f596432268
* Specification for the NMS-4 operation (updated shape infer function) * Enabled NMS-4 in the Model Optimizer * Changed opset version for NMS with dynamic outputs and namespace to be "dynamic" * Added NMS-4 * Added opset4 to the nGraph * Added unit tests for NMS-4 type infer * Renamed UpgradeNMS3ToNMS4 to UpgradeNMS3ToNMSDynamic. Added stub for ConvertNMS4ToLegacy * Make IE aware of opset4 ops * Updated NMSIE to have different shape infer function based on the NMS it was converted from. Implemented NMS4->NMSIE conversion * Apply code style * Updated StaticShapeNonMaximumSuppression op in the VPU * Introduced new version of NMSIE operation with shape infer function from v4::NMS * Fixed dynamicToStaticNonMaxSuppression transformation * Added new version of NMSIE op with updated shape infer function * Fixed NMS4 to NMSIE2 transformation * Fixed constructors for nGraph ops v4::NM and dynamic::NMS * Updated text in the opset4 specification document * Code style fixes * Fixed constructors for StaticShapeNMS + fixed test * Minor change to the NMS op in the MO * Fixed typo in the dynamic_to_static_shape_non_max_suppression transformation * Removed redundant checks * Refactored NMS infer and validate functions * Added more checks to the validate_and_infer_types functions for NMS-3 and NMS-4 * Fixed compilation issue on Windows for op NMS * Code style fixes * Fixed typos in the NMSIE and NMSIE2 to CNNLayer op conversion * Fixed typo in the ie_cnn_layer_builder_ngraph.cpp * Fixed the NMSToLegacyNMS transformation. Added unit tests * Apply code review comments * Refactored NMSIE to use visitors * Removed calling ConvertNMS4ToLegacy in the common optimizations * Moved NMS4ToNMSLegacy to convert1_to_legacy group of transformations * Removed useless include statement * Removed copy-paste issue Co-authored-by: Evgeny Lazarev <elazarev.nnov@gmail.com>
88 lines
3.5 KiB
Python
88 lines
3.5 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 logging as log
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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.graph.graph import Node, Graph
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from mo.middle.passes.convert_data_type import np_data_type_to_destination_type
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from mo.ops.op import Op
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from mo.utils.error import Error
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class NonMaxSuppression(Op):
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op = 'NonMaxSuppression'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': self.op,
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'op': self.op,
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'version': 'opset4',
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'infer': self.infer,
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'output_type': np.int64,
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'center_point_box': 0,
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'box_encoding': 'corner',
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'in_ports_count': 5,
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'out_ports_count': 1,
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'sort_result_descending': 1,
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'force_precision_in_ports': {
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2: 'int64'},
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'type_infer': self.type_infer,
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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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version = self.get_opset()
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if version in ['opset3', 'opset4']:
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return ['sort_result_descending', 'box_encoding',
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('output_type', lambda node: np_data_type_to_destination_type(node.output_type))]
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elif version == 'opset1':
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return ['sort_result_descending', 'box_encoding']
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else:
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raise Error('Unsupported operation opset version "{}"'.format(version))
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@staticmethod
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def infer(node: Node):
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boxes_shape = node.in_port(0).data.get_shape()
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assert boxes_shape is not None, 'The shape of tensor with boxes is not defined'
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scores_shape = node.in_port(1).data.get_shape()
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assert scores_shape is not None, 'The shape of tensor with scores is not defined'
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assert len(boxes_shape) == 3, 'Length of tensors with boxes must be equal to 3'
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assert len(scores_shape) == 3, 'Length of tensors with scores must be equal to 3'
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max_output_boxes_per_class = node.in_port(2).data.get_value()
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if max_output_boxes_per_class is None:
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log.info('Set default "max_output_boxes_per_class" for node {} to number of boxes'.format(node.name))
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max_output_boxes_per_class = boxes_shape[1]
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num_classes = scores_shape[1]
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num_input_boxes = boxes_shape[1]
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assert scores_shape[2] == num_input_boxes, 'Number of boxes mismatch'
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if node.get_opset() == 'opset4':
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max_number_of_boxes = min(num_input_boxes, max_output_boxes_per_class) * boxes_shape[0] * num_classes
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else:
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max_number_of_boxes = min(num_input_boxes, boxes_shape[0] * max_output_boxes_per_class * num_classes)
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node.out_port(0).data.set_shape(int64_array([max_number_of_boxes, 3]))
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@staticmethod
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def type_infer(node):
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if node.get_opset() in ['opset3', 'opset4']:
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node.out_port(0).set_data_type(node.output_type)
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else:
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node.out_port(0).set_data_type(np.int64)
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