[PaddlePaddle Hackathon] add AlexNet

python/paddle/tests/test_pretrained_model.py

@@ -52,7 +52,9 @@ def infer(self, arch):
         np.testing.assert_allclose(res['dygraph'], res['static'])
 
     def test_models(self):
-        arches = ['mobilenet_v1', 'mobilenet_v2', 'resnet18', 'vgg16']
+        arches = [
+            'mobilenet_v1', 'mobilenet_v2', 'resnet18', 'vgg16', 'alexnet'
+        ]
         for arch in arches:
             self.infer(arch)
 

python/paddle/tests/test_vision_models.py

@@ -11,7 +11,6 @@
 # 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 unittest
 import numpy as np
 
@@ -71,6 +70,9 @@ def test_resnet101(self):
     def test_resnet152(self):
         self.models_infer('resnet152')
 
+    def test_alexnet(self):
+        self.models_infer('alexnet')
+
     def test_vgg16_num_classes(self):
         vgg16 = models.__dict__['vgg16'](pretrained=False, num_classes=10)
 

python/paddle/vision/__init__.py

@@ -44,6 +44,8 @@
 from .models import vgg16  # noqa: F401
 from .models import vgg19  # noqa: F401
 from .models import LeNet  # noqa: F401
+from .models import AlexNet  # noqa: F401
+from .models import alexnet  # noqa: F401
 from .transforms import BaseTransform  # noqa: F401
 from .transforms import Compose  # noqa: F401
 from .transforms import Resize  # noqa: F401

python/paddle/vision/models/__init__.py

@@ -28,6 +28,8 @@
 from .vgg import vgg16  # noqa: F401
 from .vgg import vgg19  # noqa: F401
 from .lenet import LeNet  # noqa: F401
+from .alexnet import AlexNet  # noqa: F401
+from .alexnet import alexnet  # noqa: F401
 
 __all__ = [ #noqa
     'ResNet',
@@ -45,5 +47,7 @@
     'mobilenet_v1',
     'MobileNetV2',
     'mobilenet_v2',
-    'LeNet'
+    'LeNet',
+    'AlexNet',
+    'alexnet'
 ]

python/paddle/vision/models/alexnet.py

@@ -0,0 +1,192 @@
+# copyright (c) 2021 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 absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+from paddle.nn import Linear, Dropout, ReLU
+from paddle.nn import Conv2D, MaxPool2D
+from paddle.nn.initializer import Uniform
+from paddle.fluid.param_attr import ParamAttr
+from paddle.utils.download import get_weights_path_from_url
+
+model_urls = {
+    "alexnet": (
+        "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams",
+        "7f0f9f737132e02732d75a1459d98a43", )
+}
+
+__all__ = []
+
+
+class ConvPoolLayer(nn.Layer):
+    def __init__(self,
+                 input_channels,
+                 output_channels,
+                 filter_size,
+                 stride,
+                 padding,
+                 stdv,
+                 groups=1,
+                 act=None):
+        super(ConvPoolLayer, self).__init__()
+
+        self.relu = ReLU() if act == "relu" else None
+
+        self._conv = Conv2D(
+            in_channels=input_channels,
+            out_channels=output_channels,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
+            bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
+        self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
+
+    def forward(self, inputs):
+        x = self._conv(inputs)
+        if self.relu is not None:
+            x = self.relu(x)
+        x = self._pool(x)
+        return x
+
+
+class AlexNet(nn.Layer):
+    """AlexNet model from
+    `"ImageNet Classification with Deep Convolutional Neural Networks"
+    <https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf>`_
+
+    Args:
+        num_classes (int): Output dim of last fc layer. Default: 1000.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.vision.models import AlexNet
+
+            alexnet = AlexNet()
+
+    """
+
+    def __init__(self, num_classes=1000):
+        super(AlexNet, self).__init__()
+        self.num_classes = num_classes
+        stdv = 1.0 / math.sqrt(3 * 11 * 11)
+        self._conv1 = ConvPoolLayer(3, 64, 11, 4, 2, stdv, act="relu")
+        stdv = 1.0 / math.sqrt(64 * 5 * 5)
+        self._conv2 = ConvPoolLayer(64, 192, 5, 1, 2, stdv, act="relu")
+        stdv = 1.0 / math.sqrt(192 * 3 * 3)
+        self._conv3 = Conv2D(
+            192,
+            384,
+            3,
+            stride=1,
+            padding=1,
+            weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
+            bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
+        stdv = 1.0 / math.sqrt(384 * 3 * 3)
+        self._conv4 = Conv2D(
+            384,
+            256,
+            3,
+            stride=1,
+            padding=1,
+            weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
+            bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
+        stdv = 1.0 / math.sqrt(256 * 3 * 3)
+        self._conv5 = ConvPoolLayer(256, 256, 3, 1, 1, stdv, act="relu")
+
+        if self.num_classes > 0:
+            stdv = 1.0 / math.sqrt(256 * 6 * 6)
+            self._drop1 = Dropout(p=0.5, mode="downscale_in_infer")
+            self._fc6 = Linear(
+                in_features=256 * 6 * 6,
+                out_features=4096,
+                weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
+                bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
+
+            self._drop2 = Dropout(p=0.5, mode="downscale_in_infer")
+            self._fc7 = Linear(
+                in_features=4096,
+                out_features=4096,
+                weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
+                bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
+            self._fc8 = Linear(
+                in_features=4096,
+                out_features=num_classes,
+                weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
+                bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)))
+
+    def forward(self, inputs):
+        x = self._conv1(inputs)
+        x = self._conv2(x)
+        x = self._conv3(x)
+        x = F.relu(x)
+        x = self._conv4(x)
+        x = F.relu(x)
+        x = self._conv5(x)
+
+        if self.num_classes > 0:
+            x = paddle.flatten(x, start_axis=1, stop_axis=-1)
+            x = self._drop1(x)
+            x = self._fc6(x)
+            x = F.relu(x)
+            x = self._drop2(x)
+            x = self._fc7(x)
+            x = F.relu(x)
+            x = self._fc8(x)
+
+        return x
+
+
+def _alexnet(arch, pretrained, **kwargs):
+    model = AlexNet(**kwargs)
+
+    if pretrained:
+        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
+            arch)
+        weight_path = get_weights_path_from_url(model_urls[arch][0],
+                                                model_urls[arch][1])
+
+        param = paddle.load(weight_path)
+        model.load_dict(param)
+
+    return model
+
+
+def alexnet(pretrained=False, **kwargs):
+    """AlexNet model
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet. Default: False.
+
+    Examples:
+        .. code-block:: python
+
+            from paddle.vision.models import alexnet
+
+            # build model
+            model = alexnet()
+
+            # build model and load imagenet pretrained weight
+            # model = alexnet(pretrained=True)
+    """
+    return _alexnet('alexnet', pretrained, **kwargs)