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# @package _group_

# try to resemble mask generation of DeepFill v2
# official tf version: https://github.com/JiahuiYu/generative_inpainting/blob/master/inpaint_ops.py#L168
# pytorch version: https://github.com/zhaoyuzhi/deepfillv2/blob/62dad2c601400e14d79f4d1e090c2effcb9bf3eb/deepfillv2/dataset.py#L40
# another unofficial pytorch version: https://github.com/avalonstrel/GatedConvolution/blob/master/config/inpaint.yml
# they are a bit different, official version has slightly larger masks

batch_size: 10
val_batch_size: 2
num_workers: 3

train:
  indir: ${location.data_root_dir}/train
  out_size: 256

  mask_gen_kwargs:  # probabilities do not need to sum to 1, they are re-normalized in mask generator
    irregular_proba: 1
    irregular_kwargs:
      max_angle: 4
      max_len: 80  # math.sqrt(H*H+W*W) / 8 + math.sqrt(H*H+W*W) / 16 https://github.com/JiahuiYu/generative_inpainting/blob/master/inpaint_ops.py#L189
      max_width: 40
      max_times: 12
      min_times: 4

    box_proba: 1
    box_kwargs:
      margin: 0
      bbox_min_size: 30
      bbox_max_size: 128
      max_times: 1
      min_times: 1

    segm_proba: 0  # not working yet due to RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method

  transform_variant: default
  dataloader_kwargs:
    batch_size: ${data.batch_size}
    shuffle: True
    num_workers: ${data.num_workers}

val:
  indir: ${location.data_root_dir}/val
  img_suffix: .png
  dataloader_kwargs:
    batch_size: ${data.val_batch_size}
    shuffle: False
    num_workers: ${data.num_workers}

#extra_val:
#  random_thin_256:
#    indir: ${location.data_root_dir}/extra_val/random_thin_256
#    img_suffix: .png
#    dataloader_kwargs:
#      batch_size: ${data.val_batch_size}
#      shuffle: False
#      num_workers: ${data.num_workers}
#  random_medium_256:
#    indir: ${location.data_root_dir}/extra_val/random_medium_256
#    img_suffix: .png
#    dataloader_kwargs:
#      batch_size: ${data.val_batch_size}
#      shuffle: False
#      num_workers: ${data.num_workers}
#  random_thick_256:
#    indir: ${location.data_root_dir}/extra_val/random_thick_256
#    img_suffix: .png
#    dataloader_kwargs:
#      batch_size: ${data.val_batch_size}
#      shuffle: False
#      num_workers: ${data.num_workers}
#  random_thin_512:
#    indir: ${location.data_root_dir}/extra_val/random_thin_512
#    img_suffix: .png
#    dataloader_kwargs:
#      batch_size: ${data.val_batch_size}
#      shuffle: False
#      num_workers: ${data.num_workers}
#  random_medium_512:
#    indir: ${location.data_root_dir}/extra_val/random_medium_512
#    img_suffix: .png
#    dataloader_kwargs:
#      batch_size: ${data.val_batch_size}
#      shuffle: False
#      num_workers: ${data.num_workers}
#  random_thick_512:
#    indir: ${location.data_root_dir}/extra_val/random_thick_512
#    img_suffix: .png
#    dataloader_kwargs:
#      batch_size: ${data.val_batch_size}
#      shuffle: False
#      num_workers: ${data.num_workers}
#  segm_256:
#    indir: ${location.data_root_dir}/extra_val/segm_256
#    img_suffix: .png
#    dataloader_kwargs:
#      batch_size: ${data.val_batch_size}
#      shuffle: False
#      num_workers: ${data.num_workers}
#  segm_512:
#    indir: ${location.data_root_dir}/extra_val/segm_512
#    img_suffix: .png
#    dataloader_kwargs:
#      batch_size: ${data.val_batch_size}
#      shuffle: False
#      num_workers: ${data.num_workers}

visual_test:
  indir: ${location.data_root_dir}/visual_test
  img_suffix: _input.png
  pad_out_to_modulo: 32
  dataloader_kwargs:
    batch_size: 1
    shuffle: False
    num_workers: ${data.num_workers}