a120cb4377c48caba2d2dbb25f126a06568e01be,src/graph_transpiler/webdnn/frontend/tensorflow/ops/gen_nn_ops.py,,conv2_d_handler,#Any#Any#,118

Before Change

    ksize_hw = (w.shape_dict[Axis.KH], w.shape_dict[Axis.KW])

    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

After Change

@TensorFlowConverter.register_handler("Conv2D")
def conv2_d_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)

    w = converter.get_variable(tf_op.inputs[1])  // HWCN
    w.order.unify(Order([Axis.KH, Axis.KW, Axis.C, Axis.N]))

    ksize = (w.shape_dict[Axis.KH], w.shape_dict[Axis.KW])

    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),
                                                data_format=data_format)

    y, = Convolution2D(None, ksize=ksize, stride=stride, padding=padding)(x, w)
    converter.set_variable(tf_op.outputs[0], y)

Instances