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