chore: import upstream snapshot with attribution

python/paddle/vision/models/lenet.py

@@ -0,0 +1,83 @@
+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+)
+
+import paddle
+from paddle import nn
+
+if TYPE_CHECKING:
+    from paddle import Tensor
+
+__all__ = []
+
+
+class LeNet(nn.Layer):
+    """LeNet model from
+    `"Gradient-based learning applied to document recognition" <https://ieeexplore.ieee.org/document/726791>`_.
+
+    Args:
+        num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer
+            will not be defined. Default: 10.
+
+    Returns:
+        :ref:`api_paddle_nn_Layer`. An instance of LeNet model.
+
+    Examples:
+        .. code-block:: pycon
+
+            >>> import paddle
+            >>> from paddle.vision.models import LeNet
+
+            >>> model = LeNet()
+
+            >>> x = paddle.rand([1, 1, 28, 28])
+            >>> out = model(x)
+
+            >>> print(out.shape)
+            paddle.Size([1, 10])
+    """
+
+    num_classes: int
+
+    def __init__(self, num_classes: int = 10) -> None:
+        super().__init__()
+        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),
+            )
+
+    def forward(self, inputs: Tensor) -> Tensor:
+        x = self.features(inputs)
+
+        if self.num_classes > 0:
+            x = paddle.flatten(x, 1)
+            x = self.fc(x)
+        return x