ba201ede312a093d737fd5932624c0293d45d5ea,tensorflow2/tf2cv/models/hardnet.py,HarDNet,__init__,#HarDNet#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,352

Before Change

        self.data_format = data_format
        activation = "relu6"

        self.features = tf.keras.Sequential(name="features")
        self.features.add(HarDInitBlock(
            in_channels=in_channels,
            out_channels=init_block_channels,
            use_deptwise=use_deptwise,
            activation=activation,
            data_format=data_format,
            name="init_block"))
        for i, (in_channels_list_i, out_channels_list_i) in enumerate(zip(unit_in_channels, unit_out_channels)):
            stage = tf.keras.Sequential(name="stage{}".format(i + 1))
            for j, (in_channels_list_ij, out_channels_list_ij) in enumerate(zip(in_channels_list_i,
                                                                                out_channels_list_i)):
                use_dropout = ((j == len(in_channels_list_i) - 1) and (i == len(unit_in_channels) - 1) and
                               use_last_dropout)
                downsampling = ((j == len(in_channels_list_i) - 1) and (i != len(unit_in_channels) - 1))
                stage.add(HarDUnit(
                    in_channels_list=in_channels_list_ij,
                    out_channels_list=out_channels_list_ij,
                    links_list=unit_links[i][j],
                    use_deptwise=use_deptwise,
                    use_dropout=use_dropout,
                    downsampling=downsampling,
                    activation=activation,
                    data_format=data_format,
                    name="unit{}".format(j + 1)))
            self.features.add(stage)
        in_channels = unit_out_channels[-1][-1][-1]
        self.features.add(nn.AveragePooling2D(
            pool_size=7,
            strides=1,
            data_format=data_format,
            name="final_pool"))

        self.output1 = tf.keras.Sequential(name="output1")
        self.output1.add(nn.Dropout(
            rate=output_dropout_rate,
            name="dropout"))

After Change

        self.data_format = data_format
        activation = "relu6"

        self.features = SimpleSequential(name="features")
        self.features.add(HarDInitBlock(
            in_channels=in_channels,
            out_channels=init_block_channels,
            use_deptwise=use_deptwise,

Instances