dataset:
  use_epochs: false
  num_workers: 4
  batch_size: ${experiment.batch_size_per_gpu}
  _target_: ocl.datasets.WebdatasetDataModule
  train_shards: ${oc.env:DATASET_PREFIX}/vg_disjoint_coco/train/shard-{000000..001217}.tar
  train_size: 118287
  val_shards: ${oc.env:DATASET_PREFIX}/vg/val/shard-{000000..000037}.tar
  val_size: 5000
  test_shards: ${oc.env:DATASET_PREFIX}/vg/test/shard-{000000..000037}.tar
  test_size: 40670
  use_autopadding: true
  eval_transforms:
    03a_preprocessing:
      _target_: ocl.transforms.Map
      transform:
        _target_: torchvision.transforms.Compose
        transforms:
        - _target_: ocl.preprocessing.SelectConditioningInfoVG
          num_max_binds: ${experiment.num_slots}
          num_slots: ${experiment.num_slots}
        - _target_: ocl.preprocessing.CopyFields
          mapping:
            instance_mask: instance_mask_v2
      fields:
      - image
      - instance_mask
      - instance_bbox
      - name
      - bbox_centroids
      - name_embedding
      - selected_indices
      - contrastive_loss_mask
      - all_bbox_centroids
      batch_transform: false
    03c_preprocessing:
      _target_: ocl.transforms.SimpleTransform
      transforms:
        image:
          _target_: torchvision.transforms.Compose
          transforms:
          - '${lambda_fn:''lambda image: image.copy()''}'
          - _target_: torchvision.transforms.v2.ToImage
          - _target_: torchvision.transforms.v2.ToDtype
            dtype: ${torch_dtype:float32}
            scale: true
          - _target_: torchvision.transforms.v2.Normalize
            mean:
            - 0.485
            - 0.456
            - 0.406
            std:
            - 0.229
            - 0.224
            - 0.225
        instance_mask:
          _target_: torchvision.transforms.Compose
          transforms:
          - _target_: ocl.preprocessing.IntegerToOneHotMask
            output_axis: -3
          - _target_: ocl.preprocessing.AddEmptyMasksVG
          - _target_: ocl.preprocessing.DenseMaskToTensor
        instance_mask_v2:
          _target_: torchvision.transforms.Compose
          transforms:
          - _target_: ocl.preprocessing.IntegerToOneHotMask
            output_axis: -3
          - _target_: ocl.preprocessing.AddEmptyMasksVG
          - _target_: ocl.preprocessing.DenseMaskToTensor
      batch_transform: false
  train_transforms:
    03a_preprocessing:
      _target_: ocl.transforms.Map
      transform:
        _target_: torchvision.transforms.Compose
        transforms:
        - _target_: ocl.preprocessing.SelectConditioningInfoVG
          num_max_binds: ${experiment.num_slots}
          num_slots: ${experiment.num_slots}
        - _target_: ocl.preprocessing.CopyFields
          mapping:
            instance_mask: instance_mask_v2
      fields:
      - image
      - instance_mask
      - instance_bbox
      - name
      - bbox_centroids
      - name_embedding
      - selected_indices
      - contrastive_loss_mask
      batch_transform: false
    03b_preprocessing:
      _target_: ocl.transforms.SimpleTransform
      transforms:
        image:
          _target_: torchvision.transforms.Compose
          transforms:
          - '${lambda_fn:''lambda image: image.copy()''}'
          - _target_: torchvision.transforms.v2.ToImage
          - _target_: torchvision.transforms.v2.ToDtype
            dtype: ${torch_dtype:float32}
            scale: true
          - _target_: torchvision.transforms.v2.Normalize
            mean:
            - 0.485
            - 0.456
            - 0.406
            std:
            - 0.229
            - 0.224
            - 0.225
        name_embedding:
          _target_: torchvision.transforms.Compose
          transforms:
          - '${lambda_fn:''lambda name_embedding: name_embedding.copy()''}'
          - _target_: ocl.preprocessing.ToTensor
        bbox_centroids:
          _target_: torchvision.transforms.Compose
          transforms:
          - '${lambda_fn:''lambda bbox_centroids: bbox_centroids.copy()''}'
          - _target_: ocl.preprocessing.ToTensor
        all_bbox_centroids:
          _target_: torchvision.transforms.Compose
          transforms:
          - '${lambda_fn:''lambda all_bbox_centroids: all_bbox_centroids.copy()''}'
          - _target_: ocl.preprocessing.ToTensor
        selected_indices:
          _target_: torchvision.transforms.Compose
          transforms:
          - '${lambda_fn:''lambda selected_indices: selected_indices.copy()''}'
          - _target_: ocl.preprocessing.ToTensor
        contrastive_loss_mask:
          _target_: torchvision.transforms.Compose
          transforms:
          - '${lambda_fn:''lambda contrastive_loss_mask: contrastive_loss_mask.copy()''}'
          - _target_: ocl.preprocessing.ToTensor
        instance_mask:
          _target_: torchvision.transforms.Compose
          transforms:
          - _target_: ocl.preprocessing.IntegerToOneHotMask
            output_axis: -3
          - _target_: ocl.preprocessing.AddEmptyMasksVG
          - _target_: ocl.preprocessing.DenseMaskToTensor
        instance_mask_v2:
          _target_: torchvision.transforms.Compose
          transforms:
          - _target_: ocl.preprocessing.IntegerToOneHotMask
            output_axis: -3
          - _target_: ocl.preprocessing.AddEmptyMasksVG
          - _target_: ocl.preprocessing.DenseMaskToTensor
      batch_transform: false
models:
  feature_extractor:
    _target_: routed.ocl.feature_extractors.TimmFeatureExtractor
    model_name: ${experiment.timm_model}
    pretrained: ${when_testing:false,true}
    freeze: true
    feature_level: 12
    video_path: input.image
    dynamic_img_size: true
  mapping:
    _target_: routed.ocl.mapping.MLPMapping
    dim: ${experiment.feature_dim}
    x_path: feature_extractor
  conditioning:
    _target_: routed.ocl.conditioning.LangConditioning
    n_slots: ${experiment.num_slots}
    object_dim: ${experiment.slot_dim}
    dual_conditioning: false
    name_embedding_path: input.name_embedding
    batch_size_path: input.batch_size
    mask_path: input.contrastive_loss_mask
  perceptual_grouping:
    _target_: routed.ocl.perceptual_grouping.SlotAttentionGrouping
    feature_dim: ${.object_dim}
    object_dim: ${experiment.slot_dim}
    use_projection_bias: false
    positional_embedding:
      _target_: ocl.neural_networks.wrappers.Sequential
      _args_:
      - _target_: ocl.neural_networks.positional_embedding.DummyPositionEmbed
      - _target_: ocl.neural_networks.build_two_layer_mlp
        input_dim: ${experiment.feature_dim}
        output_dim: ${....feature_dim}
        hidden_dim: '${mul: ${experiment.feature_dim}, 2}'
        initial_layer_norm: true
    ff_mlp:
      _target_: ocl.neural_networks.build_two_layer_mlp
      input_dim: ${..object_dim}
      output_dim: ${..object_dim}
      hidden_dim: '${mul: ${..object_dim}, 4}'
      initial_layer_norm: true
      residual: true
    feature_path: mapping
    conditioning_path: conditioning
  attn_aggregation:
    _target_: routed.ocl.heads.PatchClipAttentionAggregationHead
    dim: ${experiment.feature_dim}
    attn_path: perceptual_grouping.feature_attributions
    x_path: input.image
  projector_slots:
    _target_: routed.ocl.heads.SlotProjectorHead
    dim: 512
    embedding_dim: 512
    slots_path: attn_aggregation
  dual_embedding:
    _target_: routed.ocl.heads.CLIPLangEmbeddingHead
    embedding_dim: 512
    names_batch_path: input.name
  dec_conditioning:
    _target_: routed.ocl.decoder_conditioning.EncodeLangConditioning
    dim: ${experiment.slot_dim}
    language_path: input.name_embedding
    mask_path: input.contrastive_loss_mask
  object_decoder:
    _target_: routed.ocl.decoding.PatchDecoder
    decoder:
      _target_: ocl.neural_networks.build_mlp
      _partial_: true
      features:
      - 2048
      - 2048
      - 2048
    object_dim: ${experiment.slot_dim}
    output_dim: ${experiment.feature_dim}
    num_patches: ${experiment.num_patches}
    object_features_path: perceptual_grouping.objects
    image_path: input.image
    conditioned: true
    condition_info_path: dec_conditioning
optimizers:
  opt0:
    _target_: ocl.optimization.OptimizationWrapper
    optimizer:
      _target_: torch.optim.AdamW
      _partial_: true
      lr: ${experiment.total_lr}
    lr_scheduler:
      _target_: ocl.scheduling.exponential_decay_after_optional_warmup
      _partial_: true
      decay_rate: 0.5
      decay_steps: 100000
      warmup_steps: 10000
    parameter_groups:
      _target_: ocl.optimization.ParameterGroupCreator
      param_groups:
        grouping:
          params:
          - models.perceptual_grouping
          - models.conditioning
          - models.object_decoder
          - models.dec_conditioning
          lr: ${experiment.total_lr}
          weight_decay: 0.0
        encoder:
          params:
          - models.mapping
          - models.projector_slots
          lr: ${experiment.mapping_lr}
          weight_decay: 0.0
losses:
  mse:
    _target_: routed.ocl.losses.ReconstructionLoss
    loss_type: mse
    input_path: object_decoder.reconstruction
    target_path: feature_extractor.features
  contrastive_loss:
    _target_: routed.ocl.losses.DiagonalContrastiveLoss
    x1_path: projector_slots
    x2_path: dual_embedding
    contrastive_loss_mask_path: input.contrastive_loss_mask
    temp: 0.1
    batch_contrastive: true
    weight: 0.2
visualizations:
  input:
    _target_: routed.ocl.visualizations.Image
    n_instances: 32
    denormalization:
      _target_: ocl.preprocessing.Denormalize
      mean:
      - 0.485
      - 0.456
      - 0.406
      std:
      - 0.229
      - 0.224
      - 0.225
    image_path: input.image
  masks:
    _target_: routed.ocl.visualizations.Mask
    mask_path: object_decoder.masks_as_image
  pred_segmentation:
    _target_: routed.ocl.visualizations.Segmentation
    denormalization:
      _target_: ocl.preprocessing.Denormalize
      mean:
      - 0.485
      - 0.456
      - 0.406
      std:
      - 0.229
      - 0.224
      - 0.225
    image_path: input.image
    mask_path: object_decoder.masks_as_image
  pred_segmentation_with_text:
    _target_: routed.ocl.visualizations.SegmentationWithText
    n_instances: 32
    denormalization:
      _target_: ocl.preprocessing.Denormalize
      mean:
      - 0.485
      - 0.456
      - 0.406
      std:
      - 0.229
      - 0.224
      - 0.225
    image_path: input.image
    mask_path: object_decoder.masks_as_image
    gt_masks_path: input.instance_mask_v2
    selected_indices_path: input.selected_indices
    text_path: input.name
    bbox_centroids_path: input.all_bbox_centroids
trainer:
  _target_: pytorch_lightning.trainer.trainer.Trainer
  accelerator: auto
  strategy: auto
  devices: 1
  num_nodes: 1
  precision: null
  logger:
  - _target_: pytorch_lightning.loggers.TensorBoardLogger
    save_dir: .
    name: tb
    version: ''
  - _target_: pytorch_lightning.loggers.WandbLogger
    project: ${slice:${hydra:runtime.choices.experiment},"/", 1}_${slice:${hydra:runtime.choices.experiment},"/",
      2}
    name: ${slice:${hydra:runtime.choices.experiment},"/","3:"}
    log_model: false
  callbacks: ${oc.dict.values:experiment.callbacks}
  fast_dev_run: false
  max_epochs: -1
  min_epochs: null
  max_steps: 100000
  min_steps: null
  max_time: null
  limit_train_batches: null
  limit_val_batches: null
  limit_test_batches: null
  limit_predict_batches: null
  overfit_batches: 0.0
  val_check_interval: 5000
  check_val_every_n_epoch: null
  num_sanity_val_steps: null
  log_every_n_steps: 100
  enable_checkpointing: null
  enable_progress_bar: null
  enable_model_summary: null
  accumulate_grad_batches: 1
  gradient_clip_val: 1.0
  gradient_clip_algorithm: null
  deterministic: null
  benchmark: null
  inference_mode: true
  use_distributed_sampler: true
  profiler: null
  detect_anomaly: false
  barebones: false
  plugins: null
  sync_batchnorm: false
  reload_dataloaders_every_n_epochs: 0
  default_root_dir: .
training_vis_frequency: 10000
training_metrics:
  acc_sc:
    _target_: routed.ocl.metrics.acc.EmbAccMetric
    mode: sc
    slot_emb_path: projector_slots
    ctrl_emb_path: dual_embedding
    mask_idx_path: input.contrastive_loss_mask
  acc_cs:
    _target_: routed.ocl.metrics.acc.EmbAccMetric
    mode: cs
    slot_emb_path: projector_slots
    ctrl_emb_path: dual_embedding
    mask_idx_path: input.contrastive_loss_mask
  acc_avg:
    _target_: routed.ocl.metrics.acc.EmbAccMetric
    mode: average
    slot_emb_path: projector_slots
    ctrl_emb_path: dual_embedding
    mask_idx_path: input.contrastive_loss_mask
evaluation_metrics:
  binding_hits:
    _target_: routed.ocl.metrics.BindingHits
    prediction_path: object_decoder.masks_as_image
    target_path: input.instance_mask_v2
    selected_indices_path: input.selected_indices
    use_threshold: false
    matching: best_overlap
    ignore_overlaps: false
  instance_ari:
    _target_: routed.ocl.metrics.ARIMetric
    prediction_path: object_decoder.masks_as_image
    target_path: input.instance_mask_v2
    foreground: false
    convert_target_one_hot: true
    ignore_overlaps: true
  instance_mbo:
    _target_: routed.ocl.metrics.UnsupervisedMaskIoUMetric
    prediction_path: object_decoder.masks_as_image
    target_path: input.instance_mask
    use_threshold: false
    matching: best_overlap
    ignore_overlaps: true
  gt_matched_instance_mbo:
    _target_: routed.ocl.metrics.UnsupervisedMaskIoUMetric
    prediction_path: object_decoder.masks_as_image
    target_path: input.instance_mask_v2
    selected_indices_path: input.selected_indices
    use_threshold: false
    matching: best_overlap
    ignore_overlaps: true
  acc_sc:
    _target_: routed.ocl.metrics.acc.EmbAccMetric
    mode: sc
    slot_emb_path: projector_slots
    ctrl_emb_path: dual_embedding
    mask_idx_path: input.contrastive_loss_mask
  acc_cs:
    _target_: routed.ocl.metrics.acc.EmbAccMetric
    mode: cs
    slot_emb_path: projector_slots
    ctrl_emb_path: dual_embedding
    mask_idx_path: input.contrastive_loss_mask
  acc_avg:
    _target_: routed.ocl.metrics.acc.EmbAccMetric
    mode: average
    slot_emb_path: projector_slots
    ctrl_emb_path: dual_embedding
    mask_idx_path: input.contrastive_loss_mask
load_checkpoint: null
load_checkpoint_partial: null
modules_to_load: null
trainable_models: null
seed: null
experiment:
  callbacks: {}
  checkpoint_every_n_steps: 1000
  image_size: 224
  mask_size: ${.image_size}
  batch_size_per_gpu: 64
  base_learning_rate: 0.0004
  max_num_binds: 7
  slot_dim: 256
  num_slots: 7
  timm_model: vit_small_patch14_dinov2.lvd142m
  feature_dim: '${timm_model_dim: ${.timm_model}}'
  num_patches: '${timm_model_num_patches: ${.timm_model}, ${.image_size}}'
  num_patches_per_side: '${isqrt: ${.num_patches}}'
  patch_size: '${timm_model_patch_size: ${.timm_model}}'
  total_batch_size: '${mul: ${trainer.devices}, ${.batch_size_per_gpu}}'
  total_lr: 0.0004
  mapping_lr: '${mul: 0.05, ${.total_lr}}'