dwSun commented on a change in pull request #9614: MobileNetV2
URL: https://github.com/apache/incubator-mxnet/pull/9614#discussion_r167393834
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File path: python/mxnet/gluon/model_zoo/vision/mobilenet.py
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@@ -74,13 +123,68 @@ def hybrid_forward(self, F, x):
x = self.output(x)
return x
+
+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
+ ----------
+ 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.
+ classes : int, default 1000
+ Number of classes for the output layer.
+ """
+
+ def __init__(self, multiplier=1.0, classes=1000, **kwargs):
+ super(MobileNetV2, self).__init__(**kwargs)
+ with self.name_scope():
+ self.features = nn.HybridSequential(prefix='features_')
+ with self.features.name_scope():
+ _add_conv(self.features, int(32 * multiplier), kernel=3,
stride=2, pad=1)
+
+ in_channels_group = [int(x * multiplier) for x in [32] + [16]
+ [24] * 2
+ + [32] * 3 + [64] * 4 + [96] * 3 + [160]
* 3]
+ channels_group = [int(x * multiplier) for x in [16] + [24] * 2
+ [32] * 3
+ + [64] * 4 + [96] * 3 + [160] * 3 + [320]]
+ ts = [1] + [6] * 16
+ strides = [1, 2] * 2 + [1] * 2 + [2] + [1] * 3 + [1] * 3 + [2]
+ [1] * 3
+
+ for in_c, c, t, s in zip(in_channels_group, channels_group,
ts, strides):
+ self.features.add(BottleNeck(in_channels=in_c, channels=c,
t=t, stride=s))
+
+ last_channels = int(1280 * multiplier) if multiplier > 1.0
else 1280
+ _add_conv(self.features, last_channels)
+
+ self.features.add(nn.GlobalAvgPool2D())
+
+ self.output = nn.HybridSequential(prefix='output_')
+ self.output.add(
Review comment:
A name_scope is indeed necessary.
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