[Bug]: EfficientAD Decoder image_size class attribute missing

[AlessandroPolidori]

Describe the bug

In the EfficientAD torch_model.py, Decoder object has no attribute 'img_size' and trainer.fit() fails.
In my opinion "self.img_size= img_size" is missing from the init function.
I encountered this issue using efficientad with the 001_getting_started notebook.

Dataset

MVTec

Model

Other (please specify in the field below)

Steps to reproduce the behavior

EfficientAD
anomalib/src/anomalib/models/efficientad/torch_model.py
line 144
self.image_size is missing. it should be assigned in the Decoder init function (self.image_size = image_size).

OS information

OS information:

• OS: [e.g. Ubuntu 20.04]
• Python version: [e.g. 3.8.10]
• Anomalib version: [e.g. 0.3.6]
• PyTorch version: [e.g. 1.9.0]
• CUDA/cuDNN version: [e.g. 11.1]
• GPU models and configuration: [e.g. 2x GeForce RTX 3090]
• Any other relevant information: [e.g. I'm using a custom dataset]

Expected behavior

trainer.fit should work using the 001_getting_started notebook but it doesn't.

Screenshots

No response

Pip/GitHub

GitHub

What version/branch did you use?

main EfficientAD (#1073)

Configuration YAML

dataset:
  name: mvtec
  format: mvtec
  path: ./datasets/MVTec
  category: wood
  task: segmentation
  train_batch_size: 1
  eval_batch_size: 16
  num_workers: 8
  image_size: 256 # dimensions to which images are resized (mandatory)
  center_crop: null # dimensions to which images are center-cropped after resizing (optional)
  normalization: none # 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)

model:
  name: efficientad
  teacher_out_channels: 384
  model_size: medium # options: [small, medium]
  lr: 0.0001
  weight_decay: 0.00001
  padding: true
  # generic params
  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: False # 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: 42
  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: torch, 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
  fast_dev_run: false
  accumulate_grad_batches: 1
  max_epochs: 200
  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
  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

AttributeError: 'Decoder' object has no attribute 'img_size'

Code of Conduct

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

[samet-akcay]

@AlessandroPolidori, I've created a pr for this. We'll merge once the tests pass.