python类Lambda()的实例源码

neural_style.py 文件源码 项目:examples 作者: pytorch 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
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)
base_dataset.py 文件源码 项目:DeblurGAN 作者: KupynOrest 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
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)
base_dataset.py 文件源码 项目:CycleGANwithPerceptionLoss 作者: EliasVansteenkiste 项目源码 文件源码 阅读 20 收藏 0 点赞 0 评论 0
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)
base_dataset.py 文件源码 项目:pytorch-CycleGAN-and-pix2pix 作者: junyanz 项目源码 文件源码 阅读 26 收藏 0 点赞 0 评论 0
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)
image.py 文件源码 项目:generative_models 作者: j-min 项目源码 文件源码 阅读 22 收藏 0 点赞 0 评论 0
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)
base_dataset.py 文件源码 项目:GAN_Liveness_Detection 作者: yunfan0621 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
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)
proData.py 文件源码 项目:PaintsPytorch 作者: orashi 项目源码 文件源码 阅读 27 收藏 0 点赞 0 评论 0
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)
img_loader.py 文件源码 项目:torch_light 作者: ne7ermore 项目源码 文件源码 阅读 31 收藏 0 点赞 0 评论 0
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))
img_loader.py 文件源码 项目:torch_light 作者: ne7ermore 项目源码 文件源码 阅读 30 收藏 0 点赞 0 评论 0
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")
augmentations.py 文件源码 项目:nn_tools 作者: hahnyuan 项目源码 文件源码 阅读 25 收藏 0 点赞 0 评论 0
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))
    ])
augmentations.py 文件源码 项目:nn_tools 作者: hahnyuan 项目源码 文件源码 阅读 34 收藏 0 点赞 0 评论 0
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))
    ])


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