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import torch
from torchvision import transforms
from torchvision.transforms import Compose
from PIL import Image
class ToTensor(transforms.ToTensor):
def __call__(self, input):
if not isinstance(input, dict):
return super().__call__(input)
assert 'image' in input
input['image'] = super().__call__(input['image'])
return input
class Normalize(transforms.Normalize):
def __call__(self, input):
if not isinstance(input, dict):
return super().__call__(input)
assert 'image' in input
input['image'] = super().__call__(input['image'])
return input
class NormalizeBoxCoords(transforms.ToTensor):
def __call__(self, input):
if not isinstance(input, dict):
return super().__call__(input)
assert 'image' in input and 'bbox' in input
_, H, W = input['image'].size()
input['bbox'][:, (0, 2)] /= W
input['bbox'][:, (1, 3)] /= H
if 'tr_param' not in input:
input['tr_param'] = []
input['tr_param'].append({'normalize_box_coords': (H, W)})
return input
class SquarePad(torch.nn.Module):
def __call__(self, input):
if isinstance(input, Image.Image):
raise NotImplementedError('put the SquarePad transform after ToTensor')
assert 'image' in input
_, h, w = input['image'].size()
max_wh = max(w, h)
xp = int(0.5 * (max_wh - w))
yp = int(0.5 * (max_wh - h))
padding = (xp, yp, (max_wh-xp)-w, (max_wh-yp)-h)
input['image'] = transforms.functional.pad(
input['image'], padding, fill=0, padding_mode='constant'
)
# input['image'] = transforms.functional.pad(
# input['image'], padding, padding_mode='edge'
# )
if 'mask' in input:
input['mask'] = transforms.functional.pad(
input['mask'], padding, fill=0, padding_mode='constant'
)
if 'bbox' in input:
input['bbox'][:, (0, 2)] += xp
input['bbox'][:, (1, 3)] += yp
if 'tr_param' not in input:
input['tr_param'] = []
input['tr_param'].append({'square_pad': padding})
return input
class Resize(transforms.Resize):
def __call__(self, input):
if not isinstance(input, dict):
return super().__call__(input)
assert 'image' in input
if not torch.is_tensor(input['image']):
raise NotImplementedError('put the Resize transform after ToTensor')
_, img_h, img_w = input['image'].size()
if isinstance(self.size, int):
dst_h = self.size if img_h < img_w else int(self.size * img_h / img_w)
dst_w = self.size if img_w < img_h else int(self.size * img_w / img_h)
else:
dst_h, dst_w = self.size
input['image'] = super().__call__(input['image'])
if 'mask' in input:
input['mask'] = super().__call__(input['mask'])
sx, sy = dst_w / img_w, dst_h / img_h
if 'bbox' in input:
input['bbox'][:, (0, 2)] *= sx
input['bbox'][:, (1, 3)] *= sy
if 'tr_param' not in input:
input['tr_param'] = []
input['tr_param'].append({'resize': (sx, sy)})
return input
class RandomHorizontalFlip(transforms.RandomHorizontalFlip):
def __call__(self, input):
if not isinstance(input, dict):
return super().__call__(input)
assert 'image' in input
if not torch.is_tensor(input['image']):
raise NotImplementedError('use Resize after ToTensor')
result = super().__call__(input['image'])
if result is input['image']: # not flipped
return input
input['image'] = result
if 'mask' in input:
input['mask'] = torch.flip(input['mask'], dims=(-1,))
img_w = input['image'].size(2)
if 'bbox' in input:
input['bbox'][:, (0, 2)] = img_w - input['bbox'][:, (2, 0)]
if 'expr' in input:
input['expr'] = input['expr'].replace('left', '<LEFT>').replace('right', 'left').replace('<LEFT>', 'right')
return input
class RandomAffine(transforms.RandomAffine):
def get_params(self, *args, **kwargs):
self.params = super().get_params(*args, **kwargs)
return self.params
def __call__(self, input):
if not isinstance(input, dict):
return super().__call__(input)
assert 'image' in input
if not torch.is_tensor(input['image']):
raise NotImplementedError('put the Resize transform after ToTensor')
#self.fill = input['image'].mean((1,2)) # set fill value to the mean pixel value
result = super().__call__(input['image'])
if result is input['image']: # not transformed
return input
input['image'] = result
_, img_h, img_w = input['image'].size()
angle, translate, scale, shear = self.params
center = (img_w * 0.5, img_h * 0.5)
matrix = transforms.functional._get_inverse_affine_matrix(center, angle, translate, scale, shear)
matrix = torch.FloatTensor([matrix[:3], matrix[3:], [0, 0, 1]])
matrix = torch.linalg.inv(matrix)
if 'mask' in input:
input['mask'] = transforms.functional.affine(
input['mask'], *self.params, self.interpolation, self.fill
)
if 'bbox' in input:
for i, (x1, y1, x2, y2) in enumerate(input['bbox']):
pt = matrix @ torch.FloatTensor([
[x1, y1, 1],
[x2, y1, 1],
[x2, y2, 1],
[x1, y2, 1]
]).T
x_min, y_min, _ = pt.min(dim=1).values
x_max, y_max, _ = pt.max(dim=1).values
input['bbox'][i, :] = torch.FloatTensor([x_min, y_min, x_max, y_max])
# if 'tr_param' not in input:
# input['tr_param'] = []
# input['tr_param'].append({'random_affine': matrix[:2, :].tolist()})
return input
class ColorJitter(transforms.ColorJitter):
def __call__(self, input):
if not isinstance(input, dict):
return super().__call__(input)
assert 'image' in input
input['image'] = super().__call__(input['image'])
return input
def get_transform(split, input_size=512):
mean = [0.485, 0.456, 0.406]
sdev = [0.229, 0.224, 0.225]
if split in ('train', 'trainval'):
transform = Compose([
# ColorJitter(brightness=0.5, saturation=0.5), # before normalization
ToTensor(),
Normalize(mean, sdev), # first normalize so that the mean is ~0
SquarePad(), # zero pad (approx mean pixel value)
Resize(size=(input_size, input_size)),
# RandomHorizontalFlip(p=0.5),
RandomAffine(degrees=5, translate=(0.1, 0.1), scale=(0.9, 1.1)),
NormalizeBoxCoords(),
])
elif split in ('val', 'test', 'testA', 'testB', 'testC'):
transform = Compose([
ToTensor(),
Normalize(mean, sdev),
SquarePad(),
Resize(size=(input_size, input_size)),
NormalizeBoxCoords(),
])
elif split in ('visu',):
transform = Compose([
ToTensor(),
SquarePad(),
Resize(size=(input_size, input_size)),
NormalizeBoxCoords(),
])
else:
raise ValueError(f'\'{split}\' is not a valid data split')
return transform
def denormalize(img):
mean = [0.485, 0.456, 0.406]
sdev = [0.229, 0.224, 0.225]
return Normalize(
mean=[-m/s for m, s in zip(mean, sdev)], std=[1./s for s in sdev]
)(img)
def undo_box_transforms(bbox, tr_param):
# undo validation mode transformations
bbox = bbox.clone()
for tr in tr_param[::-1]:
if 'resize' in tr:
sx, sy = tr['resize']
bbox[:, (0, 2)] /= sx
bbox[:, (1, 3)] /= sy
elif 'square_pad' in tr:
px, py, _, _ = tr['square_pad']
bbox[:, (0, 2)] -= px
bbox[:, (1, 3)] -= py
elif 'normalize_box_coords' in tr:
img_h, img_w = tr['normalize_box_coords']
bbox[:, (0, 2)] *= img_w
bbox[:, (1, 3)] *= img_h
else:
continue
return bbox
def undo_box_transforms_batch(bbox, tr_param):
output = []
for i in range(bbox.size(0)):
bb = undo_box_transforms(torch.atleast_2d(bbox[i]), tr_param[i])
output.append(bb)
return torch.cat(output, dim=0)
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