| --- |
| trainer: |
| class_path: eva.Trainer |
| init_args: |
| n_runs: &N_RUNS ${oc.env:N_RUNS, 5} |
| default_root_dir: &OUTPUT_ROOT ${oc.env:OUTPUT_ROOT, logs/${oc.env:MODEL_NAME, vit_small_patch16_224_dino}/consep} |
| max_steps: &MAX_STEPS ${oc.env:MAX_STEPS, 2000} |
| log_every_n_steps: 6 |
| checkpoint_type: ${oc.env:CHECKPOINT_TYPE, best} |
| devices: ${oc.env:NUM_DEVICES, 1} |
| callbacks: |
| - class_path: eva.callbacks.ConfigurationLogger |
| - class_path: lightning.pytorch.callbacks.TQDMProgressBar |
| init_args: |
| refresh_rate: ${oc.env:TQDM_REFRESH_RATE, 1} |
| - class_path: eva.vision.callbacks.SemanticSegmentationLogger |
| init_args: |
| log_every_n_epochs: 1 |
| 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: lightning.pytorch.callbacks.ModelCheckpoint |
| init_args: |
| filename: best |
| save_last: ${oc.env:SAVE_LAST, false} |
| save_top_k: 1 |
| monitor: &MONITOR_METRIC ${oc.env:MONITOR_METRIC, 'val/MonaiDiceScore'} |
| mode: &MONITOR_METRIC_MODE ${oc.env:MONITOR_METRIC_MODE, max} |
| - class_path: lightning.pytorch.callbacks.EarlyStopping |
| init_args: |
| min_delta: 0 |
| patience: ${oc.env:PATIENCE, 200} |
| monitor: *MONITOR_METRIC |
| mode: *MONITOR_METRIC_MODE |
| logger: |
| - class_path: lightning.pytorch.loggers.TensorBoardLogger |
| init_args: |
| save_dir: *OUTPUT_ROOT |
| name: "" |
| model: |
| class_path: eva.vision.models.modules.SemanticSegmentationModule |
| init_args: |
| encoder: |
| class_path: eva.core.models.wrappers.TorchHubModel |
| init_args: |
| repo_or_dir: facebookresearch/dinov2 |
| model_name: dinov2_vitg14_reg |
| pretrained: true |
| out_indices: 1 |
| checkpoint_path: ${oc.env:CHECKPOINT_PATH, ../checkpoints/teacher_epoch250000.pth} |
| decoder: |
| class_path: eva.vision.models.networks.decoders.segmentation.ConvDecoderWithImage |
| init_args: |
| in_features: ${oc.env:IN_FEATURES, 1536} |
| num_classes: &NUM_CLASSES 5 |
| criterion: |
| class_path: eva.vision.losses.DiceLoss |
| init_args: |
| softmax: true |
| batch: true |
| lr_multiplier_encoder: 0.0 |
| optimizer: |
| class_path: torch.optim.AdamW |
| init_args: |
| lr: ${oc.env:LR_VALUE, 0.002} |
| postprocess: |
| predictions_transforms: |
| - class_path: torch.argmax |
| init_args: |
| dim: 1 |
| metrics: |
| common: |
| - class_path: eva.metrics.AverageLoss |
| evaluation: |
| - class_path: eva.vision.metrics.defaults.MulticlassSegmentationMetrics |
| init_args: |
| num_classes: *NUM_CLASSES |
| - class_path: torchmetrics.ClasswiseWrapper |
| init_args: |
| metric: |
| class_path: eva.vision.metrics.MonaiDiceScore |
| init_args: |
| include_background: true |
| num_classes: *NUM_CLASSES |
| reduction: none |
| labels: |
| - background |
| - other |
| - inflammatory |
| - epithelial |
| - spindle-shaped |
| data: |
| class_path: eva.DataModule |
| init_args: |
| datasets: |
| train: |
| class_path: eva.vision.datasets.CoNSeP |
| init_args: &DATASET_ARGS |
| root: ${oc.env:DATA_ROOT, /block/eva-data/consep} |
| split: train |
| sampler: eva.vision.data.wsi.patching.samplers.GridSampler |
| transforms: |
| class_path: eva.vision.data.transforms.common.ResizeAndCrop |
| init_args: |
| size: ${oc.env:RESIZE_DIM, 224} |
| mean: *NORMALIZE_MEAN |
| std: *NORMALIZE_STD |
| val: |
| class_path: eva.vision.datasets.CoNSeP |
| init_args: |
| <<: *DATASET_ARGS |
| split: val |
| dataloaders: |
| train: |
| batch_size: &BATCH_SIZE ${oc.env:BATCH_SIZE, 64} |
| num_workers: &N_DATA_WORKERS ${oc.env:N_DATA_WORKERS, 4} |
| shuffle: true |
| val: |
| batch_size: *BATCH_SIZE |
| num_workers: *N_DATA_WORKERS |
|
|
