forked from PulseFocusPlatform/PulseFocusPlatform
83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
# Copyright (c) 2019 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from paddle import fluid
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from ppdet.core.workspace import register
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from ppdet.modeling.ops import BBoxAssigner, MaskAssigner
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__all__ = [
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'BBoxAssigner',
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'MaskAssigner',
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'CascadeBBoxAssigner',
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]
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@register
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class CascadeBBoxAssigner(object):
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__shared__ = ['num_classes']
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def __init__(self,
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batch_size_per_im=512,
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fg_fraction=.25,
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fg_thresh=[0.5, 0.6, 0.7],
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bg_thresh_hi=[0.5, 0.6, 0.7],
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bg_thresh_lo=[0., 0., 0.],
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bbox_reg_weights=[10, 20, 30],
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shuffle_before_sample=True,
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num_classes=81,
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class_aware=False):
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super(CascadeBBoxAssigner, self).__init__()
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self.batch_size_per_im = batch_size_per_im
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self.fg_fraction = fg_fraction
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self.fg_thresh = fg_thresh
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self.bg_thresh_hi = bg_thresh_hi
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self.bg_thresh_lo = bg_thresh_lo
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self.bbox_reg_weights = bbox_reg_weights
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self.class_nums = num_classes
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self.use_random = shuffle_before_sample
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self.class_aware = class_aware
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def __call__(self, input_rois, feed_vars, curr_stage, max_overlap=None):
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curr_bbox_reg_w = [
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1. / self.bbox_reg_weights[curr_stage],
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1. / self.bbox_reg_weights[curr_stage],
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2. / self.bbox_reg_weights[curr_stage],
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2. / self.bbox_reg_weights[curr_stage],
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]
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outs = fluid.layers.generate_proposal_labels(
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rpn_rois=input_rois,
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gt_classes=feed_vars['gt_class'],
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is_crowd=feed_vars['is_crowd'],
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gt_boxes=feed_vars['gt_bbox'],
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im_info=feed_vars['im_info'],
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batch_size_per_im=self.batch_size_per_im,
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fg_thresh=self.fg_thresh[curr_stage],
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bg_thresh_hi=self.bg_thresh_hi[curr_stage],
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bg_thresh_lo=self.bg_thresh_lo[curr_stage],
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bbox_reg_weights=curr_bbox_reg_w,
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use_random=self.use_random,
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class_nums=self.class_nums if self.class_aware else 2,
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is_cls_agnostic=not self.class_aware,
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is_cascade_rcnn=True
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if curr_stage > 0 and not self.class_aware else False,
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max_overlap=max_overlap,
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return_max_overlap=True)
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return outs
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