959372edab3020c0164b8a2da4aaff1c49ba8400,src/graph_transpiler/webdnn/frontend/keras.py,,_convert_max_pooling2d,#Any#Any#,394

Before Change

    "strides": [2, 2],
    "trainable": True}},
    
    assert len(operator.inputs) == 1
    x = operator.inputs[0]
    ksize: Tuple[int, int] = tuple(operator.specific_config["pool_size"])
    stride: Tuple[int, int] = tuple(operator.specific_config["strides"])
    padding_keras: str = operator.specific_config["padding"]  // valid or same

After Change

@KerasConverter.register_handler("AveragePooling2D")
def _convert_max_pooling2d(converter: KerasConverter, k_op: keras.layers.AveragePooling2D):
    x = converter.get_variable(converter.get_input_tensor(k_op)[0])

    if k_op.data_format == "channels_first":
        assert x.order == OrderNCHW

    elif k_op.data_format == "channels_last":
        assert x.order == OrderNHWC

    else:
        raise ValueError(f"[KerasConverter] Unknown data format: {k_op.data_format}")

    ksize = tuple(k_op.pool_size)
    stride = tuple(k_op.strides)
    if k_op.padding == "valid":
        padding = (0, 0)

    elif k_op.padding == "same":
        padding = (ksize[0] // 2, ksize[1] // 2)

    else:
        raise ValueError(f"[KerasConverter] Unknown padding: {k_op.padding}")

    y, = AveragePooling2D(None, ksize=ksize, stride=stride, padding=padding)(x)
    converter.set_variable(converter.get_output_tensor(k_op)[0], y)


@KerasConverter.register_handler("GlobalAveragePooling2D")
def convert_layer_global_average_pooling2d(converter: KerasConverter, k_op: keras.layers.GlobalAveragePooling2D):

Instances