Find the dataset but Can`t read the dataset rightly

[xiaofanfan161]

Hello, what is the reason why the program can find the dataset but cannot read it correctly, the following is the content of the dataset I read:
Creating new dataset market1501 and add it to the datasets cache.
Loading train (source) dataset
Creating new dataset market1501 and add it to the datasets cache.
=> Loaded Market1501

subset | # ids | # images | # cameras

train | 0 | 0 | 0
query | 0 | 0 | 0
gallery | 0 | 0 | 0

=> Loading test (target) dataset
Using cached dataset market1501.
Using cached dataset market1501.

**************** Summary ****************
source : ['market1501']

source datasets : 1

source ids : 0

source images : 0

source cameras : 0

target : ['market1501']

---

Building model: bpbreid
=> init weights from normal distribution
Loading pretrained ImageNet HRNet32 model at pretrained_models/hrnetv2_w32_imagenet_pretrained.pth
=> loading pretrained model pretrained_models/hrnetv2_w32_imagenet_pretrained.pth
/home/Anaconda-env/bpbreid/lib/python3.9/site-packages/torch/nn/init.py:405: UserWarning: Initializing zero-element tensors is a no-op
warnings.warn("Initializing zero-element tensors is a no-op")
Model complexity: params=34,848,838 flops=8,000,198,656
Successfully loaded pretrained weights from "/home/WYF/BPBReID/bpbreid-main/pretrained_models/bpbreid_market1501_hrnet32_10642.pth"
** The following layers are discarded due to unmatched keys or layer size: ['global_identity_classifier.classifier.weight', 'background_identity_classifier.classifier.weight', 'foreground_identity_classifier.classifier.weight', 'concat_parts_identity_classifier.classifier.weight', 'parts_identity_classifier.0.classifier.weight', 'parts_identity_classifier.1.classifier.weight', 'parts_identity_classifier.2.classifier.weight', 'parts_identity_classifier.3.classifier.weight', 'parts_identity_classifier.4.classifier.weight']
Building part_based-engine for image-reid
Starting experiment 3cdb81db-1431-4221-8e47-408497a470ef with job id 778978385 and creation date 2024_05_12_03_13_36_13S

Evaluating market1501 (source)

Extracting features from query set ...
Batches processed: 0it [00:00, ?it/s]
Traceback (most recent call last):
File "/home/WYF/BPBReID/bpbreid-main/torchreid/scripts/main.py", line 274, in
main()
File "/home/WYF/BPBReID/bpbreid-main/torchreid/scripts/main.py", line 183, in main
engine.run(**engine_run_kwargs(cfg))
File "/home/WYF/BPBReID/bpbreid-main/torchreid/engine/engine.py", line 176, in run
self.test(
File "/home/WYF/BPBReID/bpbreid-main/torchreid/engine/engine.py", line 334, in test
cmc, mAP, ssmd, avg_pxl_pred_accuracy = self._evaluate(
File "/home/Anaconda-env/bpbreid/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/home/WYF/BPBReID/bpbreid-main/torchreid/engine/image/part_based_engine.py", line 188, in evaluate
qf, q_pids, q_camids, qf_parts_visibility, q_parts_masks, q_pxl_scores, q_anns = self.feature_extraction(query_loader)
File "/home/WYF/BPBReID/bpbreid-main/torchreid/engine/image/part_based_engine.py", line 159, in feature_extraction
parts_visibility = torch.cat(parts_visibility, 0)
RuntimeError: torch.cat(): expected a non-empty list of Tensors