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253
sam3/train/configs/odinw13/odinw_text_only.yaml
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253
sam3/train/configs/odinw13/odinw_text_only.yaml
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# @package _global_
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defaults:
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- _self_
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# ============================================================================
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# Paths Configuration (Chage this to your own paths)
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# ============================================================================
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# python sam3/train/train.py -c configs/odinw_text_only.yaml --use-cluster 1 --partition ${PARTITION} --account ${ACCOUNT} --qos ${QoS}
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paths:
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odinw_data_root: <YOUR_DATA_DIR>
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experiment_log_dir: <YOUR EXPERIMENET LOG_DIR>
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bpe_path: <BPE_PATH> # This should be under assets/bpe_simple_vocab_16e6.txt.gz
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supercategory_tuple: ${all_odinw_supercategories.${string:${submitit.job_array.task_index}}}
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# Validation transforms pipeline
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val_transforms:
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- _target_: sam3.train.transforms.basic_for_api.ComposeAPI
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transforms:
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- _target_: sam3.train.transforms.basic_for_api.RandomResizeAPI
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sizes: ${scratch.resolution}
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max_size:
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_target_: sam3.train.transforms.basic.get_random_resize_max_size
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size: ${scratch.resolution}
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square: true
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consistent_transform: False
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- _target_: sam3.train.transforms.basic_for_api.ToTensorAPI
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- _target_: sam3.train.transforms.basic_for_api.NormalizeAPI
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mean: ${scratch.val_norm_mean}
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std: ${scratch.val_norm_std}
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# ============================================================================
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# Different helper parameters and functions
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# ============================================================================
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scratch:
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enable_segmentation: True
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# Box processing
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use_presence_eval: True
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original_box_postprocessor:
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_target_: sam3.eval.postprocessors.PostProcessImage
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max_dets_per_img: -1 # infinite detections
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use_original_ids: true
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use_original_sizes_box: true
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use_presence: ${scratch.use_presence_eval}
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# Image processing parameters
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resolution: 1008
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# Normalization parameters
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val_norm_mean: [0.5, 0.5, 0.5]
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val_norm_std: [0.5, 0.5, 0.5]
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# Training parameters
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val_batch_size: 2
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num_val_workers: 0
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gather_pred_via_filesys: false
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# ============================================================================
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# Trainer Configuration
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# ============================================================================
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trainer:
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_target_: sam3.train.trainer.Trainer
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skip_saving_ckpts: true
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empty_gpu_mem_cache_after_eval: True
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max_epochs: 1
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accelerator: cuda
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seed_value: 123
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mode: val
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distributed:
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backend: nccl
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find_unused_parameters: True
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gradient_as_bucket_view: True
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loss:
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default:
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_target_: sam3.train.loss.sam3_loss.DummyLoss
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data:
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val:
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_target_: sam3.train.data.torch_dataset.TorchDataset
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dataset:
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_target_: sam3.train.data.sam3_image_dataset.Sam3ImageDataset
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coco_json_loader:
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_target_: sam3.train.data.coco_json_loaders.COCO_FROM_JSON
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prompts: ${odinw35_prompts.${supercategory_tuple.name}}
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include_negatives: true
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category_chunk_size: 20 # Note: Since we are doing AP +ve we need to include all categories!
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_partial_: true
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img_folder: ${paths.odinw_data_root}/${supercategory_tuple.val.img_folder}
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ann_file:
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_target_: sam3.eval.coco_reindex.reindex_coco_to_temp
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input_json_path: ${paths.odinw_data_root}/${supercategory_tuple.val.json}
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transforms: ${val_transforms}
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max_ann_per_img: 100000
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multiplier: 1
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training: false
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shuffle: False
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batch_size: ${scratch.val_batch_size}
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num_workers: ${scratch.num_val_workers}
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pin_memory: False
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drop_last: False
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collate_fn:
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_target_: sam3.train.data.collator.collate_fn_api
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_partial_: true
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repeats: 1
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dict_key: odinw35
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model:
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_target_: sam3.model_builder.build_sam3_image_model
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bpe_path: ${paths.bpe_path}
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device: cpus
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eval_mode: true # Set to false if training
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enable_segmentation: ${scratch.enable_segmentation} # Warning: Enable this if using segmentation.
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meters:
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val:
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odinw35:
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detection:
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_target_: sam3.eval.coco_writer.PredictionDumper
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iou_type: "bbox"
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dump_dir: ${launcher.experiment_log_dir}/dumps/odinw/${supercategory_tuple.name}
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merge_predictions: True
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postprocessor: ${scratch.original_box_postprocessor}
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gather_pred_via_filesys: ${scratch.gather_pred_via_filesys}
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maxdets: 100
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pred_file_evaluators:
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- _target_: sam3.eval.coco_eval_offline.CocoEvaluatorOfflineWithPredFileEvaluators
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gt_path:
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_target_: sam3.eval.coco_reindex.reindex_coco_to_temp
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input_json_path: ${paths.odinw_data_root}/${supercategory_tuple.val.json}
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tide: False
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iou_type: "bbox"
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positive_split: False
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checkpoint:
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save_dir: ${launcher.experiment_log_dir}/checkpoints
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save_freq: 0 # 0 only last checkpoint is saved.
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logging:
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tensorboard_writer:
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_target_: sam3.train.utils.logger.make_tensorboard_logger
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log_dir: ${launcher.experiment_log_dir}/tensorboard
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flush_secs: 120
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should_log: True
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wandb_writer: null
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log_dir: ${launcher.experiment_log_dir}/logs/${supercategory_tuple.name}
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log_freq: 10
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# ============================================================================
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# Launcher and Submitit Configuration
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# ============================================================================
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launcher:
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num_nodes: 1
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gpus_per_node: 2
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experiment_log_dir: ${paths.experiment_log_dir}
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multiprocessing_context: forkserver
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submitit:
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account: null
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partition: null
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qos: null
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timeout_hour: 72
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use_cluster: True
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cpus_per_task: 10
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port_range: [10000, 65000]
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constraint: null
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job_array:
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num_tasks: 13
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task_index: 0
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# ============================================================================
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# ODinW13 Supercategories
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# ============================================================================
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all_odinw_supercategories:
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- name: AerialMaritimeDrone_large
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val:
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img_folder: AerialMaritimeDrone/large/test/
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json: AerialMaritimeDrone/large/test/annotations_without_background.json
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- name: Aquarium
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val:
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img_folder: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/
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json: Aquarium/Aquarium Combined.v2-raw-1024.coco/test/annotations_without_background.json
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- name: CottontailRabbits
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val:
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img_folder: CottontailRabbits/test/
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json: CottontailRabbits/test/annotations_without_background.json
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- name: EgoHands_generic
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val:
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img_folder: EgoHands/generic/test/
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json: EgoHands/generic/test/annotations_without_background.json
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- name: NorthAmericaMushrooms
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val:
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img_folder: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/
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json: NorthAmericaMushrooms/North American Mushrooms.v1-416x416.coco/test/annotations_without_background.json
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- name: Packages
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val:
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img_folder: Packages/Raw/test/
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json: Packages/Raw/test/annotations_without_background.json
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- name: PascalVOC
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val:
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img_folder: PascalVOC/valid/
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json: PascalVOC/valid/annotations_without_background.json
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- name: Raccoon
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val:
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img_folder: Raccoon/Raccoon.v2-raw.coco/test/
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json: Raccoon/Raccoon.v2-raw.coco/test/annotations_without_background.json
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- name: ShellfishOpenImages
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val:
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img_folder: ShellfishOpenImages/raw/test/
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json: ShellfishOpenImages/raw/test/annotations_without_background.json
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- name: VehiclesOpenImages
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val:
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img_folder: VehiclesOpenImages/416x416/test/
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json: VehiclesOpenImages/416x416/test/annotations_without_background.json
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- name: pistols
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val:
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img_folder: pistols/export/
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json: pistols/export/test_annotations_without_background.json
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- name: pothole
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val:
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img_folder: pothole/test/
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json: pothole/test/annotations_without_background.json
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- name: thermalDogsAndPeople
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val:
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img_folder: thermalDogsAndPeople/test/
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json: thermalDogsAndPeople/test/annotations_without_background.json
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odinw35_prompts:
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AerialMaritimeDrone_large: '[{"id": 1, "name": "boat", "supercategory": "movable-objects"},
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{"id": 2, "name": "car", "supercategory": "movable-objects"}, {"id": 3, "name": "dock",
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"supercategory": "movable-objects"}, {"id": 4, "name": "jet ski", "supercategory": "movable-objects"},
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{"id": 5, "name": "boat lift", "supercategory": "movable-objects"}]'
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Aquarium: null
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CottontailRabbits: null
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EgoHands_generic: null
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NorthAmericaMushrooms: '[{''id'': 1, ''name'':
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''chicken of the woods'', ''supercategory'': ''mushroom''}, {''id'': 2, ''name'': ''chanterelle'', ''supercategory'': ''mushroom''}]'
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Packages: null
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PascalVOC: null
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Raccoon: null
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ShellfishOpenImages: null
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VehiclesOpenImages: null
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pistols: null
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pothole: null
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thermalDogsAndPeople: null
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