openvino/model-optimizer/extensions/ops/proposal.py

70 lines
2.4 KiB
Python

# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
from mo.front.common.partial_infer.utils import int64_array
from mo.front.extractor import attr_getter, bool_to_str
from mo.graph.graph import Node, Graph
from mo.ops.op import Op
class ProposalOp(Op):
op = 'Proposal'
def __init__(self, graph: Graph, attrs: dict):
mandatory_props = {
'type': __class__.op,
'op': __class__.op,
'version': 'opset4',
'post_nms_topn': 300, # default in caffe-shared
'infer': ProposalOp.proposal_infer,
'in_ports_count': 3,
'out_ports_count': 1 if attrs.get('version') == 'opset1' else 2,
'normalize': False,
'clip_before_nms': True,
'clip_after_nms': False,
}
super().__init__(graph, mandatory_props, attrs)
def supported_attrs(self):
return [
'feat_stride',
'base_size',
'min_size',
'ratio',
'scale',
'pre_nms_topn',
'post_nms_topn',
'nms_thresh',
]
def backend_attrs(self):
return [
'feat_stride',
'base_size',
'min_size',
('ratio', lambda node: attr_getter(node, 'ratio')),
('scale', lambda node: attr_getter(node, 'scale')),
'pre_nms_topn',
'post_nms_topn',
'nms_thresh',
'framework',
'box_coordinate_scale',
'box_size_scale',
('normalize', lambda node: bool_to_str(node, 'normalize')),
('clip_after_nms', lambda node: bool_to_str(node, 'clip_after_nms')),
('clip_before_nms', lambda node: bool_to_str(node, 'clip_before_nms')),
]
@staticmethod
def proposal_infer(node: Node):
input_shape = node.in_node(0).shape
out_shape = int64_array([input_shape[0] * node.post_nms_topn, 5])
# rois blob: holds R regions of interest, each is a 5 - tuple
# (n, x1, y1, x2, y2) specifying an image batch index n and a
# rectangle(x1, y1, x2, y2)
node.out_port(0).data.set_shape(out_shape)
# the second optional output contains box probabilities
if len(node.out_ports()) == 2 and not node.out_port(1).disconnected():
node.out_port(1).data.set_shape(int64_array([input_shape[0] * node.post_nms_topn]))