def _convert_max(converter: ChainerConverter, c_op: "chainer.functions.Max"):
x = converter.get_variable(c_op.inputs[0])
for axis in list(x.order.axes) if c_op.axis is None else [
x.order.axes[i] for i in c_op.axis
]:
x, = Max(None, axis=axis)(x)
if not c_op.keepdims and x.ndim > 1:
x = x.squeeze(axis)
converter.set_variable(c_op.outputs[0](), x)
def _convert_reduce_max(converter: ONNXConverter, onnx_op: INodeProto):
x = converter.get_variable(onnx_op.input[0])
attrs = attribute_dict(onnx_op)
axes = attrs["axes"].ints
keepdims = (attrs["keepdims"].i if "keepdims" in attrs else 1) == 1
for a in axes:
x, = Max(None, axis=x.order.axes[a])(x)
if not keepdims:
x = x.squeeze(axis=[x.order.axes[i] for i in axes])
converter.set_variable(onnx_op.output[0], x)
def max_handler(converter: TensorFlowConverter, tf_op: "tf.Operation"):
x = converter.get_variable(tf_op.inputs[0])
axis = converter.get_variable(tf_op.inputs[1])
assert isinstance(
axis, ConstantVariable
), "[TensorFlowConverter] Operation 'Max' with dynamic axis is not supported yet."
for axis in [
x.order.axes[i] for i in axis.data.astype(int).flatten().tolist()
]:
x, = Max(None, axis=axis)(x)
if not tf_op.get_attr("keep_dims") and x.ndim > 1:
x = x.squeeze(axis)
converter.set_variable(tf_op.outputs[0], x)
