code reformat use autopep8 and isort.

fix pylint style check error.
add docstring, disable some check.
delete duplicate code in example.

example/image-classification/symbols/mobilenetv2.py

@@ -14,132 +14,44 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+
 # -*- coding:utf-8 -*-
 '''
 MobileNetV2, implemented in Gluon.
 
 Reference:
-Inverted Residuals and Linear Bottlenecks: Mobile Networks for  Classification, Detection and Segmentation
+Inverted Residuals and Linear Bottlenecks:
+Mobile Networks for Classification, Detection and Segmentation
 https://arxiv.org/abs/1801.04381
 '''
 __author__ = 'dwSun'
-__date__ = '18/1/29'
+__date__ = '18/1/31'
 
 import mxnet as mx
-import mxnet.gluon.nn as nn
-
-__all__ = ['MobileNetV2', 'get_symbol']
-
-
-class BottleNeck(nn.HybridBlock):
-    def __init__(self, c_in, c_out, t,  s, **kwargs):
-        super(BottleNeck, self).__init__(**kwargs)
-        self.use_shortcut = s == 1 and c_in == c_out
-        with self.name_scope():
-            self.out = nn.HybridSequential()
-            self.out.add(
-                nn.Conv2D(c_in * t, 1, padding=0, use_bias=False),
-                nn.BatchNorm(scale=True),
-                nn.Activation('relu'),
 
-                nn.Conv2D(c_in * t, 3, strides=s, padding=1, groups=c_in * t, use_bias=False),
-                nn.BatchNorm(scale=True),
-                nn.Activation('relu'),
-
-                nn.Conv2D(c_out, 1, padding=0, use_bias=False),
-                nn.BatchNorm(scale=True),
-            )
-
-    def hybrid_forward(self, F, x):
-        out = self.out(x)
-        if self.use_shortcut:
-            out = F.elemwise_add(out, x)
-        return out
-
-
-class MobileNetV2(nn.HybridBlock):
-    r"""MobileNetV2 model from the
-    `"Inverted Residuals and Linear Bottlenecks: Mobile Networks for  Classification, Detection and Segmentation"
-    <https://arxiv.org/abs/1801.04381>`_ paper.
-
-    Parameters
-    ----------
-    num_classes : int, default 1000
-        Number of classes for the output layer.
-    w : float, default 1.0
-        The width multiplier for controling the model size. The actual number of channels
-        is equal to the original channel size multiplied by this multiplier.
-    """
-    def __init__(self, num_classes=1000, w=1.0, **kwargs):
-        super(MobileNetV2, self).__init__(**kwargs)
-        with self.name_scope():
-            self.features = nn.HybridSequential(prefix='features_')
-            with self.features.name_scope():
-                self.features.add(
-                    nn.Conv2D(int(32 * w), 3, strides=2, padding=1, use_bias=False),
-                    nn.BatchNorm(scale=True),
-                    nn.Activation('relu')
-                )
+from mxnet.gluon.model_zoo.vision.mobilenet import MobileNetV2
 
-                self.features.add(BottleNeck(c_in=int(32 * w), c_out=int(16 * w), t=1, s=1))
 
-                self.features.add(BottleNeck(c_in=int(16 * w), c_out=int(24 * w), t=6, s=2))
-                self.features.add(BottleNeck(c_in=int(24 * w), c_out=int(24 * w), t=6, s=1))
-
-                self.features.add(BottleNeck(c_in=int(24 * w), c_out=int(32 * w), t=6, s=2))
-                self.features.add(BottleNeck(c_in=int(32 * w), c_out=int(32 * w), t=6, s=1))
-                self.features.add(BottleNeck(c_in=int(32 * w), c_out=int(32 * w), t=6, s=1))
-
-                self.features.add(BottleNeck(c_in=int(32 * w), c_out=int(64 * w), t=6, s=2))
-                self.features.add(BottleNeck(c_in=int(64 * w), c_out=int(64 * w), t=6, s=1))
-                self.features.add(BottleNeck(c_in=int(64 * w), c_out=int(64 * w), t=6, s=1))
-                self.features.add(BottleNeck(c_in=int(64 * w), c_out=int(64 * w), t=6, s=1))
-
-                self.features.add(BottleNeck(c_in=int(64 * w), c_out=int(96 * w), t=6, s=1))
-                self.features.add(BottleNeck(c_in=int(96 * w), c_out=int(96 * w), t=6, s=1))
-                self.features.add(BottleNeck(c_in=int(96 * w), c_out=int(96 * w), t=6, s=1))
-
-                self.features.add(BottleNeck(c_in=int(96 * w), c_out=int(160 * w), t=6, s=2))
-                self.features.add(BottleNeck(c_in=int(160 * w), c_out=int(160 * w), t=6, s=1))
-                self.features.add(BottleNeck(c_in=int(160 * w), c_out=int(160 * w), t=6, s=1))
-
-                self.features.add(BottleNeck(c_in=int(160 * w), c_out=int(320 * w), t=6, s=1))
-
-                last_channels = int(1280 * w) if w > 1.0 else 1280
-
-                self.features.add(
-                    nn.Conv2D(last_channels, 1, strides=1, padding=0, use_bias=False),
-                    nn.BatchNorm(scale=True),
-                    nn.Activation('relu'),
-                )
-                self.features.add(nn.GlobalAvgPool2D())
-
-            self.output = nn.Conv2D(channels=num_classes, kernel_size=1, strides=1, padding=0, use_bias=False, prefix='pred_')
-            self.flatten = nn.Flatten(prefix='flat_')
-
-    def hybrid_forward(self, F, x):
-        x = self.features(x)
-        x = self.output(x)
-        x = self.flatten(x)
-        return x
+__all__ = ['MobileNetV2', 'get_symbol']
 
 
-def get_symbol(num_classes=1000, w=1.0, ctx=mx.cpu()):
+def get_symbol(num_classes=1000, multiplier=1.0, ctx=mx.cpu(), **kwargs):
     r"""MobileNetV2 model from the
-    `"Inverted Residuals and Linear Bottlenecks: Mobile Networks for  Classification, Detection and Segmentation"
+    `"Inverted Residuals and Linear Bottlenecks:
+      Mobile Networks for  Classification, Detection and Segmentation"
     <https://arxiv.org/abs/1801.04381>`_ paper.
 
     Parameters
     ----------
     num_classes : int, default 1000
         Number of classes for the output layer.
-    w : float, default 1.0
+    multiplier : float, default 1.0
         The width multiplier for controling the model size. The actual number of channels
         is equal to the original channel size multiplied by this multiplier.
     ctx : Context, default CPU
         The context in which to initialize the model weights.
     """
-    net = MobileNetV2(num_classes, w)
+    net = MobileNetV2(multiplier=multiplier, classes=num_classes, **kwargs)
     net.initialize(ctx=ctx, init=mx.init.Xavier())
     net.hybridize()
 
@@ -149,11 +61,16 @@ def get_symbol(num_classes=1000, w=1.0, ctx=mx.cpu()):
     return sym
 
 
-if __name__ == '__main__':
-    net = MobileNetV2(1000, prefix='mob_')
-
-    data = mx.sym.var('data')
-    sym = net(data)
+def plot_net():
+    """
+    Visualize the network.
+    """
+    sym = get_symbol(1000, prefix='mob_')
 
     # plot network graph
-    mx.viz.plot_network(sym, shape={'data': (8, 3, 224, 224)}, node_attrs={'shape': 'oval', 'fixedsize': 'fasl==false'}).view()
+    mx.viz.plot_network(sym, shape={'data': (8, 3, 224, 224)},
+                        node_attrs={'shape': 'oval', 'fixedsize': 'fasl==false'}).view()
+
+
+if __name__ == '__main__':
+    plot_net()

python/mxnet/gluon/model_zoo/vision/__init__.py

@@ -138,9 +138,8 @@ def get_model(name, **kwargs):
               'mobilenet1.0': mobilenet1_0,
               'mobilenet0.75': mobilenet0_75,
               'mobilenet0.5': mobilenet0_5,
-              'mobilenet0.25': mobilenet0_25,
-              'mobilenetv2_1.0': mobilenetv2_1_0
-              }
+              'mobilenet0.25': mobilenet0_25
+             }
     name = name.lower()
     if name not in models:
         raise ValueError(