szha commented on a change in pull request #9614: MobileNetV2
URL: https://github.com/apache/incubator-mxnet/pull/9614#discussion_r165114975
##########
File path: python/mxnet/gluon/model_zoo/vision/mobilenet.py
##########
@@ -74,13 +126,102 @@ 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():
+ self.features.add(
+ nn.Conv2D(int(32 * multiplier), 3, strides=2, padding=1,
use_bias=False),
+ nn.BatchNorm(scale=True),
+ nn.Activation('relu')
+ )
+
+ c_bgn = int(32 * multiplier)
+ c_end = int(16 * multiplier)
+ self.features.add(BottleNeck(c_in=c_bgn, c_out=c_end, t=1,
s=1))
+
+ c_bgn = int(16 * multiplier)
+ c_end = int(24 * multiplier)
+ self.features.add(BottleNeck(c_in=c_bgn, c_out=c_end, t=6,
s=2))
+ self.features.add(BottleNeck(c_in=c_end, c_out=c_end, t=6,
s=1))
+
+ c_bgn = int(24 * multiplier)
+ c_end = int(32 * multiplier)
+ self.features.add(BottleNeck(c_in=c_bgn, c_out=c_end, t=6,
s=2))
+ self.features.add(BottleNeck(c_in=c_end, c_out=c_end, t=6,
s=1))
+ self.features.add(BottleNeck(c_in=c_end, c_out=c_end, t=6,
s=1))
+
+ c_bgn = int(32 * multiplier)
+ c_end = int(64 * multiplier)
+ self.features.add(BottleNeck(c_in=c_bgn, c_out=c_end, t=6,
s=2))
+ self.features.add(BottleNeck(c_in=c_end, c_out=c_end, t=6,
s=1))
+ self.features.add(BottleNeck(c_in=c_end, c_out=c_end, t=6,
s=1))
+ self.features.add(BottleNeck(c_in=c_end, c_out=c_end, t=6,
s=1))
+
+ c_bgn = int(64 * multiplier)
+ c_end = int(96 * multiplier)
+ self.features.add(BottleNeck(c_in=c_bgn, c_out=c_end, t=6,
s=1))
+ self.features.add(BottleNeck(c_in=c_end, c_out=c_end, t=6,
s=1))
+ self.features.add(BottleNeck(c_in=c_end, c_out=c_end, t=6,
s=1))
+
+ c_bgn = int(96 * multiplier)
+ c_end = int(160 * multiplier)
+ self.features.add(BottleNeck(c_in=c_bgn, c_out=c_end, t=6,
s=2))
+ self.features.add(BottleNeck(c_in=c_end, c_out=c_end, t=6,
s=1))
+ self.features.add(BottleNeck(c_in=c_end, c_out=c_end, t=6,
s=1))
+
+ c_bgn = int(160 * multiplier)
+ c_end = int(320 * multiplier)
+ self.features.add(BottleNeck(c_in=c_bgn, c_out=c_end, t=6,
s=1))
Review comment:
the construction looks like it could be refactored. can you use similar
implementation from mobilenet v1?
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