56 lines
1.8 KiB
Python
56 lines
1.8 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.ops.op import Op
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class ExperimentalDetectronROIFeatureExtractor(Op):
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op = 'ExperimentalDetectronROIFeatureExtractor'
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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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in_ports_count=5,
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out_ports_count=2,
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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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'distribute_rois_between_levels',
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('pyramid_scales', lambda node: ','.join(map(str, node['pyramid_scales']))),
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'image_id',
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'output_size',
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'sampling_ratio',
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'preserve_rois_order',
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'aligned']
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@staticmethod
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def infer(node):
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input_rois_shape = node.in_node(0).shape
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rois_num = input_rois_shape[0]
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input_features_level_0_shape = node.in_node(1).shape
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channels_num = input_features_level_0_shape[1]
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node.out_node(0).shape = np.array([rois_num, channels_num, node.output_size, node.output_size], dtype=np.int64)
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if not node.out_port(1).disconnected():
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node.out_node(1).shape = np.array([rois_num, 4], dtype=np.int64)
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