Python torchvision.transforms.RandomVerticalFlip() Examples
The following are 13
code examples of torchvision.transforms.RandomVerticalFlip().
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Example #1
| Source File: dataset.py From sepconv with MIT License | 6 votes |
|
def __init__(self, patches, use_cache, augment_data):
super(PatchDataset, self).__init__()
self.patches = patches
self.crop = CenterCrop(config.CROP_SIZE)
if augment_data:
self.random_transforms = [RandomRotation((90, 90)), RandomVerticalFlip(1.0), RandomHorizontalFlip(1.0),
(lambda x: x)]
self.get_aug_transform = (lambda: random.sample(self.random_transforms, 1)[0])
else:
# Transform does nothing. Not sure if horrible or very elegant...
self.get_aug_transform = (lambda: (lambda x: x))
if use_cache:
self.load_patch = data_manager.load_cached_patch
else:
self.load_patch = data_manager.load_patch
print('Dataset ready with {} tuples.'.format(len(patches)))
Example #2
| Source File: data_loader.py From real-world-sr with MIT License | 6 votes |
|
def __init__(self, noisy_dir, crop_size, upscale_factor=4, cropped=False, flips=False, rotations=False, **kwargs):
super(TrainDataset, self).__init__()
# get all directories used for training
if isinstance(noisy_dir, str):
noisy_dir = [noisy_dir]
self.files = []
for n_dir in noisy_dir:
self.files += [join(n_dir, x) for x in listdir(n_dir) if utils.is_image_file(x)]
# intitialize image transformations and variables
self.input_transform = T.Compose([
T.RandomVerticalFlip(0.5 if flips else 0.0),
T.RandomHorizontalFlip(0.5 if flips else 0.0),
T.RandomCrop(crop_size)
])
self.crop_transform = T.RandomCrop(crop_size // upscale_factor)
self.upscale_factor = upscale_factor
self.cropped = cropped
self.rotations = rotations
Example #3
| Source File: utils_model.py From HistoGAN with GNU General Public License v3.0 | 6 votes |
|
def get_data_transforms():
data_transforms = {
'train': transforms.Compose([
transforms.CenterCrop(config.patch_size),
transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.2),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
Random90Rotation(),
transforms.ToTensor(),
transforms.Normalize([0.7, 0.6, 0.7], [0.15, 0.15, 0.15]) #mean and standard deviations for lung adenocarcinoma resection slides
]),
'val': transforms.Compose([
transforms.CenterCrop(config.patch_size),
transforms.ToTensor(),
transforms.Normalize([0.7, 0.6, 0.7], [0.15, 0.15, 0.15])
]),
'unnormalize': transforms.Compose([
transforms.Normalize([1/0.15, 1/0.15, 1/0.15], [1/0.15, 1/0.15, 1/0.15])
]),
}
return data_transforms
#printing the model
Example #4
| Source File: data_loader.py From real-world-sr with MIT License | 5 votes |
|
def __init__(self, dataset_dir, crop_size, upscale_factor=4, flips=False, rotations=False, **kwargs):
super(DiscDataset, self).__init__()
self.files = [join(dataset_dir, x) for x in listdir(dataset_dir) if utils.is_image_file(x)]
self.input_transform = T.Compose([
T.RandomVerticalFlip(0.5 if flips else 0.0),
T.RandomHorizontalFlip(0.5 if flips else 0.0),
T.RandomCrop(crop_size // upscale_factor)
])
self.rotations = rotations
Example #5
| Source File: preproc.py From pt.darts with MIT License | 5 votes |
|
def data_transforms(dataset, cutout_length):
dataset = dataset.lower()
if dataset == 'cifar10':
MEAN = [0.49139968, 0.48215827, 0.44653124]
STD = [0.24703233, 0.24348505, 0.26158768]
transf = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip()
]
elif dataset == 'mnist':
MEAN = [0.13066051707548254]
STD = [0.30810780244715075]
transf = [
transforms.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=0.1)
]
elif dataset == 'fashionmnist':
MEAN = [0.28604063146254594]
STD = [0.35302426207299326]
transf = [
transforms.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=0.1),
transforms.RandomVerticalFlip()
]
else:
raise ValueError('not expected dataset = {}'.format(dataset))
normalize = [
transforms.ToTensor(),
transforms.Normalize(MEAN, STD)
]
train_transform = transforms.Compose(transf + normalize)
valid_transform = transforms.Compose(normalize)
if cutout_length > 0:
train_transform.transforms.append(Cutout(cutout_length))
return train_transform, valid_transform
Example #6
| Source File: utils.py From CAE-ADMM with MIT License | 5 votes |
|
def __init__(self, folder_path):
self.files = sorted(glob.glob('%s/*.*' % folder_path))
self.transform = transforms.Compose([
transforms.RandomCrop(128),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ToTensor()
])
Example #7
| Source File: loader.py From nni with MIT License | 5 votes |
|
def get_tta_transforms(index, pad_mode):
tta_transforms = {
0: [],
1: [transforms.RandomHorizontalFlip(p=2.)],
2: [transforms.RandomVerticalFlip(p=2.)],
3: [transforms.RandomHorizontalFlip(p=2.), transforms.RandomVerticalFlip(p=2.)]
}
if pad_mode == 'resize':
return transforms.Compose([transforms.Resize((H, W)), *(tta_transforms[index]), *img_transforms])
else:
return transforms.Compose([*(tta_transforms[index]), *img_transforms])
Example #8
| Source File: basic_dataset.py From srntt-pytorch with Apache License 2.0 | 5 votes |
|
def __init__(self, data_dir, scale_factor, patch_size=0, mode='train'):
assert patch_size % scale_factor == 0
assert (mode == 'train' and patch_size != 0) or mode == 'eval'
if isinstance(data_dir, str):
data_dir = Path(data_dir)
self.filenames = [f for f in data_dir.glob('*') if is_image(f)]
self.scale_factor = scale_factor
if mode == 'train':
self.transforms = transforms.Compose([
transforms.RandomCrop(
patch_size, pad_if_needed=True, padding_mode='reflect'),
transforms.RandomApply([
functools.partial(TF.rotate, angle=0),
functools.partial(TF.rotate, angle=90),
functools.partial(TF.rotate, angle=180),
functools.partial(TF.rotate, angle=270),
]),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
])
elif mode == 'eval':
self.filenames.sort()
if patch_size > 0:
self.transforms = transforms.Compose([
transforms.CenterCrop(patch_size)
])
else:
self.transforms = transforms.Compose([
functools.partial(pad, scale=scale_factor)
])
else:
raise NotImplementedError
Example #9
| Source File: utils.py From models-comparison.pytorch with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def __init__(self, opts, scale=0.875, random_crop=False, random_hflip=False, random_vflip=False):
if type(opts) == dict:
opts = munchify(opts)
self.input_size = opts.input_size
self.input_space = opts.input_space
self.input_range = opts.input_range
self.mean = opts.mean
self.std = opts.std
# https://github.com/tensorflow/models/blob/master/research/inception/inception/image_processing.py#L294
self.scale = scale
self.random_crop = random_crop
self.random_hflip = random_hflip
self.random_vflip = random_vflip
tfs = []
tfs.append(transforms.Resize(int(math.floor(max(self.input_size)/self.scale))))
if random_crop:
tfs.append(transforms.RandomCrop(max(self.input_size)))
else:
tfs.append(transforms.CenterCrop(max(self.input_size)))
if random_hflip:
tfs.append(transforms.RandomHorizontalFlip())
if random_vflip:
tfs.append(transforms.RandomVerticalFlip())
tfs.append(transforms.ToTensor())
tfs.append(ToSpaceBGR(self.input_space=='BGR'))
tfs.append(ToRange255(max(self.input_range)==255))
tfs.append(transforms.Normalize(mean=self.mean, std=self.std))
self.tf = transforms.Compose(tfs)
Example #10
| Source File: utils.py From pretorched-x with MIT License | 4 votes |
|
def __init__(self, opts, scale=0.875, random_crop=False,
random_hflip=False, random_vflip=False,
preserve_aspect_ratio=True):
if type(opts) == dict:
opts = munchify(opts)
self.input_size = opts.input_size
self.input_space = opts.input_space
self.input_range = opts.input_range
self.mean = opts.mean
self.std = opts.std
# https://github.com/tensorflow/models/blob/master/research/inception/inception/image_processing.py#L294
self.scale = scale
self.random_crop = random_crop
self.random_hflip = random_hflip
self.random_vflip = random_vflip
tfs = []
if preserve_aspect_ratio:
tfs.append(transforms.Resize(int(math.floor(max(self.input_size)/self.scale))))
else:
height = int(self.input_size[1] / self.scale)
width = int(self.input_size[2] / self.scale)
tfs.append(transforms.Resize((height, width)))
if random_crop:
tfs.append(transforms.RandomCrop(max(self.input_size)))
else:
tfs.append(transforms.CenterCrop(max(self.input_size)))
if random_hflip:
tfs.append(transforms.RandomHorizontalFlip())
if random_vflip:
tfs.append(transforms.RandomVerticalFlip())
tfs.append(transforms.ToTensor())
tfs.append(ToSpaceBGR(self.input_space=='BGR'))
tfs.append(ToRange255(max(self.input_range)==255))
tfs.append(transforms.Normalize(mean=self.mean, std=self.std))
self.tf = transforms.Compose(tfs)
Example #11
| Source File: utils.py From pretrained-models.pytorch with BSD 3-Clause "New" or "Revised" License | 4 votes |
|
def __init__(self, opts, scale=0.875, random_crop=False,
random_hflip=False, random_vflip=False,
preserve_aspect_ratio=True):
if type(opts) == dict:
opts = munchify(opts)
self.input_size = opts.input_size
self.input_space = opts.input_space
self.input_range = opts.input_range
self.mean = opts.mean
self.std = opts.std
# https://github.com/tensorflow/models/blob/master/research/inception/inception/image_processing.py#L294
self.scale = scale
self.random_crop = random_crop
self.random_hflip = random_hflip
self.random_vflip = random_vflip
tfs = []
if preserve_aspect_ratio:
tfs.append(transforms.Resize(int(math.floor(max(self.input_size)/self.scale))))
else:
height = int(self.input_size[1] / self.scale)
width = int(self.input_size[2] / self.scale)
tfs.append(transforms.Resize((height, width)))
if random_crop:
tfs.append(transforms.RandomCrop(max(self.input_size)))
else:
tfs.append(transforms.CenterCrop(max(self.input_size)))
if random_hflip:
tfs.append(transforms.RandomHorizontalFlip())
if random_vflip:
tfs.append(transforms.RandomVerticalFlip())
tfs.append(transforms.ToTensor())
tfs.append(ToSpaceBGR(self.input_space=='BGR'))
tfs.append(ToRange255(max(self.input_range)==255))
tfs.append(transforms.Normalize(mean=self.mean, std=self.std))
self.tf = transforms.Compose(tfs)
Example #12
| Source File: preproc.py From NAS-Benchmark with GNU General Public License v3.0 | 4 votes |
|
def data_transforms(dataset, cutout_length):
dataset = dataset.lower()
if dataset == 'cifar10' or dataset == 'cifar100':
MEAN = [0.49139968, 0.48215827, 0.44653124]
STD = [0.24703233, 0.24348505, 0.26158768]
transf_train = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip()
]
transf_val = []
elif dataset == 'mnist':
MEAN = [0.13066051707548254]
STD = [0.30810780244715075]
transf_train = [
transforms.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=0.1)
]
transf_val=[]
elif dataset == 'fashionmnist':
MEAN = [0.28604063146254594]
STD = [0.35302426207299326]
transf_train = [
transforms.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1), shear=0.1),
transforms.RandomVerticalFlip()
]
transf_val = []
#Same preprocessing for ImageNet, Sport8 and MIT67
elif dataset in utils.LARGE_DATASETS:
MEAN = [0.485, 0.456, 0.406]
STD = [0.229, 0.224, 0.225]
transf_train = [
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.2)
]
transf_val = [
transforms.Resize(256),
transforms.CenterCrop(224),
]
else:
raise ValueError('not expected dataset = {}'.format(dataset))
normalize = [
transforms.ToTensor(),
transforms.Normalize(MEAN, STD)
]
train_transform = transforms.Compose(transf_train + normalize)
valid_transform = transforms.Compose(transf_val + normalize) # FIXME validation is not set to square proportions, is this an issue?
if cutout_length > 0:
train_transform.transforms.append(Cutout(cutout_length))
return train_transform, valid_transform
Example #13
| Source File: datasets.py From ultra-thin-PRM with MIT License | 4 votes |
|
def image_transform(
image_size: Union[int, List[int]],
augmentation: dict = {},
mean: List[float] = [0.485, 0.456, 0.406],
std: List[float] = [0.229, 0.224, 0.225]) -> Callable:
"""Image transforms.
"""
if isinstance(image_size, int):
image_size = (image_size, image_size)
else:
image_size = tuple(image_size)
# data augmentations
horizontal_flip = augmentation.pop('horizontal_flip', None)
if horizontal_flip is not None:
assert isinstance(horizontal_flip, float) and 0 <= horizontal_flip <= 1
vertical_flip = augmentation.pop('vertical_flip', None)
if vertical_flip is not None:
assert isinstance(vertical_flip, float) and 0 <= vertical_flip <= 1
random_crop = augmentation.pop('random_crop', None)
if random_crop is not None:
assert isinstance(random_crop, dict)
center_crop = augmentation.pop('center_crop', None)
if center_crop is not None:
assert isinstance(center_crop, (int, list))
if len(augmentation) > 0:
raise NotImplementedError('Invalid augmentation options: %s.' % ', '.join(augmentation.keys()))
t = [
transforms.Resize(image_size) if random_crop is None else transforms.RandomResizedCrop(image_size[0], **random_crop),
transforms.CenterCrop(center_crop) if center_crop is not None else None,
transforms.RandomHorizontalFlip(horizontal_flip) if horizontal_flip is not None else None,
transforms.RandomVerticalFlip(vertical_flip) if vertical_flip is not None else None,
transforms.ToTensor(),
transforms.Normalize(mean, std)]
return transforms.Compose([v for v in t if v is not None])
