【MXNet Gluon】使用预训练好的模型fine-tune

【承接图像分类、检测、分割、生成相关项目，私信。】

finetune关键代码

prenet=ResNet(466)
net=ResNet(3400)
ctx = [mx.gpu(i) for i in range(3)]
if finetune ==1:
    prenet.load_params('params/net-%d.params' % (start_iter),ctx)
    #features为需要保留的模型参数，output为修改为新数据集类别数的Dense层
    net.features=prenet.features
    net.output.initialize(mx.init.Xavier(), ctx)
else:
	 net.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx)

ResNet代码参考

class ResNetV2(HybridBlock):
    def __init__(self, block, layers, channels, classes=1000, thumbnail=False, **kwargs):
        super(ResNetV2, self).__init__(**kwargs)
        assert len(layers) == len(channels) - 1
        with self.name_scope():
            self.features = nn.HybridSequential(prefix='')
            self.features.add(nn.BatchNorm(scale=False, center=False))
            if thumbnail:
                self.features.add(_conv3x3(channels[0], 1, 0))
            else:
                self.features.add(nn.Conv2D(channels[0], 7, 2, 3, use_bias=False))
                self.features.add(nn.BatchNorm())
                self.features.add(nn.Activation('relu'))
                self.features.add(nn.MaxPool2D(3, 2, 1))

            in_channels = channels[0]
            for i, num_layer in enumerate(layers):
                stride = 1 if i == 0 else 2
                self.features.add(self._make_layer(block, num_layer, channels[i+1],
                                                   stride, i+1, in_channels=in_channels))
                in_channels = channels[i+1]
            self.features.add(nn.BatchNorm())
            self.features.add(nn.Activation('relu'))
            self.features.add(nn.GlobalAvgPool2D())
            self.features.add(nn.Flatten())

            self.output = nn.Dense(classes, in_units=in_channels)

    def _make_layer(self, block, layers, channels, stride, stage_index, in_channels=0):
        layer = nn.HybridSequential(prefix='stage%d_'%stage_index)
        with layer.name_scope():
            layer.add(block(channels, stride, channels != in_channels, in_channels=in_channels,
                            prefix=''))
            for _ in range(layers-1):
                layer.add(block(channels, 1, False, in_channels=channels, prefix=''))
        return layer

    def hybrid_forward(self, F, x):
        x = self.features(x)
        x = self.output(x)
        return x

参考文献
http://zh.gluon.ai/chapter_computer-vision/fine-tuning.html