configs/evaluation/projects/bridging/outputs_coco_ccrop_sa.yaml

# @package _global_
# Save model predictions on COCO with slot attention models
dataset:
  eval_transforms:
    02a_preprocessing:
      _target_: ocl.transforms.Map
      transform: "${lambda_fn:'lambda data: {\"orig_image\": data[\"image\"], **data}'}"
      fields:
        - image
      batch_transform: false
defaults:
  - /evaluation_config  # (1)!
  - /evaluation/projects/bridging/_base_metrics # (2)!
  - /evaluation/projects/bridging/_preprocessing_coco # (3)!
  - /dataset: coco  # (4)!
  - _self_

eval_batch_size: 1

save_outputs: true
skip_metrics: true
n_samples_to_store: 500
outputs_to_store:
  - input.orig_image
  - input.instance_mask
  - masks_resized
  - object_decoder.reconstruction

modules:
  masks_resized:
    patch_mode: false

plugins:
  03b_preprocessing:
    evaluation_transforms:
      orig_image:
        _target_: torchvision.transforms.Compose
        transforms:
          - _target_: torchvision.transforms.ToTensor
          - _target_: ocl.preprocessing.OrigCenterCrop
      image:
        _target_: torchvision.transforms.Compose
        transforms:
          - _target_: torchvision.transforms.ToTensor
          - _target_: torchvision.transforms.Resize
            size: 128
            interpolation: ${torchvision_interpolation_mode:BILINEAR}
          - _target_: torchvision.transforms.CenterCrop
            size: 128
          - _target_: torchvision.transforms.Normalize
            mean: [0.5, 0.5, 0.5]
            std: [0.5, 0.5, 0.5]
      instance_mask:
        _target_: torchvision.transforms.Compose
        transforms:
          - _target_: ocl.preprocessing.DenseMaskToTensor
          - _target_: ocl.preprocessing.OrigCenterCrop

      segmentation_mask:
        _target_: torchvision.transforms.Compose
        transforms:
          - _target_: ocl.preprocessing.DenseMaskToTensor
          - _target_: ocl.preprocessing.OrigCenterCrop