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  • pytorch torchvision.transforms.Resize

    千次阅读 2020-08-25 20:20:20
    CLASS torchvision.transforms.Resize(size, interpolation=2) 参数 描述 size (sequence or int) 如果size是sequence(h,w),则输出为此尺寸:如果size是int,则暗比例缩放 interpolation (int, optional...

    API

    CLASS torchvision.transforms.Resize(size, interpolation=2)
    
    参数 描述
    size (sequence or int) 如果size是sequence(h,w),则输出为此尺寸:如果size是int,则暗比例缩放
    interpolation (int, optional) 插值方法,默认是双线性

    参考:
    https://pytorch.org/docs/stable/torchvision/transforms.html#torchvision.transforms.Resize
    https://blog.csdn.net/w55100/article/details/88415637

    展开全文
  • Pytorch transforms.Resize()的简单用法

    千次阅读 2021-04-02 10:50:26
    transforms.Resize(x) 而一般输入深度网络的特征图长宽是相等的,就不能采取等比例缩放的方式了,需要同时指定长宽: transforms.Resize([h, w]) 例如transforms.Resize([224, 224])就能将输入图片转化成224×224...

    简单来说就是调整PILImage对象的尺寸,注意不能是用io.imread或者cv2.imread读取的图片,这两种方法得到的是ndarray。

    将图片短边缩放至x,长宽比保持不变:

    transforms.Resize(x)
    

    而一般输入深度网络的特征图长宽是相等的,就不能采取等比例缩放的方式了,需要同时指定长宽:

    transforms.Resize([h, w])
    

    例如transforms.Resize([224, 224])就能将输入图片转化成224×224的输入特征图。

    这样虽然会改变图片的长宽比,但是本身并没有发生裁切,仍可以通过resize方法返回原来的形状:

    from PIL import Image
    from torchvision import transforms
    
    img = Image.open('1.jpg')
    w, h = img.size
    resize = transforms.Resize([224,244])
    img = resize(img)
    img.save('2.jpg')
    resize2 = transforms.Resize([h, w])
    img = resize2(img)
    img.save('3.jpg')
    

    需要注意的一点是PILImage对象size属性返回的是w, h,而resize的参数顺序是h, w。

    展开全文
  • transforms.Resize()的用法

    2021-06-25 16:40:22
    调整PILImage对象的尺寸 ...transforms.Resize([h, w]) 将图片短边缩放至x,长宽比保持不变: transforms.Resize(x) 例如transforms.Resize([224, 224])就能将输入图片转化成224×224的输入特征图。 ...

    调整PILImage对象的尺寸
    提示:不能是用io.imread或者cv2.imread读取的图片,这两种方法得到的是ndarray

    一般输入深度网络的特征图长宽是相等的,就不能采取等比例缩放的方式了,需要同时指定长宽:

    transforms.Resize([h, w])

    将图片短边缩放至x,长宽比保持不变:

    transforms.Resize(x)

    例如transforms.Resize([224, 224])就能将输入图片转化成224×224的输入特征图。

    需要注意的一点是PILImage对象size属性返回的是w, h,而resize的参数顺序是h, w。

    def load_data(root_path, dir, batch_size, phase):
     transform_dict = {
     'src': transforms.Compose(
     [transforms.RandomResizedCrop(224),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ]),
     'tar': transforms.Compose(
     [transforms.Resize(224),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ])}
     data = datasets.ImageFolder(root=root_path + dir, transform=transform_dict[phase])
     data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=4)
     return data_loader
    def get_screen(self, env):
     screen = env.render(mode='rgb_array').transpose((2, 0, 1)) # transpose into torch order (CHW)
     # Strip off the top and bottom of the screen
     screen = screen[:, 160:320]
     view_width = 320
     cart_location = self.get_cart_location(env)
     if cart_location < view_width // 2:
     slice_range = slice(view_width)
     elif cart_location > (self.screen_width - view_width // 2):
     slice_range = slice(-view_width, None)
     else:
     slice_range = slice(cart_location - view_width // 2,
     cart_location + view_width // 2)
     # Strip off the edges, so that we have a square image centered on a cart
     screen = screen[:, :, slice_range]
     # Convert to float, rescale, convert to torch tensor
     screen = np.ascontiguousarray(screen, dtype=np.float32) / 255
     screen = torch.from_numpy(screen)
     # Resize, and add a batch dimension (BCHW)
     return resize(screen).unsqueeze(0)
    def load_data(data_folder, batch_size, phase='train', train_val_split=True, train_ratio=.8):
     transform_dict = {
     'train': transforms.Compose(
     [transforms.Resize(256),
     transforms.RandomCrop(224),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ]),
     'test': transforms.Compose(
     [transforms.Resize(224),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ])}
     data = datasets.ImageFolder(root=data_folder, transform=transform_dict[phase])
     if phase == 'train':
     if train_val_split:
     train_size = int(train_ratio * len(data))
     test_size = len(data) - train_size
     data_train, data_val = torch.utils.data.random_split(data, [train_size, test_size])
     train_loader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, shuffle=True, drop_last=True,
     num_workers=4)
     val_loader = torch.utils.data.DataLoader(data_val, batch_size=batch_size, shuffle=False, drop_last=False,
     num_workers=4)
     return [train_loader, val_loader]
     else:
     train_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=True,
     num_workers=4)
     return train_loader
     else:
     test_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=False, drop_last=False,
     num_workers=4)
     return test_loader
     ## Below are for ImageCLEF datasets
    def load_imageclef_train(root_path, domain, batch_size, phase):
     transform_dict = {
     'src': transforms.Compose(
     [transforms.Resize((256, 256)),
     transforms.RandomCrop(224),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ]),
     'tar': transforms.Compose(
     [transforms.Resize((224, 224)),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ])}
     data = ImageCLEF(root_dir=root_path, domain=domain, transform=transform_dict[phase])
     train_size = int(0.8 * len(data))
     test_size = len(data) - train_size
     data_train, data_val = torch.utils.data.random_split(data, [train_size, test_size])
     train_loader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, shuffle=True, drop_last=False,
     num_workers=4)
     val_loader = torch.utils.data.DataLoader(data_val, batch_size=batch_size, shuffle=True, drop_last=False,
     num_workers=4)
     return train_loader, val_loader
    def load_imageclef_test(root_path, domain, batch_size, phase):
     transform_dict = {
     'src': transforms.Compose(
     [transforms.Resize((256,256)),
     transforms.RandomCrop(224),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ]),
     'tar': transforms.Compose(
     [transforms.Resize((224, 224)),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ])}
     data = ImageCLEF(root_dir=root_path, domain=domain, transform=transform_dict[phase])
     data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=4)
     return data_loader
    def load_imageclef_test(root_path, domain, batch_size, phase):
     transform_dict = {
     'src': transforms.Compose(
     [transforms.Resize((256,256)),
     transforms.RandomCrop(224),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ]),
     'tar': transforms.Compose(
     [transforms.Resize((224, 224)),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ])}
     data = ImageCLEF(root_dir=root_path, domain=domain, transform=transform_dict[phase])
     data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=4)
     return data_loader
    def load_training(root_path, dir, batch_size, kwargs):
     transform = transforms.Compose(
     [transforms.Resize([256, 256]),
     transforms.RandomCrop(224),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor()])
     data = datasets.ImageFolder(root=root_path + dir, transform=transform)
     train_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=True, **kwargs)
     return train_loader
    def load_data(data_folder, batch_size, train, kwargs):
     transform = {
     'train': transforms.Compose(
     [transforms.Resize([256, 256]),
     transforms.RandomCrop(224),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225])]),
     'test': transforms.Compose(
     [transforms.Resize([224, 224]),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225])])
     }
     data = datasets.ImageFolder(root = data_folder, transform=transform['train' if train else 'test'])
     data_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, **kwargs, drop_last = True if train else False)
     return data_loader
    def load_train(root_path, dir, batch_size, phase):
     transform_dict = {
     'src': transforms.Compose(
     [transforms.RandomResizedCrop(224),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ]),
     'tar': transforms.Compose(
     [transforms.Resize(224),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406],
     std=[0.229, 0.224, 0.225]),
     ])}
     data = datasets.ImageFolder(root=root_path + dir, transform=transform_dict[phase])
     train_size = int(0.8 * len(data))
     test_size = len(data) - train_size
     data_train, data_val = torch.utils.data.random_split(data, [train_size, test_size])
     train_loader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=4)
     val_loader = torch.utils.data.DataLoader(data_val, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=4)
     return train_loader, val_loader
    def __init__(self, train_mode, loader_params, dataset_params, augmentation_params):
     super().__init__(train_mode, loader_params, dataset_params, augmentation_params)
     self.image_transform = transforms.Compose([transforms.Resize((self.dataset_params.h, self.dataset_params.w)),
     transforms.Grayscale(num_output_channels=3),
     transforms.ToTensor(),
     transforms.Normalize(mean=self.dataset_params.MEAN,
     std=self.dataset_params.STD),
     ])
     self.mask_transform = transforms.Compose([transforms.Resize((self.dataset_params.h, self.dataset_params.w),
     interpolation=0),
     transforms.Lambda(to_array),
     transforms.Lambda(to_tensor),
     ])
     self.image_augment_train = ImgAug(self.augmentation_params['image_augment_train'])
     self.image_augment_with_target_train = ImgAug(self.augmentation_params['image_augment_with_target_train'])
     if self.dataset_params.target_format == 'png':
     self.dataset = ImageSegmentationPngDataset
     elif self.dataset_params.target_format == 'json':
     self.dataset = ImageSegmentationJsonDataset
     else:
     raise Exception('files must be png or json')
    def get_transform2(dataset_name, net_transform, downscale):
     "Returns image and label transform to downscale, crop and prepare for net."
     orig_size = get_orig_size(dataset_name)
     transform = []
     target_transform = []
     if downscale is not None:
     transform.append(transforms.Resize(orig_size // downscale))
     target_transform.append(
     transforms.Resize(orig_size // downscale,
     interpolation=Image.NEAREST))
     transform.extend([transforms.Resize(orig_size), net_transform])
     target_transform.extend([transforms.Resize(orig_size, interpolation=Image.NEAREST),
     to_tensor_raw])
     transform = transforms.Compose(transform)
     target_transform = transforms.Compose(target_transform)
     return transform, target_transform
    def get_transform(params, image_size, num_channels):
     # Transforms for PIL Images: Gray RGB
     Gray2RGB = transforms.Lambda(lambda x: x.convert('RGB'))
     RGB2Gray = transforms.Lambda(lambda x: x.convert('L'))
     transform = []
     # Does size request match original size?
     if not image_size == params.image_size:
     transform.append(transforms.Resize(image_size))
     # Does number of channels requested match original?
     if not num_channels == params.num_channels:
     if num_channels == 1:
     transform.append(RGB2Gray)
     elif num_channels == 3:
     transform.append(Gray2RGB)
     else:
     print('NumChannels should be 1 or 3', num_channels)
     raise Exception
     transform += [transforms.ToTensor(),
     transforms.Normalize((params.mean,), (params.std,))]
     return transforms.Compose(transform)
    def get_mnist_dataloaders(batch_size=128):
     """MNIST dataloader with (32, 32) sized images."""
     # Resize images so they are a power of 2
     all_transforms = transforms.Compose([
     transforms.Resize(32),
     transforms.ToTensor()
     ])
     # Get train and test data
     train_data = datasets.MNIST('../data', train=True, download=True,
     transform=all_transforms)
     test_data = datasets.MNIST('../data', train=False,
     transform=all_transforms)
     # Create dataloaders
     train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
     test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)
     return train_loader, test_loader
    def get_fashion_mnist_dataloaders(batch_size=128):
     """Fashion MNIST dataloader with (32, 32) sized images."""
     # Resize images so they are a power of 2
     all_transforms = transforms.Compose([
     transforms.Resize(32),
     transforms.ToTensor()
     ])
     # Get train and test data
     train_data = datasets.FashionMNIST('../fashion_data', train=True, download=True,
     transform=all_transforms)
     test_data = datasets.FashionMNIST('../fashion_data', train=False,
     transform=all_transforms)
     # Create dataloaders
     train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
     test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)
     return train_loader, test_loader
    def get_lsun_dataloader(path_to_data='../lsun', dataset='bedroom_train',
     batch_size=64):
     """LSUN dataloader with (128, 128) sized images.
     path_to_data : str
     One of 'bedroom_val' or 'bedroom_train'
     """
     # Compose transforms
     transform = transforms.Compose([
     transforms.Resize(128),
     transforms.CenterCrop(128),
     transforms.ToTensor()
     ])
     # Get dataset
     lsun_dset = datasets.LSUN(db_path=path_to_data, classes=[dataset],
     transform=transform)
     # Create dataloader
     return DataLoader(lsun_dset, batch_size=batch_size, shuffle=True)
    def save_distorted(method=gaussian_noise):
     for severity in range(1, 6):
     print(method.__name__, severity)
     distorted_dataset = DistortImageFolder(
     root="/share/data/vision-greg/ImageNet/clsloc/images/val",
     method=method, severity=severity,
     transform=trn.Compose([trn.Resize(256), trn.CenterCrop(224)]))
     distorted_dataset_loader = torch.utils.data.DataLoader(
     distorted_dataset, batch_size=100, shuffle=False, num_workers=4)
     for _ in distorted_dataset_loader: continue
     # /// End Further Setup ///
     # /// Display Results ///
    def save_distorted(method=gaussian_noise):
     for severity in range(1, 6):
     print(method.__name__, severity)
     distorted_dataset = DistortImageFolder(
     root="./imagenet_val_bbox_crop/",
     method=method, severity=severity,
     transform=trn.Compose([trn.Resize((64, 64))]))
     distorted_dataset_loader = torch.utils.data.DataLoader(
     distorted_dataset, batch_size=100, shuffle=False, num_workers=6)
     for _ in distorted_dataset_loader: continue
     # /// End Further Setup ///
     # /// Display Results ///
    def save_distorted(method=gaussian_noise):
     for severity in range(1, 6):
     print(method.__name__, severity)
     distorted_dataset = DistortImageFolder(
     root="/share/data/vision-greg/ImageNet/clsloc/images/val",
     method=method, severity=severity,
     transform=trn.Compose([trn.Resize((64, 64))]))
     distorted_dataset_loader = torch.utils.data.DataLoader(
     distorted_dataset, batch_size=100, shuffle=False, num_workers=6)
     for _ in distorted_dataset_loader: continue
     # /// End Further Setup ///
     # /// Display Results ///
    def get_transform():
     transform_image_list = [
     transforms.Resize((256, 256), 3),
     transforms.ToTensor(),
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
     ]
     transform_gt_list = [
     transforms.Resize((256, 256), 0),
     transforms.Lambda(lambda img: np.asarray(img, dtype=np.uint8)),
     ]
     data_transforms = {
     'img': transforms.Compose(transform_image_list),
     'gt': transforms.Compose(transform_gt_list),
     }
     return data_transforms
    def get_data(train):
     data_raw = datasets.CIFAR10('../data/dl/', train=train, download=True, transform=transforms.Compose([
     transforms.Grayscale(),
     transforms.Resize((20, 20)),
     transforms.ToTensor(),
     lambda x: x.numpy().flatten()]))
     data_x, data_y = zip(*data_raw)
     data_x = np.array(data_x)
     data_y = np.array(data_y, dtype='int32').reshape(-1, 1)
     # binarize
     label_0 = data_y < 5
     label_1 = ~label_0
     data_y[label_0] = 0
     data_y[label_1] = 1
     data = pd.DataFrame(data_x)
     data[COLUMN_LABEL] = data_y
     return data, data_x.mean(), data_x.std()
     #---
    def get_data(train):
     data_raw = datasets.CIFAR10('../data/dl/', train=train, download=True, transform=transforms.Compose([
     transforms.Grayscale(),
     transforms.Resize((20, 20)),
     transforms.ToTensor(),
     lambda x: x.numpy().flatten()]))
     data_x, data_y = zip(*data_raw)
     data_x = np.array(data_x)
     data_y = np.array(data_y, dtype='int32').reshape(-1, 1)
     data = pd.DataFrame(data_x)
     data[COLUMN_LABEL] = data_y
     return data, data_x.mean(), data_x.std()
     #---
    def initialize_dataset(clevr_dir, dictionaries, state_description=True):
     if not state_description:
     train_transforms = transforms.Compose([transforms.Resize((128, 128)),
     transforms.Pad(8),
     transforms.RandomCrop((128, 128)),
     transforms.RandomRotation(2.8), # .05 rad
     transforms.ToTensor()])
     test_transforms = transforms.Compose([transforms.Resize((128, 128)),
     transforms.ToTensor()])
     clevr_dataset_train = ClevrDataset(clevr_dir, True, dictionaries, train_transforms)
     clevr_dataset_test = ClevrDataset(clevr_dir, False, dictionaries, test_transforms)
     else:
     clevr_dataset_train = ClevrDatasetStateDescription(clevr_dir, True, dictionaries)
     clevr_dataset_test = ClevrDatasetStateDescription(clevr_dir, False, dictionaries)
     return clevr_dataset_train, clevr_dataset_test

    最常用:

    def build_image_transforms(self):
     self.image_transform = transforms.Compose([
     transforms.Resize((224, 224)),
     transforms.ToTensor(),
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
     ])
    def load_data(domain, root_dir, batch_size):
     src_train_img, src_train_label, src_test_img, src_test_label = load_dataset(domain['src'], root_dir)
     tar_train_img, tar_train_label, tar_test_img, tar_test_label = load_dataset(domain['tar'], root_dir)
     transform = transforms.Compose([
     transforms.Resize(32),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
     ])
     data_src_train, data_src_test = GetDataset(src_train_img, src_train_label,
     transform), GetDataset(src_test_img,
     src_test_label,
     transform)
     data_tar_train, data_tar_test = GetDataset(tar_train_img, tar_train_label,
     transform), GetDataset(tar_test_img,
     tar_test_label,
     transform)
     dataloaders = {}
     dataloaders['src'] = torch.utils.data.DataLoader(data_src_train, batch_size=batch_size, shuffle=True,
     drop_last=False,
     num_workers=4)
     dataloaders['val'] = torch.utils.data.DataLoader(data_src_test, batch_size=batch_size, shuffle=True,
     drop_last=False,
     num_workers=4)
     dataloaders['tar'] = torch.utils.data.DataLoader(data_tar_train, batch_size=batch_size, shuffle=True,
     drop_last=False,
     num_workers=4)
     return dataloaders
    def loader(path, batch_size=16, num_workers=1, pin_memory=True):
     normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
     return data.DataLoader(
     datasets.ImageFolder(path,
     transforms.Compose([
     transforms.Resize(256),
     transforms.RandomResizedCrop(224),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor(),
     normalize,
     ])),
     batch_size=batch_size,
     shuffle=True,
     num_workers=num_workers,
     pin_memory=pin_memory)
    def test_loader(path, batch_size=16, num_workers=1, pin_memory=True):
     normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
     return data.DataLoader(
     datasets.ImageFolder(path,
     transforms.Compose([
     transforms.Resize(256),
     transforms.CenterCrop(224),
     transforms.ToTensor(),
     normalize,
     ])),
     batch_size=batch_size,
     shuffle=False,
     num_workers=num_workers,
     pin_memory=pin_memory)
    def load_training(root_path, dir, batch_size, kwargs):
     transform = transforms.Compose(
     [transforms.Resize([256, 256]),
     transforms.RandomCrop(224),
     transforms.RandomHorizontalFlip(),
     transforms.ToTensor()])
     data = datasets.ImageFolder(root=root_path + dir, transform=transform)
     train_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, drop_last=True, **kwargs)
     return train_loader
    def load_testing(root_path, dir, batch_size, kwargs):
     transform = transforms.Compose(
     [transforms.Resize([224, 224]),
     transforms.ToTensor()])
     data = datasets.ImageFolder(root=root_path + dir, transform=transform)
     test_loader = torch.utils.data.DataLoader(data, batch_size=batch_size, shuffle=True, **kwargs)
     return test_loader
    展开全文
  • pytorch transforms.Resize([224, 224])

    千次阅读 2019-12-17 15:56:24
    记住图像尺度统一为224×224时,要用transforms.Resize([224, 224]),不能写成transforms.Resize(224),transforms.Resize(224)表示把图像的短边统一为224,另外一边做同样倍速缩放,不一定为224 ...

    记住图像尺度统一为224×224时,要用transforms.Resize([224, 224]),不能写成transforms.Resize(224),transforms.Resize(224)表示把图像的短边统一为224,另外一边做同样倍速缩放,不一定为224

    展开全文
  • 使用Pytorch训练通常使用Torchvision的transforms进行预处理,transforms提供了丰富的数据增强手段,但是transforms.Resize和OpenCV的resize会有一定差异,这会导致我们使用C++去部署pytorch训练的model时候可能会有...
  • torchvision.transforms.Resize()函数解读

    万次阅读 2019-09-21 15:17:25
    对于PIL Image对象进行resize的运算。 函数源码 class Resize(object): """Resize the input PIL Image to the given size. Args: size (sequence or int): Desired output size. If size is a sequence ...

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