def convert_upsample_bilinear(params, w_name, scope_name, inputs, layers, weights, names):
    """
    Convert upsample_bilinear2d layer.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
    """
    print('Converting upsample...')

    if names == 'short':
        tf_name = 'UPSL' + random_string(4)
    elif names == 'keep':
        tf_name = w_name
    else:
        tf_name = w_name + str(random.random())

    output_size = params['output_size']
    align_corners = params['align_corners'] > 0

    def target_layer(x, size=output_size, align_corners=align_corners):
        import tensorflow as tf
        x = tf.transpose(x, [0, 2, 3, 1])
        x = tf.image.resize_images(x, size, align_corners=align_corners)
        x = tf.transpose(x, [0, 3, 1, 2])
        return x

    lambda_layer = keras.layers.Lambda(target_layer)
    layers[scope_name] = lambda_layer(layers[inputs[0]])
def convert_upsample_bilinear(params, w_name, scope_name, inputs, layers, weights, names):
    """
    Convert upsample_bilinear2d layer.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
    """
    print('Converting upsample...')

    if names == 'short':
        tf_name = 'UPSL' + random_string(4)
    elif names == 'keep':
        tf_name = w_name
    else:
        tf_name = w_name + str(random.random())

    output_size = params['output_size']
    align_corners = params['align_corners'] > 0

    def target_layer(x, size=output_size, align_corners=align_corners):
        import tensorflow as tf
        x = tf.transpose(x, [0, 2, 3, 1])
        x = tf.image.resize_images(x, size, align_corners=align_corners)
        x = tf.transpose(x, [0, 3, 1, 2])
        return x

    lambda_layer = keras.layers.Lambda(target_layer)
    layers[scope_name] = lambda_layer(layers[inputs[0]])
def convert_upsample_bilinear ( params , w_name , scope_name , inputs , layers , weights , names ) : print ( 'Converting upsample...' ) if names == 'short' : tf_name = 'UPSL' + random_string ( 4 ) elif names == 'keep' : tf_name = w_name else : tf_name = w_name + str ( random . random ( ) ) output_size = params [ 'output_size' ] align_corners = params [ 'align_corners' ] > 0 def target_layer ( x , size = output_size , align_corners = align_corners ) : import tensorflow as tf x = tf . transpose ( x , [ 0 , 2 , 3 , 1 ] ) x = tf . image . resize_images ( x , size , align_corners = align_corners ) x = tf . transpose ( x , [ 0 , 3 , 1 , 2 ] ) return x lambda_layer = keras . layers . Lambda ( target_layer ) layers [ scope_name ] = lambda_layer ( layers [ inputs [ 0 ] ] )
Convert upsample_bilinear2d layer.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
Convert upsample_bilinear2d layer .