67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import numpy as np
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from mo.front.common.partial_infer.utils import int64_array
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from mo.ops.op import Op
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class ExperimentalDetectronDetectionOutput(Op):
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op = 'ExperimentalDetectronDetectionOutput'
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enabled = True
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def __init__(self, graph, attrs):
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mandatory_props = dict(
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type=__class__.op,
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op=__class__.op,
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version='experimental',
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infer=__class__.infer,
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type_infer=self.type_infer,
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in_ports_count=4,
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out_ports_count=3,
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)
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super().__init__(graph, mandatory_props, attrs)
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def backend_attrs(self):
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return [
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'class_agnostic_box_regression',
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'max_detections_per_image',
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'nms_threshold',
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'num_classes',
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'post_nms_count',
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'score_threshold',
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'max_delta_log_wh',
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('deltas_weights', lambda node: ','.join(map(str, node['deltas_weights'])))]
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@staticmethod
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def infer(node):
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rois_num = node.max_detections_per_image
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# boxes
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node.out_node(0).shape = np.array([rois_num, 4], dtype=np.int64)
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# classes, scores, batch indices
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for port_ind in range(1, 3):
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if not node.out_port(port_ind).disconnected():
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node.out_port(port_ind).data.set_shape(int64_array([rois_num]))
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@staticmethod
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def type_infer(node):
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in_data_type = node.in_port(0).get_data_type()
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node.out_port(0).set_data_type(in_data_type)
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node.out_port(1).set_data_type(np.int32) # the second output contains class indices
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node.out_port(2).set_data_type(in_data_type)
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