68 lines
1.9 KiB
Python
68 lines
1.9 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.graph.graph import Node, Graph
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from mo.ops.op import Op, PermuteAttrs
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class ArgMaxOp(Op):
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op = 'ArgMax'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': __class__.op,
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'op': __class__.op,
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'infer': ArgMaxOp.argmax_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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@staticmethod
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def argmax_infer(node: Node):
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shape = node.in_node(0).shape
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if shape is None:
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return
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# there are two inputs in TensorFlow. The second input is the axis for ArgMax
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if len(node.in_nodes()) == 2:
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if node.in_node(1).value is None:
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log.debug('The second argument to ArgMax is None')
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return
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node.axis = node.in_node(1).value.item()
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# remove the unnecessary input
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node.graph.remove_edge(node.in_node(1).id, node.id)
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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=int)
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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 = np.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.out_max_val:
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out_shape[1] = 2
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node.out_node().shape = out_shape
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