Python torchvision.transforms.ColorJitter() Examples
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code examples of torchvision.transforms.ColorJitter().
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Example #1
| Source File: data_loader.py From self-supervised-da with MIT License | 7 votes |
|
def get_rot_train_transformers(args):
size = args.img_transform.random_resize_crop.size
scale = args.img_transform.random_resize_crop.scale
img_tr = [transforms.RandomResizedCrop((int(size[0]), int(size[1])), (scale[0], scale[1]))]
if args.img_transform.random_horiz_flip > 0.0:
img_tr.append(transforms.RandomHorizontalFlip(args.img_transform.random_horiz_flip))
if args.img_transform.jitter > 0.0:
img_tr.append(transforms.ColorJitter(
brightness=args.img_transform.jitter, contrast=args.img_transform.jitter,
saturation=args.jitter, hue=min(0.5, args.jitter)))
mean = args.normalize.mean
std = args.normalize.std
img_tr += [transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]
return transforms.Compose(img_tr)
Example #2
| Source File: datasets.py From garbageClassifier with MIT License | 6 votes |
|
def __init__(self, data_dir, image_size, is_train=True, **kwargs): self.image_size = image_size self.image_paths = [] self.image_labels = [] self.classes = sorted(os.listdir(data_dir)) for idx, cls_ in enumerate(self.classes): self.image_paths += glob.glob(os.path.join(data_dir, cls_, '*.*')) self.image_labels += [idx] * len(glob.glob(os.path.join(data_dir, cls_, '*.*'))) self.indexes = list(range(len(self.image_paths))) if is_train: random.shuffle(self.indexes) self.transform = transforms.Compose([transforms.RandomResizedCrop(image_size), transforms.RandomHorizontalFlip(), transforms.ColorJitter(brightness=1, contrast=1, saturation=0.5, hue=0.5), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) else: self.transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
Example #3
| Source File: utils.py From NAS-Benchmark with GNU General Public License v3.0 | 6 votes |
|
def data_transforms_food101():
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_transform = transforms.Compose([
transforms.RandomResizedCrop(128), # default bilinear for interpolation
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=0.2,
contrast=0.2,
saturation=0.2,
hue=0.2),
transforms.ToTensor(),
normalize,
])
valid_transform = transforms.Compose([
transforms.Resize(128),
transforms.CenterCrop(128),
transforms.ToTensor(),
normalize,
])
return train_transform, valid_transform
Example #4
| Source File: data_loader_stargan.py From adversarial-object-removal with MIT License | 6 votes |
|
def __init__(self, transform, mode, select_attrs=[], out_img_size=64, bbox_out_size=32, randomrotate=0, scaleRange=[0.1, 0.9], squareAspectRatio=False, use_celeb=False):
self.image_path = os.path.join('data','mnist')
self.mode = mode
self.iouThresh = 0.5
self.maxDigits= 1
self.minDigits = 1
self.use_celeb = use_celeb
self.scaleRange = scaleRange
self.squareAspectRatio = squareAspectRatio
self.nc = 1 if not self.use_celeb else 3
transList = [transforms.RandomHorizontalFlip(), transforms.RandomRotation(randomrotate,resample=Image.BICUBIC)]#, transforms.ColorJitter(0.5,0.5,0.5,0.3)
self.digitTransforms = transforms.Compose(transList)
self.dataset = MNIST(self.image_path,train=True, transform=self.digitTransforms) if not use_celeb else CelebDataset('./data/celebA/images', './data/celebA/list_attr_celeba.txt', self.digitTransforms, mode)
self.num_data = len(self.dataset)
self.metadata = {'images':[]}
self.catid2attr = {}
self.out_img_size = out_img_size
self.bbox_out_size = bbox_out_size
self.selected_attrs = select_attrs
print ('Start preprocessing dataset..!')
self.preprocess()
print ('Finished preprocessing dataset..!')
Example #5
| Source File: datasets.py From amdim-public with MIT License | 6 votes |
|
def __init__(self):
# flipping image along vertical axis
self.flip_lr = transforms.RandomHorizontalFlip(p=0.5)
# image augmentation functions
normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
col_jitter = transforms.RandomApply([
transforms.ColorJitter(0.4, 0.4, 0.4, 0.2)], p=0.8)
img_jitter = transforms.RandomApply([
RandomTranslateWithReflect(4)], p=0.8)
rnd_gray = transforms.RandomGrayscale(p=0.25)
# main transform for self-supervised training
self.train_transform = transforms.Compose([
img_jitter,
col_jitter,
rnd_gray,
transforms.ToTensor(),
normalize
])
# transform for testing
self.test_transform = transforms.Compose([
transforms.ToTensor(),
normalize
])
Example #6
| Source File: utils.py From NAS-Benchmark with GNU General Public License v3.0 | 6 votes |
|
def data_transforms_imagenet():
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_transform = transforms.Compose([
transforms.RandomResizedCrop(128),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.2),
transforms.ToTensor(),
normalize,
])
valid_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(128),
transforms.ToTensor(),
normalize,
])
return train_transform, valid_transform
Example #7
| Source File: datasets.py From amdim-public with MIT License | 6 votes |
|
def __init__(self):
# image augmentation functions
self.flip_lr = transforms.RandomHorizontalFlip(p=0.5)
rand_crop = \
transforms.RandomResizedCrop(128, scale=(0.3, 1.0), ratio=(0.7, 1.4),
interpolation=INTERP)
col_jitter = transforms.RandomApply([
transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8)
rnd_gray = transforms.RandomGrayscale(p=0.25)
post_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.test_transform = transforms.Compose([
transforms.Resize(146, interpolation=INTERP),
transforms.CenterCrop(128),
post_transform
])
self.train_transform = transforms.Compose([
rand_crop,
col_jitter,
rnd_gray,
post_transform
])
Example #8
| Source File: preprocess.py From pytorch_quantization with MIT License | 6 votes |
|
def imgnet_transform(is_training=True):
if is_training:
transform_list = transforms.Compose([transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.5,
contrast=0.5,
saturation=0.3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
else:
transform_list = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
return transform_list
Example #9
| Source File: imagenet.py From nasnet-pytorch with MIT License | 6 votes |
|
def preprocess(self):
if self.train:
return transforms.Compose([
transforms.RandomResizedCrop(self.image_size),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2),
transforms.ToTensor(),
transforms.Normalize(self.mean, self.std),
])
else:
return transforms.Compose([
transforms.Resize((int(self.image_size / 0.875), int(self.image_size / 0.875))),
transforms.CenterCrop(self.image_size),
transforms.ToTensor(),
transforms.Normalize(self.mean, self.std),
])
Example #10
| Source File: data_loader.py From self-supervised-da with MIT License | 6 votes |
|
def get_jig_train_transformers(args):
size = args.img_transform.random_resize_crop.size
scale = args.img_transform.random_resize_crop.scale
img_tr = [transforms.RandomResizedCrop((int(size[0]), int(size[1])), (scale[0], scale[1]))]
if args.img_transform.random_horiz_flip > 0.0:
img_tr.append(transforms.RandomHorizontalFlip(args.img_transform.random_horiz_flip))
if args.img_transform.jitter > 0.0:
img_tr.append(transforms.ColorJitter(
brightness=args.img_transform.jitter, contrast=args.img_transform.jitter,
saturation=args.jitter, hue=min(0.5, args.jitter)))
tile_tr = []
if args.jig_transform.tile_random_grayscale:
tile_tr.append(transforms.RandomGrayscale(args.jig_transform.tile_random_grayscale))
mean = args.normalize.mean
std = args.normalize.std
tile_tr = tile_tr + [transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]
return transforms.Compose(img_tr), transforms.Compose(tile_tr)
Example #11
| Source File: deep_globe.py From GLNet with MIT License | 6 votes |
|
def __init__(self, root, ids, label=False, transform=False):
super(DeepGlobe, self).__init__()
"""
Args:
fileDir(string): directory with all the input images.
transform(callable, optional): Optional transform to be applied on a sample
"""
self.root = root
self.label = label
self.transform = transform
self.ids = ids
self.classdict = {1: "urban", 2: "agriculture", 3: "rangeland", 4: "forest", 5: "water", 6: "barren", 0: "unknown"}
self.color_jitter = transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.04)
self.resizer = transforms.Resize((2448, 2448))
Example #12
| Source File: cifar10_cls_dataset.py From imgclsmob with MIT License | 6 votes |
|
def cifar10_train_transform(ds_metainfo,
mean_rgb=(0.4914, 0.4822, 0.4465),
std_rgb=(0.2023, 0.1994, 0.2010),
jitter_param=0.4):
assert (ds_metainfo is not None)
assert (ds_metainfo.input_image_size[0] == 32)
return transforms.Compose([
transforms.RandomCrop(
size=32,
padding=4),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=jitter_param,
contrast=jitter_param,
saturation=jitter_param),
transforms.ToTensor(),
transforms.Normalize(
mean=mean_rgb,
std=std_rgb)
])
Example #13
| Source File: utils.py From WS-DAN.PyTorch with MIT License | 6 votes |
|
def get_transform(resize, phase='train'):
if phase == 'train':
return transforms.Compose([
transforms.Resize(size=(int(resize[0] / 0.875), int(resize[1] / 0.875))),
transforms.RandomCrop(resize),
transforms.RandomHorizontalFlip(0.5),
transforms.ColorJitter(brightness=0.126, saturation=0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
else:
return transforms.Compose([
transforms.Resize(size=(int(resize[0] / 0.875), int(resize[1] / 0.875))),
transforms.CenterCrop(resize),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
Example #14
| 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 #15
| Source File: datasets.py From nni with MIT License | 6 votes |
|
def build_train_transform(self, distort_color, resize_scale):
print('Color jitter: %s' % distort_color)
if distort_color == 'strong':
color_transform = transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1)
elif distort_color == 'normal':
color_transform = transforms.ColorJitter(brightness=32. / 255., saturation=0.5)
else:
color_transform = None
if color_transform is None:
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(self.image_size, scale=(resize_scale, 1.0)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
self.normalize,
])
else:
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(self.image_size, scale=(resize_scale, 1.0)),
transforms.RandomHorizontalFlip(),
color_transform,
transforms.ToTensor(),
self.normalize,
])
return train_transforms
Example #16
| Source File: preprocessing.py From pytorch_DoReFaNet with MIT License | 6 votes |
|
def imgnet_transform(is_training=True):
if is_training:
transform_list = transforms.Compose([transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.5,
contrast=0.5,
saturation=0.3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
else:
transform_list = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
return transform_list
Example #17
| Source File: utils.py From NAS-Benchmark with GNU General Public License v3.0 | 5 votes |
|
def data_transforms_large(dataset, cutout_length):
dataset = dataset.lower()
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),
]
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 #18
| Source File: dataset.py From EAST with MIT License | 5 votes |
|
def __getitem__(self, index):
with open(self.gt_files[index], 'r') as f:
lines = f.readlines()
vertices, labels = extract_vertices(lines)
img = Image.open(self.img_files[index])
img, vertices = adjust_height(img, vertices)
img, vertices = rotate_img(img, vertices)
img, vertices = crop_img(img, vertices, labels, self.length)
transform = transforms.Compose([transforms.ColorJitter(0.5, 0.5, 0.5, 0.25), \
transforms.ToTensor(), \
transforms.Normalize(mean=(0.5,0.5,0.5),std=(0.5,0.5,0.5))])
score_map, geo_map, ignored_map = get_score_geo(img, vertices, labels, self.scale, self.length)
return transform(img), score_map, geo_map, ignored_map
Example #19
| Source File: utils.py From instance-segmentation-pytorch with GNU General Public License v3.0 | 5 votes |
|
def image_random_color_jitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2):
return transforms.ColorJitter(brightness=brightness, contrast=contrast, saturation=saturation, hue=hue)
Example #20
| Source File: train.py From Holocron with MIT License | 5 votes |
|
def load_data(datadir):
# Data loading code
print("Loading data")
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
print("Loading training data")
st = time.time()
dataset = VOCDetection(datadir, image_set='train', download=True,
transforms=Compose([VOCTargetTransform(classes),
Resize(512), RandomResizedCrop(416), RandomHorizontalFlip(),
convert_to_relative,
ImageTransform(transforms.ColorJitter(brightness=0.3, contrast=0.3,
saturation=0.1, hue=0.02)),
ImageTransform(transforms.ToTensor()), ImageTransform(normalize)]))
print("Took", time.time() - st)
print("Loading validation data")
st = time.time()
dataset_test = VOCDetection(datadir, image_set='val', download=True,
transforms=Compose([VOCTargetTransform(classes),
Resize(416), CenterCrop(416),
convert_to_relative,
ImageTransform(transforms.ToTensor()), ImageTransform(normalize)]))
print("Took", time.time() - st)
print("Creating data loaders")
train_sampler = torch.utils.data.RandomSampler(dataset)
test_sampler = torch.utils.data.SequentialSampler(dataset_test)
return dataset, dataset_test, train_sampler, test_sampler
Example #21
| Source File: train.py From Holocron with MIT License | 5 votes |
|
def load_data(datadir):
# Data loading code
print("Loading data")
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
base_size = 320
crop_size = 256
min_size = int(0.5 * base_size)
max_size = int(2.0 * base_size)
print("Loading training data")
st = time.time()
dataset = VOCSegmentation(datadir, image_set='train', download=True,
transforms=Compose([RandomResize(min_size, max_size),
RandomHorizontalFlip(0.5),
RandomCrop(crop_size),
SampleTransform(transforms.ColorJitter(brightness=0.3,
contrast=0.3,
saturation=0.1,
hue=0.02)),
ToTensor(),
SampleTransform(normalize)]))
print("Took", time.time() - st)
print("Loading validation data")
st = time.time()
dataset_test = VOCSegmentation(datadir, image_set='val', download=True,
transforms=Compose([RandomResize(base_size, base_size),
ToTensor(),
SampleTransform(normalize)]))
print("Took", time.time() - st)
print("Creating data loaders")
train_sampler = torch.utils.data.RandomSampler(dataset)
test_sampler = torch.utils.data.SequentialSampler(dataset_test)
return dataset, dataset_test, train_sampler, test_sampler
Example #22
| Source File: charadesrgb.py From actor-observer with GNU General Public License v3.0 | 5 votes |
|
def get(cls, args, scale=(0.08, 1.0)):
""" Entry point. Call this function to get all Charades dataloaders """
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_file = args.train_file
val_file = args.val_file
train_dataset = cls(
args.data, 'train', train_file, args.cache, args.cache_buster,
transform=transforms.Compose([
transforms.RandomResizedCrop(args.inputsize, scale),
transforms.ColorJitter(
brightness=0.4, contrast=0.4, saturation=0.4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), # missing PCA lighting jitter
normalize,
]))
val_dataset = cls(
args.data, 'val', val_file, args.cache, args.cache_buster,
transform=transforms.Compose([
transforms.Resize(int(256. / 224 * args.inputsize)),
transforms.CenterCrop(args.inputsize),
transforms.ToTensor(),
normalize,
]))
valvideo_dataset = cls(
args.data, 'val_video', val_file, args.cache, args.cache_buster,
transform=transforms.Compose([
transforms.Resize(int(256. / 224 * args.inputsize)),
transforms.CenterCrop(args.inputsize),
transforms.ToTensor(),
normalize,
]))
return train_dataset, val_dataset, valvideo_dataset
Example #23
| Source File: train.py From Holocron with MIT License | 5 votes |
|
def load_data(traindir, valdir, half=False):
# Data loading code
print("Loading data")
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
print("Loading training data")
st = time.time()
dataset = torchvision.datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.1, hue=0.02),
transforms.ToTensor(),
normalize,
# transforms.RandomErasing(p=0.9, value='random')
]))
print("Took", time.time() - st)
print("Loading validation data")
dataset_test = torchvision.datasets.ImageFolder(
valdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize
]))
print("Creating data loaders")
train_sampler = torch.utils.data.RandomSampler(dataset)
test_sampler = torch.utils.data.SequentialSampler(dataset_test)
return dataset, dataset_test, train_sampler, test_sampler
Example #24
| Source File: datasets.py From amdim-public with MIT License | 5 votes |
|
def __init__(self):
# flipping image along vertical axis
self.flip_lr = transforms.RandomHorizontalFlip(p=0.5)
normalize = transforms.Normalize(mean=(0.43, 0.42, 0.39), std=(0.27, 0.26, 0.27))
# image augmentation functions
col_jitter = transforms.RandomApply([
transforms.ColorJitter(0.4, 0.4, 0.4, 0.2)], p=0.8)
rnd_gray = transforms.RandomGrayscale(p=0.25)
rand_crop = \
transforms.RandomResizedCrop(64, scale=(0.3, 1.0), ratio=(0.7, 1.4),
interpolation=INTERP)
self.test_transform = transforms.Compose([
transforms.Resize(70, interpolation=INTERP),
transforms.CenterCrop(64),
transforms.ToTensor(),
normalize
])
self.train_transform = transforms.Compose([
rand_crop,
col_jitter,
rnd_gray,
transforms.ToTensor(),
normalize
])
Example #25
| Source File: model.py From derplearning with MIT License | 5 votes |
|
def compose_transforms(transform_config):
""" Apply all image transforms """
transform_list = []
for perturb_config in transform_config:
if perturb_config["name"] == "colorjitter":
transform = transforms.ColorJitter(
brightness=perturb_config["brightness"],
contrast=perturb_config["contrast"],
saturation=perturb_config["saturation"],
hue=perturb_config["hue"],
)
transform_list.append(transform)
transform_list.append(transforms.ToTensor())
return transforms.Compose(transform_list)
Example #26
| Source File: transforms.py From RCRNet-Pytorch with MIT License | 5 votes |
|
def __init__(self, image_mode, **kwargs):
super(ColorJitter, self).__init__(**kwargs)
self.transform = None
self.image_mode = image_mode
Example #27
| Source File: imagenet.py From nni with MIT License | 5 votes |
|
def _imagenet_dataset(config):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_dir = os.path.join(config.data_dir, "train")
test_dir = os.path.join(config.data_dir, "val")
if hasattr(config, "use_aa") and config.use_aa:
train_data = dset.ImageFolder(
train_dir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
ImageNetPolicy(),
transforms.ToTensor(),
normalize,
]))
else:
train_data = dset.ImageFolder(
train_dir,
transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
hue=0.2),
transforms.ToTensor(),
normalize,
]))
test_data = dset.ImageFolder(
test_dir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]))
return train_data, test_data
Example #28
| Source File: augmentation.py From nni with MIT License | 5 votes |
|
def test_color_trans():
img_id = '00abc623a.jpg'
img = Image.open(os.path.join(settings.TRAIN_IMG_DIR, img_id)).convert('RGB')
trans = ColorJitter(0.1, 0.1, 0.1, 0.1)
img2 = trans(img)
img.show()
img2.show()
Example #29
| Source File: loader.py From nni with MIT License | 5 votes |
|
def get_train_loaders(ifold, batch_size=8, dev_mode=False, pad_mode='edge', meta_version=1, pseudo_label=False, depths=False):
train_shuffle = True
train_meta, val_meta = get_nfold_split(ifold, nfold=10, meta_version=meta_version)
if pseudo_label:
test_meta = get_test_meta()
train_meta = train_meta.append(test_meta, sort=True)
if dev_mode:
train_shuffle = False
train_meta = train_meta.iloc[:10]
val_meta = val_meta.iloc[:10]
#print(val_meta[X_COLUMN].values[:5])
#print(val_meta[Y_COLUMN].values[:5])
print(train_meta.shape, val_meta.shape)
img_mask_aug_train, img_mask_aug_val = get_img_mask_augments(pad_mode, depths)
train_set = ImageDataset(True, train_meta,
augment_with_target=img_mask_aug_train,
image_augment=transforms.ColorJitter(0.2, 0.2, 0.2, 0.2),
image_transform=get_image_transform(pad_mode),
mask_transform=get_mask_transform(pad_mode))
train_loader = data.DataLoader(train_set, batch_size=batch_size, shuffle=train_shuffle, num_workers=4, collate_fn=train_set.collate_fn, drop_last=True)
train_loader.num = len(train_set)
val_set = ImageDataset(True, val_meta,
augment_with_target=img_mask_aug_val,
image_augment=None,
image_transform=get_image_transform(pad_mode),
mask_transform=get_mask_transform(pad_mode))
val_loader = data.DataLoader(val_set, batch_size=batch_size, shuffle=False, num_workers=4, collate_fn=val_set.collate_fn)
val_loader.num = len(val_set)
val_loader.y_true = read_masks(val_meta[ID_COLUMN].values)
return train_loader, val_loader
Example #30
| Source File: face_sketch_data.py From Face-Sketch-Wild with MIT License | 5 votes |
|
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0, sharp=0.0):
super(ColorJitter, self).__init__(brightness, contrast, saturation, hue)
self.sharp = sharp
