11c6bf1dbd051d087519ff771b39dac600c0d96d,batchflow/models/tf/encoder_decoder.py,EncoderDecoder,head,#Any#Any#Any#Any#,163

Before Change

                filters = channels
            elif isinstance(filters, int):
                if filters != channels and "layout" in kwargs:
                    filters = [filters] * filters_needed(kwargs["layout"])

            if isinstance(filters, (list, tuple)) and len(filters) > 0:
                if isinstance(filters, tuple):

After Change

            channels = cls.num_channels(targets)
            if cls.num_channels(x) != channels:
                with tf.variable_scope("final"):
                    x = cls.crop(x, targets, kwargs["data_format"])
                    args = cls.combine_kwargs(kwargs, dict(layout="c", kernel_size=1, filters=channels))
                    x = conv_block(x, **args)

        return x

    @classmethod
    def block(cls, inputs, name="block", **kwargs):

Instances