Patchcore multiple test batch size is not supported.

I have another problem after dealing with #243 That is: ValueError: Either preds and target both should have the (same) shape (N, …), or target should be (N, …) and preds should be (N, C, …). Epoch 0: 100%|██████████| 34/34 [09:17<00:00, 16.39s/it, loss=nan]

From: File “/home/devadmin/haobo/anomalib_venv/lib/python3.8/site-packages/torchmetrics/utilities/checks.py”, line 269, in _check_classification_inputs case, implied_classes = _check_shape_and_type_consistency(preds, target) File “/home/devadmin/haobo/anomalib_venv/lib/python3.8/site-packages/torchmetrics/utilities/checks.py”, line 115, in _check_shape_and_type_consistency

Then I print preds and target: Epoch 0: 68%|████████████████Aggregating the embedding extracted from the training set. 2.13it/s, loss=nan] Creating CoreSet Sampler via k-Center Greedy Getting the coreset from the main embedding. Assigning the coreset as the memory bank. Epoch 0: 100%|█████████████████████████████████████████████████████| 34/34 [08:59<00:00, 15.85s/it, loss=nan] preds is: tensor([1.4457])00%|███████████████████████████████████████████████| 11/11 [08:48<00:00, 48.02s/it] target is: tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int32)

my patchcore config.yaml is:

dataset:
  name: wafer_line #options: [mvtec, btech, folder]
  format: folder
  path: ../data/wafer_line/
  normal_dir: "train/Negative"
  abnormal_dir: "test/Positive"
  normal_test_dir: "test/Negative"
  task: segmentation
  mask: ../data/wafer_line/ground_truth/Positive
  extensions: ".jpg"
  split_ratio: 0.1
  seed: 0
  image_size: 256
  train_batch_size: 16
  test_batch_size: 16
  num_workers: 20
  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
trainer:
  accelerator: "gpu" # <"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: true
  benchmark: false
  check_val_every_n_epoch: 1 # Don't validate before extracting features.
  default_root_dir: null
  detect_anomaly: false
  deterministic: false
  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: 0.05
  limit_val_batches: 1.0
  log_every_n_steps: 50
  log_gpu_memory: null
  max_epochs: 10000
  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: 1
  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.

Thank you for your patience in reading and answering！

[samet-akcay]

@fujikosu, we have just merged #580, which should address the multiple batch size problem. Let us know if you still encounter any issues. Thanks!

[samet-akcay]

should I set test_batch_size=1？

Yes, if you set test_batch_size: 1, it would work. We’ll investigate why it doesn’t work for multiple batch sizes.