Add AlexNet to paddle vision

python/paddle/tests/test_vision_models.py

@@ -41,6 +41,9 @@ def test_mobilenetv2_pretrained(self):
     def test_mobilenetv1(self):
         self.models_infer('mobilenet_v1')
 
+    def test_alexnet(self):
+        self.models_infer('AlexNet')
+
     def test_vgg11(self):
         self.models_infer('vgg11')
 

python/paddle/vision/models/__init__.py

@@ -28,6 +28,7 @@
 from .vgg import vgg16  # noqa: F401
 from .vgg import vgg19  # noqa: F401
 from .lenet import LeNet  # noqa: F401
+from .alexnet import AlexNet  # noqa: F401
 
 __all__ = [ #noqa
     'ResNet',
@@ -45,5 +46,6 @@
     'mobilenet_v1',
     'MobileNetV2',
     'mobilenet_v2',
-    'LeNet'
+    'LeNet',
+    'AlexNet'
 ]

python/paddle/vision/models/alexnet.py

@@ -0,0 +1,71 @@
+#  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.
+
+import paddle
+import paddle.nn as nn
+
+__all__ = []
+
+
+class AlexNet(nn.Layer):
+  def __init__(self, num_classes=10):
+    """AlexNet model architecture from the
+    `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.
+
+    Args:
+        num_classes (int): output dim of the classifier. If num_classes <=0, the classifier
+                            will not be defined. Default: 10.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.vision.models import AlexNet
+
+            model = AlexNet()
+    """
+    super(AlexNet, self).__init__()
+    self.num_classes = num_classes
+    self.features = nn.Sequential(
+      nn.Conv2D(3, 64, kernel_size=3, stride=2, padding=1),
+      nn.ReLU(),
+      nn.MaxPool2D(kernel_size=3, stride=2),
+      nn.Conv2D(64, 192, kernel_size=3, stride=1, padding=1),
+      nn.ReLU(),
+      nn.MaxPool2D(kernel_size=3, stride=2),
+      nn.Conv2D(192, 384, kernel_size=3, stride=1, padding=1),
+      nn.ReLU(),
+      nn.Conv2D(384, 256, kernel_size=3, stride=1, padding=1),
+      nn.ReLU(),
+      nn.Conv2D(256, 256, kernel_size=3, stride=1, padding=1),
+      nn.ReLU(),
+      nn.MaxPool2D(kernel_size=3, stride=2),
+    )
+
+    if num_classes > 0:
+        self.classifier = nn.Sequential(
+        nn.Dropout(),
+        nn.Linear(256 * 1 * 1, 4096),
+        nn.ReLU(),
+        nn.Dropout(),
+        nn.Linear(4096, 4096),
+        nn.ReLU(),
+        nn.Linear(4096, num_classes),
+        )
+
+  def forward(self, x):
+    x = self.features(x)
+    if self.num_classes > 0:
+        x = paddle.flatten(x, 1)
+        x = self.classifier(x)
+    return x