72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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import numpy as np
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from mo.front.caffe.extractors.utils import get_canonical_axis_index
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Node, Graph
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from mo.ops.op import Op, PermuteAttrs
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def arg_ops_infer(node: Node):
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shape = node.in_port(0).data.get_shape()
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node_name = node.soft_get('name', node.id)
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assert shape is not None, "Input shape for the node {} is None".format(node_name)
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# there are two inputs in TensorFlow. The second input is the axis for ArgMax
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connected_in_ports = [port for port in node.in_ports().values() if not port.disconnected()]
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if len(connected_in_ports) == 2:
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axis = node.in_port(1).data.get_value()
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if axis is None:
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log.debug('The second argument to {} is None'.format(node.soft_get('name', node.id)))
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return
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node.axis = axis
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# remove the unnecessary input
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node.in_port(1).disconnect()
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num_top_axes = shape.size
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if num_top_axes < 3:
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num_top_axes = 3
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out_shape = np.ones(num_top_axes, dtype=np.int64)
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if node.has_valid('axis'):
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axis = get_canonical_axis_index(shape, node.axis)
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node.axis = axis
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out_shape = int64_array(shape)
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out_shape[axis] = node.top_k
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PermuteAttrs.create_permute_attrs(node, attrs=[('axis', 'input:0')])
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else:
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out_shape[0] = shape[0]
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out_shape[2] = node.top_k
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if node.has_and_set('out_max_val'):
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out_shape[1] = 2
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node.out_port(0).data.set_shape(out_shape)
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class ArgMaxOp(Op):
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op = 'ArgMax'
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enabled = False
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': None,
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'op': self.op,
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'infer': arg_ops_infer,
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'output_type': np.int64,
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'in_ports_count': 2,
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'out_ports_count': 1,
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}
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super().__init__(graph, mandatory_props, attrs)
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def supported_attrs(self):
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return [
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'out_max_val',
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'top_k',
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'axis',
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]
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