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"""
Copyright (c) 2017-2018 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import logging as log
import networkx as nx
import numpy as np
from mo.front.caffe.extractors.utils import get_canonical_axis_index
from mo.graph.graph import Node
from mo.ops.op import Op, PermuteAttrs
class ArgMaxOp(Op):
op = 'ArgMax'
def __init__(self, graph: nx.MultiDiGraph, attrs: dict):
mandatory_props = {
'type': __class__.op,
'op': __class__.op,
'infer': ArgMaxOp.argmax_infer
}
super().__init__(graph, mandatory_props, attrs)
def supported_attrs(self):
return [
'out_max_val',
'top_k',
'axis',
]
@staticmethod
def argmax_infer(node: Node):
shape = node.in_node(0).shape
if shape is None:
return
# there are two inputs in TensorFlow. The second input is the axis for ArgMax
if len(node.in_nodes()) == 2:
if node.in_node(1).value is None:
log.debug('The second argument to ArgMax is None')
return
node.axis = node.in_node(1).value.item()
# remove the unnecessary input
node.graph.remove_edge(node.in_node(1).id, node.id)
num_top_axes = shape.size
if num_top_axes < 3:
num_top_axes = 3
out_shape = np.ones(num_top_axes, dtype=int)
if node.has_valid('axis'):
axis = get_canonical_axis_index(shape, node.axis)
node.axis = axis
out_shape = np.array(shape)
out_shape[axis] = node.top_k
PermuteAttrs.create_permute_attrs(node, attrs=[('axis', 'input:0')])
else:
out_shape[0] = shape[0]
out_shape[2] = node.top_k
if node.out_max_val:
out_shape[1] = 2
node.out_node().shape = out_shape
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