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update leaderboard and documentation with WSI segmentation datasets (#…
…596) * updated configs * updated docs * addressed comment
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,143 @@ | ||
| --- | ||
| trainer: | ||
| class_path: eva.Trainer | ||
| init_args: | ||
| n_runs: &N_RUNS ${oc.env:N_RUNS, 1} | ||
| default_root_dir: &OUTPUT_ROOT ${oc.env:OUTPUT_ROOT, logs/phikon/bcss} | ||
| max_steps: &MAX_STEPS ${oc.env:MAX_STEPS, 2000} | ||
| log_every_n_steps: 6 | ||
| callbacks: | ||
| - class_path: eva.callbacks.ConfigurationLogger | ||
| - class_path: eva.vision.callbacks.SemanticSegmentationLogger | ||
| init_args: | ||
| log_every_n_epochs: 1 | ||
| log_images: false | ||
| - class_path: lightning.pytorch.callbacks.ModelCheckpoint | ||
| init_args: | ||
| filename: best | ||
| save_last: true | ||
| save_top_k: 1 | ||
| monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, val/GeneralizedDiceScore} | ||
| mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max} | ||
| - class_path: lightning.pytorch.callbacks.EarlyStopping | ||
| init_args: | ||
| min_delta: 0 | ||
| patience: 8 | ||
| monitor: *MONITOR_METRIC | ||
| mode: *MONITOR_METRIC_MODE | ||
| - class_path: eva.callbacks.SegmentationEmbeddingsWriter | ||
| init_args: | ||
| output_dir: &DATASET_EMBEDDINGS_ROOT ${oc.env:EMBEDDINGS_ROOT, ./data/embeddings}/phikon/bcss | ||
| dataloader_idx_map: | ||
| 0: train | ||
| 1: val | ||
| 2: test | ||
| metadata_keys: ["coords"] | ||
| overwrite: true | ||
| backbone: | ||
| class_path: eva.models.HuggingFaceModel | ||
| init_args: | ||
| model_name_or_path: owkin/phikon | ||
| tensor_transforms: | ||
| class_path: eva.core.models.networks.transforms.ExtractPatchFeatures | ||
| logger: | ||
| - class_path: lightning.pytorch.loggers.TensorBoardLogger | ||
| init_args: | ||
| save_dir: *OUTPUT_ROOT | ||
| name: "" | ||
| model: | ||
| class_path: eva.vision.models.modules.SemanticSegmentationModule | ||
| init_args: | ||
| decoder: | ||
| class_path: eva.vision.models.networks.decoders.segmentation.ConvDecoderMS | ||
| init_args: | ||
| in_features: ${oc.env:DECODER_IN_FEATURES, 768} | ||
| num_classes: &NUM_CLASSES 6 | ||
| criterion: | ||
| class_path: eva.vision.losses.DiceLoss | ||
| init_args: | ||
| softmax: true | ||
| batch: true | ||
| optimizer: | ||
| class_path: torch.optim.AdamW | ||
| init_args: | ||
| lr: 0.0001 | ||
| lr_scheduler: | ||
| class_path: torch.optim.lr_scheduler.PolynomialLR | ||
| init_args: | ||
| total_iters: *MAX_STEPS | ||
| power: 0.9 | ||
| postprocess: | ||
| predictions_transforms: | ||
| - class_path: torch.argmax | ||
| init_args: | ||
| dim: 1 | ||
| metrics: | ||
| common: | ||
| - class_path: eva.metrics.AverageLoss | ||
| evaluation: | ||
| - class_path: eva.core.metrics.defaults.MulticlassSegmentationMetrics | ||
| init_args: | ||
| num_classes: *NUM_CLASSES | ||
| - class_path: torchmetrics.ClasswiseWrapper | ||
| init_args: | ||
| metric: | ||
| class_path: eva.core.metrics.GeneralizedDiceScore | ||
| init_args: | ||
| num_classes: *NUM_CLASSES | ||
| input_format: index | ||
| weight_type: linear | ||
| per_class: true | ||
| data: | ||
| class_path: eva.DataModule | ||
| init_args: | ||
| datasets: | ||
| train: | ||
| class_path: eva.vision.datasets.EmbeddingsSegmentationDataset | ||
| init_args: &DATASET_ARGS | ||
| root: *DATASET_EMBEDDINGS_ROOT | ||
| manifest_file: manifest.csv | ||
| split: train | ||
| val: | ||
| class_path: eva.vision.datasets.EmbeddingsSegmentationDataset | ||
| init_args: | ||
| <<: *DATASET_ARGS | ||
| split: val | ||
| test: | ||
| class_path: eva.vision.datasets.EmbeddingsSegmentationDataset | ||
| init_args: | ||
| <<: *DATASET_ARGS | ||
| split: test | ||
| predict: | ||
| - class_path: eva.vision.datasets.BCSS | ||
| init_args: &PREDICT_DATASET_ARGS | ||
| root: ${oc.env:DATA_ROOT, ./data}/bcss | ||
| split: train | ||
| sampler: | ||
| class_path: eva.vision.data.wsi.patching.samplers.GridSampler | ||
| init_args: | ||
| max_samples: 1000 | ||
| transforms: | ||
| class_path: eva.vision.data.transforms.common.ResizeAndCrop | ||
| init_args: | ||
| size: ${oc.env:RESIZE_DIM, 224} | ||
| mean: &NORMALIZE_MEAN ${oc.env:NORMALIZE_MEAN, [0.485, 0.456, 0.406]} | ||
| std: &NORMALIZE_STD ${oc.env:NORMALIZE_STD, [0.229, 0.224, 0.225]} | ||
| - class_path: eva.vision.datasets.BCSS | ||
| init_args: | ||
| <<: *PREDICT_DATASET_ARGS | ||
| split: val | ||
| - class_path: eva.vision.datasets.BCSS | ||
| init_args: | ||
| <<: *PREDICT_DATASET_ARGS | ||
| split: test | ||
| dataloaders: | ||
| train: | ||
| batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 64} | ||
| shuffle: true | ||
| val: | ||
| batch_size: *BATCH_SIZE | ||
| test: | ||
| batch_size: *BATCH_SIZE | ||
| predict: | ||
| batch_size: &PREDICT_BATCH_SIZE ${oc.env:PREDICT_BATCH_SIZE, 64} |
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