我们从Python开源项目中，提取了以下11个代码示例，用于说明如何使用torchvision.transforms.Lambda()。
def stylize(args): content_image = utils.load_image(args.content_image, scale=args.content_scale) content_transform = transforms.Compose([ transforms.ToTensor(), transforms.Lambda(lambda x: x.mul(255)) ]) content_image = content_transform(content_image) content_image = content_image.unsqueeze(0) if args.cuda: content_image = content_image.cuda() content_image = Variable(content_image, volatile=True) style_model = TransformerNet() style_model.load_state_dict(torch.load(args.model)) if args.cuda: style_model.cuda() output = style_model(content_image) if args.cuda: output = output.cpu() output_data = output.data[0] utils.save_image(args.output_image, output_data)
def get_transform(opt): transform_list = [] if opt.resize_or_crop == 'resize_and_crop': osize = [opt.loadSizeX, opt.loadSizeY] 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.loadSizeX))) 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)
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)
def get_transform(resize_crop='resize_and_crop', flip=True, loadSize=286, fineSize=256): transform_list = [] if resize_crop == 'resize_and_crop': osize = [loadSize, loadSize] transform_list.append(transforms.Resize(osize, Image.BICUBIC)) transform_list.append(transforms.RandomCrop(fineSize)) elif resize_crop == 'crop': transform_list.append(transforms.RandomCrop(fineSize)) elif resize_crop == 'scale_width': transform_list.append(transforms.Lambda( lambda img: __scale_width(img, fineSize))) elif resize_crop == 'scale_width_and_crop': transform_list.append(transforms.Lambda( lambda img: __scale_width(img, loadSize))) transform_list.append(transforms.RandomCrop(fineSize)) if 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)
def CreateDataLoader(opt): random.seed(opt.manualSeed) # folder dataset CTrans = transforms.Compose([ transforms.Scale(opt.imageSize, Image.BICUBIC), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) VTrans = transforms.Compose([ RandomSizedCrop(opt.imageSize // 4, Image.BICUBIC), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) def jitter(x): ran = random.uniform(0.7, 1) return x * ran + 1 - ran STrans = transforms.Compose([ transforms.Scale(opt.imageSize, Image.BICUBIC), transforms.ToTensor(), transforms.Lambda(jitter), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) ]) dataset = ImageFolder(rootC=opt.datarootC, rootS=opt.datarootS, transform=CTrans, vtransform=VTrans, stransform=STrans ) assert dataset return data.DataLoader(dataset, batch_size=opt.batchSize, shuffle=True, num_workers=int(opt.workers), drop_last=True)
def toTensor(self, img): encode = transforms.Compose([transforms.Scale(self.img_size), transforms.ToTensor(), transforms.Lambda(lambda x: x[torch.LongTensor([2,1,0])]), transforms.Normalize(mean=[0.40760392, 0.45795686, 0.48501961], std=[1,1,1]), transforms.Lambda(lambda x: x.mul_(255)), ]) return encode(Image.open(img))
def tensor2img(self, tensor): decode = transforms.Compose([transforms.Lambda(lambda x: x.mul_(1./255)), transforms.Normalize(mean=[-0.40760392, -0.45795686, -0.48501961], std=[1,1,1]), transforms.Lambda(lambda x: x[torch.LongTensor([2,1,0])]), ]) tensor = decode(tensor) loader = transforms.Compose([transforms.ToPILImage()]) img = loader(tensor.clamp_(0, 1)) img.save(self.img_path + "/result.jpg")
def get_hue_transform(dim, mean_values): swap = (2, 1, 0) return transforms.Compose([ transforms.Scale(dim), transforms.ToTensor(), transforms.Lambda(lambda x: x.mul(255)), RandomHue(), SwapChannels(swap), transforms.Normalize(mean_values, (1, 1, 1)) ])
def get_advanced_transform(dim, mean_values): # loader must be cv2 loader swap = (2, 1, 0) return transforms.Compose([ Scale(dim), Padding(5), RandomCrop(5), transforms.ToTensor(), transforms.Lambda(lambda x: x.mul(255)), RandomHue(), SwapChannels(swap), transforms.Normalize(mean_values, (1, 1, 1)) ])
