reverse paddle.vision.xxx import (#34489)

python/paddle/__init__.py

@@ -404,7 +404,6 @@
            'logical_xor',
            'exp',
            'bernoulli',
-           'summary',
            'sinh',
            'round',
            'DataParallel',

python/paddle/vision/__init__.py

@@ -20,50 +20,63 @@
 from .image import set_image_backend  # noqa: F401
 from .image import get_image_backend  # noqa: F401
 from .image import image_load  # noqa: F401
-from .models import LeNet as models_LeNet
-import paddle.utils.deprecated as deprecated
+from .datasets import DatasetFolder  # noqa: F401
+from .datasets import ImageFolder  # noqa: F401
+from .datasets import MNIST  # noqa: F401
+from .datasets import FashionMNIST  # noqa: F401
+from .datasets import Flowers  # noqa: F401
+from .datasets import Cifar10  # noqa: F401
+from .datasets import Cifar100  # noqa: F401
+from .datasets import VOC2012  # noqa: F401
+from .models import ResNet  # noqa: F401
+from .models import resnet18  # noqa: F401
+from .models import resnet34  # noqa: F401
+from .models import resnet50  # noqa: F401
+from .models import resnet101  # noqa: F401
+from .models import resnet152  # noqa: F401
+from .models import MobileNetV1  # noqa: F401
+from .models import mobilenet_v1  # noqa: F401
+from .models import MobileNetV2  # noqa: F401
+from .models import mobilenet_v2  # noqa: F401
+from .models import VGG  # noqa: F401
+from .models import vgg11  # noqa: F401
+from .models import vgg13  # noqa: F401
+from .models import vgg16  # noqa: F401
+from .models import vgg19  # noqa: F401
+from .models import LeNet  # noqa: F401
+from .transforms import BaseTransform  # noqa: F401
+from .transforms import Compose  # noqa: F401
+from .transforms import Resize  # noqa: F401
+from .transforms import RandomResizedCrop  # noqa: F401
+from .transforms import CenterCrop  # noqa: F401
+from .transforms import RandomHorizontalFlip  # noqa: F401
+from .transforms import RandomVerticalFlip  # noqa: F401
+from .transforms import Transpose  # noqa: F401
+from .transforms import Normalize  # noqa: F401
+from .transforms import BrightnessTransform  # noqa: F401
+from .transforms import SaturationTransform  # noqa: F401
+from .transforms import ContrastTransform  # noqa: F401
+from .transforms import HueTransform  # noqa: F401
+from .transforms import ColorJitter  # noqa: F401
+from .transforms import RandomCrop  # noqa: F401
+from .transforms import Pad  # noqa: F401
+from .transforms import RandomRotation  # noqa: F401
+from .transforms import Grayscale  # noqa: F401
+from .transforms import ToTensor  # noqa: F401
+from .transforms import to_tensor  # noqa: F401
+from .transforms import hflip  # noqa: F401
+from .transforms import vflip  # noqa: F401
+from .transforms import resize  # noqa: F401
+from .transforms import pad  # noqa: F401
+from .transforms import rotate  # noqa: F401
+from .transforms import to_grayscale  # noqa: F401
+from .transforms import crop  # noqa: F401
+from .transforms import center_crop  # noqa: F401
+from .transforms import adjust_brightness  # noqa: F401
+from .transforms import adjust_contrast  # noqa: F401
+from .transforms import adjust_hue  # noqa: F401
+from .transforms import normalize  # noqa: F401
 
 __all__ = [  #noqa
     'set_image_backend', 'get_image_backend', 'image_load'
 ]
-
-
-class LeNet(models_LeNet):
-    """LeNet model from
-    `"LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.`_
-
-    Args:
-        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
-                            will not be defined. Default: 10.
-
-    Examples:
-        .. code-block:: python
-
-            from paddle.vision.models import LeNet
-
-            model = LeNet()
-    """
-
-    @deprecated(
-        since="2.0.0",
-        update_to="paddle.vision.models.LeNet",
-        level=1,
-        reason="Please use new API in models, paddle.vision.LeNet will be removed in future"
-    )
-    def __init__(self, num_classes=10):
-        super(LeNet, self).__init__(num_classes=10)
-        self.num_classes = num_classes
-        self.features = nn.Sequential(
-            nn.Conv2D(
-                1, 6, 3, stride=1, padding=1),
-            nn.ReLU(),
-            nn.MaxPool2D(2, 2),
-            nn.Conv2D(
-                6, 16, 5, stride=1, padding=0),
-            nn.ReLU(),
-            nn.MaxPool2D(2, 2))
-
-        if num_classes > 0:
-            self.fc = nn.Sequential(
-                nn.Linear(400, 120),
-                nn.Linear(120, 84), nn.Linear(84, num_classes))