177 lines
6.4 KiB
Python
177 lines
6.4 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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import copy
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import math
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import numpy as np
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import paddle
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from ppdet.core.workspace import register, create
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from .meta_arch import BaseArch
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from ..keypoint_utils import affine_transform
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from ppdet.data.transform.op_helper import gaussian_radius, gaussian2D, draw_umich_gaussian
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__all__ = ['CenterTrack']
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@register
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class CenterTrack(BaseArch):
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"""
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CenterTrack network, see http://arxiv.org/abs/2004.01177
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Args:
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detector (object): 'CenterNet' instance
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plugin_head (object): 'CenterTrackHead' instance
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tracker (object): 'CenterTracker' instance
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"""
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__category__ = 'architecture'
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__shared__ = ['mot_metric']
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def __init__(self,
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detector='CenterNet',
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plugin_head='CenterTrackHead',
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tracker='CenterTracker',
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mot_metric=False):
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super(CenterTrack, self).__init__()
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self.detector = detector
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self.plugin_head = plugin_head
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self.tracker = tracker
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self.mot_metric = mot_metric
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self.pre_image = None
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self.deploy = False
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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detector = create(cfg['detector'])
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detector_out_shape = detector.neck and detector.neck.out_shape or detector.backbone.out_shape
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kwargs = {'input_shape': detector_out_shape}
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plugin_head = create(cfg['plugin_head'], **kwargs)
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tracker = create(cfg['tracker'])
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return {
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'detector': detector,
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'plugin_head': plugin_head,
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'tracker': tracker,
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}
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def _forward(self):
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if self.training:
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det_outs = self.detector(self.inputs)
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neck_feat = det_outs['neck_feat']
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losses = {}
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for k, v in det_outs.items():
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if 'loss' not in k: continue
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losses.update({k: v})
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plugin_outs = self.plugin_head(neck_feat, self.inputs)
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for k, v in plugin_outs.items():
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if 'loss' not in k: continue
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losses.update({k: v})
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losses['loss'] = det_outs['det_loss'] + plugin_outs['plugin_loss']
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return losses
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else:
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if not self.mot_metric:
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# detection, support bs>=1
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det_outs = self.detector(self.inputs)
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return {
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'bbox': det_outs['bbox'],
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'bbox_num': det_outs['bbox_num']
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}
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else:
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# MOT, only support bs=1
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if not self.deploy:
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if self.pre_image is None:
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self.pre_image = self.inputs['image']
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# initializing tracker for the first frame
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self.tracker.init_track([])
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self.inputs['pre_image'] = self.pre_image
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self.pre_image = self.inputs[
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'image'] # Note: update for next image
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# render input heatmap from tracker status
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pre_hm = self.get_additional_inputs(
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self.tracker.tracks, self.inputs, with_hm=True)
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self.inputs['pre_hm'] = paddle.to_tensor(pre_hm)
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# model inference
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det_outs = self.detector(self.inputs)
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neck_feat = det_outs['neck_feat']
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result = self.plugin_head(
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neck_feat, self.inputs, det_outs['bbox'],
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det_outs['bbox_inds'], det_outs['topk_clses'],
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det_outs['topk_ys'], det_outs['topk_xs'])
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if not self.deploy:
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# convert the cropped and 4x downsampled output coordinate system
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# back to the input image coordinate system
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result = self.plugin_head.centertrack_post_process(
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result, self.inputs, self.tracker.out_thresh)
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return result
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def get_pred(self):
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return self._forward()
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def get_loss(self):
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return self._forward()
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def reset_tracking(self):
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self.tracker.reset()
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self.pre_image = None
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def get_additional_inputs(self, dets, meta, with_hm=True):
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# Render input heatmap from previous trackings.
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trans_input = meta['trans_input'][0].numpy()
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inp_width, inp_height = int(meta['inp_width'][0]), int(meta[
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'inp_height'][0])
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input_hm = np.zeros((1, inp_height, inp_width), dtype=np.float32)
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for det in dets:
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if det['score'] < self.tracker.pre_thresh:
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continue
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bbox = affine_transform_bbox(det['bbox'], trans_input, inp_width,
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inp_height)
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h, w = bbox[3] - bbox[1], bbox[2] - bbox[0]
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if (h > 0 and w > 0):
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radius = gaussian_radius(
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(math.ceil(h), math.ceil(w)), min_overlap=0.7)
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radius = max(0, int(radius))
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ct = np.array(
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[(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2],
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dtype=np.float32)
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ct_int = ct.astype(np.int32)
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if with_hm:
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input_hm[0] = draw_umich_gaussian(input_hm[0], ct_int,
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radius)
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if with_hm:
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input_hm = input_hm[np.newaxis]
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return input_hm
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def affine_transform_bbox(bbox, trans, width, height):
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bbox = np.array(copy.deepcopy(bbox), dtype=np.float32)
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bbox[:2] = affine_transform(bbox[:2], trans)
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bbox[2:] = affine_transform(bbox[2:], trans)
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bbox[[0, 2]] = np.clip(bbox[[0, 2]], 0, width - 1)
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bbox[[1, 3]] = np.clip(bbox[[1, 3]], 0, height - 1)
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return bbox
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