anomalib: [Bug]: Key-error during HPO with WandB

Describe the bug

I get a key-error when doing HPO using WandB.

Dataset

MVTec

Model

PatchCore

Steps to reproduce the behavior

1. Install anomalib
2. python tools/hpo/sweep.py --model patchcore --model_config src/anomalib/models/patchcore/config.yaml --sweep_config tools/hpo/configs/wandb.yaml

Expected behavior

I would expect the sweep to start…

Screenshots

No response

Pip/GitHub

pip

What version/branch did you use?

No response

Configuration YAML

---------
sweep.yml
---------
observation_budget: 10
method: bayes
metric:
  name: pixel_AUROC
  goal: maximize
parameters:
  learning_rate:
    min: 1e-5
    max: 1e-1
  optimizer:
    values: ["adam","sgd"]
  dataset:
    category: capsule
  model:
    backbone:
      values: [resnet18, wide_resnet50_2]


--------
PatchCore config
--------
dataset:
  name: mvtec
  format: mvtec
  path: ./datasets/MVTec
  task: segmentation
  category: bottle
  train_batch_size: 32
  test_batch_size: 32
  num_workers: 1
  image_size: 256 # dimensions to which images are resized (mandatory)
  center_crop: 224 # dimensions to which images are center-cropped after resizing (optional)
  normalization: imagenet # data distribution to which the images will be normalized: [none, imagenet]
  transform_config:
    train: null
    eval: null
  test_split_mode: from_dir # options: [from_dir, synthetic]
  test_split_ratio: 0.2 # fraction of train images held out testing (usage depends on test_split_mode)
  val_split_mode: same_as_test # options: [same_as_test, from_test, synthetic]
  val_split_ratio: 0.5 # fraction of train/test images held out for validation (usage depends on val_split_mode)
  tiling:
    apply: false
    tile_size: null
    stride: null
    remove_border_count: 0
    use_random_tiling: False
    random_tile_count: 16

model:
  name: patchcore
  backbone: wide_resnet50_2
  pre_trained: False
  layers:
    - layer3
  coreset_sampling_ratio: 0.1
  num_neighbors: 9
  normalization_method: min_max # options: [null, min_max, cdf]

metrics:
  image:
    - F1Score
    - AUROC
  pixel:
    - F1Score
    - AUROC
  threshold:
    method: adaptive #options: [adaptive, manual]
    manual_image: null
    manual_pixel: null

visualization:
  show_images: False # show images on the screen
  save_images: True # save images to the file system
  log_images: True # log images to the available loggers (if any)
  image_save_path: patchcore/mvtec/bottle/run/images # path to which images will be saved
  mode: full # options: ["full", "simple"]

project:
  seed: 0
  path: ./results

logging:
  logger: [wandb] # options: [comet, tensorboard, wandb, csv] or combinations.
  log_graph: True  # Logs the model graph to respective logger.

optimization:
  export_mode: null # options: onnx, openvino

# PL Trainer Args. Don't add extra parameter here.
trainer:
  enable_checkpointing: true
  default_root_dir: null
  gradient_clip_val: 0
  gradient_clip_algorithm: norm
  num_nodes: 1
  devices: 1
  enable_progress_bar: true
  overfit_batches: 0.0
  track_grad_norm: -1
  check_val_every_n_epoch: 1 # Don't validate before extracting features.
  fast_dev_run: false
  accumulate_grad_batches: 1
  max_epochs: 50
  min_epochs: null
  max_steps: -1
  min_steps: null
  max_time: null
  limit_train_batches: 1.0
  limit_val_batches: 1.0
  limit_test_batches: 1.0
  limit_predict_batches: 1.0
  val_check_interval: 1.0 # Don't validate before extracting features.
  log_every_n_steps: 50
  accelerator: auto # <"cpu", "gpu", "tpu", "ipu", "hpu", "auto">
  strategy: null
  sync_batchnorm: false
  precision: 32
  enable_model_summary: true
  num_sanity_val_steps: 0
  profiler: null
  benchmark: false
  deterministic: false
  reload_dataloaders_every_n_epochs: 0
  auto_lr_find: false
  replace_sampler_ddp: true
  detect_anomaly: false
  auto_scale_batch_size: false
  plugins: null
  move_metrics_to_cpu: false
  multiple_trainloader_mode: max_size_cycle

Logs

/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/requests/__init__.py:109: RequestsDependencyWarning: urllib3 (1.26.14) or chardet (None)/charset_normalizer (3.1.0) doesn't match a supported version!
  warnings.warn(
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/anomalib/config/config.py:238: UserWarning: The seed value is now fixed to 0. Up to v0.3.7, the seed was not fixed when the seed value was set to 0. If you want to use the random seed, please select `None` for the seed value (`null` in the YAML file) or remove the `seed` key from the YAML file.
  warn(
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/anomalib/config/config.py:275: UserWarning: config.project.unique_dir is set to False. This does not ensure that your results will be written in an empty directory and you may overwrite files.
  warn(
[rank: 0] Global seed set to 0
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/requests/__init__.py:109: RequestsDependencyWarning: urllib3 (1.26.14) or chardet (None)/charset_normalizer (3.1.0) doesn't match a supported version!
  warnings.warn(
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/anomalib/config/config.py:238: UserWarning: The seed value is now fixed to 0. Up to v0.3.7, the seed was not fixed when the seed value was set to 0. If you want to use the random seed, please select `None` for the seed value (`null` in the YAML file) or remove the `seed` key from the YAML file.
  warn(
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/anomalib/config/config.py:275: UserWarning: config.project.unique_dir is set to False. This does not ensure that your results will be written in an empty directory and you may overwrite files.
  warn(
[rank: 1] Global seed set to 0
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/requests/__init__.py:109: RequestsDependencyWarning: urllib3 (1.26.14) or chardet (None)/charset_normalizer (3.1.0) doesn't match a supported version!
  warnings.warn(
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/requests/__init__.py:109: RequestsDependencyWarning: urllib3 (1.26.14) or chardet (None)/charset_normalizer (3.1.0) doesn't match a supported version!
  warnings.warn(
wandb: Agent Starting Run: tfvdtelj with config:
wandb: 	learning_rate: 0.05701225120472204
wandb: 	model.backbone: wide_resnet50_2
wandb: 	optimizer: adam
wandb: Agent Starting Run: 6edtodhp with config:
wandb: 	learning_rate: 0.0268687292737814
wandb: 	model.backbone: resnet18
wandb: 	optimizer: sgd
Run tfvdtelj errored: AttributeError("'function' object has no attribute 'keys'")
wandb: ERROR Run tfvdtelj errored: AttributeError("'function' object has no attribute 'keys'")
wandb: Currently logged in as: tor-arnth. Use `wandb login --relogin` to force relogin
wandb: WARNING Ignored wandb.init() arg project when running a sweep.
wandb: wandb version 0.14.2 is available!  To upgrade, please run:
wandb:  $ pip install wandb --upgrade
wandb: Tracking run with wandb version 0.14.0
wandb: Run data is saved locally in ./wandb/run-20230410_200952-6edtodhp
wandb: Run `wandb offline` to turn off syncing.
wandb: Agent Starting Run: b65xplws with config:
wandb: 	learning_rate: 0.030693667871486703
wandb: 	model.backbone: resnet18
wandb: 	optimizer: adam
Run b65xplws errored: AttributeError("'function' object has no attribute 'keys'")
wandb: ERROR Run b65xplws errored: AttributeError("'function' object has no attribute 'keys'")
wandb: Resuming run sweepy-sweep-1
wandb: ⭐️ View project at https://wandb.ai/tor-arnth/patchcore_mvtec
wandb: 🧹 View sweep at https://wandb.ai/tor-arnth/patchcore_mvtec/sweeps/l2e38kmv
wandb: 🚀 View run at https://wandb.ai/tor-arnth/patchcore_mvtec/runs/6edtodhp
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `PrecisionRecallCurve` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.
  warnings.warn(*args, **kwargs)
FeatureExtractor is deprecated. Use TimmFeatureExtractor instead. Both FeatureExtractor and TimmFeatureExtractor will be removed in a future release.
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..
`Trainer(limit_val_batches=1.0)` was configured so 100% of the batches will be used..
`Trainer(limit_test_batches=1.0)` was configured so 100% of the batches will be used..
`Trainer(limit_predict_batches=1.0)` was configured so 100% of the batches will be used..
`Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..
You are using a CUDA device ('NVIDIA A30') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
wandb: Sweep Agent: Waiting for job.
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/torchmetrics/utilities/prints.py:36: UserWarning: Metric `ROC` will save all targets and predictions in buffer. For large datasets this may lead to large memory footprint.
  warnings.warn(*args, **kwargs)
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/pytorch_lightning/core/optimizer.py:183: UserWarning: `LightningModule.configure_optimizers` returned `None`, this fit will run with no optimizer
  rank_zero_warn(

  | Name            | Type                     | Params
-------------------------------------------------------------
0 | image_threshold | AnomalyScoreThreshold    | 0
1 | pixel_threshold | AnomalyScoreThreshold    | 0
2 | model           | PatchcoreModel           | 2.8 M
3 | image_metrics   | AnomalibMetricCollection | 0
4 | pixel_metrics   | AnomalibMetricCollection | 0
-------------------------------------------------------------
2.8 M     Trainable params
0         Non-trainable params
2.8 M     Total params
11.131    Total estimated model params size (MB)
wandb: WARNING Config item 'learning_rate' was locked by 'sweep' (ignored update).
wandb: WARNING Config item 'optimizer' was locked by 'sweep' (ignored update).
SLURM auto-requeueing enabled. Setting signal handlers.
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 96 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1609: PossibleUserWarning: The number of training batches (7) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
  rank_zero_warn(
/home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 96 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
Create sweep with ID: l2e38kmv
Sweep URL: https://wandb.ai/tor-arnth/patchcore_mvtec/sweeps/l2e38kmv
wandb: Job received.
wandb: Agent Starting Run: w5ckatl4 with config:
wandb: 	learning_rate: 0.05802934016250325
wandb: 	model.backbone: resnet18
wandb: 	optimizer: sgd
Create sweep with ID: ya55wjys
Sweep URL: https://wandb.ai/tor-arnth/patchcore_mvtec/sweeps/ya55wjys
Run w5ckatl4 errored: AttributeError("'function' object has no attribute 'keys'")
wandb: ERROR Run w5ckatl4 errored: AttributeError("'function' object has no attribute 'keys'")
Detected 3 failed runs in the first 60 seconds, killing sweep.
wandb: ERROR Detected 3 failed runs in the first 60 seconds, killing sweep.
wandb: To disable this check set WANDB_AGENT_DISABLE_FLAPPING=true
Epoch 0:  60%|██████    | 6/10 [00:08<00:05,  1.42s/it, loss=nan, /home/toap/.conda/envs/anomaliv_env2/lib/python3.8/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:138: UserWarning: `training_step` returned `None`. If this was on purpose, ignore this warning...
  self.warning_cache.warn("`training_step` returned `None`. If this was on purpose, ignore this warning...")
Epoch 0: 100%|██████████| 10/10 [00:15<00:00,  1.54s/it, loss=nan, v_num=odhp, pEpoch 0: 100%|██████████| 10/10 [00:15<00:00,  1.54s/it, loss=nan, v_num=odhp, pEpoch 0:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 1:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 1:  10%|█         | 1/10 [00:01<00:13,  1.47s/it, loss=nan, v_num=odhp, piEpoch 1:  10%|█         | 1/10 [00:01<00:13,  1.48s/it, loss=nan, v_num=odhp, piEpoch 1:  20%|██        | 2/10 [00:02<00:08,  1.09s/it, loss=nan, v_num=odhp, piEpoch 1:  20%|██        | 2/10 [00:02<00:08,  1.09s/it, loss=nan, v_num=odhp, piEpoch 1:  30%|███       | 3/10 [00:02<00:06,  1.03it/s, loss=nan, v_num=odhp, piEpoch 1:  30%|███       | 3/10 [00:02<00:06,  1.02it/s, loss=nan, v_num=odhp, piEpoch 1:  40%|████      | 4/10 [00:03<00:05,  1.09it/s, loss=nan, v_num=odhp, piEpoch 1:  40%|████      | 4/10 [00:03<00:05,  1.09it/s, loss=nan, v_num=odhp, piEpoch 1:  50%|█████     | 5/10 [00:04<00:04,  1.14it/s, loss=nan, v_num=odhp, piEpoch 1:  50%|█████     | 5/10 [00:04<00:04,  1.13it/s, loss=nan, v_num=odhp, piEpoch 1:  60%|██████    | 6/10 [00:05<00:03,  1.17it/s, loss=nan, v_num=odhp, piEpoch 1:  60%|██████    | 6/10 [00:05<00:03,  1.17it/s, loss=nan, v_num=odhp, piEpoch 1:  70%|███████   | 7/10 [00:05<00:02,  1.26it/s, loss=nan, v_num=odhp, piEpoch 1:  70%|███████   | 7/10 [00:05<00:02,  1.26it/s, loss=nan, v_num=odhp, piEpoch 1:  80%|████████  | 8/10 [00:13<00:03,  1.70s/it, loss=nan, v_num=odhp, piEpoch 1:  90%|█████████ | 9/10 [00:14<00:01,  1.60s/it, loss=nan, v_num=odhp, piEpoch 1: 100%|██████████| 10/10 [00:14<00:00,  1.48s/it, loss=nan, v_num=odhp, pEpoch 1: 100%|██████████| 10/10 [00:15<00:00,  1.56s/it, loss=nan, v_num=odhp, pEpoch 1: 100%|██████████| 10/10 [00:15<00:00,  1.56s/it, loss=nan, v_num=odhp, pEpoch 1:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 2:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 2:  10%|█         | 1/10 [00:01<00:11,  1.32s/it, loss=nan, v_num=odhp, piEpoch 2:  10%|█         | 1/10 [00:01<00:12,  1.34s/it, loss=nan, v_num=odhp, piEpoch 2:  20%|██        | 2/10 [00:02<00:08,  1.02s/it, loss=nan, v_num=odhp, piEpoch 2:  20%|██        | 2/10 [00:02<00:08,  1.02s/it, loss=nan, v_num=odhp, piEpoch 2:  30%|███       | 3/10 [00:02<00:06,  1.08it/s, loss=nan, v_num=odhp, piEpoch 2:  30%|███       | 3/10 [00:02<00:06,  1.08it/s, loss=nan, v_num=odhp, piEpoch 2:  40%|████      | 4/10 [00:03<00:05,  1.13it/s, loss=nan, v_num=odhp, piEpoch 2:  40%|████      | 4/10 [00:03<00:05,  1.13it/s, loss=nan, v_num=odhp, piEpoch 2:  50%|█████     | 5/10 [00:04<00:04,  1.17it/s, loss=nan, v_num=odhp, piEpoch 2:  50%|█████     | 5/10 [00:04<00:04,  1.17it/s, loss=nan, v_num=odhp, piEpoch 2:  60%|██████    | 6/10 [00:05<00:03,  1.19it/s, loss=nan, v_num=odhp, piEpoch 2:  60%|██████    | 6/10 [00:05<00:03,  1.19it/s, loss=nan, v_num=odhp, piEpoch 2:  70%|███████   | 7/10 [00:05<00:02,  1.29it/s, loss=nan, v_num=odhp, piEpoch 2:  70%|███████   | 7/10 [00:05<00:02,  1.29it/s, loss=nan, v_num=odhp, piEpoch 2:  80%|████████  | 8/10 [00:20<00:05,  2.56s/it, loss=nan, v_num=odhp, piEpoch 2:  90%|█████████ | 9/10 [00:21<00:02,  2.36s/it, loss=nan, v_num=odhp, piEpoch 2: 100%|██████████| 10/10 [00:21<00:00,  2.17s/it, loss=nan, v_num=odhp, pEpoch 2: 100%|██████████| 10/10 [00:22<00:00,  2.26s/it, loss=nan, v_num=odhp, pEpoch 2: 100%|██████████| 10/10 [00:22<00:00,  2.27s/it, loss=nan, v_num=odhp, pEpoch 2:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 3:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 3:  10%|█         | 1/10 [00:01<00:11,  1.33s/it, loss=nan, v_num=odhp, piEpoch 3:  10%|█         | 1/10 [00:01<00:12,  1.34s/it, loss=nan, v_num=odhp, piEpoch 3:  20%|██        | 2/10 [00:02<00:08,  1.02s/it, loss=nan, v_num=odhp, piEpoch 3:  20%|██        | 2/10 [00:02<00:08,  1.02s/it, loss=nan, v_num=odhp, piEpoch 3:  30%|███       | 3/10 [00:02<00:06,  1.08it/s, loss=nan, v_num=odhp, piEpoch 3:  30%|███       | 3/10 [00:02<00:06,  1.08it/s, loss=nan, v_num=odhp, piEpoch 3:  40%|████      | 4/10 [00:03<00:05,  1.14it/s, loss=nan, v_num=odhp, piEpoch 3:  40%|████      | 4/10 [00:03<00:05,  1.14it/s, loss=nan, v_num=odhp, piEpoch 3:  50%|█████     | 5/10 [00:04<00:04,  1.18it/s, loss=nan, v_num=odhp, piEpoch 3:  50%|█████     | 5/10 [00:04<00:04,  1.18it/s, loss=nan, v_num=odhp, piEpoch 3:  60%|██████    | 6/10 [00:04<00:03,  1.20it/s, loss=nan, v_num=odhp, piEpoch 3:  60%|██████    | 6/10 [00:05<00:03,  1.20it/s, loss=nan, v_num=odhp, piEpoch 3:  70%|███████   | 7/10 [00:05<00:02,  1.30it/s, loss=nan, v_num=odhp, piEpoch 3:  70%|███████   | 7/10 [00:05<00:02,  1.30it/s, loss=nan, v_num=odhp, piEpoch 3:  80%|████████  | 8/10 [00:29<00:07,  3.69s/it, loss=nan, v_num=odhp, piEpoch 3:  90%|█████████ | 9/10 [00:30<00:03,  3.38s/it, loss=nan, v_num=odhp, piEpoch 3: 100%|██████████| 10/10 [00:30<00:00,  3.09s/it, loss=nan, v_num=odhp, pEpoch 3: 100%|██████████| 10/10 [00:31<00:00,  3.16s/it, loss=nan, v_num=odhp, pEpoch 3: 100%|██████████| 10/10 [00:31<00:00,  3.16s/it, loss=nan, v_num=odhp, pEpoch 3:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 4:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 4:  10%|█         | 1/10 [00:01<00:12,  1.34s/it, loss=nan, v_num=odhp, piEpoch 4:  10%|█         | 1/10 [00:01<00:12,  1.36s/it, loss=nan, v_num=odhp, piEpoch 4:  20%|██        | 2/10 [00:02<00:08,  1.03s/it, loss=nan, v_num=odhp, piEpoch 4:  20%|██        | 2/10 [00:02<00:08,  1.03s/it, loss=nan, v_num=odhp, piEpoch 4:  30%|███       | 3/10 [00:02<00:06,  1.08it/s, loss=nan, v_num=odhp, piEpoch 4:  30%|███       | 3/10 [00:02<00:06,  1.07it/s, loss=nan, v_num=odhp, piEpoch 4:  40%|████      | 4/10 [00:03<00:05,  1.14it/s, loss=nan, v_num=odhp, piEpoch 4:  40%|████      | 4/10 [00:03<00:05,  1.13it/s, loss=nan, v_num=odhp, piEpoch 4:  50%|█████     | 5/10 [00:04<00:04,  1.17it/s, loss=nan, v_num=odhp, piEpoch 4:  50%|█████     | 5/10 [00:04<00:04,  1.17it/s, loss=nan, v_num=odhp, piEpoch 4:  60%|██████    | 6/10 [00:05<00:03,  1.20it/s, loss=nan, v_num=odhp, piEpoch 4:  60%|██████    | 6/10 [00:05<00:03,  1.20it/s, loss=nan, v_num=odhp, piEpoch 4:  70%|███████   | 7/10 [00:05<00:02,  1.30it/s, loss=nan, v_num=odhp, piEpoch 4:  70%|███████   | 7/10 [00:05<00:02,  1.29it/s, loss=nan, v_num=odhp, piEpoch 4:  80%|████████  | 8/10 [00:42<00:10,  5.29s/it, loss=nan, v_num=odhp, piEpoch 4:  90%|█████████ | 9/10 [00:43<00:04,  4.79s/it, loss=nan, v_num=odhp, piEpoch 4: 100%|██████████| 10/10 [00:43<00:00,  4.35s/it, loss=nan, v_num=odhp, pEpoch 4: 100%|██████████| 10/10 [00:44<00:00,  4.43s/it, loss=nan, v_num=odhp, pEpoch 4: 100%|██████████| 10/10 [00:44<00:00,  4.43s/it, loss=nan, v_num=odhp, pEpoch 4:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 5:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 5:  10%|█         | 1/10 [00:01<00:12,  1.35s/it, loss=nan, v_num=odhp, piEpoch 5:  10%|█         | 1/10 [00:01<00:12,  1.36s/it, loss=nan, v_num=odhp, piEpoch 5:  20%|██        | 2/10 [00:02<00:08,  1.02s/it, loss=nan, v_num=odhp, piEpoch 5:  20%|██        | 2/10 [00:02<00:08,  1.02s/it, loss=nan, v_num=odhp, piEpoch 5:  30%|███       | 3/10 [00:02<00:06,  1.08it/s, loss=nan, v_num=odhp, piEpoch 5:  30%|███       | 3/10 [00:02<00:06,  1.08it/s, loss=nan, v_num=odhp, piEpoch 5:  40%|████      | 4/10 [00:03<00:05,  1.13it/s, loss=nan, v_num=odhp, piEpoch 5:  40%|████      | 4/10 [00:03<00:05,  1.13it/s, loss=nan, v_num=odhp, piEpoch 5:  50%|█████     | 5/10 [00:04<00:04,  1.17it/s, loss=nan, v_num=odhp, piEpoch 5:  50%|█████     | 5/10 [00:04<00:04,  1.17it/s, loss=nan, v_num=odhp, piEpoch 5:  60%|██████    | 6/10 [00:05<00:03,  1.20it/s, loss=nan, v_num=odhp, piEpoch 5:  60%|██████    | 6/10 [00:05<00:03,  1.19it/s, loss=nan, v_num=odhp, piEpoch 5:  70%|███████   | 7/10 [00:05<00:02,  1.29it/s, loss=nan, v_num=odhp, piEpoch 5:  70%|███████   | 7/10 [00:05<00:02,  1.29it/s, loss=nan, v_num=odhp, piEpoch 5:  80%|████████  | 8/10 [00:58<00:14,  7.26s/it, loss=nan, v_num=odhp, piEpoch 5:  90%|█████████ | 9/10 [00:58<00:06,  6.54s/it, loss=nan, v_num=odhp, piEpoch 5: 100%|██████████| 10/10 [00:59<00:00,  5.93s/it, loss=nan, v_num=odhp, pEpoch 5: 100%|██████████| 10/10 [01:00<00:00,  6.01s/it, loss=nan, v_num=odhp, pEpoch 5: 100%|██████████| 10/10 [01:00<00:00,  6.01s/it, loss=nan, v_num=odhp, pEpoch 5:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 6:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 6:  10%|█         | 1/10 [00:01<00:14,  1.58s/it, loss=nan, v_num=odhp, piEpoch 6:  10%|█         | 1/10 [00:01<00:14,  1.58s/it, loss=nan, v_num=odhp, piEpoch 6:  20%|██        | 2/10 [00:02<00:09,  1.14s/it, loss=nan, v_num=odhp, piEpoch 6:  20%|██        | 2/10 [00:02<00:09,  1.14s/it, loss=nan, v_num=odhp, piEpoch 6:  30%|███       | 3/10 [00:03<00:07,  1.01s/it, loss=nan, v_num=odhp, piEpoch 6:  30%|███       | 3/10 [00:03<00:07,  1.01s/it, loss=nan, v_num=odhp, piEpoch 6:  40%|████      | 4/10 [00:03<00:05,  1.06it/s, loss=nan, v_num=odhp, piEpoch 6:  40%|████      | 4/10 [00:03<00:05,  1.06it/s, loss=nan, v_num=odhp, piEpoch 6:  50%|█████     | 5/10 [00:04<00:04,  1.11it/s, loss=nan, v_num=odhp, piEpoch 6:  50%|█████     | 5/10 [00:04<00:04,  1.11it/s, loss=nan, v_num=odhp, piEpoch 6:  60%|██████    | 6/10 [00:05<00:03,  1.14it/s, loss=nan, v_num=odhp, piEpoch 6:  60%|██████    | 6/10 [00:05<00:03,  1.14it/s, loss=nan, v_num=odhp, piEpoch 6:  70%|███████   | 7/10 [00:05<00:02,  1.24it/s, loss=nan, v_num=odhp, piEpoch 6:  70%|███████   | 7/10 [00:05<00:02,  1.24it/s, loss=nan, v_num=odhp, piEpoch 6:  80%|████████  | 8/10 [01:16<00:19,  9.56s/it, loss=nan, v_num=odhp, piEpoch 6:  90%|█████████ | 9/10 [01:17<00:08,  8.58s/it, loss=nan, v_num=odhp, piEpoch 6: 100%|██████████| 10/10 [01:17<00:00,  7.77s/it, loss=nan, v_num=odhp, pEpoch 6: 100%|██████████| 10/10 [01:18<00:00,  7.85s/it, loss=nan, v_num=odhp, pEpoch 6: 100%|██████████| 10/10 [01:18<00:00,  7.85s/it, loss=nan, v_num=odhp, pEpoch 6:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 7:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 7:  10%|█         | 1/10 [00:01<00:12,  1.40s/it, loss=nan, v_num=odhp, piEpoch 7:  10%|█         | 1/10 [00:01<00:12,  1.41s/it, loss=nan, v_num=odhp, piEpoch 7:  20%|██        | 2/10 [00:02<00:08,  1.06s/it, loss=nan, v_num=odhp, piEpoch 7:  20%|██        | 2/10 [00:02<00:08,  1.06s/it, loss=nan, v_num=odhp, piEpoch 7:  30%|███       | 3/10 [00:02<00:06,  1.05it/s, loss=nan, v_num=odhp, piEpoch 7:  30%|███       | 3/10 [00:02<00:06,  1.05it/s, loss=nan, v_num=odhp, piEpoch 7:  40%|████      | 4/10 [00:03<00:05,  1.11it/s, loss=nan, v_num=odhp, piEpoch 7:  40%|████      | 4/10 [00:03<00:05,  1.11it/s, loss=nan, v_num=odhp, piEpoch 7:  50%|█████     | 5/10 [00:04<00:04,  1.15it/s, loss=nan, v_num=odhp, piEpoch 7:  50%|█████     | 5/10 [00:04<00:04,  1.15it/s, loss=nan, v_num=odhp, piEpoch 7:  60%|██████    | 6/10 [00:05<00:03,  1.18it/s, loss=nan, v_num=odhp, piEpoch 7:  60%|██████    | 6/10 [00:05<00:03,  1.17it/s, loss=nan, v_num=odhp, piEpoch 7:  70%|███████   | 7/10 [00:05<00:02,  1.27it/s, loss=nan, v_num=odhp, piEpoch 7:  70%|███████   | 7/10 [00:05<00:02,  1.27it/s, loss=nan, v_num=odhp, piEpoch 7:  80%|████████  | 8/10 [01:36<00:24, 12.12s/it, loss=nan, v_num=odhp, piEpoch 7:  90%|█████████ | 9/10 [01:37<00:10, 10.89s/it, loss=nan, v_num=odhp, piEpoch 7: 100%|██████████| 10/10 [01:38<00:00,  9.84s/it, loss=nan, v_num=odhp, pEpoch 7: 100%|██████████| 10/10 [01:39<00:00,  9.92s/it, loss=nan, v_num=odhp, pEpoch 7: 100%|██████████| 10/10 [01:39<00:00,  9.92s/it, loss=nan, v_num=odhp, pEpoch 7:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 8:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 8:  10%|█         | 1/10 [00:01<00:12,  1.36s/it, loss=nan, v_num=odhp, piEpoch 8:  10%|█         | 1/10 [00:01<00:12,  1.36s/it, loss=nan, v_num=odhp, piEpoch 8:  20%|██        | 2/10 [00:02<00:08,  1.04s/it, loss=nan, v_num=odhp, piEpoch 8:  20%|██        | 2/10 [00:02<00:08,  1.04s/it, loss=nan, v_num=odhp, piEpoch 8:  30%|███       | 3/10 [00:03<00:07,  1.02s/it, loss=nan, v_num=odhp, piEpoch 8:  30%|███       | 3/10 [00:03<00:07,  1.02s/it, loss=nan, v_num=odhp, piEpoch 8:  40%|████      | 4/10 [00:03<00:05,  1.05it/s, loss=nan, v_num=odhp, piEpoch 8:  40%|████      | 4/10 [00:03<00:05,  1.05it/s, loss=nan, v_num=odhp, piEpoch 8:  50%|█████     | 5/10 [00:04<00:04,  1.04it/s, loss=nan, v_num=odhp, piEpoch 8:  50%|█████     | 5/10 [00:04<00:04,  1.04it/s, loss=nan, v_num=odhp, piEpoch 8:  60%|██████    | 6/10 [00:05<00:03,  1.08it/s, loss=nan, v_num=odhp, piEpoch 8:  60%|██████    | 6/10 [00:05<00:03,  1.08it/s, loss=nan, v_num=odhp, piEpoch 8:  70%|███████   | 7/10 [00:05<00:02,  1.17it/s, loss=nan, v_num=odhp, piEpoch 8:  70%|███████   | 7/10 [00:05<00:02,  1.17it/s, loss=nan, v_num=odhp, piEpoch 8:  80%|████████  | 8/10 [02:00<00:30, 15.07s/it, loss=nan, v_num=odhp, piEpoch 8:  90%|█████████ | 9/10 [02:01<00:13, 13.50s/it, loss=nan, v_num=odhp, piEpoch 8: 100%|██████████| 10/10 [02:01<00:00, 12.19s/it, loss=nan, v_num=odhp, pEpoch 8: 100%|██████████| 10/10 [02:02<00:00, 12.27s/it, loss=nan, v_num=odhp, pEpoch 8: 100%|██████████| 10/10 [02:02<00:00, 12.27s/it, loss=nan, v_num=odhp, pEpoch 8:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 9:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 9:  10%|█         | 1/10 [00:01<00:12,  1.36s/it, loss=nan, v_num=odhp, piEpoch 9:  10%|█         | 1/10 [00:01<00:12,  1.38s/it, loss=nan, v_num=odhp, piEpoch 9:  20%|██        | 2/10 [00:02<00:09,  1.15s/it, loss=nan, v_num=odhp, piEpoch 9:  20%|██        | 2/10 [00:02<00:09,  1.15s/it, loss=nan, v_num=odhp, piEpoch 9:  30%|███       | 3/10 [00:03<00:07,  1.02s/it, loss=nan, v_num=odhp, piEpoch 9:  30%|███       | 3/10 [00:03<00:07,  1.02s/it, loss=nan, v_num=odhp, piEpoch 9:  40%|████      | 4/10 [00:04<00:06,  1.01s/it, loss=nan, v_num=odhp, piEpoch 9:  40%|████      | 4/10 [00:04<00:06,  1.01s/it, loss=nan, v_num=odhp, piEpoch 9:  50%|█████     | 5/10 [00:04<00:04,  1.04it/s, loss=nan, v_num=odhp, piEpoch 9:  50%|█████     | 5/10 [00:04<00:04,  1.04it/s, loss=nan, v_num=odhp, piEpoch 9:  60%|██████    | 6/10 [00:05<00:03,  1.08it/s, loss=nan, v_num=odhp, piEpoch 9:  60%|██████    | 6/10 [00:05<00:03,  1.07it/s, loss=nan, v_num=odhp, piEpoch 9:  70%|███████   | 7/10 [00:06<00:02,  1.12it/s, loss=nan, v_num=odhp, piEpoch 9:  70%|███████   | 7/10 [00:06<00:02,  1.12it/s, loss=nan, v_num=odhp, piEpoch 9:  80%|████████  | 8/10 [02:28<00:37, 18.58s/it, loss=nan, v_num=odhp, piEpoch 9:  90%|█████████ | 9/10 [02:29<00:16, 16.61s/it, loss=nan, v_num=odhp, piEpoch 9: 100%|██████████| 10/10 [02:30<00:00, 15.01s/it, loss=nan, v_num=odhp, pEpoch 9: 100%|██████████| 10/10 [02:30<00:00, 15.09s/it, loss=nan, v_num=odhp, pEpoch 9: 100%|██████████| 10/10 [02:30<00:00, 15.09s/it, loss=nan, v_num=odhp, pEpoch 9:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1ScEpoch 10:   0%|          | 0/10 [00:00<?, ?it/s, loss=nan, v_num=odhp, pixel_F1SEpoch 10:  10%|█         | 1/10 [00:01<00:12,  1.38s/it, loss=nan, v_num=odhp, pEpoch 10:  10%|█         | 1/10 [00:01<00:12,  1.40s/it, loss=nan, v_num=odhp, pEpoch 10:  20%|██        | 2/10 [00:02<00:08,  1.06s/it, loss=nan, v_num=odhp, pEpoch 10:  20%|██        | 2/10 [00:02<00:08,  1.06s/it, loss=nan, v_num=odhp, pEpoch 10:  30%|███       | 3/10 [00:02<00:06,  1.05it/s, loss=nan, v_num=odhp, pEpoch 10:  30%|███       | 3/10 [00:02<00:06,  1.05it/s, loss=nan, v_num=odhp, pEpoch 10:  40%|████      | 4/10 [00:03<00:05,  1.10it/s, loss=nan, v_num=odhp, pEpoch 10:  40%|████      | 4/10 [00:03<00:05,  1.10it/s, loss=nan, v_num=odhp, pEpoch 10:  50%|█████     | 5/10 [00:04<00:04,  1.14it/s, loss=nan, v_num=odhp, pEpoch 10:  50%|█████     | 5/10 [00:04<00:04,  1.14it/s, loss=nan, v_num=odhp, pEpoch 10:  60%|██████    | 6/10 [00:05<00:03,  1.17it/s, loss=nan, v_num=odhp, pEpoch 10:  60%|██████    | 6/10 [00:05<00:03,  1.17it/s, loss=nan, v_num=odhp, pEpoch 10:  70%|███████   | 7/10 [00:05<00:02,  1.26it/s, loss=nan, v_num=odhp, pEpoch 10:  70%|███████   | 7/10 [00:05<00:02,  1.26it/s, loss=nan, v_num=odhp, pixel_F1Score=0.414, pixel_AUROC=0.798]

Code of Conduct

• I agree to follow this project’s Code of Conduct