a120cb4377c48caba2d2dbb25f126a06568e01be,src/graph_transpiler/webdnn/frontend/tensorflow/ops/gen_nn_ops.py,,max_pool_handler,#Any#Any#,331

Before Change

    ksize_nhwc = tf_op.get_attr("ksize")  // type: List[int]
    assert ksize_nhwc[0] == 1
    assert ksize_nhwc[3] == 1
    ksize_hw = ksize_nhwc[1:3]

    stride_nhwc = tf_op.get_attr("strides")  // type: List[int]
    assert stride_nhwc[0] == 1
    assert stride_nhwc[3] == 1
    stride_hw = stride_nhwc[1:3]

    padding_name = tf_op.get_attr("padding")  // type: str
    if padding_name == b"SAME":
        padding = (padding_same(x.shape_dict[Axis.H], ksize_hw[0], stride_hw[0]),
                   padding_same(x.shape_dict[Axis.W], ksize_hw[1], stride_hw[1]))
    elif padding_name == b"VALID":
        padding = (0, 0)
    else:
        raise NotImplementedError(f"[TensorFlowConverter] MaxPool: padding "{padding_name}" is not supported yet.")

    y, = MaxPooling2D(None, ksize=ksize_hw, stride=stride_hw, padding=padding)(x)
    converter.set_variable(tf_op.outputs[0], y)

After Change

    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]])

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

Instances