def test_normalize_data_format(): assert keras_utils.normalize_data_format( "Channels_Last") == "channels_last" assert keras_utils.normalize_data_format( "CHANNELS_FIRST") == "channels_first" with pytest.raises(ValueError, match="The `data_format` argument must be one of"): keras_utils.normalize_data_format("invalid")
def __init__(self, reduce_function: Callable, output_size: Union[int, Iterable[int]], data_format=None, **kwargs): self.data_format = conv_utils.normalize_data_format(data_format) self.reduce_function = reduce_function self.output_size = conv_utils.normalize_tuple(output_size, 1, "output_size") super().__init__(**kwargs)
def __init__(self, bins: Union[Iterable[int], Iterable[Iterable[int]]], data_format=None, *args, **kwargs): self.bins = [conv_utils.normalize_tuple(bin, 2, "bin") for bin in bins] self.data_format = conv_utils.normalize_data_format(data_format) self.pool_layers = [] for bin in self.bins: self.pool_layers.append( AdaptiveAveragePooling2D(bin, self.data_format)) super().__init__(*args, **kwargs)
def _norm_params(images, mask_size, data_format): mask_size = tf.convert_to_tensor(mask_size) if tf.executing_eagerly(): tf.assert_equal( tf.reduce_any(mask_size % 2 != 0), False, "mask_size should be divisible by 2", ) if tf.rank(mask_size) == 0: mask_size = tf.stack([mask_size, mask_size]) data_format = keras_utils.normalize_data_format(data_format) image_height, image_width = _get_image_wh(images, data_format) return mask_size, data_format, image_height, image_width