wandb log artifact fails at end of run

[jveitchmichaelis]

🐛 Bug

When training using a wandb sweep, the call to log_artifact at the end of the run fails with a ValueError that the path is not correct.

To Reproduce (REQUIRED)

Run a sweep e.g:

wandb sweep ../configs/sweep.yaml --project <>--name test
wandb agent etc

Example config:

2021-09-04 20:49:21,924 - wandb.wandb_agent - INFO - About to run command: /usr/bin/env python utils/loggers/wandb/sweep.py --anchor_t=2.91 --batch_size=32 --box=0.0296 --cl
s=0.243 --cls_pw=0.631 --copy_paste=0 --data=../configs/dataset_final_ultralytics.yaml --degrees=0.373 --epochs=200 --fl_gamma=0 --fliplr=0.5 --flipud=0.5 --hsv_h=0.0138 --
hsv_s=0.664 --hsv_v=0.464 --img=640 --iou_t=0.2 --lr0=0.0016 --lrf=0.06 --mixup=0.342 --momentum=0.843 --mosaic=1 --obj=0.301 --obj_pw=0.911 --patience=150 --perspective=0 -
-scale=0.898 --shear=0.602 --translate=0.245 --warmup_bias_lr=0.05 --warmup_epochs=5 --warmup_momentum=0.5 --weight_decay=0.00036 --weights=yolov5s.pt --workers=8
wandb: 

Output (presumably at an early-stopping point as the epoch number is always less than max):

    51/199     4.79G   0.02315  0.004203 0.0005895        69       640: 100%|██████████████████████████████████████████████████████████████| 186/186 [00:35<00:00,  5.26it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100%|████████████████████████████████████████████████| 23/23 [00:05<00:00,  4.34it/s]
                 all       1415       3611       0.35      0.351      0.331      0.164
Traceback (most recent call last):
  File "/home/josh/code/yolo/yolov5/utils/loggers/wandb/sweep.py", line 33, in <module>
    sweep()
  File "/home/josh/code/yolo/yolov5/utils/loggers/wandb/sweep.py", line 29, in sweep
    train(hyp_dict, opt, device)
  File "/home/josh/code/yolo/yolov5/train.py", line 421, in train
    callbacks.on_train_end(last, best, plots, epoch)
  File "/home/josh/code/yolo/yolov5/utils/callbacks.py", line 173, in on_train_end
    self.run_callbacks('on_train_end', *args, **kwargs)
  File "/home/josh/code/yolo/yolov5/utils/callbacks.py", line 71, in run_callbacks
    logger['callback'](*args, **kwargs)
  File "/home/josh/code/yolo/yolov5/utils/loggers/__init__.py", line 143, in on_train_end
    wandb.log_artifact(str(best if best.exists() else last), type='model',
  File "/home/josh/anaconda3/envs/yolo/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 2147, in log_artifact
    return self._log_artifact(artifact_or_path, name, type, aliases)
  File "/home/josh/anaconda3/envs/yolo/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 2266, in _log_artifact
    artifact, aliases = self._prepare_artifact(
  File "/home/josh/anaconda3/envs/yolo/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 2343, in _prepare_artifact
    raise ValueError(
ValueError: path must be a file, directory or externalreference like s3://bucket/path

wandb: Waiting for W&B process to finish, PID 26001
wandb: Program failed with code 1.  Press ctrl-c to abort syncing.
wandb:
wandb: Find user logs for this run at: /home/josh/code/yolo/yolov5/wandb/run-20210904_151552-m0kgmv8u/logs/debug.log
wandb: Find internal logs for this run at: /home/josh/code/yolo/yolov5/wandb/run-20210904_151552-m0kgmv8u/logs/debug-internal.log
wandb: Run summary:
wandb:                 train/box_loss 0.02315
wandb:                 train/obj_loss 0.0042
wandb:                 train/cls_loss 0.00059
wandb:              metrics/precision 0.3496
wandb:                 metrics/recall 0.35147
wandb:                metrics/mAP_0.5 0.33073
wandb:           metrics/mAP_0.5:0.95 0.164
wandb:                   val/box_loss 0.03803
wandb:                   val/obj_loss 0.00572

Adding a try/except block around the call gives the following path:

runs/train/exp38/weights/last.pt

but the weight folder is empty. So for some reason the run is terminating, but it's not saving the final checkpoint?

Expected behavior

The artifact (e.g. best/last) model is uploaded.

Environment

If applicable, add screenshots to help explain your problem.

• OS: Ubuntu 18.04
• GPU: GTX 2070 Super

[github-actions]

👋 Hello @jveitchmichaelis, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@jveitchmichaelis thanks for raising this issue!

@AyushExel can you take a look at this please? Thanks!

[AyushExel]

I'll try to reproduce this and push a fix today

[jveitchmichaelis]

For what it's worth, checkpoints are saved if I run training directly (eg `python train.py`)

…

On Mon, Sep 6, 2021, 21:32 Ayush Chaurasia ***@***.***> wrote: I'll try to reproduce this and push a fix today — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#4672 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAYDMJYWSPOT4JGKN2YOFQTUASDDZANCNFSM5DNM5ACQ> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>.

[AyushExel]

@jveitchmichaelis I wasn't able to reproduce it. I'm currently executing this command from inside the yolov5 folder.

wandb sweep utils/loggers/wandb/sweep.yaml --name sweep_test
wandb agent {sweep id here}

How are you running the sweep? Can you share your sweep.yaml file contents? thanks!

[jveitchmichaelis]

Sure, here's the sweep:

# Hyperparameters for training
# To set range- 
# Provide min and max values as:
#      parameter:
#         
#         min: scalar
#         max: scalar
# OR
#
# Set a specific list of search space-
#     parameter: 
#         values: [scalar1, scalar2, scalar3...]
#         
# You can use grid, bayesian and hyperopt search strategy 
# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration

program: utils/loggers/wandb/sweep.py
method: grid
metric:
  name: metrics/mAP_0.5
  goal: maximize

parameters:
  # hyperparameters: set either min, max range or values list
  weights:
    value: yolov5s.pt
  data:
    values: ["../configs/dataset_10.yaml", "../configs/dataset_5.yaml", "../configs/dataset.yaml"]
  batch_size:
    values: [4, 8, 16]
  epochs:
    distribution: constant
    value: 150
  workers:
    distribution: constant
    value: 2
  img:
    distribution: constant
    value: 640
  lr0:
    distribution: constant
    value: 0.0016
  lrf: # final cyclic training rate, lr0 * lrf!
    distribution: constant
    value: 0.06
  momentum:
    distribution: constant
    value: 0.843
  weight_decay:
    distribution: constant
    value: 0.00036
  warmup_epochs:
    distribution: constant
    value: 5
  warmup_momentum:
    distribution: constant
    value: 0.5
  warmup_bias_lr:
    distribution: constant
    value: 0.05
  box:
    distribution: constant
    value: 0.0296
  cls:
    distribution: constant
    value: 0.243
  cls_pw:
    distribution: constant
    value: 0.631
  obj:
    distribution: constant
    value: 0.301
  obj_pw:
    distribution: constant
    value: 0.911
  iou_t:
    distribution: constant
    value: 0.2
  anchor_t:
    distribution: constant
    value: 2.91

  # Augmentation
  fl_gamma:
    distribution: constant
    value: 0.0
  hsv_h:
    distribution: constant
    value: 0.0138
  hsv_s:
    distribution: constant
    value: 0.664
  hsv_v:
    distribution: constant
    value: 0.464
  degrees:
    distribution: constant
    value: 0.373
  translate:
    distribution: constant
    value: 0.245
  scale:
    distribution: constant
    value: 0.898
  shear:
    distribution: constant
    value: 0.602
  perspective:
    distribution: constant
    value: 0.0
  flipud:
    distribution: constant
    value: 0.5
  fliplr:
    distribution: constant
    value: 0.5
  mosaic:
    distribution: constant
    value: 1.0
  mixup:
    distribution: constant
    value: 0.342
  copy_paste:
    distribution: constant
    value: 0.0

and yep, I'm running from the folder with:

wandb sweep ../configs/sweep_constant.yaml  --name etc #above
wandb agent {sweep id}

I have yolov5 as a submodule inside my dataset repository, but I don't think it should make a difference

[AyushExel]

Thanks I'm trying to repro. I'll update soon

[AyushExel]

@jveitchmichaelis Okay I tried running the sweep for 150 epochs and I didn't see any error. Is this error reproducible from your end? Run the same command again to repro ( if you haven't done this already)

[jveitchmichaelis]

Yeah I'll try again. There are some other odd things which I'm trying to dig into. Here's an output from a non-sweep run:

python train.py --img 640 --device 0 --batch 8 --epochs 200 --workers 4 --data ../configs/final_ultralytics_5.yaml --weights yolov5m.pt --hyp ../configs/hyp.finetune.yaml --entity <me> --project project_name--save_period 10

the run stops early (fine):

     Epoch   gpu_mem       box       obj       cls    labels  img_size
    32/199     6.01G   0.01985  0.003756 0.0005924         2       640: 100%|██████████████████████████████████████████████████████████████████████| 742/742 [02:59<00:00,  4.14it/s]
               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100%|████████████████████████████████████████████████████████| 89/89 [00:13<00:00,  6.60it/s]
                 all       1415       3611      0.384       0.35      0.335       0.15
EarlyStopping patience 30 exceeded, stopping training.

33 epochs completed in 1.782 hours.
Optimizer stripped from <project_name>/exp10/weights/last.pt, 93.7MB
Optimizer stripped from <project_name>/exp10/weights/best.pt, 93.7MB

wandb: Waiting for W&B process to finish, PID 27174
wandb: Program ended successfully.

...

you can see that the weights are created - here's the output folder:

(yolo) user@mimir:~/code/yolov5/<project_name>/exp10$ tree -L 2
.
├── events.out.tfevents.1631138661.mimir.27098.0
├── hyp.yaml
├── labels_correlogram.jpg
├── labels.jpg
├── opt.yaml
├── results.csv
├── results.png
├── train_batch0.jpg
├── train_batch1.jpg
├── train_batch2.jpg
└── weights
    ├── best.pt
    └── last.pt

1 directory, 12 files

So that's OK. But oddly there is no confusion matrix, per-class-AP is not printed to the log file, and I suppose as a result, the matrix image doesn't get uploaded to wandb either.

EDIT: It looks like this got fixed a few days ago with updates to the early stopping functionality. I'll test again...

[glenn-jocher]

@jveitchmichaelis some of your questions are addressed in EarlyStopping updates PR #4679, which increases the default patience to 100 and fixes the lack of validation results you mentioned (no confusion matrix etc.)

[jveitchmichaelis]

@glenn-jocher Yep - I just saw, thanks! The fix was only a few days ago, so I've re-pulled and will test again. It's possible that the same issue was causing weights to not be created.

@AyushExel - I'll try a sweep with e.g. 1 epoch and see if it still occurs

[github-actions]

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