def transform_tr(self, sample):
# if (sample['image'].width>self.args.base_size*2) and (sample['image'].height>self.args.base_size*2):
# composed_transforms = transforms.Compose([
# tr.RandomHorizontalFlip(),
# tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size),
# tr.RandomGaussianBlur(),
# tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
# tr.ToTensor()])
# else:
# composed_transforms = transforms.Compose([
# # tr.FixScaleCrop(crop_size=self.args.crop_size),
# tr.RandomHorizontalFlip(),
# tr.RandomGaussianBlur(),
# tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
# tr.ToTensor()])
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.args.base_size,
crop_size=self.args.crop_size),
tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_tr(self, sample):
"""Transformations for images
sample: {image:img, annotation:ann}
Note: the mean and std is from imagenet
"""
if self.args.no_flip:
composed_transforms = transforms.Compose([
tr.RandomScaleCrop(base_size=self.args.base_size,
crop_size=self.args.crop_size,
scale_ratio=self.args.scale_ratio,
fill=0),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)
else:
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.args.base_size,
crop_size=self.args.crop_size,
scale_ratio=self.args.scale_ratio,
fill=0),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()])
return composed_transforms(sample)
def create_transforms(relax_crop, zero_crop):
# Preparation of the data loaders
first = [
tr.CropFromMask(crop_elems=('image', 'gt'),
relax=relax_crop,
zero_pad=zero_crop),
tr.FixedResize(resolutions={
'crop_image': (512, 512),
'crop_gt': (512, 512)
})
]
second = [
tr.ToImage(norm_elem='extreme_points'),
tr.ConcatInputs(elems=('crop_image', 'extreme_points')),
tr.ToTensor()
]
train_tf = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.ScaleNRotate(rots=(-20, 20), scales=(.75, 1.25)), *first,
tr.ExtremePoints(sigma=10, pert=5, elem='crop_gt'), *second
])
test_tf = transforms.Compose(
[*first,
tr.ExtremePoints(sigma=10, pert=0, elem='crop_gt'), *second])
return train_tf, test_tf
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size),
tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225)),
tr.ToTensor()])
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
#tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255),
#tr.FixScaleCrop(crop_size=self.args.crop_size),
#tr.FixedResize(size=self.args.crop_size),
#tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))
])
img = composed_transforms(sample)
data = img['image']
label = img['label']
p = np.random.rand(1)[0]
if p < 0.25:
data = np.rot90(data, 1, (0, 1)).copy()
label = np.rot90(label, 1, (0, 1)).copy()
elif p >= 0.25 and p < 0.5:
data = np.rot90(data, 2, (0, 1)).copy()
label = np.rot90(label, 2, (0, 1)).copy()
elif p >= 0.5 and p < 0.75:
data = np.rot90(data, 3, (0, 1)).copy()
label = np.rot90(label, 3, (0, 1)).copy()
data = torch.from_numpy(data.transpose(2, 0, 1))
label = torch.from_numpy(label)
return {'image': data, 'label': label}
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomCrop(self.par.base_size, self.par.crop_size, fill=255),
tr.RandomColorJitter(),
tr.RandomHorizontalFlip(),
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()])
return composed_transforms(sample)
def transform_tr(self, sample): # eventually, according to the condition of split in self.split, then split == 'train'
composed_transforms = transforms.Compose([ # define transform_tr
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size), # random scale crop, we have to calcualte base_size and crop_size based on argparse
tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()])
return composed_transforms(sample) # return composed_transforms
def transform_train(self):
temp = []
temp.append(tr.Resize(self.args.input_size))
temp.append(tr.RandomHorizontalFlip())
temp.append(tr.RandomRotate(15))
temp.append(tr.RandomCrop(self.args.input_size))
temp.append(tr.ToTensor())
composed_transforms = transforms.Compose(temp)
return composed_transforms
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.FixedResize(size=self.args['crop_size']),
tr.Normalize(mean=self.mean, std=self.std),
tr.ToTensor()
])
return composed_transforms(sample)
def transform(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.cfg.DATASET.BASE_SIZE, crop_size=self.cfg.DATASET.CROP_SIZE),
tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()])
return composed_transforms(sample)
def transform_tr(self, sample: dict):
sample_transforms = transforms.Compose([
ctr.RandomCrop(size=self.settings['rnd_crop_size']),
ctr.RandomHorizontalFlip(p=0.5),
ctr.ToTensor(),
ctr.Normalize(**self.settings['normalize_params'],
apply_to=['image']),
ctr.Squeeze(apply_to=['label']),
])
return sample_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.FixedCrop(x1=280, x2=1000, y1=50, y2=562),
tr.RandomHorizontalFlip(),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms_tr = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.ScaleNRotate(rots=(-20, 20), scales=(.75, 1.25)),
tr.CropFromMask(crop_elems=('image', 'gt'), relax=20, zero_pad=True),
tr.FixedResize(resolutions={'crop_image': (256, 256), 'crop_gt': (256, 256)}),
tr.Normalize(elems='crop_image'),
tr.ToTensor()
])
return composed_transforms_tr(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.args.base_size,
crop_size=self.args.crop_size),
tr.RandomGaussianBlur(),
tr.Resize_normalize_train(mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5))
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.FixedResize(size=(1024, 2048)),
tr.ColorJitter(),
tr.RandomGaussianBlur(),
tr.RandomMotionBlur(),
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255),
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.FixedNoMaskResize(size=self.args.crop_size),
tr.RandomColorJeter(0.3, 0.3, 0.3, 0.3),
tr.RandomHorizontalFlip(),
# tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size),
tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
tr.ToTensor()])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
#tr.FixScaleCrop(crop_size=self.args['crop_size']),
tr.FixedResize(size=self.args['crop_size']),
#tr.RandomScaleCrop(base_size=self.args['base_size'], crop_size=self.args['crop_size'], min_scale = 1.0, max_scale = 1.5),
#tr.RandomGaussianBlur(),
tr.Normalize(mean=self.mean, std=self.std),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_tr_part1_1(self, sample):
if self.args.use_small:
composed_transforms = transforms.Compose(
[tr.FixScaleCrop(crop_size=self.args.crop_size)])
else:
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.args.base_size,
crop_size=self.args.crop_size)
]) # Zhiwei
return composed_transforms(sample)
def transform_train(self, sample):
composed_transforms = transforms.Compose([
self.scalecrop,
tr.RandomHorizontalFlip(),
#tr.RandomScaleCrop(base_size=self.base_size, crop_size=self.crop_size, fill=255),
tr.RandomGaussianBlur(),
tr.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_train(self, sample):
composed_transforms = transforms.Compose([
tr.FixScaleCrop(crop_size=self.crop_size),
tr.RandomHorizontalFlip(),
tr.RandomGaussianBlur(),
tr.Normalize(mean=[0.4911], std=[0.1658]),
tr.ToTensor()
])
transformed = composed_transforms(sample)
transformed['image'] = transformed['image'].unsqueeze(0)
return transformed
def transforms_train_esp(self, sample):
composed_transforms = transforms.Compose([
tr.RandomVerticalFlip(),
tr.RandomHorizontalFlip(),
tr.RandomAffine(degrees=40, scale=(.9, 1.1), shear=30),
tr.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5),
tr.FixedResize(size=self.input_size),
tr.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.base_size,
crop_size=self.crop_size,
fill=255),
tr.RandomDarken(self.cfg, self.darken),
#tr.RandomGaussianBlur(), #TODO Not working for depth channel
tr.Normalize(mean=self.data_mean, std=self.data_std),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
# transforms.ColorJitter(brightness=(-1,1),contrast=(-1, 1),saturation=(-0.3, 0.3), hue=(-0.3, 0.3)),
# transforms.ColorJitter(brightness=0.4, contrast=0.4,saturation=0.4),
tr.RandomHorizontalFlip(),
tr.GaussianNoise(),
tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
tr.PatchToTensor()
])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(
), # given PIL image randomly with a given probability
tr.RandomScaleCrop(base_size=self.args.base_size,
crop_size=self.args.crop_size),
tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_tr(self, sample):
#print(sample)
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.FixScaleCrop(crop_size=self.args.crop_size),
#tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size),
tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
tr.ToTensor(),
tr.Lablize(high_confidence=self.args.high_confidence)
])
return composed_transforms(sample)
def transform_train(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=self.args.base_size,
crop_size=self.args.crop_size,
fill=255),
tr.RandomRotate(degree=10),
tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_train(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomVerticalFlip(),
# tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size, fill=255),
# tr.FixedResize(size=self.args.crop_size),
tr.RandomRotate(),
tr.RandomGammaTransform(),
tr.RandomGaussianBlur(),
tr.RandomNoise(),
tr.Normalize(mean=(0.544650, 0.352033, 0.384602, 0.352311), std=(0.249456, 0.241652, 0.228824, 0.227583)),
tr.ToTensor()])
return composed_transforms(sample)
def transform_tr(self, sample):
composed_transforms = transforms.Compose([
tr.RandomHorizontalFlip(),
tr.RandomScaleCrop(base_size=513, crop_size=513),
tr.ColorJitter(brightness=0.3,
contrast=0.3,
saturation=0.3,
hue=0.3,
gamma=0.3),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225)),
tr.ToTensor()
])
return composed_transforms(sample)
def transform_tr(self, sample):
if self.csplit == 'all': ignores = []
elif self.csplit == 'seen': ignores = classes['unseen']
else: raise RuntimeError("Training Unseen data is not legal.")
composed_transforms = transforms.Compose([
tr.MaskIgnores(ignores=ignores, mask=255),
tr.RandomHorizontalFlip(),
# tr.RandomScaleCrop(base_size=self.args.base_size, crop_size=self.args.crop_size),
# tr.RandomGaussianBlur(),
tr.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
seg=True),
tr.ToTensor()
])
return composed_transforms(sample)
