ed8577a6e4afec9f68bf69dc5d506b425ad7ff17,batchflow/models/tf/mobilenet.py,MobileNet_v1,body,#Any#Any#Any#,103

Before Change

        with tf.variable_scope(name):
            x = inputs
            for i, strides in enumerate(sep_strides):
                x = cls.block(x, strides=strides, double_filters=double_filters[i], width_factor=width_factor,
                              name="block-%d" % i, **kwargs)
        return x

    @classmethod
    def block(cls, inputs, strides=1, double_filters=False, width_factor=1, name=None, **kwargs):

After Change

            x = inputs
            i = 0
            for block in layout:
                repeats = block.pop("repeats")
                block["width_factor"] = width_factor
                for k in range(repeats):
                    if k > 0:
                        block["strides"] = 1
                    residual = block.pop("residual", False) or k > 0
                    block = {**kwargs, **block}
                    x = cls.block(x, **block, residual=residual, name="block-%d" % i)
                    i += 1
        return x

    @classmethod
    def block(cls, inputs, filters, residual=False, strides=1, expansion_factor=4, width_factor=1, kernel_size=3,

Instances