nn/nn_utils.py
def crop_upsample_concat(input_a, input_b, margin, name):
    """
    Upsample tensor a, crop tensor b and concatenate them
    :param input_a:
    :param input_b:
    :param margin: the margin tensor b need to be cropped
    :param name: name for this variable scope
    :return:
    """
    with tf.variable_scope('crop_upsample_concat'):
        _, w, h, _ = input_b.get_shape().as_list()
        input_b_crop = tf.image.resize_image_with_crop_or_pad(input_b, w - margin, h - margin)
        return upsample_concat(input_a, input_b_crop, name)

    Upsample tensor a, crop tensor b and concatenate them
    :param input_a:
    :param input_b:
    :param margin: the margin tensor b need to be cropped
    :param name: name for this variable scope
    :return:
    
Upsample tensor a, crop tensor b and concatenate them
Upsample tensor a crop tensor b and concatenate them
def crop_upsample_concat(input_a, input_b, margin, name):
    with tf.variable_scope('crop_upsample_concat'):
        _, w, h, _ = input_b.get_shape().as_list()
        input_b_crop = tf.image.resize_image_with_crop_or_pad(input_b, w - margin, h - margin)
        return upsample_concat(input_a, input_b_crop, name)
def crop_upsample_concat ( input_a , input_b , margin , name ) : with tf . variable_scope ( 'crop_upsample_concat' ) : _ , w , h , _ = input_b . get_shape ( ) . as_list ( ) input_b_crop = tf . image . resize_image_with_crop_or_pad ( input_b , w - margin , h - margin ) return upsample_concat ( input_a , input_b_crop , name )
Upsample tensor a, crop tensor b and concatenate them
Upsample tensor a crop tensor b and concatenate them
"""
    Upsample tensor a, crop tensor b and concatenate them
    :param input_a:
    :param input_b:
    :param margin: the margin tensor b need to be cropped
    :param name: name for this variable scope
    :return:
    """
None
input_a
None
input_b
None
margin
the margin tensor b need to be cropped
the margin tensor b need to be cropped
name
name for this variable scope
name for this variable scope