[PaddlePaddle Hackathon] add GoogLeNet

python/paddle/tests/test_pretrained_model.py

@@ -54,7 +54,7 @@ def infer(self, arch):
     def test_models(self):
         arches = [
             'mobilenet_v1', 'mobilenet_v2', 'resnet18', 'vgg16', 'alexnet',
-            'resnext50_32x4d', 'inception_v3', 'densenet121'
+            'resnext50_32x4d', 'inception_v3', 'densenet121', 'googlenet'
         ]
         for arch in arches:
             self.infer(arch)

python/paddle/tests/test_vision_models.py

@@ -109,6 +109,9 @@ def test_resnext152_64x4d(self):
     def test_inception_v3(self):
         self.models_infer('inception_v3')
 
+    def test_googlenet(self):
+        self.models_infer('googlenet')
+
     def test_vgg16_num_classes(self):
         vgg16 = models.__dict__['vgg16'](pretrained=False, num_classes=10)
 

python/paddle/vision/__init__.py

@@ -61,6 +61,8 @@
 from .models import resnext152_64x4d  # noqa: F401
 from .models import InceptionV3  # noqa: F401
 from .models import inception_v3  # noqa: F401
+from .models import GoogLeNet  # noqa: F401
+from .models import googlenet  # 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

@@ -45,6 +45,8 @@
 from .resnext import resnext152_64x4d  # noqa: F401
 from .inceptionv3 import InceptionV3  # noqa: F401
 from .inceptionv3 import inception_v3  # noqa: F401
+from .googlenet import GoogLeNet  # noqa: F401
+from .googlenet import googlenet  # noqa: F401
 
 __all__ = [ #noqa
     'ResNet',
@@ -79,5 +81,7 @@
     'resnext152_32x4d',
     'resnext152_64x4d',
     'InceptionV3',
-    'inception_v3'
+    'inception_v3',
+    'GoogLeNet',
+    'googlenet',
 ]

python/paddle/vision/models/googlenet.py

@@ -0,0 +1,254 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 division
+from __future__ import print_function
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+from paddle.nn import Conv2D, Linear, Dropout
+from paddle.nn import MaxPool2D, AvgPool2D, AdaptiveAvgPool2D
+from paddle.nn.initializer import Uniform
+from paddle.fluid.param_attr import ParamAttr
+from paddle.utils.download import get_weights_path_from_url
+
+__all__ = []
+
+model_urls = {
+    "googlenet":
+    ("https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams",
+     "80c06f038e905c53ab32c40eca6e26ae")
+}
+
+
+def xavier(channels, filter_size):
+    stdv = (3.0 / (filter_size**2 * channels))**0.5
+    param_attr = ParamAttr(initializer=Uniform(-stdv, stdv))
+    return param_attr
+
+
+class ConvLayer(nn.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1):
+        super(ConvLayer, self).__init__()
+
+        self._conv = Conv2D(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            bias_attr=False)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        return y
+
+
+class Inception(nn.Layer):
+    def __init__(self, input_channels, output_channels, filter1, filter3R,
+                 filter3, filter5R, filter5, proj):
+        super(Inception, self).__init__()
+
+        self._conv1 = ConvLayer(input_channels, filter1, 1)
+        self._conv3r = ConvLayer(input_channels, filter3R, 1)
+        self._conv3 = ConvLayer(filter3R, filter3, 3)
+        self._conv5r = ConvLayer(input_channels, filter5R, 1)
+        self._conv5 = ConvLayer(filter5R, filter5, 5)
+        self._pool = MaxPool2D(kernel_size=3, stride=1, padding=1)
+
+        self._convprj = ConvLayer(input_channels, proj, 1)
+
+    def forward(self, inputs):
+        conv1 = self._conv1(inputs)
+
+        conv3r = self._conv3r(inputs)
+        conv3 = self._conv3(conv3r)
+
+        conv5r = self._conv5r(inputs)
+        conv5 = self._conv5(conv5r)
+
+        pool = self._pool(inputs)
+        convprj = self._convprj(pool)
+
+        cat = paddle.concat([conv1, conv3, conv5, convprj], axis=1)
+        cat = F.relu(cat)
+        return cat
+
+
+class GoogLeNet(nn.Layer):
+    """GoogLeNet (Inception v1) model architecture from
+    `"Going Deeper with Convolutions" <https://arxiv.org/pdf/1409.4842.pdf>`_
+
+    Args:
+        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
+                            will not be defined. Default: 1000.
+        with_pool (bool, optional): use pool before the last fc layer or not. Default: True.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            from paddle.vision.models import GoogLeNet
+
+            # build model
+            model = GoogLeNet()
+
+            x = paddle.rand([1, 3, 224, 224])
+            out, out1, out2 = model(x)
+
+            print(out.shape)
+    """
+
+    def __init__(self, num_classes=1000, with_pool=True):
+        super(GoogLeNet, self).__init__()
+        self.num_classes = num_classes
+        self.with_pool = with_pool
+
+        self._conv = ConvLayer(3, 64, 7, 2)
+        self._pool = MaxPool2D(kernel_size=3, stride=2)
+        self._conv_1 = ConvLayer(64, 64, 1)
+        self._conv_2 = ConvLayer(64, 192, 3)
+
+        self._ince3a = Inception(192, 192, 64, 96, 128, 16, 32, 32)
+        self._ince3b = Inception(256, 256, 128, 128, 192, 32, 96, 64)
+
+        self._ince4a = Inception(480, 480, 192, 96, 208, 16, 48, 64)
+        self._ince4b = Inception(512, 512, 160, 112, 224, 24, 64, 64)
+        self._ince4c = Inception(512, 512, 128, 128, 256, 24, 64, 64)
+        self._ince4d = Inception(512, 512, 112, 144, 288, 32, 64, 64)
+        self._ince4e = Inception(528, 528, 256, 160, 320, 32, 128, 128)
+
+        self._ince5a = Inception(832, 832, 256, 160, 320, 32, 128, 128)
+        self._ince5b = Inception(832, 832, 384, 192, 384, 48, 128, 128)
+
+        if with_pool:
+            # out
+            self._pool_5 = AdaptiveAvgPool2D(1)
+            # out1
+            self._pool_o1 = AvgPool2D(kernel_size=5, stride=3)
+            # out2
+            self._pool_o2 = AvgPool2D(kernel_size=5, stride=3)
+
+        if num_classes > 0:
+            # out
+            self._drop = Dropout(p=0.4, mode="downscale_in_infer")
+            self._fc_out = Linear(
+                1024, num_classes, weight_attr=xavier(1024, 1))
+
+            # out1
+            self._conv_o1 = ConvLayer(512, 128, 1)
+            self._fc_o1 = Linear(1152, 1024, weight_attr=xavier(2048, 1))
+            self._drop_o1 = Dropout(p=0.7, mode="downscale_in_infer")
+            self._out1 = Linear(1024, num_classes, weight_attr=xavier(1024, 1))
+
+            # out2
+            self._conv_o2 = ConvLayer(528, 128, 1)
+            self._fc_o2 = Linear(1152, 1024, weight_attr=xavier(2048, 1))
+            self._drop_o2 = Dropout(p=0.7, mode="downscale_in_infer")
+            self._out2 = Linear(1024, num_classes, weight_attr=xavier(1024, 1))
+
+    def forward(self, inputs):
+        x = self._conv(inputs)
+        x = self._pool(x)
+        x = self._conv_1(x)
+        x = self._conv_2(x)
+        x = self._pool(x)
+
+        x = self._ince3a(x)
+        x = self._ince3b(x)
+        x = self._pool(x)
+
+        ince4a = self._ince4a(x)
+        x = self._ince4b(ince4a)
+        x = self._ince4c(x)
+        ince4d = self._ince4d(x)
+        x = self._ince4e(ince4d)
+        x = self._pool(x)
+
+        x = self._ince5a(x)
+        ince5b = self._ince5b(x)
+
+        out, out1, out2 = ince5b, ince4a, ince4d
+
+        if self.with_pool:
+            out = self._pool_5(out)
+            out1 = self._pool_o1(out1)
+            out2 = self._pool_o2(out2)
+
+        if self.num_classes > 0:
+            out = self._drop(out)
+            out = paddle.squeeze(out, axis=[2, 3])
+            out = self._fc_out(out)
+
+            out1 = self._conv_o1(out1)
+            out1 = paddle.flatten(out1, start_axis=1, stop_axis=-1)
+            out1 = self._fc_o1(out1)
+            out1 = F.relu(out1)
+            out1 = self._drop_o1(out1)
+            out1 = self._out1(out1)
+
+            out2 = self._conv_o2(out2)
+            out2 = paddle.flatten(out2, start_axis=1, stop_axis=-1)
+            out2 = self._fc_o2(out2)
+            out2 = self._drop_o2(out2)
+            out2 = self._out2(out2)
+
+        return [out, out1, out2]
+
+
+def googlenet(pretrained=False, **kwargs):
+    """GoogLeNet (Inception v1) model architecture from
+    `"Going Deeper with Convolutions" <https://arxiv.org/pdf/1409.4842.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            from paddle.vision.models import googlenet
+
+            # build model
+            model = googlenet()
+
+            # build model and load imagenet pretrained weight
+            # model = googlenet(pretrained=True)
+
+            x = paddle.rand([1, 3, 224, 224])
+            out, out1, out2 = model(x)
+
+            print(out.shape)
+    """
+    model = GoogLeNet(**kwargs)
+    arch = "googlenet"
+    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.set_dict(param)
+    return model