 1333.
T.Compose([
T.RandomResize([400, 500,
600]), # Originally: [400, 500, 600].
T.RandomSizeCrop(384, 600), # Originally: 384, 600.
T.RandomResize(scales, max_size=640), # Originally: 1333.
])),
normalize,
])
if image_set == 'val':
return T.Compose([
T.RandomResize([640], max_size=640), # Originally: [800], 1333.
normalize,
])
raise ValueError(f'unknown {image_set}')
def make_coco_transforms(image_set):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
if image_set == 'train':
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomSelect(
T.RandomResize(scales, max_size=1333),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=1333),
])
),
normalize,
])
if image_set == 'val':
return T.Compose([
T.RandomResize([800], max_size=1333),
normalize,
])
raise ValueError(f'unknown {image_set}')