ceedc39e529dbd8a66bdb3950af131b75b430683,tensorflow_/tensorflowcv/models/shufflenetv2b.py,ShuffleNetV2b,__call__,#ShuffleNetV2b#Any#Any#,218

Before Change

            out_channels=self.final_block_channels,
            training=training,
            name="features/final_block")
        x = tf.layers.average_pooling2d(
            inputs=x,
            pool_size=7,
            strides=1,
            data_format="channels_first",
            name="features/final_pool")

        x = tf.layers.flatten(x)
        x = tf.layers.dense(
            inputs=x,
            units=self.classes,
            name="output")

After Change

            in_channels=in_channels,
            out_channels=self.init_block_channels,
            training=training,
            data_format=self.data_format,
            name="features/init_block")
        in_channels = self.init_block_channels
        for i, channels_per_stage in enumerate(self.channels):
            for j, out_channels in enumerate(channels_per_stage):
                downsample = (j == 0)
                x = shuffle_unit(
                    x=x,
                    in_channels=in_channels,
                    out_channels=out_channels,
                    downsample=downsample,
                    use_se=self.use_se,
                    use_residual=self.use_residual,
                    shuffle_group_first=self.shuffle_group_first,
                    training=training,
                    data_format=self.data_format,
                    name="features/stage{}/unit{}".format(i + 1, j + 1))
                in_channels = out_channels
        x = conv1x1_block(
            x=x,
            in_channels=in_channels,
            out_channels=self.final_block_channels,
            training=training,
            data_format=self.data_format,
            name="features/final_block")
        x = tf.layers.average_pooling2d(
            inputs=x,
            pool_size=7,
            strides=1,
            data_format=self.data_format,
            name="features/final_pool")

        // x = tf.layers.flatten(x)
        x = flatten(
            x=x,
            out_channels=in_channels,
            data_format=self.data_format)
        x = tf.layers.dense(
            inputs=x,
            units=self.classes,
            name="output")

Instances