82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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import numpy as np
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from ppdet.core.workspace import register, serializable
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from .. import ops
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@register
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@serializable
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class ProposalGenerator(object):
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"""
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Proposal generation module
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For more details, please refer to the document of generate_proposals
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in ppdet/modeing/ops.py
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Args:
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pre_nms_top_n (int): Number of total bboxes to be kept per
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image before NMS. default 6000
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post_nms_top_n (int): Number of total bboxes to be kept per
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image after NMS. default 1000
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nms_thresh (float): Threshold in NMS. default 0.5
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min_size (flaot): Remove predicted boxes with either height or
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width < min_size. default 0.1
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eta (float): Apply in adaptive NMS, if adaptive `threshold > 0.5`,
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`adaptive_threshold = adaptive_threshold * eta` in each iteration.
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default 1.
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topk_after_collect (bool): whether to adopt topk after batch
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collection. If topk_after_collect is true, box filter will not be
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used after NMS at each image in proposal generation. default false
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"""
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def __init__(self,
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pre_nms_top_n=12000,
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post_nms_top_n=2000,
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nms_thresh=.5,
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min_size=.1,
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eta=1.,
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topk_after_collect=False):
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super(ProposalGenerator, self).__init__()
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self.pre_nms_top_n = pre_nms_top_n
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self.post_nms_top_n = post_nms_top_n
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self.nms_thresh = nms_thresh
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self.min_size = min_size
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self.eta = eta
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self.topk_after_collect = topk_after_collect
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def __call__(self, scores, bbox_deltas, anchors, im_shape):
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top_n = self.pre_nms_top_n if self.topk_after_collect else self.post_nms_top_n
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variances = paddle.ones_like(anchors)
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rpn_rois, rpn_rois_prob, rpn_rois_num = ops.generate_proposals(
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scores,
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bbox_deltas,
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im_shape,
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anchors,
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variances,
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pre_nms_top_n=self.pre_nms_top_n,
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post_nms_top_n=top_n,
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nms_thresh=self.nms_thresh,
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min_size=self.min_size,
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eta=self.eta,
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return_rois_num=True)
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return rpn_rois, rpn_rois_prob, rpn_rois_num, self.post_nms_top_n
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