139 lines
4.7 KiB
Python
139 lines
4.7 KiB
Python
# Copyright (c) 2022 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 ppdet.core.workspace import register, create
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from .meta_arch import BaseArch
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import random
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import paddle
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import paddle.nn.functional as F
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import paddle.distributed as dist
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__all__ = ['YOLOX']
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@register
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class YOLOX(BaseArch):
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"""
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YOLOX network, see https://arxiv.org/abs/2107.08430
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Args:
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backbone (nn.Layer): backbone instance
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neck (nn.Layer): neck instance
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head (nn.Layer): head instance
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for_mot (bool): whether used for MOT or not
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input_size (list[int]): initial scale, will be reset by self._preprocess()
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size_stride (int): stride of the size range
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size_range (list[int]): multi-scale range for training
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random_interval (int): interval of iter to change self._input_size
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"""
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__category__ = 'architecture'
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def __init__(self,
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backbone='CSPDarkNet',
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neck='YOLOCSPPAN',
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head='YOLOXHead',
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for_mot=False,
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input_size=[640, 640],
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size_stride=32,
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size_range=[15, 25],
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random_interval=10):
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super(YOLOX, self).__init__()
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self.backbone = backbone
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self.neck = neck
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self.head = head
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self.for_mot = for_mot
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self.input_size = input_size
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self._input_size = paddle.to_tensor(input_size)
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self.size_stride = size_stride
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self.size_range = size_range
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self.random_interval = random_interval
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self._step = 0
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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# backbone
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backbone = create(cfg['backbone'])
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# fpn
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kwargs = {'input_shape': backbone.out_shape}
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neck = create(cfg['neck'], **kwargs)
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# head
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kwargs = {'input_shape': neck.out_shape}
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head = create(cfg['head'], **kwargs)
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return {
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'backbone': backbone,
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'neck': neck,
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"head": head,
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}
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def _forward(self):
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if self.training:
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self._preprocess()
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body_feats = self.backbone(self.inputs)
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neck_feats = self.neck(body_feats, self.for_mot)
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if self.training:
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yolox_losses = self.head(neck_feats, self.inputs)
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yolox_losses.update({'size': self._input_size[0]})
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return yolox_losses
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else:
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head_outs = self.head(neck_feats)
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bbox, bbox_num = self.head.post_process(
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head_outs, self.inputs['im_shape'], self.inputs['scale_factor'])
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return {'bbox': bbox, 'bbox_num': bbox_num}
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def get_loss(self):
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return self._forward()
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def get_pred(self):
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return self._forward()
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def _preprocess(self):
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# YOLOX multi-scale training, interpolate resize before inputs of the network.
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self._get_size()
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scale_y = self._input_size[0] / self.input_size[0]
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scale_x = self._input_size[1] / self.input_size[1]
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if scale_x != 1 or scale_y != 1:
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self.inputs['image'] = F.interpolate(
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self.inputs['image'],
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size=self._input_size,
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mode='bilinear',
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align_corners=False)
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gt_bboxes = self.inputs['gt_bbox']
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for i in range(len(gt_bboxes)):
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if len(gt_bboxes[i]) > 0:
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gt_bboxes[i][:, 0::2] = gt_bboxes[i][:, 0::2] * scale_x
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gt_bboxes[i][:, 1::2] = gt_bboxes[i][:, 1::2] * scale_y
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self.inputs['gt_bbox'] = gt_bboxes
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def _get_size(self):
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# random_interval = 10 as default, every 10 iters to change self._input_size
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image_ratio = self.input_size[1] * 1.0 / self.input_size[0]
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if self._step % self.random_interval == 0:
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size_factor = random.randint(*self.size_range)
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size = [
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self.size_stride * size_factor,
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self.size_stride * int(size_factor * image_ratio)
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]
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self._input_size = paddle.to_tensor(size)
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self._step += 1
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