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7b7527a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
from ppdet.core.workspace import register, serializable
from .. import ops
@register
@serializable
class ProposalGenerator(object):
"""
Proposal generation module
For more details, please refer to the document of generate_proposals
in ppdet/modeing/ops.py
Args:
pre_nms_top_n (int): Number of total bboxes to be kept per
image before NMS. default 6000
post_nms_top_n (int): Number of total bboxes to be kept per
image after NMS. default 1000
nms_thresh (float): Threshold in NMS. default 0.5
min_size (flaot): Remove predicted boxes with either height or
width < min_size. default 0.1
eta (float): Apply in adaptive NMS, if adaptive `threshold > 0.5`,
`adaptive_threshold = adaptive_threshold * eta` in each iteration.
default 1.
topk_after_collect (bool): whether to adopt topk after batch
collection. If topk_after_collect is true, box filter will not be
used after NMS at each image in proposal generation. default false
"""
def __init__(self,
pre_nms_top_n=12000,
post_nms_top_n=2000,
nms_thresh=.5,
min_size=.1,
eta=1.,
topk_after_collect=False):
super(ProposalGenerator, self).__init__()
self.pre_nms_top_n = pre_nms_top_n
self.post_nms_top_n = post_nms_top_n
self.nms_thresh = nms_thresh
self.min_size = min_size
self.eta = eta
self.topk_after_collect = topk_after_collect
def __call__(self, scores, bbox_deltas, anchors, im_shape):
top_n = self.pre_nms_top_n if self.topk_after_collect else self.post_nms_top_n
variances = paddle.ones_like(anchors)
if hasattr(paddle.vision.ops, "generate_proposals"):
generate_proposals = getattr(paddle.vision.ops,
"generate_proposals")
else:
generate_proposals = ops.generate_proposals
rpn_rois, rpn_rois_prob, rpn_rois_num = generate_proposals(
scores,
bbox_deltas,
im_shape,
anchors,
variances,
pre_nms_top_n=self.pre_nms_top_n,
post_nms_top_n=top_n,
nms_thresh=self.nms_thresh,
min_size=self.min_size,
eta=self.eta,
return_rois_num=True)
return rpn_rois, rpn_rois_prob, rpn_rois_num, self.post_nms_top_n
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