def _transforms(name, scale, mean, std, gray_probability, blur_probability, sigma): # standard size: (1333, 800) if name == 'basic_train': return data.BasicPairTransforms(train=True, scale=scale, mean=mean, std=std) elif name == 'basic_test': return data.BasicPairTransforms(train=False, scale=scale, mean=mean, std=std) elif name == 'extra_partial': return data.ExtraPairTransforms( with_photometric=True, with_expand=False, with_crop=False) elif name == 'extra_full': return data.ExtraPairTransforms() elif name == 'extra_partial_boost': return data.ExtraPairTransforms( with_photometric=True, with_expand=False, with_crop=False, with_grayscale=True, with_blur=True, gray_probability=gray_probability, sigma=sigma, blur_probability=blur_probability ) elif name == 'extra_full_boost': return data.ExtraPairTransforms( with_grayscale=True, with_blur=True, gray_probability=gray_probability, sigma=sigma, blur_probability=blur_probability ) else: raise KeyError('Unknown transform:', name)
def _transforms(name): # standard size: (1333, 800) if name == 'basic_train': return data.BasicPairTransforms(train=True) elif name == 'basic_test': return data.BasicPairTransforms(train=False) elif name == 'extra_partial': return data.ExtraPairTransforms(with_photometric=True, with_expand=False, with_crop=False) elif name == 'extra_full': return data.ExtraPairTransforms() else: raise KeyError('Unknown transform:', name)
def Golbal_Track_init(self, image, init_box): cfg_file = os.path.join(base_path, 'DiMP_LTMU/Global_Track/configs/qg_rcnn_r50_fpn.py') ckp_file = os.path.join(base_path, 'DiMP_LTMU/Global_Track/checkpoints/qg_rcnn_r50_fpn_coco_got10k_lasot.pth') transforms = data.BasicPairTransforms(train=False) self.Global_Tracker = GlobalTrack( cfg_file, ckp_file, transforms, name_suffix='qg_rcnn_r50_fpn') self.Global_Tracker.init(image, init_box)
def Golbal_Track_init(self, image, init_box): init_box = [ init_box[0], init_box[1], init_box[0] + init_box[2], init_box[1] + init_box[3] ] cfg_file = 'Global_Track/configs/qg_rcnn_r50_fpn.py' ckp_file = 'Global_Track/checkpoints/qg_rcnn_r50_fpn_coco_got10k_lasot.pth' transforms = data.BasicPairTransforms(train=False) self.Global_Tracker = GlobalTrack(cfg_file, ckp_file, transforms, name_suffix='qg_rcnn_r50_fpn') self.Global_Tracker.init(image, init_box)
def test_global_track(self): # settings cfg_files = [ 'configs/qg_rpn_r50_fpn.py', 'configs/qg_rcnn_r50_fpn.py', 'configs/qg_rpn_r18_fpn.py'] ckp_files = [ 'checkpoints/qg_rpn_r50_fpn_coco_got10k_lasot.pth', 'checkpoints/qg_rcnn_r50_fpn_coco_got10k_lasot.pth', 'work_dirs/qg_rpn_r18_fpn/epoch_12.pth'] transforms = data.BasicPairTransforms(train=False) # run evaluation over different settings for cfg_file, ckp_file in zip(cfg_files, ckp_files): tracker = GlobalTrack(cfg_file, ckp_file, transforms) self.evaluator.run(tracker, visualize=self.visualize) self.evaluator.report(tracker.name)
def test_mmdet_transforms(self): for transforms in [ data.BasicPairTransforms(), data.ExtraPairTransforms() ]: dataset = data.Seq2Pair(self.seqs, transforms=transforms) indices = np.random.choice(len(dataset), 10) for i in indices: item = dataset[i] img_z = ops.stretch_color(item['img_z'].permute(1, 2, 0).numpy()) img_x = ops.stretch_color(item['img_x'].permute(1, 2, 0).numpy()) bboxes_z = item['gt_bboxes_z'][0].numpy() bboxes_x = item['gt_bboxes_x'][0].numpy() if self.visualize: ops.show_image(img_z, bboxes_z, fig=1, delay=1) ops.show_image(img_x, bboxes_x, fig=2, delay=0)
import _init_paths import neuron.data as data from trackers import * from mmcv import Config if __name__ == '__main__': cfg_file = 'configs/dmtrackGS_dla34_fpn.py' cfg = Config.fromfile(cfg_file) ckp_file = 'work_dirs/dmtrack_dla34_fpn/dmtrackGS.pth' transforms = data.BasicPairTransforms(scale=cfg.data.test['scale'], train=cfg.data.test['train']) tracker = DMTrack(cfg_file, ckp_file, transforms, name_suffix='dmtrack_dla34_fpn') evaluators = [ data.EvaluatorLaSOT(frame_stride=10), ] for e in evaluators: e.run(tracker, visualize=False, return_all=False) e.report(tracker.name, return_all=False)
import _init_paths import neuron.data as data from trackers import * if __name__ == '__main__': cfg_file = 'configs/qg_rcnn_r50_fpn.py' ckp_file = 'checkpoints/qg_rcnn_r50_fpn_coco_got10k_lasot.pth' transforms = data.BasicPairTransforms(train=False) tracker = GlobalTrack(cfg_file, ckp_file, transforms, name_suffix='qg_rcnn_r50_fpn') evaluators = [ # data.EvaluatorOTB(version=2015, root_dir="/disk/xuxiang/GlobalTrack/data/OTB100"), # data.EvaluatorLaSOT(frame_stride=10, root_dir="/disk/xuxiang/GlobalTrack/data/LaSOTBenchmark"), # data.EvaluatorGOT10k(subset='test', root_dir="/disk/xuxiang/GlobalTrack/data/GOT-10k"), data.EvaluatorTLP(root_dir="/disk/xuxiang/GlobalTrack/data/TLP") ] for e in evaluators: e.run(tracker, visualize=False) e.report(tracker.name)