Hi, I got some models and results by running './scripts/train_voc_aug.sh -l 1323 -g 4 -b 50'. How can I get the testing results on Pascal VOC? Is the valid_Mean_IoU (0.7005) same as testing result? Run summary: global_step 23119 learning_rate_0 1e-05 learning_rate_1 1e-05 loss_sup 0.05151 loss_unsup 0.0 mIoU_labeled 0.932 mIoU_unlabeled 0.619 pixel_acc_labeled 0.98 pixel_acc_unlabeled 0.886 ramp_up 1.0 valid_Mean_IoU 0.7005 valid_Pixel_Accuracy 0.9316

Hello author, could you provide the download link of ResNet50 and ResNet101? I don't know the bucket_namespace and bucket_name in the download script. I downloaded two pretrained models from other place but the size dosen't match. Thanks.

I run with ./scripts/train_voc_aug.sh -l 1323 -g 4 -b 101 but get error：

ID 3 Warm (4) | Ls 0.51 |: 98%|█████████████████████████████████████████████████████████████████████▎ | 40/41 [01:04<00:00, 1.18it/s] ID 3 Warm (4) | Ls 0.51 |: 98%|█████████████████████████████████████████████████████████████████████▎ | 40/41 [01:14<00:00, 1.18it/s] ID 3 Warm (4) | Ls 0.51 |: 100%|███████████████████████████████████████████████████████████████████████| 41/41 [01:14<00:00, 3.65s/it] ID 3 Warm (4) | Ls 0.51 |: 100%|███████████████████████████████████████████████████████████████████████| 41/41 [01:14<00:00, 1.81s/it]

0%| | 0/289 [00:00<?, ?it/s]wandb: Network error (ConnectionError), entering retry loop.

How can I solve this problem？Can I run without wandb?

Hello, thank you for your excellent work, I read your paper, I have a place is not very clear, is the experiment mentioned in the sliding evaluation is what it means, hope to get your reply, thank you!

Sorry to bother you. I train with bash ./scripts/train_city.sh -l 372 -g 4 -b 50,but get error:

availble_gpus= [0, 1, 2, 3]
  0%|                                                                                                           | 0/93 [00:00<?, ?it/s]
  0%|                                                                                                           | 0/93 [00:05<?, ?it/s]
wandb: Waiting for W&B process to finish... (failed 1).
wandb: - 0.000 MB of 0.000 MB uploaded (0.000 MB deduped)
wandb: \ 0.000 MB of 0.000 MB uploaded (0.000 MB deduped)
wandb: | 0.000 MB of 0.000 MB uploaded (0.000 MB deduped)
wandb:                                                                                
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /homedjy/PS-MT/wandb/offline-run-20220908_195002-2iykpb0m
wandb: Find logs at: ./wandb/offline-run-20220908_195002-2iykpb0m/logs
Traceback (most recent call last):
  File "CityCode/main.py", line 199, in <module>
    main(-1, 1, config, args)
  File "CityCode/main.py", line 116, in main
    trainer.train()
  File "/home/PS-MT/CityCode/Base/base_trainer.py", line 145, in train
    _ = self._warm_up(epoch, id=1)
  File "/homedjy/PS-MT/CityCode/train.py", line 173, in _warm_up
    curr_iter=batch_idx, epoch=epoch-1, id=id, warm_up=True)
  File "/home/.conda/envs/ps-mt/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/.conda/envs/ps-mt/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 155, in forward
    "them on device: {}".format(self.src_device_obj, t.device))
RuntimeError: module must have its parameters and buffers on device cuda:0 (device_ids[0]) but found one of them on device: cpu

I try to fix it but no effect.I want use GPU5,6,7,8,because GPU0123 is occupied.But when print availble_gpus,it's still [0, 1, 2, 3]. I can train model on VOC in same case. Do you have any ideas?

The code works fine when I train with one gpu. The _warm_up process works fine when using multi-gpus distributed training，but the _train_epoch process gets stuck. Gpus and cpus are still running normally. Have you encountered the same problem?

Hi,

Did you apply any extra techniques to your supervised baseline, such as setting output stride as 8, auxiliary loss, or OHEM? Since your reported baseline results are very high on the Pascal dataset, according to Figure 3.

HI! I train with ./scripts/train_voc_aug.sh -l 1323 -g 2 -b 101，and I use 4*V100(16G),batchsize=4,lr=0.0025. In your code,it's 2*V100(32G),batchsize=8,lr=0.0025.But miou in result is just75.29%.When I set lr=0.00125 ,the miou=76.33%,lower than you 78.20%.

I set batchsize in scripts/train_voc_aug.sh line 49：

nohup python3 VocCode/main.py --labeled_examples="${labelled}" --gpus=${gpus} --backbone=${backbone} --warm_up=5 --batch_size=4 --semi_p_th=.6 --semi_n_th=.0 --learning-rate=1.25e-3 \
--epochs=${max_epochs} --unsup_weight=${unsup_weight} > voc_aug_"${labelled}"_"${backbone}".out &

In addition, I have done experiments on Cityscapes(372), and only with batchsize set to 2 does not get error:out of GUP memory.(gpu=4*v100(16G),lr=0.0045).In your code it's gpu=2*v100(32G),lr=0.0045,batchsize=8

Can you give me some advice?

Hi,thank you for your great work! If I use 4GPU,and batchsize=16 learning-rate=1e-2 ,will it be the best configuration?

And why you said 2GPU can get better performance rather than 4GPU?

Hi, thank you for your work. I have a question about the CuTMIx.

Since the batch size for each GPU is 1 when using 8GPUs (conventional setting), I wonder how to perform CutMix when only one unlabeled image is in a GPU? Can you give me some advice?

I recently read your paper and it's a great job. I have a question about the Conf-CE loss in the paper. Can you explain what the c(w) formula means? I confused y~ and y^ in the c(w), formula(4), figure 2.