[Bug]: Tricky behavior when `config.project.seed` is set to `0`

Describe the bug

Hi,

Thanks for the great work 😃 I have just encountered unexpected behavior in seed specification, so here is the issue description.

In the current implementation, when config.project.seed is set to 0, a seed is not fixed to 0 but randomized, as shown in the following code:

https://github.com/openvinotoolkit/anomalib/blob/764f97dd3bb6f082a51422a946a2582edf07d511/tools/train.py#L50-L51

https://github.com/openvinotoolkit/anomalib/blob/764f97dd3bb6f082a51422a946a2582edf07d511/tools/hpo/sweep.py#L38-L39

I feel that the seed should be randomized either

• when there is no seed key under config.project, or
• when config.project.seed is explicitly set to None.

So how about rewriting the above code as follows?

if config.project.get("seed") is not None:
    seed_everything(config.project.seed)

If this change doesn’t bother somebody who already uses 0 to randomize seeds, I will create a pull request.

What do you think?

Dataset

N/A

Model

N/A

Steps to reproduce the behavior

1. Run python tools/train.py --model patchcore

The seed is set to 0 in patchcore’s config.yaml, so the seed is randomized.

https://github.com/openvinotoolkit/anomalib/blob/764f97dd3bb6f082a51422a946a2582edf07d511/anomalib/models/patchcore/config.yaml#L54

OS information

OS information:

• OS: macOS Monterey
• Python version: 3.8.13
• Anomalib version: 0.3.7
• PyTorch version: 1.11.0
• CUDA/cuDNN version: N/A
• GPU models and configuration: N/A
• Any other relevant information: N/A

Expected behavior

When a seed is set to 0, the seed should be fixed to 0 instead of using a random seed.

Screenshots

No response

Pip/GitHub

GitHub

What version/branch did you use?

No response

Configuration YAML

dataset:
  name: mvtec #options: [mvtec, btech, folder]
  format: mvtec
  path: ./datasets/MVTec
  task: segmentation
  category: bottle
  image_size: 224
  train_batch_size: 32
  test_batch_size: 1
  num_workers: 8
  transform_config:
    train: null
    val: null
  create_validation_set: false
  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: true
  layers:
    - layer2
    - 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: null # path to which images will be saved
  mode: full # options: ["full", "simple"]

project:
  seed: 0
  path: ./results

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

optimization:
  export_mode: null # options: onnx, openvino

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

Logs

N/A

Code of Conduct

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

[samet-akcay]

Yes please

[tanemaki]

@samet-akcay Oh, really? Thanks for letting me know. Then, do I need to press “Ready for review” button to convert the draft PR to the formal PR?