您好，我用crowdhuman来训练cstrack模型的时候，训练速度很慢，gpu使用率波动很大，一下百分之百，一下百分之零，我尝试调整num_work数量，但是cpu使用率始终只用了一个核，不知道要怎么解决这个问题；第二个问题是我用yolov5l做预训练模型，但训练效果很不理想，recall一直降低，不知道要怎么解决这个问题；第三个问题是您网站上提供的yolov5l模型和cstrack.pt模型是在哪个数据集上训练的结果呢

您好，我有几个小问题需要向您请教一下，西北工业大学在读学生，直博生在读，博士课题就是多目标跟踪。 第一个问题是，我在mot benchmark上注册一直没有回应，注册了很多次，用学校邮箱或者其他邮箱，都一年了，没有任何回应。 第二个问题是，现在在使用很早之前一个师兄注册的账号，但是最近突然发现提交不上去了，能到用户界面，也能load 结果文件，提交的时候也会显示提交成功，界面说是会在几分钟内返回结果，但是没有任何回应，刷新个人用户界面时，显示的提交次数是0，最近两周试了很多次，都是这样。 比较好奇是因为西工大邮箱或者ip问题还是怎么回事儿，其他同方向的伙伴们可以提交和注册吗

Thank you for your nice recent paper, "One More Check: Making “Fake Background” Be Tracked Again" which shows high improvement in performance compared to the current state of the art in Multi-object tracking. The link on the paper direct to this repository is where we can find the implementation, however, I didn't find its implementation.

Also on the paper " https://arxiv.org/pdf/2104.09441v1.pdf "stated that other information can be found in Supplementary material, but also I didn't find those supplementary materials.

Can you share those supplementary materials? Also, when do you expect to release the code of this paper??

Thank you again for your nice work.

Hello! I have a question about max_index setting in LoadImagesAndLabels

for ds, label_paths in self.label_files_dict.items():
          max_index = -1
          for lp in label_paths:
              lb = np.loadtxt(lp)
              if len(lb) < 1:
                  continue
              if len(lb.shape) < 2:
                  img_max = lb[1]
              else:
                  img_max = np.max(lb[:, 1])
              if img_max > max_index:
                  max_index = img_max
          self.tid_num[ds] = max_index + 1 # here

      last_index = 0
      for i, (k, v) in enumerate(self.tid_num.items()):
                self.tid_start_index[k] = last_index
                last_index += v

when self.tid_num[ds]=max_index+1 is applied to all dataset, index i think that is not used occur.

ex) |dataset|id range|max_index + 1|tid_start_index| |:------:|:---:|:---:|:---:| |mot17|1~517|518|0| |mot20|1~2215|2216|518| |mot16|1~517|518|2734|

Between mot20 and mot16, an index 2733 is not used.

Does you have any other reason why self.tid_num[ds]=max_index+1 is applied to all dataset, not applying only first dataset?

Download the link in the pre-training weights is compressed package, how should this be used? RuntimeError: snapshot/Ocean.pth is a zip archive (did you mean to use torch.jit.load()?)

具体代码见： 这一行。 我仔细阅读了数据集这里的代码，发现制作分类的label这里，如果不使用这里aug_apply返回的real_param，制作出的标签似乎脱离了groundtruth，这里是否应该改为crop_bbox, param = aug_apply(Corner(*crop_bbox), param, shape)?如果是我的理解有误，请多多指教！

Hi Really admire the work "One More Check: Making “Fake Background” Be Tracked Again". Just kindly ask, any plans to release the corresponding code? Thanks.

Thanks for your wonderful work! I find that on MOTChallenge dataset clipping bbox may lead to degraded results. I comment all the clip bbox operations in https://github.com/JudasDie/SOTS/blob/master/lib/dataset/cstrack.py and meet the following training error. Can you help me fix this? Thanks very much. @H11zang

Traceback (most recent call last):
File "train_cstrack.py", line 517, in
train(hyp, opt, device, tb_writer)
File "train_cstrack.py", line 319, in train
loss, loss_items = mot_loss(pred, targets.to(device), model) # scaled by batch_size
File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/opt/tiger/demo/SOTS/tracking/../lib/core/mot/base_trainer.py", line 355, in forward
giou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # giou(prediction, target)
File "/opt/tiger/demo/SOTS/tracking/../lib/core/mot/base_trainer.py", line 67, in bbox_iou
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1
RuntimeError: CUDA error: device-side assert triggered
/pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:84: operator(): block: [79,0,0], thread: [96,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed. /pytorch/aten/src/ATen/native/cuda/IndexKernel.cu:84: operator(): block: [79,0,0], thread: [97,0,0] Assertion index >= -sizes[i] && index < sizes[i] && "index out of bounds" failed.

当我按照cstrack.md中的步骤进行模型的测试时，我的命令如下： python test_cstrack.py --nms_thres 0.6 --conf_thres 0.5 --weights ../weights/cstrack.pt --data_dir ../dataset --device 0 --test_mot17 True --vis_state 1 得到的结果为： 2022-05-06 10:40:17 [INFO]: start seq: MOT17-01-SDP Creating model...

78,863,078 total parameters. 2022-05-06 10:40:19 [INFO]: Processing frame 0 (100000.00 fps) 2022-05-06 10:40:54 [INFO]: Processing frame 220 (8.14 fps) 2022-05-06 10:40:57 [INFO]: Processing frame 240 (8.16 fps) 2022-05-06 10:41:00 [INFO]: Processing frame 260 (8.18 fps) 2022-05-06 10:41:03 [INFO]: Processing frame 280 (8.20 fps) 2022-05-06 10:41:06 [INFO]: Processing frame 300 (8.21 fps) 2022-05-06 10:41:09 [INFO]: Processing frame 320 (8.21 fps) 2022-05-06 10:41:30 [INFO]: Time elapsed: 54.46 seconds, FPS: 8.26 IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP IDt IDa IDm MOT17-01-SDP 0.0% 0.0% nan% nan% 0.0% 0 0 0 0 4293 0 0 0 -inf% nan 0 0 0 OVERALL 0.0% 0.0% nan% nan% 0.0% 0 0 0 0 4293 0 0 0 -inf% nan 0 0 0 detection_num: [4293] 4293 id_num: [53] 53 请问是什么原因造成这样的结果，数据需要进行预处理吗，非常感谢你突出的工作！

Hi. Thank you for your great work. May I have the access to the cstrack pre-trained model? I cannot use my google drive account to download from your link. Please add my gmail [email protected].

您好，感谢您提出的卓越工作！ 这里有一些让我存在困惑的结果问题，希望向您请教一下！

我在 Ocean 论文中得到到的VOT2019结果为

offline版本 A 0.590 | R 0.376 | EAO 0.327
online版本 A 0.594 | R 0.316 | EAO 0.350

但您提供的 Google Drive 中的结果只有 Ocean_VOT2019_0.323 文件夹，测试的指标为

A 0.598 | R  0.401 | EAO 0.323

请问 EAO 0.323 的是哪一个版本呢？为什么和论文会有这么大的差异呢？

谢谢！💖

/home/lsw/miniconda3/envs/SOTS/bin/python /home/lsw/SOT/SOTS/tracking/test_sot.py model backbone: ResNet50Dilated model neck: ShrinkChannelS3S4 model head: Learn2Match model build done! SiamInference( (backbone): ResNet50Dilated( (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False) (layer1): Sequential( (0): Bottleneck( (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer2): Sequential( (0): Bottleneck( (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), bias=False) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (3): Bottleneck( (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) (layer3): Sequential( (0): Bottleneck( (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) (downsample): Sequential( (0): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) ) (1): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (2): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (3): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (4): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) (5): Bottleneck( (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False) (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False) (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (relu): ReLU(inplace=True) ) ) ) (neck): ShrinkChannelS3S4( (downsample): Sequential( (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) (downsample_s3): Sequential( (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU(inplace=True) ) ) (head): Learn2Match( (regression): L2Mregression( (reg_encode): SimpleMatrix( (matrix11_k): Sequential( (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (matrix11_s): Sequential( (0): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (roi_te): roi_template( (fea_encoder): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (fea_encoder_s3): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (spatial_conv): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) ) (spatial_conv_s3): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) ) ) (LTM): LTM( (FiLM): FiLM( (s_embed): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (conv_g): Sequential( (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (1): LeakyReLU(negative_slope=0.1) ) (conv_b): Sequential( (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (1): LeakyReLU(negative_slope=0.1) ) ) (PC): PairRelation( (s_embed): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) (t_embed): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)) (down): Conv2d(225, 256, kernel_size=(1, 1), stride=(1, 1)) ) (embed2): Sequential( (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) ) ) (bbox_tower): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): ReLU() (6): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (7): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (8): ReLU() ) (bbox_pred): Conv2d(256, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (classification): L2Mclassification( (LTM): LTM( (Transformer): SimpleSelfAtt( (s_embed): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (t_embed_v): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (t_embed): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (trans): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) ) (FiLM): FiLM( (s_embed): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (conv_g): Sequential( (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (1): LeakyReLU(negative_slope=0.1) ) (conv_b): Sequential( (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (1): LeakyReLU(negative_slope=0.1) ) ) (embed2): Sequential( (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) ) ) (roi_cls): roi_classification( (fea_encoder): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) (3): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): LeakyReLU(negative_slope=0.1) ) (fea_encoder_s3): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) (3): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): LeakyReLU(negative_slope=0.1) ) (down_spatial_conv): Sequential( (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) ) (down_spatial_linear): Sequential( (0): Linear(in_features=128, out_features=128, bias=True) (1): LeakyReLU(negative_slope=0.1) ) (down_spatial_conv_s3): Sequential( (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1)) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) ) (down_target_s3): Sequential( (0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) ) (down_target_s4): Sequential( (0): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) ) (down_spatial_linear_s3): Sequential( (0): Linear(in_features=128, out_features=128, bias=True) (1): LeakyReLU(negative_slope=0.1) ) (merge_s3s4_s2): Sequential( (0): Linear(in_features=256, out_features=256, bias=True) (1): LeakyReLU(negative_slope=0.1) (2): Linear(in_features=256, out_features=256, bias=True) (3): LeakyReLU(negative_slope=0.1) ) (merge_s3s4_s1): Sequential( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): LeakyReLU(negative_slope=0.1) (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (5): LeakyReLU(negative_slope=0.1) ) (pred_s1): Conv2d(256, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (pred_s2): Linear(in_features=256, out_features=1, bias=True) ) ) ) ) ===> init Siamese <==== load pretrained model from ../snapshot/AutoMatch.pth remove prefix 'module.' change features.features to backbone 2022-12-02 22:47:17.534 | INFO | lib.utils.model_helper:check_keys:178 - missing keys:[] Traceback (most recent call last): File "/home/lsw/SOT/SOTS/tracking/test_sot.py", line 150, in main() File "/home/lsw/SOT/SOTS/tracking/test_sot.py", line 135, in main siam_net = loader.load_pretrain(siam_net, resume, addhead=True, print_unuse=False) File "/home/lsw/SOT/SOTS/tracking/../lib/utils/model_helper.py", line 209, in load_pretrain model.load_state_dict(pretrained_dict, strict=True) File "/home/lsw/miniconda3/envs/Unicorn/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1483, in load_state_dict self.class.name, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for SiamInference: Unexpected key(s) in state_dict: "head.regression.LTM.GuidedSP.s_embed.weight", "head.regression.LTM.GuidedSP.s_embed.bias", "head.regression.LTM.GuidedSP.t_embed.weight", "head.regression.LTM.GuidedSP.t_embed.bias", "head.regression.LTM.PointDW.s_embed.weight", "head.regression.LTM.PointDW.s_embed.bias", "head.regression.LTM.PointDW.t_embed.weight", "head.regression.LTM.PointDW.t_embed.bias", "head.regression.LTM.PointAdd.s_embed.weight", "head.regression.LTM.PointAdd.s_embed.bias", "head.regression.LTM.PointAdd.t_embed.weight", "head.regression.LTM.PointAdd.t_embed.bias", "head.regression.LTM.Transformer.s_embed.weight", "head.regression.LTM.Transformer.s_embed.bias", "head.regression.LTM.Transformer.t_embed_v.weight", "head.regression.LTM.Transformer.t_embed_v.bias", "head.regression.LTM.Transformer.t_embed.weight", "head.regression.LTM.Transformer.t_embed.bias", "head.regression.LTM.Transformer.trans.in_proj_weight", "head.regression.LTM.Transformer.trans.in_proj_bias", "head.regression.LTM.Transformer.trans.out_proj.weight", "head.regression.LTM.Transformer.trans.out_proj.bias", "head.classification.LTM.GuidedSP.s_embed.weight", "head.classification.LTM.GuidedSP.s_embed.bias", "head.classification.LTM.GuidedSP.t_embed.weight", "head.classification.LTM.GuidedSP.t_embed.bias", "head.classification.LTM.PointDW.s_embed.weight", "head.classification.LTM.PointDW.s_embed.bias", "head.classification.LTM.PointDW.t_embed.weight", "head.classification.LTM.PointDW.t_embed.bias", "head.classification.LTM.PointAdd.s_embed.weight", "head.classification.LTM.PointAdd.s_embed.bias", "head.classification.LTM.PointAdd.t_embed.weight", "head.classification.LTM.PointAdd.t_embed.bias", "head.classification.LTM.PC.s_embed.weight", "head.classification.LTM.PC.s_embed.bias", "head.classification.LTM.PC.t_embed.weight", "head.classification.LTM.PC.t_embed.bias", "head.classification.LTM.PC.down.weight", "head.classification.LTM.PC.down.bias".

Process finished with exit code 1

Hello,i met this problem when i try to testing ,but i can not solve it.Could you help me to solve it if you have some time?

hi！When i run tune-sot.py, it reports:

(pid=84596) 2022-12-01 19:00:31,528 ERROR function_manager.py:496 -- Failed to load actor class ImplicitFunc. (pid=84596) Traceback (most recent call last): (pid=84596) File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs (pid=84596) actor_class = pickle.loads(pickled_class) (pid=84596) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport (pid=84596) import(name) (pid=84596) ModuleNotFoundError: No module named 'tracker' (pid=84597) 2022-12-01 19:00:31,513 ERROR function_manager.py:496 -- Failed to load actor class ImplicitFunc. (pid=84597) Traceback (most recent call last): (pid=84597) File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs (pid=84597) actor_class = pickle.loads(pickled_class) (pid=84597) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport (pid=84597) import(name) (pid=84597) ModuleNotFoundError: No module named 'tracker' (pid=84599) 2022-12-01 19:00:31,525 ERROR function_manager.py:496 -- Failed to load actor class ImplicitFunc. (pid=84599) Traceback (most recent call last): (pid=84599) File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs (pid=84599) actor_class = pickle.loads(pickled_class) (pid=84599) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport (pid=84599) import(name) (pid=84599) ModuleNotFoundError: No module named 'tracker' 2022-12-01 19:00:31,564 ERROR trial_runner.py:523 -- Trial fitness_5f069808: Error processing event. Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/trial_runner.py", line 471, in _process_trial result = self.trial_executor.fetch_result(trial) File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 430, in fetch_result result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT) File "/root/miniconda3/lib/python3.8/site-packages/ray/worker.py", line 1538, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(RuntimeError): ray::TemporaryActor.train() (pid=84596, ip=172.17.0.7) File "python/ray/_raylet.pyx", line 439, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 474, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 478, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 432, in ray._raylet.execute_task.function_executor RuntimeError: The actor with name ImplicitFunc failed to be imported, and so cannot execute this method. 2022-12-01 19:00:31,570 WARNING worker.py:1134 -- Failed to unpickle actor class 'ImplicitFunc' for actor ID 676d0b440100. Traceback: Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs actor_class = pickle.loads(pickled_class) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport import(name) ModuleNotFoundError: No module named 'tracker'

2022-12-01 19:00:31,586 ERROR trial_runner.py:523 -- Trial fitness_5f0f687a: Error processing event. Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/trial_runner.py", line 471, in _process_trial result = self.trial_executor.fetch_result(trial) File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 430, in fetch_result result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT) File "/root/miniconda3/lib/python3.8/site-packages/ray/worker.py", line 1538, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(RuntimeError): ray::TemporaryActor.train() (pid=84599, ip=172.17.0.7) File "python/ray/_raylet.pyx", line 439, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 474, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 478, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 432, in ray._raylet.execute_task.function_executor RuntimeError: The actor with name ImplicitFunc failed to be imported, and so cannot execute this method. 2022-12-01 19:00:31,616 ERROR trial_runner.py:523 -- Trial fitness_5f3270ea: Error processing event. Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/trial_runner.py", line 471, in _process_trial result = self.trial_executor.fetch_result(trial) File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 430, in fetch_result result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT) File "/root/miniconda3/lib/python3.8/site-packages/ray/worker.py", line 1538, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(RuntimeError): ray::TemporaryActor.train() (pid=84601, ip=172.17.0.7) File "python/ray/_raylet.pyx", line 439, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 474, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 478, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 432, in ray._raylet.execute_task.function_executor RuntimeError: The actor with name ImplicitFunc failed to be imported, and so cannot execute this method. (pid=84601) 2022-12-01 19:00:31,567 ERROR function_manager.py:496 -- Failed to load actor class ImplicitFunc. (pid=84601) Traceback (most recent call last): (pid=84601) File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs (pid=84601) actor_class = pickle.loads(pickled_class) (pid=84601) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport (pid=84601) import(name) (pid=84601) ModuleNotFoundError: No module named 'tracker' 2022-12-01 19:00:31,857 ERROR trial_runner.py:523 -- Trial fitness_5f40249c: Error processing event. Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/trial_runner.py", line 471, in _process_trial result = self.trial_executor.fetch_result(trial) File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 430, in fetch_result result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT) File "/root/miniconda3/lib/python3.8/site-packages/ray/worker.py", line 1538, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(RuntimeError): ray::TemporaryActor.train() (pid=84626, ip=172.17.0.7) File "python/ray/_raylet.pyx", line 439, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 474, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 478, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 432, in ray._raylet.execute_task.function_executor RuntimeError: The actor with name ImplicitFunc failed to be imported, and so cannot execute this method. 2022-12-01 19:00:31,860 WARNING worker.py:1134 -- Failed to unpickle actor class 'ImplicitFunc' for actor ID 552068930100. Traceback: Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs actor_class = pickle.loads(pickled_class) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport import(name) ModuleNotFoundError: No module named 'tracker'

(pid=84626) 2022-12-01 19:00:31,850 ERROR function_manager.py:496 -- Failed to load actor class ImplicitFunc. (pid=84626) Traceback (most recent call last): (pid=84626) File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs (pid=84626) actor_class = pickle.loads(pickled_class) (pid=84626) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport (pid=84626) import(name) (pid=84626) ModuleNotFoundError: No module named 'tracker' 2022-12-01 19:00:31,922 WARNING worker.py:1134 -- Failed to unpickle actor class 'ImplicitFunc' for actor ID adb556570100. Traceback: Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs actor_class = pickle.loads(pickled_class) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport import(name) ModuleNotFoundError: No module named 'tracker'

2022-12-01 19:00:31,926 ERROR trial_runner.py:523 -- Trial fitness_5f744182: Error processing event. Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/trial_runner.py", line 471, in _process_trial result = self.trial_executor.fetch_result(trial) File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 430, in fetch_result result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT) File "/root/miniconda3/lib/python3.8/site-packages/ray/worker.py", line 1538, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(RuntimeError): ray::TemporaryActor.train() (pid=84611, ip=172.17.0.7) File "python/ray/_raylet.pyx", line 439, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 474, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 478, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 432, in ray._raylet.execute_task.function_executor RuntimeError: The actor with name ImplicitFunc failed to be imported, and so cannot execute this method. 2022-12-01 19:00:31,949 WARNING worker.py:1134 -- Failed to unpickle actor class 'ImplicitFunc' for actor ID 2f6e56200100. Traceback: Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs actor_class = pickle.loads(pickled_class) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport import(name) ModuleNotFoundError: No module named 'tracker'

2022-12-01 19:00:31,954 ERROR trial_runner.py:523 -- Trial fitness_5f78b94c: Error processing event. Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/trial_runner.py", line 471, in _process_trial result = self.trial_executor.fetch_result(trial) File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 430, in fetch_result result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT) File "/root/miniconda3/lib/python3.8/site-packages/ray/worker.py", line 1538, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(RuntimeError): ray::TemporaryActor.train() (pid=84610, ip=172.17.0.7) File "python/ray/_raylet.pyx", line 439, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 474, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 478, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 432, in ray._raylet.execute_task.function_executor RuntimeError: The actor with name ImplicitFunc failed to be imported, and so cannot execute this method. (pid=84610) 2022-12-01 19:00:31,941 ERROR function_manager.py:496 -- Failed to load actor class ImplicitFunc. (pid=84610) Traceback (most recent call last): (pid=84610) File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs (pid=84610) actor_class = pickle.loads(pickled_class) (pid=84610) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport (pid=84610) import(name) (pid=84610) ModuleNotFoundError: No module named 'tracker' (pid=84611) 2022-12-01 19:00:31,919 ERROR function_manager.py:496 -- Failed to load actor class ImplicitFunc. (pid=84611) Traceback (most recent call last): (pid=84611) File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs (pid=84611) actor_class = pickle.loads(pickled_class) (pid=84611) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport (pid=84611) import(name) (pid=84611) ModuleNotFoundError: No module named 'tracker' 2022-12-01 19:00:31,977 ERROR trial_runner.py:523 -- Trial fitness_5f7c0872: Error processing event. Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/trial_runner.py", line 471, in _process_trial result = self.trial_executor.fetch_result(trial) File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 430, in fetch_result result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT) File "/root/miniconda3/lib/python3.8/site-packages/ray/worker.py", line 1538, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(RuntimeError): ray::TemporaryActor.train() (pid=84629, ip=172.17.0.7) File "python/ray/_raylet.pyx", line 439, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 474, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 478, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 432, in ray._raylet.execute_task.function_executor RuntimeError: The actor with name ImplicitFunc failed to be imported, and so cannot execute this method. 2022-12-01 19:00:31,985 WARNING worker.py:1134 -- Failed to unpickle actor class 'ImplicitFunc' for actor ID c39997530100. Traceback: Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs actor_class = pickle.loads(pickled_class) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport import(name) ModuleNotFoundError: No module named 'tracker'

(pid=84629) 2022-12-01 19:00:31,971 ERROR function_manager.py:496 -- Failed to load actor class ImplicitFunc. (pid=84629) Traceback (most recent call last): (pid=84629) File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs (pid=84629) actor_class = pickle.loads(pickled_class) (pid=84629) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport (pid=84629) import(name) (pid=84629) ModuleNotFoundError: No module named 'tracker' 2022-12-01 19:00:32,220 WARNING worker.py:1134 -- Failed to unpickle actor class 'ImplicitFunc' for actor ID 98ba875f0100. Traceback: Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs actor_class = pickle.loads(pickled_class) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport import(name) ModuleNotFoundError: No module named 'tracker'

2022-12-01 19:00:32,225 ERROR trial_runner.py:523 -- Trial fitness_5f80b8b8: Error processing event. Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/trial_runner.py", line 471, in _process_trial result = self.trial_executor.fetch_result(trial) File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 430, in fetch_result result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT) File "/root/miniconda3/lib/python3.8/site-packages/ray/worker.py", line 1538, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(RuntimeError): ray::TemporaryActor.train() (pid=84607, ip=172.17.0.7) File "python/ray/_raylet.pyx", line 439, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 474, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 478, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 432, in ray._raylet.execute_task.function_executor RuntimeError: The actor with name ImplicitFunc failed to be imported, and so cannot execute this method. (pid=84607) 2022-12-01 19:00:32,217 ERROR function_manager.py:496 -- Failed to load actor class ImplicitFunc. (pid=84607) Traceback (most recent call last): (pid=84607) File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs (pid=84607) actor_class = pickle.loads(pickled_class) (pid=84607) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport (pid=84607) import(name) (pid=84607) ModuleNotFoundError: No module named 'tracker' 2022-12-01 19:00:32,300 WARNING worker.py:1134 -- Failed to unpickle actor class 'ImplicitFunc' for actor ID 842595fe0100. Traceback: Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs actor_class = pickle.loads(pickled_class) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport import(name) ModuleNotFoundError: No module named 'tracker'

2022-12-01 19:00:32,303 ERROR trial_runner.py:523 -- Trial fitness_5fa5f254: Error processing event. Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/trial_runner.py", line 471, in _process_trial result = self.trial_executor.fetch_result(trial) File "/root/miniconda3/lib/python3.8/site-packages/ray/tune/ray_trial_executor.py", line 430, in fetch_result result = ray.get(trial_future[0], DEFAULT_GET_TIMEOUT) File "/root/miniconda3/lib/python3.8/site-packages/ray/worker.py", line 1538, in get raise value.as_instanceof_cause() ray.exceptions.RayTaskError(RuntimeError): ray::TemporaryActor.train() (pid=84608, ip=172.17.0.7) File "python/ray/_raylet.pyx", line 439, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 474, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 478, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 479, in ray._raylet.execute_task File "python/ray/_raylet.pyx", line 432, in ray._raylet.execute_task.function_executor RuntimeError: The actor with name ImplicitFunc failed to be imported, and so cannot execute this method. (pid=84608) 2022-12-01 19:00:32,297 ERROR function_manager.py:496 -- Failed to load actor class ImplicitFunc. (pid=84608) Traceback (most recent call last): (pid=84608) File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs (pid=84608) actor_class = pickle.loads(pickled_class) (pid=84608) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport (pid=84608) import(name) (pid=84608) ModuleNotFoundError: No module named 'tracker' 2022-12-01 19:00:32,625 WARNING worker.py:1134 -- Failed to unpickle actor class 'ImplicitFunc' for actor ID 688d7fcd0100. Traceback: Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs actor_class = pickle.loads(pickled_class) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport import(name) ModuleNotFoundError: No module named 'tracker'

2022-12-01 19:00:32,625 WARNING worker.py:1134 -- Failed to unpickle actor class 'ImplicitFunc' for actor ID 3261819f0100. Traceback: Traceback (most recent call last): File "/root/miniconda3/lib/python3.8/site-packages/ray/function_manager.py", line 494, in _load_actor_class_from_gcs actor_class = pickle.loads(pickled_class) File "/root/miniconda3/lib/python3.8/site-packages/ray/cloudpickle/cloudpickle.py", line 1096, in subimport import(name) ModuleNotFoundError: No module named 'tracker'

I have run many times, somtimes it shows ModuleNotFoundError: No module named 'utils' I have tied many ways, like changing the version of ray for 0.8.4 to 0.6.0 or appending the utils to system path. it still didn't work. I'm not sure what causes this problem.

thank you, expect your reply!

I really appreciate your great work and sharing OMC.

Would you mind sharing HOTA and IDsw of OMC on the MOT16/17/20 test results which are not available from the paper?

Thank you.

Thanks for your contributions. (1) I train the OMC according to training and testing turorial. When I train with the re-check network,if I use best.pt, the MOTA on MOT16 test dataset is only 69.0. But if I use last.pt, the MOTA on MOT16 test dataset is 74.9.Why? (2) The best MOTA on the MOT16 test is only 74.9,it's much lower than paper. And I don't modify the code.How to adjust training process to improve the MOTA?