Python torchvision.transforms.Scale() Examples
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Example #1
Source File: data_loader.py From mnist-svhn-transfer with MIT License | 9 votes |
def get_loader(config): """Builds and returns Dataloader for MNIST and SVHN dataset.""" transform = transforms.Compose([ transforms.Scale(config.image_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) svhn = datasets.SVHN(root=config.svhn_path, download=True, transform=transform) mnist = datasets.MNIST(root=config.mnist_path, download=True, transform=transform) svhn_loader = torch.utils.data.DataLoader(dataset=svhn, batch_size=config.batch_size, shuffle=True, num_workers=config.num_workers) mnist_loader = torch.utils.data.DataLoader(dataset=mnist, batch_size=config.batch_size, shuffle=True, num_workers=config.num_workers) return svhn_loader, mnist_loader
Example #2
Source File: nyu_walkable_surface_dataset.py From dogTorch with MIT License | 7 votes |
def __init__(self, args, train=True): self.root_dir = args.data if train: self.data_set_list = train_set_list elif args.use_test_for_val: self.data_set_list = test_set_list else: self.data_set_list = val_set_list self.data_set_list = ['%06d.png' % (x) for x in self.data_set_list] self.args = args self.read_features = args.read_features self.features_dir = args.features_dir self.transform = transforms.Compose([ transforms.Scale((args.image_size, args.image_size)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) self.transform_segmentation = transforms.Compose([ transforms.Scale((args.segmentation_size, args.segmentation_size)), transforms.ToTensor(), ])
Example #3
Source File: data_loader.py From HistoGAN with GNU General Public License v3.0 | 6 votes |
def __init__(self, root, scale_size, data_type, skip_pix2pix_processing=False): self.root = root if not os.path.exists(self.root): raise Exception("[!] {} not exists.".format(root)) self.name = os.path.basename(root) if self.name in PIX2PIX_DATASETS and not skip_pix2pix_processing: pix2pix_split_images(self.root) self.paths = glob(os.path.join(self.root, '{}/*'.format(data_type))) if len(self.paths) == 0: raise Exception("No images are found in {}".format(self.root)) self.shape = list(Image.open(self.paths[0]).size) + [3] self.transform = transforms.Compose([ transforms.Scale(scale_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ])
Example #4
Source File: test_preprocessor.py From open-reid with MIT License | 6 votes |
def test_getitem(self): import torchvision.transforms as t from reid.datasets.viper import VIPeR from reid.utils.data.preprocessor import Preprocessor root, split_id, num_val = '/tmp/open-reid/viper', 0, 100 dataset = VIPeR(root, split_id=split_id, num_val=num_val, download=True) preproc = Preprocessor(dataset.train, root=dataset.images_dir, transform=t.Compose([ t.Scale(256), t.CenterCrop(224), t.ToTensor(), t.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])) self.assertEquals(len(preproc), len(dataset.train)) img, pid, camid = preproc[0] self.assertEquals(img.size(), (3, 224, 224))
Example #5
Source File: img_to_vec.py From img2vec with MIT License | 6 votes |
def __init__(self, cuda=False, model='resnet-18', layer='default', layer_output_size=512): """ Img2Vec :param cuda: If set to True, will run forward pass on GPU :param model: String name of requested model :param layer: String or Int depending on model. See more docs: https://github.com/christiansafka/img2vec.git :param layer_output_size: Int depicting the output size of the requested layer """ self.device = torch.device("cuda" if cuda else "cpu") self.layer_output_size = layer_output_size self.model_name = model self.model, self.extraction_layer = self._get_model_and_layer(model, layer) self.model = self.model.to(self.device) self.model.eval() self.scaler = transforms.Scale((224, 224)) self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) self.to_tensor = transforms.ToTensor()
Example #6
Source File: dataloader.py From FewShotWithoutForgetting with MIT License | 6 votes |
def __init__(self, split='train'): self.split = split assert(split=='train' or split=='val') self.name = 'ImageNet_Split_' + split print('Loading ImageNet dataset - split {0}'.format(split)) transforms_list = [] transforms_list.append(transforms.Scale(256)) transforms_list.append(transforms.CenterCrop(224)) transforms_list.append(lambda x: np.asarray(x)) transforms_list.append(transforms.ToTensor()) mean_pix = [0.485, 0.456, 0.406] std_pix = [0.229, 0.224, 0.225] transforms_list.append(transforms.Normalize(mean=mean_pix, std=std_pix)) self.transform = transforms.Compose(transforms_list) traindir = os.path.join(_IMAGENET_DATASET_DIR, 'train') valdir = os.path.join(_IMAGENET_DATASET_DIR, 'val') self.data = datasets.ImageFolder( traindir if split=='train' else valdir, self.transform) self.labels = [item[1] for item in self.data.imgs]
Example #7
Source File: datasets.py From Recipe2ImageGAN with MIT License | 6 votes |
def get_imgs(imageIndex, imsize, file_name,transform=None, normalize=None): f = h5py.File(file_name,'r') images = f['images'] img = images[imageIndex] # rotate axis to (256,256,3) img = np.moveaxis(img, 0, -1) # convert to PIL Image img = Image.fromarray(img, 'RGB') if transform is not None: img = transform(img) ret = [] for i in range(cfg.TREE.BRANCH_NUM): if i < (cfg.TREE.BRANCH_NUM - 1): re_img = transforms.Scale(imsize[i])(img) else: re_img = img ret.append(normalize(re_img)) rec_id = f['recIDs'][imageIndex] img_id = f['imagesIDs'][imageIndex] return ret, rec_id, img_id
Example #8
Source File: unaligned_data_loader.py From MCD_DA with MIT License | 6 votes |
def initialize(self, source, target, batch_size1, batch_size2, scale=32): transform = transforms.Compose([ transforms.Scale(scale), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) dataset_source = Dataset(source['imgs'], source['labels'], transform=transform) dataset_target = Dataset(target['imgs'], target['labels'], transform=transform) # dataset_source = tnt.dataset.TensorDataset([source['imgs'], source['labels']]) # dataset_target = tnt.dataset.TensorDataset([target['imgs'], target['labels']]) data_loader_s = torch.utils.data.DataLoader( dataset_source, batch_size=batch_size1, shuffle=True, num_workers=4) data_loader_t = torch.utils.data.DataLoader( dataset_target, batch_size=batch_size2, shuffle=True, num_workers=4) self.dataset_s = dataset_source self.dataset_t = dataset_target self.paired_data = PairedData(data_loader_s, data_loader_t, float("inf"))
Example #9
Source File: base_dataset.py From non-stationary_texture_syn with MIT License | 6 votes |
def get_transform(opt): transform_list = [] if opt.resize_or_crop == 'resize_and_crop': osize = [opt.loadSize, opt.loadSize] transform_list.append(transforms.Scale(osize, Image.BICUBIC)) transform_list.append(transforms.RandomCrop(opt.fineSize)) elif opt.resize_or_crop == 'crop': transform_list.append(transforms.RandomCrop(opt.fineSize)) elif opt.resize_or_crop == 'scale_width': transform_list.append(transforms.Lambda( lambda img: __scale_width(img, opt.fineSize))) elif opt.resize_or_crop == 'scale_width_and_crop': transform_list.append(transforms.Lambda( lambda img: __scale_width(img, opt.loadSize))) transform_list.append(transforms.RandomCrop(opt.fineSize)) # if opt.isTrain and not opt.no_flip: # transform_list.append(transforms.RandomHorizontalFlip()) transform_list += [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] return transforms.Compose(transform_list)
Example #10
Source File: imsitu_loader.py From verb-attributes with MIT License | 6 votes |
def transform(is_train=True, normalize=True): """ Returns a transform object """ filters = [] filters.append(Scale(256)) if is_train: filters.append(RandomCrop(224)) else: filters.append(CenterCrop(224)) if is_train: filters.append(RandomHorizontalFlip()) filters.append(ToTensor()) if normalize: filters.append(Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])) return Compose(filters)
Example #11
Source File: base.py From Sound-of-Pixels with MIT License | 6 votes |
def _init_transform(self): mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] if self.split == 'train': self.img_transform = transforms.Compose([ transforms.Scale(int(self.imgSize * 1.2)), transforms.RandomCrop(self.imgSize), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean, std)]) else: self.img_transform = transforms.Compose([ transforms.Scale(self.imgSize), transforms.CenterCrop(self.imgSize), transforms.ToTensor(), transforms.Normalize(mean, std)])
Example #12
Source File: base_dataset.py From Recycle-GAN with MIT License | 6 votes |
def get_transform(opt): transform_list = [] if opt.resize_or_crop == 'resize_and_crop': osize = [opt.loadSize, opt.loadSize] transform_list.append(transforms.Scale(osize, Image.BICUBIC)) transform_list.append(transforms.RandomCrop(opt.fineSize)) elif opt.resize_or_crop == 'crop': transform_list.append(transforms.RandomCrop(opt.fineSize)) elif opt.resize_or_crop == 'scale_width': transform_list.append(transforms.Lambda( lambda img: __scale_width(img, opt.fineSize))) elif opt.resize_or_crop == 'scale_width_and_crop': transform_list.append(transforms.Lambda( lambda img: __scale_width(img, opt.loadSize))) transform_list.append(transforms.RandomCrop(opt.fineSize)) if opt.isTrain and not opt.no_flip: transform_list.append(transforms.RandomHorizontalFlip()) transform_list += [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] return transforms.Compose(transform_list)
Example #13
Source File: trainer.py From pggan-pytorch with MIT License | 6 votes |
def feed_interpolated_input(self, x): if self.phase == 'gtrns' and floor(self.resl)>2 and floor(self.resl)<=self.max_resl: alpha = self.complete['gen']/100.0 transform = transforms.Compose( [ transforms.ToPILImage(), transforms.Scale(size=int(pow(2,floor(self.resl)-1)), interpolation=0), # 0: nearest transforms.Scale(size=int(pow(2,floor(self.resl))), interpolation=0), # 0: nearest transforms.ToTensor(), ] ) x_low = x.clone().add(1).mul(0.5) for i in range(x_low.size(0)): x_low[i] = transform(x_low[i]).mul(2).add(-1) x = torch.add(x.mul(alpha), x_low.mul(1-alpha)) # interpolated_x if self.use_cuda: return x.cuda() else: return x
Example #14
Source File: img_to_vec.py From LaSO with BSD 3-Clause "New" or "Revised" License | 6 votes |
def __init__(self, model='inception', layer='default', layer_output_size=2048, data="top10", transform=None): """ Img2Vec :param model: String name of requested model :param layer: String or Int depending on model. See more docs: https://github.com/christiansafka/img2vec.git :param layer_output_size: Int depicting the output size of the requested layer """ cuda = True if torch.cuda.is_available() else False self.device = torch.device("cuda" if cuda else "cpu") self.layer_output_size = layer_output_size # self.model_path = '/dccstor/alfassy/saved_models/inception_traincocoInceptionT10Half2018.9.1.9:30epoch:71' # self.model_path = '/dccstor/alfassy/saved_models/inception_trainCocoIncHalf2018.10.3.13:39best' # self.model_path = '/dccstor/alfassy/saved_models/inception_trainCocoIncHalf2018.10.8.12:46best' self.model_path = '/dccstor/alfassy/saved_models/inception_trainCocoIncHalf642018.10.9.13:44epoch:30' self.model, self.extraction_layer = self._get_model_and_layer(model, layer, data) self.model = self.model.to(self.device) self.model.eval() #self.scaler = transforms.Resize(224, 224) #self.scaler = transforms.Scale((224, 224)) self.transform = transform self.model_name = model
Example #15
Source File: train.py From AlacGAN with MIT License | 6 votes |
def __call__(self, img): for attempt in range(10): area = img.size[0] * img.size[1] target_area = random.uniform(0.9, 1.) * area aspect_ratio = random.uniform(7. / 8, 8. / 7) w = int(round(math.sqrt(target_area * aspect_ratio))) h = int(round(math.sqrt(target_area / aspect_ratio))) if random.random() < 0.5: w, h = h, w if w <= img.size[0] and h <= img.size[1]: x1 = random.randint(0, img.size[0] - w) y1 = random.randint(0, img.size[1] - h) img = img.crop((x1, y1, x1 + w, y1 + h)) assert (img.size == (w, h)) return img.resize((self.size, self.size), self.interpolation) # Fallback scale = Scale(self.size, interpolation=self.interpolation) crop = CenterCrop(self.size) return crop(scale(img))
Example #16
Source File: preprocess.py From DSGN with MIT License | 5 votes |
def scale_crop(input_size, scale_size=None, normalize=__imagenet_stats): t_list = [ transforms.ToTensor(), transforms.Normalize(**normalize), ] #if scale_size != input_size: #t_list = [transforms.Scale((960,540))] + t_list return transforms.Compose(t_list)
Example #17
Source File: data_loader.py From torch-light with MIT License | 5 votes |
def __init__(self, path, img_size, batch_size, is_cuda): self._img_files = os.listdir(path) self._path = path self._is_cuda = is_cuda self._step = 0 self._batch_size = batch_size self.sents_size = len(self._img_files) self._stop_step = self.sents_size // batch_size self._encode = transforms.Compose([ transforms.Scale(img_size), transforms.RandomCrop(img_size), transforms.ToTensor() ])
Example #18
Source File: preprocess.py From DeepLiDAR with MIT License | 5 votes |
def scale_random_crop(input_size, scale_size=None, normalize=__imagenet_stats): t_list = [ transforms.RandomCrop(input_size), transforms.ToTensor(), transforms.Normalize(**normalize), ] if scale_size != input_size: t_list = [transforms.Scale(scale_size)] + t_list transforms.Compose(t_list)
Example #19
Source File: preprocess.py From DSGN with MIT License | 5 votes |
def scale_random_crop(input_size, scale_size=None, normalize=__imagenet_stats): t_list = [ transforms.RandomCrop(input_size), transforms.ToTensor(), transforms.Normalize(**normalize), ] if scale_size != input_size: t_list = [transforms.Scale(scale_size)] + t_list transforms.Compose(t_list)
Example #20
Source File: utils.py From pggan-pytorch with MIT License | 5 votes |
def resize(x, size): transform = transforms.Compose([ transforms.ToPILImage(), transforms.Scale(size), transforms.ToTensor(), ]) return transform(x)
Example #21
Source File: preprocess.py From DeepLiDAR with MIT License | 5 votes |
def scale_crop(input_size, scale_size=None, normalize=__imagenet_stats): t_list = [ # transforms.RandomHorizontalFlip(), transforms.ToTensor(), # transforms.Normalize(**normalize), ] #if scale_size != input_size: #t_list = [transforms.Scale((960,540))] + t_list return transforms.Compose(t_list)
Example #22
Source File: preprocess.py From StereoNet-ActiveStereoNet with MIT License | 5 votes |
def scale_crop(input_size, scale_size=None, normalize=__imagenet_stats): t_list = [ transforms.ToTensor(), transforms.Normalize(**normalize), ] #if scale_size != input_size: #t_list = [transforms.Scale((960,540))] + t_list return transforms.Compose(t_list)
Example #23
Source File: classify.py From drn with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_model(args): # create model model = models.__dict__[args.arch](args.pretrained) model = torch.nn.DataParallel(model).cuda() if args.resume: if os.path.isfile(args.resume): print("=> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume) args.start_epoch = checkpoint['epoch'] best_prec1 = checkpoint['best_prec1'] model.load_state_dict(checkpoint['state_dict']) print("=> loaded checkpoint '{}' (epoch {})" .format(args.resume, checkpoint['epoch'])) else: print("=> no checkpoint found at '{}'".format(args.resume)) cudnn.benchmark = True # Data loading code valdir = os.path.join(args.data, 'val') normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) t = transforms.Compose([ transforms.Scale(args.scale_size), transforms.CenterCrop(args.crop_size), transforms.ToTensor(), normalize]) val_loader = torch.utils.data.DataLoader( datasets.ImageFolder(valdir, t), batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True) criterion = nn.CrossEntropyLoss().cuda() validate(args, val_loader, model, criterion)
Example #24
Source File: datasets.py From attn-gan with MIT License | 5 votes |
def get_imgs(img_path, imsize, bbox=None, transform=None, normalize=None): img = Image.open(img_path).convert('RGB') width, height = img.size if bbox is not None: r = int(np.maximum(bbox[2], bbox[3]) * 0.75) center_x = int((2 * bbox[0] + bbox[2]) / 2) center_y = int((2 * bbox[1] + bbox[3]) / 2) y1 = np.maximum(0, center_y - r) y2 = np.minimum(height, center_y + r) x1 = np.maximum(0, center_x - r) x2 = np.minimum(width, center_x + r) img = img.crop([x1, y1, x2, y2]) if transform is not None: img = transform(img) ret = [] if cfg.GAN.B_DCGAN: ret = [normalize(img)] else: for i in range(cfg.TREE.BRANCH_NUM): # print(imsize[i]) if i < (cfg.TREE.BRANCH_NUM - 1): re_img = transforms.Scale(imsize[i])(img) else: re_img = img ret.append(normalize(re_img)) return ret
Example #25
Source File: preprocess.py From bigBatch with MIT License | 5 votes |
def scale_random_crop(input_size, scale_size=None, normalize=__imagenet_stats): t_list = [ transforms.RandomCrop(input_size), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(**normalize), ] if scale_size != input_size: t_list = [transforms.Scale(scale_size)] + t_list return transforms.Compose(t_list)
Example #26
Source File: preprocess.py From bigBatch with MIT License | 5 votes |
def scale_crop(input_size, scale_size=None, normalize=__imagenet_stats): t_list = [ transforms.CenterCrop(input_size), transforms.ToTensor(), transforms.Normalize(**normalize), ] if scale_size != input_size: t_list = [transforms.Scale(scale_size)] + t_list return transforms.Compose(t_list)
Example #27
Source File: preprocess.py From StereoNet-ActiveStereoNet with MIT License | 5 votes |
def scale_random_crop(input_size, scale_size=None, normalize=__imagenet_stats): t_list = [ transforms.RandomCrop(input_size), transforms.ToTensor(), transforms.Normalize(**normalize), ] if scale_size != input_size: t_list = [transforms.Scale(scale_size)] + t_list transforms.Compose(t_list)
Example #28
Source File: preprocess.py From StereoNet-ActiveStereoNet with MIT License | 5 votes |
def scale_crop(input_size, scale_size=None, normalize=__imagenet_stats): t_list = [ transforms.ToTensor(), transforms.Normalize(**normalize), ] # if scale_size != input_size: # t_list = [transforms.Scale((960,540))] + t_list return transforms.Compose(t_list)
Example #29
Source File: data.py From VSE-C with MIT License | 5 votes |
def get_transform(data_name, split_name, opt): normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) t_list = [] if split_name == 'train': t_list = [transforms.RandomSizedCrop(opt.crop_size), transforms.RandomHorizontalFlip()] elif split_name == 'val': t_list = [transforms.Scale(256), transforms.CenterCrop(224)] elif split_name == 'test': t_list = [transforms.Scale(256), transforms.CenterCrop(224)] t_end = [transforms.ToTensor(), normalizer] transform = transforms.Compose(t_list + t_end) return transform
Example #30
Source File: preprocess.py From StereoNet-ActiveStereoNet with MIT License | 5 votes |
def scale_random_crop(input_size, scale_size=None, normalize=__imagenet_stats): t_list = [ transforms.RandomCrop(input_size), transforms.ToTensor(), transforms.Normalize(**normalize), ] if scale_size != input_size: t_list = [transforms.Scale(scale_size)] + t_list transforms.Compose(t_list)