!6688 vgg16 hub support

Merge pull request !6688 from caojian05/ms_master_vgg16_hub

model_zoo/official/cv/vgg16/mindspore_hub_conf.py

@@ -0,0 +1,26 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""hub config."""
+from src.vgg import vgg16 as VGG16
+
+
+def vgg16(*args, **kwargs):
+    return VGG16(*args, **kwargs)
+
+
+def create_network(name, *args, **kwargs):
+    if name == "vgg16":
+        return vgg16(*args, **kwargs)
+    raise NotImplementedError(f"{name} is not implemented in the repo")

model_zoo/official/cv/vgg16/src/vgg.py

@@ -60,6 +60,7 @@ class Vgg(nn.Cell):
         num_classes (int): Class numbers. Default: 1000.
         batch_norm (bool): Whether to do the batchnorm. Default: False.
         batch_size (int): Batch size. Default: 1.
+        include_top(bool): Whether to include the 3 fully-connected layers at the top of the network. Default: True.
 
     Returns:
         Tensor, infer output tensor.
@@ -69,10 +70,12 @@ class Vgg(nn.Cell):
         >>>     num_classes=1000, batch_norm=False, batch_size=1)
     """
 
-    def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1, args=None, phase="train"):
+    def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1, args=None, phase="train",
+                 include_top=True):
         super(Vgg, self).__init__()
         _ = batch_size
         self.layers = _make_layer(base, args, batch_norm=batch_norm)
+        self.include_top = include_top
         self.flatten = nn.Flatten()
         dropout_ratio = 0.5
         if not args.has_dropout or phase == "test":
@@ -91,6 +94,7 @@ class Vgg(nn.Cell):
 
     def construct(self, x):
         x = self.layers(x)
+        if self.include_top:
             x = self.flatten(x)
             x = self.classifier(x)
         return x