cb454682666fda678e5f2fabd4256d18bae6e50c,src/graph_transpiler/webdnn/frontend/tensorflow/ops/gen_nn_ops.py,,avg_pool_handler,#Any#Any#,33

Before Change

    assert stride[x.order.axes_dict[Axis.C]] == 1
    stride = (stride[x.order.axes_dict[Axis.H]], stride[x.order.axes_dict[Axis.W]])

    x, padding = convolution_handler_preprocess(x, ksize=ksize, padding=tf_op.get_attr("padding"), dilation_rate=(1, 1),
                                                data_format=data_format)

    y, = AveragePooling2D(None, ksize=ksize, stride=stride, padding=padding, cover_all=False)(x)
    converter.set_variable(tf_op.outputs[0], y)

After Change

@TensorFlowConverter.register_handler("AvgPool")
def avg_pool_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"):
    x = converter.get_variable(tf_op.inputs[0])
    data_format = tf_op.get_attr("data_format")
    check_data_format(x, data_format)

    ksize = tuple(tf_op.get_attr("ksize"))  // type: Tuple[int,...]
    assert ksize[x.order.axes_dict[Axis.N]] == 1
    assert ksize[x.order.axes_dict[Axis.C]] == 1
    ksize = (ksize[x.order.axes_dict[Axis.H]], ksize[x.order.axes_dict[Axis.W]])

    stride = tuple(tf_op.get_attr("strides"))  // type: Tuple[int,...]
    assert stride[x.order.axes_dict[Axis.N]] == 1
    assert stride[x.order.axes_dict[Axis.C]] == 1
    stride = (stride[x.order.axes_dict[Axis.H]], stride[x.order.axes_dict[Axis.W]])

    paddings = (
        parse_padding(tf_op.get_attr("padding"), ksize[0], 1),
        parse_padding(tf_op.get_attr("padding"), ksize[1], 1),
    )
    x, paddings = convert_odd_padding_to_concat(x, paddings=paddings)

    if any(p > 0 for p in paddings):
        console.warning(
            "[KerasConverter] keras.layers.AveragePooling computes average by dividing number of valid elements in window "

Instances