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a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/cli.py.metadata b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/cli.py.metadata index 3c32cf10f8e9dadc08ccc73fb2e8be91b24f1742..68db81904e22bf0b59639c71ed3883cb233cbe7f 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/cli.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/cli.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db -8a5798cb0338c2ceb3060276d12d5c0157599933 -1781874441.3974206 +c48b211a983203f99f058ce6a5ae649db2561a43 +f2afe680f9be4c32d0b764cec33e954b9405c36a +1781597428.2136478 diff --git a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/data.py.metadata b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/data.py.metadata index 319ef3ac1a1ff25108e219ad20c56735acc45a8b..de0ee26848dafb57a03975360cb3f4a0345f04a5 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/data.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/data.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db -eec459f9a2ebb5248657e8717c82b8bcd736cf04 -1781874441.4294744 +c48b211a983203f99f058ce6a5ae649db2561a43 +18280e8278d5c3309d25807787869b5399200cfa +1781597428.513635 diff --git a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/engine.py.metadata b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/engine.py.metadata index 09a418a21ddea62abdc712cb2679dfec61ee9fba..004f2e95e746598dc5dd1d6b30331d3430ef6432 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/engine.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/engine.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db 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b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/losses.py.metadata index 94b33b4a1cb3ef240434ef8b4157254b730f262d..0759ba98d23a02c711e7ce78102fce434f943971 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/losses.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/losses.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db -a9f264a556310e3492c808a55e3091fda8b37a01 -1781874441.681009 +c48b211a983203f99f058ce6a5ae649db2561a43 +8b6d627c534d71d99d855b29c65bc294b87c6303 +1781597428.79422 diff --git a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/metrics.py.metadata b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/metrics.py.metadata index b9ca800d765db96c20b0e6b7e8281e7263b51566..77d5839c442968d4733cf7ed8d073b214e5d6935 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/metrics.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/metrics.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db -350b8c2c12a305b4d908d8bd904a8fc484f83bfd -1781874441.9017274 +c48b211a983203f99f058ce6a5ae649db2561a43 +e9c3f3d079aa08915f21fa758bf05891d861c1bd +1781597428.8403232 diff --git a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/model_setup.py.metadata b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/model_setup.py.metadata index 35470171ac7dc1b6896925b9947f3bf0573ce927..437b1e6628aed55c9c307c13ddc31cb26663c5f6 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/model_setup.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/model_setup.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db 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b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/runner.py.metadata index 7dcca2cb7a1f51cc106427601e114803faf4c0fe..0de9277c59f50d0f121e4d47dcb260ec0f819948 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/runner.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/runner.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db -9d4afea6ec9edfb4e65c5c54135ab748d4e6f866 -1781874443.812626 +c48b211a983203f99f058ce6a5ae649db2561a43 +4b39d6e160bc3dab5624ef6da0e572de33b72980 +1781597429.1600146 diff --git a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/train_milk10k_effb2_dual_metadata.py.metadata b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/train_milk10k_effb2_dual_metadata.py.metadata index 6d3e01faca69b6ad837c80374a9dd9e22652ff7c..9d548aa2379b264bff6f549e38fccae80aa05559 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/train_milk10k_effb2_dual_metadata.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/train_milk10k_effb2_dual_metadata.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db +c48b211a983203f99f058ce6a5ae649db2561a43 da1072ea523e7c2a806284dfafacbb6dba6a1293 -1781874443.932739 +1781597429.3428674 diff --git a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/training.py.metadata b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/training.py.metadata index 76bef51a0b679d602447b02a4077b68b6f7f49ea..59101ea48a845883cd9193fcd65fcd44b129e4d7 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/training.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/training.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db -c04d11f5a74450e1e61c75fc9d97985a0606c7a3 -1781874443.9688227 +c48b211a983203f99f058ce6a5ae649db2561a43 +72e7f7b777bddfa3c6e4ed06cf40cf230d8696ee +1781597429.706166 diff --git a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/training_utils.py.metadata b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/training_utils.py.metadata index 93acd17a76aeef84bdf782a1fa360dab88ba35c6..3f5e64125d8b43ac02ace9653023aa217c636cb1 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/training_utils.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/milk10k_effb2_metadata/training_utils.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db -096cf0ef6f52826e0233290e2d03532b28d99d3b -1781874443.9975698 +c48b211a983203f99f058ce6a5ae649db2561a43 +96df26e95c0a775a4d5d85ced5c23db69a0e3687 +1781597429.9209738 diff --git a/milk10k_effb2_metadata/.cache/huggingface/download/predict_milk10k_effb2_dual_metadata.py.metadata b/milk10k_effb2_metadata/.cache/huggingface/download/predict_milk10k_effb2_dual_metadata.py.metadata index 3038f160ed8c0f2ff6061dbcbbaa0878a7e7872d..d77985bdf9e9ff265aebc1a6710ff304dfb081b7 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/predict_milk10k_effb2_dual_metadata.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/predict_milk10k_effb2_dual_metadata.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db +c48b211a983203f99f058ce6a5ae649db2561a43 15f35146cc23e29e3348f85ea82d19bcfbc4f1f0 -1781874444.1152453 +1781597430.34601 diff --git a/milk10k_effb2_metadata/.cache/huggingface/download/train_milk10k_effb2_dual_metadata.py.metadata b/milk10k_effb2_metadata/.cache/huggingface/download/train_milk10k_effb2_dual_metadata.py.metadata index 9e4bd0d2aa94a2106e16210041769922d47e0ecb..21c33b4116ab09dc5610d8b01b8b3acda50db6cd 100644 --- a/milk10k_effb2_metadata/.cache/huggingface/download/train_milk10k_effb2_dual_metadata.py.metadata +++ b/milk10k_effb2_metadata/.cache/huggingface/download/train_milk10k_effb2_dual_metadata.py.metadata @@ -1,3 +1,3 @@ -f50543d6bf12766c60a351d928232a984930a1db +c48b211a983203f99f058ce6a5ae649db2561a43 da1072ea523e7c2a806284dfafacbb6dba6a1293 -1781874444.2369857 +1781597429.9241464 diff --git a/milk10k_effb2_metadata/__pycache__/datasets.cpython-310.pyc b/milk10k_effb2_metadata/__pycache__/datasets.cpython-310.pyc index c6fdfa3ca691f80fd83a0164b86d2eac7204cfb5..a5dc6a3932a33def9aa46409dea85adff4882d54 100644 Binary files a/milk10k_effb2_metadata/__pycache__/datasets.cpython-310.pyc and b/milk10k_effb2_metadata/__pycache__/datasets.cpython-310.pyc differ diff --git a/milk10k_effb2_metadata/__pycache__/datasets.cpython-314.pyc b/milk10k_effb2_metadata/__pycache__/datasets.cpython-314.pyc index af1626140821300808b58ce972715723c2a60ee5..7c899675657ed636be33d38441b52ecc7af3da1d 100644 Binary files a/milk10k_effb2_metadata/__pycache__/datasets.cpython-314.pyc and b/milk10k_effb2_metadata/__pycache__/datasets.cpython-314.pyc differ diff --git a/milk10k_effb2_metadata/__pycache__/predict_milk10k_effb2_dual_metadata.cpython-314.pyc b/milk10k_effb2_metadata/__pycache__/predict_milk10k_effb2_dual_metadata.cpython-314.pyc index f701b787b1d57cb6267f2f694e12d07ca677223c..780148ea5671d140d1df75f90b50ca942e1fd206 100644 Binary files a/milk10k_effb2_metadata/__pycache__/predict_milk10k_effb2_dual_metadata.cpython-314.pyc and b/milk10k_effb2_metadata/__pycache__/predict_milk10k_effb2_dual_metadata.cpython-314.pyc differ diff --git a/milk10k_effb2_metadata/__pycache__/runner.cpython-314.pyc b/milk10k_effb2_metadata/__pycache__/runner.cpython-314.pyc index 0476e356f6ce87dfcac6828c0e69e07f5ebaa53d..65651337e797ee668410e425a822a0bc5a1af405 100644 Binary files a/milk10k_effb2_metadata/__pycache__/runner.cpython-314.pyc and b/milk10k_effb2_metadata/__pycache__/runner.cpython-314.pyc differ diff --git a/milk10k_effb2_metadata/__pycache__/train_milk10k_effb2_dual_metadata.cpython-314.pyc b/milk10k_effb2_metadata/__pycache__/train_milk10k_effb2_dual_metadata.cpython-314.pyc index efcf05096adb4bc4336a1ab06ba147107fac35d8..d870ee8c47d65d69a1cb49164d7007c902f65636 100644 Binary files a/milk10k_effb2_metadata/__pycache__/train_milk10k_effb2_dual_metadata.cpython-314.pyc and b/milk10k_effb2_metadata/__pycache__/train_milk10k_effb2_dual_metadata.cpython-314.pyc differ diff --git a/milk10k_effb2_metadata/cli.py b/milk10k_effb2_metadata/cli.py index 81241763f338b05e2d92806364fd131131f35006..6b0fcf00152ad3777d87928cc783669a5e77e3f5 100644 --- a/milk10k_effb2_metadata/cli.py +++ b/milk10k_effb2_metadata/cli.py @@ -243,4 +243,8 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--calibration-step", type=float, default=0.25) parser.add_argument("--calibration-passes", type=int, default=3) parser.add_argument("--patience", type=int, default=6) + parser.add_argument("--tau", type=float, default=0.0, help="Logit adjustment parameter for Generalized Balanced Softmax") + parser.add_argument("--lws-epochs", type=int, default=0, help="Number of epochs to train Learnable Weight Scaling (LWS) post-training") + parser.add_argument("--ema", action="store_true", help="Enable Exponential Moving Average (EMA) for model weights") + parser.add_argument("--ema-decay", type=float, default=0.999, help="Decay rate for EMA") return parser.parse_args() diff --git a/milk10k_effb2_metadata/engine.py b/milk10k_effb2_metadata/engine.py index 94f44506481569978083f06d29641d91160c68da..74a04954dde7a2fcc61cdab89781af79f38c139e 100644 --- a/milk10k_effb2_metadata/engine.py +++ b/milk10k_effb2_metadata/engine.py @@ -35,6 +35,7 @@ def run_epoch( use_amp: bool = False, tail_class_indices: list[int] | None = None, class_names: list[str] | None = None, + ema_model: nn.Module | None = None, ) -> dict[str, float]: training = optimizer is not None model.train(training) @@ -65,6 +66,8 @@ def run_epoch( loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) optimizer.step() + if ema_model is not None: + ema_model.update_parameters(model) batch_size = labels.size(0) total_loss += float(loss.detach().item()) * batch_size @@ -161,6 +164,7 @@ def save_checkpoint( metadata_spec: dict[str, Any], args: argparse.Namespace, extra: dict[str, Any] | None = None, + ema_model: nn.Module | None = None, ) -> None: payload = { "epoch": epoch, @@ -176,6 +180,8 @@ def save_checkpoint( "metadata_spec": metadata_spec, "args": json_safe(vars(args)), } + if ema_model is not None: + payload["ema_model_state"] = ema_model.state_dict() if extra: payload.update(json_safe(extra)) torch.save(payload, path) @@ -202,6 +208,7 @@ def train_phase( tail_class_names: list[str] | None = None, train_class_counts: dict[str, int] | None = None, best_val_tail_recall: float = float("-inf"), + ema_model: nn.Module | None = None, ) -> tuple[int, float, float]: if num_epochs <= 0: return start_epoch, best_val_f1, best_val_tail_recall @@ -237,9 +244,11 @@ def train_phase( use_amp, tail_class_indices, class_names, + ema_model=ema_model, ) + eval_model = ema_model if ema_model is not None else model val_stats = run_epoch( - model, + eval_model, val_loader, criterion, device, @@ -288,6 +297,7 @@ def train_phase( label_to_idx, metadata_spec, args, + ema_model=ema_model, ) print( f"Saved best checkpoint: phase={phase} epoch={epoch:03d} " @@ -316,6 +326,7 @@ def train_phase( "train_class_counts": train_class_counts or {}, "selection_metric": "val_tail_recall_macro", }, + ema_model=ema_model, ) print( f"Saved tail checkpoint: phase={phase} epoch={epoch:03d} " @@ -337,6 +348,7 @@ def train_phase( "last_selection_metric": float(val_stats[selection_metric]), "last_val_stats": val_stats, }, + ema_model=ema_model, ) print( f"Saved last checkpoint: phase={phase} epoch={epoch:03d} " diff --git a/milk10k_effb2_metadata/losses.py b/milk10k_effb2_metadata/losses.py index a9f264a556310e3492c808a55e3091fda8b37a01..9083875d36c8ec9bdc73e55298e7b63cbe369cdc 100644 --- a/milk10k_effb2_metadata/losses.py +++ b/milk10k_effb2_metadata/losses.py @@ -27,6 +27,28 @@ class FocalLoss(nn.Module): return loss.mean() +class GeneralizedBalancedSoftmaxLoss(nn.Module): + def __init__( + self, + class_counts: torch.Tensor, + tau: float = 1.0, + weight: torch.Tensor | None = None, + ) -> None: + super().__init__() + self.tau = tau + self.weight = weight + counts = class_counts.float().clamp_min(1.0) + self.register_buffer("log_counts", torch.log(counts)) + + def forward(self, logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + if self.training and self.tau > 0.0: + log_counts = self.log_counts.to(device=logits.device, dtype=logits.dtype) + adjusted_logits = logits + self.tau * log_counts + else: + adjusted_logits = logits + return F.cross_entropy(adjusted_logits, labels, weight=self.weight) + + class LDAMLoss(nn.Module): """LDAM with deferred effective-number reweighting.""" @@ -179,7 +201,12 @@ def build_loss(train_df: pd.DataFrame, label_to_idx: dict[str, int], args: argpa y = np.array([label_to_idx[label] for label in train_df["label"]]) weights = compute_class_weight(class_weight="balanced", classes=np.arange(len(label_to_idx)), y=y) weight = torch.tensor(weights, dtype=torch.float32, device=device) - ce_loss: nn.Module = nn.CrossEntropyLoss(weight=weight) + + if getattr(args, "tau", 0.0) > 0.0: + counts = class_count_tensor(train_df, label_to_idx, device) + ce_loss: nn.Module = GeneralizedBalancedSoftmaxLoss(counts, tau=args.tau, weight=weight) + else: + ce_loss: nn.Module = nn.CrossEntropyLoss(weight=weight) if args.loss == "focal": return FocalLoss(weight=weight, gamma=args.focal_gamma) if args.loss == "ce_dice": diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/MILK10K_EFFB2_METADATA_CLI.md b/milk10k_effb2_metadata/milk10k_effb2_metadata/MILK10K_EFFB2_METADATA_CLI.md index 8c947189acfefb69aa5e3d33a4e853f26124d359..a989064dc6d719ebc7875caf21f6e56b6aba660a 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/MILK10K_EFFB2_METADATA_CLI.md +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/MILK10K_EFFB2_METADATA_CLI.md @@ -20,16 +20,11 @@ Training code is split by responsibility: ```text training.py Thin entry facade: normalize args, load dataframe, choose single run vs k-fold. runner.py Full split runner: split CSVs, loaders, loss, train phases, final metrics/files. -engine.py Epoch/phase loop: run_epoch, train_phase, save best/last checkpoints. +engine.py Epoch/phase loop: run_epoch, train_phase, save best checkpoint. model_setup.py Backend detection, model construction, resume checkpoint, optimizer param groups. training_utils.py JSON-safe serialization, run_config.json, kfold_summary.csv/json. ``` -Training always writes both checkpoint types to the run directory: - -- `best.pt`: epoch with the best configured validation selection metric. -- `last.pt`: most recently completed epoch, updated after every epoch and also available when early stopping triggers. - Common places to edit: ```text @@ -145,22 +140,6 @@ Metadata fusion can be combined with every image fusion mode: Normal online image transforms keep the original metadata vector. If you materialize offline transform augmentations, duplicate the original row metadata unchanged. Do not invent metadata for generated synthetic lesions in this trainer; use real-row metadata or disable metadata for synthetic-only experiments. -For synthetic paired augmentations, prefer appending a small train-only subset instead of replacing the whole training CSV: - -```bash -python train_milk10k_effb2_dual_metadata.py \ - --data-dir /marimo/milk10k \ - --augmented-data-dir /marimo/milk10k_augmented \ - --augmented-classes INF BEN_OTH DF VASC \ - --augmented-max-per-class 25 \ - --zero-augmented-metadata \ - --loss ce_f1 \ - --f1-weight 0.3 \ - --f1-ignore-classes MAL_OTH -``` - -`--zero-augmented-metadata` only zeros metadata vectors for appended augmented rows; real rows still use metadata normally. - ## 3. Class Weight Only ```bash @@ -184,28 +163,6 @@ python train_milk10k_effb2_dual_metadata.py \ --output-dir milk10k_effb2_sampler_p05 ``` -## Hybrid epoch balancing - -Use hybrid balancing when the largest class dominates training but inverse-frequency -loss weights make predictions too soft: - -```bash -python train_milk10k_effb2_dual_metadata.py \ - --data-dir /path/to/milk10k \ - --balance-mode hybrid \ - --balance-head-ratio 2.0 \ - --balance-tail-floor 100 \ - --balance-min-source-count 20 \ - --loss ce \ - --output-dir milk10k_effb2_hybrid_balance -``` - -The largest class is sampled without replacement up to twice the second-largest -class. Eligible classes below 100 rows are sampled with replacement to 100 rows -and receive the stronger train transform. Classes with fewer than 20 source rows -are left unchanged. Sampling changes by epoch and is reproducible from `--seed`. -Validation is never resampled. 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a/milk10k_effb2_metadata/milk10k_effb2_metadata/__pycache__/training_utils.cpython-314.pyc and b/milk10k_effb2_metadata/milk10k_effb2_metadata/__pycache__/training_utils.cpython-314.pyc differ diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/checkpoints.py b/milk10k_effb2_metadata/milk10k_effb2_metadata/checkpoints.py index 56361fdc15cbf21893f290d5a0a4414a3dfc6876..ca041bd29958b95380013b4c1ab77a377241a5b6 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/checkpoints.py +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/checkpoints.py @@ -9,8 +9,8 @@ from typing import Any import torch from torch import nn -CHECKPOINT_STATE_KEYS = ("encoder_state_dict", "model_state", "model_state_dict", "state_dict") -PREFIXES_TO_STRIP = ("module.", "model.", "encoder.", "backbone.", "_orig_mod.") +CHECKPOINT_STATE_KEYS = ("model_state", "model_state_dict", "state_dict") +PREFIXES_TO_STRIP = ("module.", "model.", "_orig_mod.") def extract_state_dict(checkpoint: Any) -> dict[str, torch.Tensor]: @@ -91,3 +91,4 @@ def load_encoder_checkpoint(path: Path, encoder: nn.Module, branch_name: str, de target_state.update(matched) encoder.load_state_dict(target_state) print(f"{branch_name}: loaded {len(matched)} keys from {path}; skipped {skipped} keys") + diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/cli.py b/milk10k_effb2_metadata/milk10k_effb2_metadata/cli.py index 81241763f338b05e2d92806364fd131131f35006..7425f4c09508c76776a55d26f0dec3ee2add1b77 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/cli.py +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/cli.py @@ -9,18 +9,6 @@ from pathlib import Path def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Train MILK10k dual-image backbones with metadata fusion.") parser.add_argument("--data-dir", type=Path, default=None) - parser.add_argument( - "--dermoscopic-mask-dir", - type=Path, - default=None, - help="Optional directory containing _dermoscopic_mask.png files.", - ) - parser.add_argument( - "--min-dermoscopic-mask-ratio", - type=float, - default=0.01, - help="Fallback to the original dermoscopic image when mask foreground ratio is below this value.", - ) parser.add_argument( "--clinical-checkpoint", type=Path, @@ -47,10 +35,7 @@ def parse_args() -> argparse.Namespace: parser.add_argument( "--backbone", default="efficientnet_b2", - help=( - "Backbone model architecture (efficientnet_b2, tf_efficientnetv2_b2, " - "efficientnet_b1, resnet50, convnext_base)." - ), + help="Backbone model architecture (efficientnet_b2, efficientnet_b1, resnet50, convnext_base).", ) parser.add_argument( "--num-workers", @@ -110,42 +95,10 @@ def parse_args() -> argparse.Namespace: action="store_true", help="Keep synthetic lesion IDs containing __sdpair_ in train only; validation is split from real lesions.", ) - parser.add_argument( - "--augmented-data-dir", - type=Path, - default=None, - help="Optional augmented MILK10k-style data dir. Only extra lesion IDs are appended to the train split.", - ) - parser.add_argument( - "--augmented-max-per-class", - type=int, - default=0, - help="Cap extra augmented lesions per class. 0 keeps all extra rows from --augmented-data-dir.", - ) - parser.add_argument( - "--augmented-classes", - nargs="*", - default=[], - help="Optional class-name allowlist for appended augmented lesions, e.g. --augmented-classes INF BEN_OTH DF VASC.", - ) - parser.add_argument( - "--zero-augmented-metadata", - action="store_true", - help="Set metadata vectors to all zeros for rows appended from --augmented-data-dir.", - ) parser.add_argument("--seed", type=int, default=42) parser.add_argument("--branch-dim", type=int, default=512) parser.add_argument("--metadata-dim", type=int, default=64) parser.add_argument("--classifier-hidden-dim", type=int, default=512) - parser.add_argument( - "--classifier-style", - choices=["legacy", "simple"], - default="legacy", - help=( - "Final fused classifier architecture. legacy keeps the existing LayerNorm/GELU head; " - "simple uses Linear-ReLU-Dropout-Linear." - ), - ) parser.add_argument("--dropout", type=float, default=0.3) parser.add_argument( "--logit-fusion-mode", @@ -159,30 +112,6 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--class-weight", action="store_true") parser.add_argument("--weighted-sampler", action="store_true") parser.add_argument("--sampler-power", type=float, default=1.0) - parser.add_argument( - "--balance-mode", - choices=["none", "hybrid"], - default="none", - help="Train-only epoch balancing. hybrid caps the largest class and mildly oversamples eligible tail classes.", - ) - parser.add_argument( - "--balance-head-ratio", - type=float, - default=2.0, - help="In hybrid mode, cap the largest class at this multiple of the second-largest class.", - ) - parser.add_argument( - "--balance-tail-floor", - type=int, - default=100, - help="In hybrid mode, oversample eligible classes below this count up to this many rows per epoch.", - ) - parser.add_argument( - "--balance-min-source-count", - type=int, - default=20, - help="Do not oversample a class with fewer real train rows than this value.", - ) parser.add_argument("--loss", choices=["ce", "focal", "ldam", "ce_dice", "ce_f1"], default="ce") parser.add_argument("--focal-gamma", type=float, default=2.0) parser.add_argument("--dice-weight", type=float, default=0.3) diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/data.py b/milk10k_effb2_metadata/milk10k_effb2_metadata/data.py index 7e4403653b4a13796af567eac02389dc3d8a0976..18280e8278d5c3309d25807787869b5399200cfa 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/data.py +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/data.py @@ -11,7 +11,7 @@ import pandas as pd import torch from PIL import Image, ImageFile from sklearn.model_selection import StratifiedKFold, train_test_split -from torch.utils.data import DataLoader, Dataset, Sampler, WeightedRandomSampler +from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler from torchvision import transforms from datasets import LABEL_COLUMNS, normalize_image_type @@ -19,110 +19,6 @@ from datasets import LABEL_COLUMNS, normalize_image_type ImageFile.LOAD_TRUNCATED_IMAGES = True METADATA_COLUMNS = ("age_approx", "sex", "skin_tone_class", "site") -DERMOSCOPIC_MASK_PATH_COLUMN = "dermoscopic_mask_path" -DERMOSCOPIC_MASK_RATIO_COLUMN = "dermoscopic_mask_ratio" -DERMOSCOPIC_MASK_STATUS_COLUMN = "dermoscopic_mask_status" - - -def apply_dermoscopic_mask(image: Image.Image, mask_path: str | Path | None) -> Image.Image: - """Return an RGB image with non-mask pixels black, or the original RGB image on read failure.""" - image = image.convert("RGB") - if not isinstance(mask_path, (str, Path)) or not str(mask_path): - return image - try: - with Image.open(mask_path) as mask_image: - mask = mask_image.convert("L") - if mask.size != image.size: - return image - binary_mask = mask.point(lambda value: 255 if value else 0) - return Image.composite(image, Image.new("RGB", image.size), binary_mask) - except (OSError, ValueError): - return image - - -def audit_dermoscopic_masks( - df: pd.DataFrame, - mask_dir: Path, - min_foreground_ratio: float = 0.01, - mask_id_column: str = "lesion_id", - mask_suffix: str = "_dermoscopic_mask.png", -) -> tuple[pd.DataFrame, pd.DataFrame]: - """Attach valid mask paths and return one audit row per paired dermoscopic image.""" - if not 0.0 <= min_foreground_ratio <= 1.0: - raise ValueError("--min-dermoscopic-mask-ratio must be between 0 and 1.") - mask_dir = mask_dir.expanduser().resolve() - if not mask_dir.is_dir(): - raise FileNotFoundError(f"Dermoscopic mask directory does not exist: {mask_dir}") - if mask_id_column not in df.columns: - raise ValueError(f"Mask ID column is missing from dataframe: {mask_id_column}") - - audited_df = df.copy() - mask_paths: list[str | None] = [] - ratios: list[float | None] = [] - statuses: list[str] = [] - audit_rows: list[dict[str, Any]] = [] - - for _, row in audited_df.iterrows(): - lesion_id = str(row["lesion_id"]) - mask_id = str(row[mask_id_column]) - image_path = Path(row["dermoscopic_path"]) - mask_path = mask_dir / f"{mask_id}{mask_suffix}" - ratio: float | None = None - status = "valid" - image_size: tuple[int, int] | None = None - mask_size: tuple[int, int] | None = None - - if not mask_path.is_file(): - status = "missing" - else: - try: - with Image.open(image_path) as image: - image_size = image.size - with Image.open(mask_path) as mask_image: - mask = mask_image.convert("L") - mask.load() - mask_size = mask.size - histogram = mask.histogram() - total_pixels = mask.width * mask.height - ratio = (total_pixels - histogram[0]) / total_pixels if total_pixels else 0.0 - if mask_size != image_size: - status = "size_mismatch" - elif ratio < min_foreground_ratio: - status = "too_small" - except (OSError, ValueError): - status = "unreadable" - - valid_path = str(mask_path) if status == "valid" else None - mask_paths.append(valid_path) - ratios.append(ratio) - statuses.append(status) - audit_rows.append( - { - "lesion_id": lesion_id, - "mask_id": mask_id, - "dermoscopic_path": str(image_path), - "mask_path": str(mask_path), - "foreground_ratio": ratio, - "status": status, - "image_size": None if image_size is None else f"{image_size[0]}x{image_size[1]}", - "mask_size": None if mask_size is None else f"{mask_size[0]}x{mask_size[1]}", - } - ) - - audited_df[DERMOSCOPIC_MASK_PATH_COLUMN] = mask_paths - audited_df[DERMOSCOPIC_MASK_RATIO_COLUMN] = ratios - audited_df[DERMOSCOPIC_MASK_STATUS_COLUMN] = statuses - return audited_df, pd.DataFrame(audit_rows) - - -def print_mask_audit_summary(audit_df: pd.DataFrame, min_foreground_ratio: float) -> None: - counts = audit_df["status"].value_counts().sort_index().to_dict() - valid = int(counts.get("valid", 0)) - print( - "Dermoscopic masks: " - f"total={len(audit_df)}, valid={valid}, fallback={len(audit_df) - valid}, " - f"min_foreground_ratio={min_foreground_ratio:.6f}, status_counts={counts}" - ) class PairedMilk10kMetadataDataset(Dataset): @@ -132,97 +28,29 @@ class PairedMilk10kMetadataDataset(Dataset): label_to_idx: dict[str, int], metadata_spec: dict[str, Any], transform=None, - strong_transform=None, - strong_augment_labels: set[int] | None = None, ) -> None: self.df = df.reset_index(drop=True) self.labels = [label_to_idx[label] for label in self.df["label"].tolist()] self.metadata = np.stack([metadata_vector(row, metadata_spec) for _, row in self.df.iterrows()]) - if "ignore_metadata" in self.df.columns: - ignore_mask = self.df["ignore_metadata"].fillna(False).astype(bool).to_numpy() - self.metadata[ignore_mask] = 0.0 self.transform = transform - self.strong_transform = strong_transform - self.strong_augment_labels = strong_augment_labels or set() def __len__(self) -> int: return len(self.df) - def _load_image( - self, - path: str, - mask_path: str | Path | None = None, - transform=None, - ) -> torch.Tensor: + def _load_image(self, path: str) -> torch.Tensor: with Image.open(path) as img: - image = apply_dermoscopic_mask(img, mask_path) - transform = self.transform if transform is None else transform - if transform is not None: - image = transform(image) + image = img.convert("RGB") + if self.transform is not None: + image = self.transform(image) return image def __getitem__(self, idx: int) -> dict[str, torch.Tensor]: row = self.df.iloc[idx] - label = self.labels[idx] - transform = self.strong_transform if label in self.strong_augment_labels else self.transform return { - "clinical": self._load_image(row["clinical_path"], transform=transform), - "dermoscopic": self._load_image( - row["dermoscopic_path"], - row.get(DERMOSCOPIC_MASK_PATH_COLUMN), - transform, - ), + "clinical": self._load_image(row["clinical_path"]), + "dermoscopic": self._load_image(row["dermoscopic_path"]), "metadata": torch.from_numpy(self.metadata[idx]), - "label": torch.tensor(label, dtype=torch.long), - } - - -class HybridEpochSampler(Sampler[int]): - """Cap the largest class and oversample eligible tail classes per epoch.""" - - def __init__( - self, - labels: list[int], - target_counts: np.ndarray, - seed: int, - label_names: dict[int, str] | None = None, - ) -> None: - self.labels = np.asarray(labels, dtype=np.int64) - self.target_counts = np.asarray(target_counts, dtype=np.int64) - self.seed = int(seed) - self.epoch = 0 - self.label_names = label_names or {} - self.class_indices = [np.flatnonzero(self.labels == idx) for idx in range(len(self.target_counts))] - self.original_counts = np.asarray([len(indices) for indices in self.class_indices], dtype=np.int64) - - def __len__(self) -> int: - return int(self.target_counts.sum()) - - def set_epoch(self, epoch: int) -> None: - self.epoch = int(epoch) - - def __iter__(self): - generator = torch.Generator().manual_seed(self.seed + self.epoch) - selected: list[torch.Tensor] = [] - for indices, target in zip(self.class_indices, self.target_counts): - source = torch.as_tensor(indices, dtype=torch.long) - target = int(target) - if target <= len(source): - selected.append(source[torch.randperm(len(source), generator=generator)[:target]]) - continue - full_repeats, remainder = divmod(target, len(source)) - chunks = [source[torch.randperm(len(source), generator=generator)] for _ in range(full_repeats)] - if remainder: - chunks.append(source[torch.randperm(len(source), generator=generator)[:remainder]]) - selected.append(torch.cat(chunks)) - epoch_indices = torch.cat(selected) - order = torch.randperm(len(epoch_indices), generator=generator) - return iter(epoch_indices[order].tolist()) - - def exposure_summary(self) -> dict[str, int]: - return { - self.label_names.get(idx, str(idx)): int(count) - for idx, count in enumerate(self.target_counts) + "label": torch.tensor(self.labels[idx], dtype=torch.long), } @@ -398,62 +226,6 @@ def make_transforms(image_size: int): return train_transform, eval_transform -def make_strong_train_transform(image_size: int): - """A conservative stronger variant used only for oversampled tail classes.""" - normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - return transforms.Compose( - [ - transforms.RandomResizedCrop(image_size, scale=(0.65, 1.0), ratio=(1.15, 1.5)), - transforms.RandomHorizontalFlip(), - transforms.RandomVerticalFlip(), - transforms.RandomRotation(30), - transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.25), - transforms.RandomAffine(degrees=0, translate=(0.05, 0.05), scale=(0.95, 1.05)), - transforms.ToTensor(), - normalize, - ] - ) - - -def hybrid_target_counts(labels: list[int], args: argparse.Namespace) -> tuple[np.ndarray, set[int]]: - """Return per-class epoch targets and classes eligible for strong augmentation.""" - counts = np.bincount(np.asarray(labels, dtype=np.int64)) - if np.any(counts == 0): - raise ValueError("Cannot build hybrid sampler because at least one class has zero training samples.") - targets = counts.copy() - - if len(counts) >= 2: - descending = np.argsort(-counts, kind="stable") - head_idx, second_idx = int(descending[0]), int(descending[1]) - head_cap = max(1, int(np.floor(counts[second_idx] * args.balance_head_ratio))) - targets[head_idx] = min(int(counts[head_idx]), head_cap) - - strong_labels: set[int] = set() - for idx, count in enumerate(counts): - if args.balance_min_source_count <= count < args.balance_tail_floor: - targets[idx] = args.balance_tail_floor - strong_labels.add(idx) - return targets, strong_labels - - -def hybrid_balance_summary( - labels: list[int], - label_names: dict[int, str], - args: argparse.Namespace, -) -> dict[str, Any]: - counts = np.bincount(np.asarray(labels, dtype=np.int64)) - targets, strong_labels = hybrid_target_counts(labels, args) - return { - "mode": "hybrid", - "original_class_counts": {label_names[idx]: int(count) for idx, count in enumerate(counts)}, - "effective_class_counts_per_epoch": { - label_names[idx]: int(count) for idx, count in enumerate(targets) - }, - "strong_augmentation_classes": [label_names[idx] for idx in sorted(strong_labels)], - "effective_rows_per_epoch": int(targets.sum()), - } - - def make_loaders( train_df: pd.DataFrame, val_df: pd.DataFrame, @@ -462,24 +234,7 @@ def make_loaders( args: argparse.Namespace, ) -> tuple[DataLoader, DataLoader]: train_transform, eval_transform = make_transforms(args.image_size) - label_names = {idx: label for label, idx in label_to_idx.items()} - train_labels = [label_to_idx[label] for label in train_df["label"].tolist()] - sampler = None - strong_transform = None - strong_labels: set[int] = set() - if args.balance_mode == "hybrid": - targets, strong_labels = hybrid_target_counts(train_labels, args) - sampler = HybridEpochSampler(train_labels, targets, args.seed, label_names) - strong_transform = make_strong_train_transform(args.image_size) - - train_ds = PairedMilk10kMetadataDataset( - train_df, - label_to_idx, - metadata_spec, - train_transform, - strong_transform=strong_transform, - strong_augment_labels=strong_labels, - ) + train_ds = PairedMilk10kMetadataDataset(train_df, label_to_idx, metadata_spec, train_transform) val_ds = PairedMilk10kMetadataDataset(val_df, label_to_idx, metadata_spec, eval_transform) common = dict( batch_size=args.batch_size, @@ -487,8 +242,7 @@ def make_loaders( pin_memory=torch.cuda.is_available(), drop_last=False, ) - if args.weighted_sampler: - sampler = build_weighted_sampler(train_ds, args) + sampler = build_weighted_sampler(train_ds, args) if args.weighted_sampler else None train_loader = DataLoader(train_ds, shuffle=sampler is None, sampler=sampler, **common) val_loader = DataLoader(val_ds, shuffle=False, **common) return train_loader, val_loader diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/engine.py b/milk10k_effb2_metadata/milk10k_effb2_metadata/engine.py index 94f44506481569978083f06d29641d91160c68da..729093ac2a6ba64edcad71867cc8192da3bf5622 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/engine.py +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/engine.py @@ -222,11 +222,6 @@ def train_phase( continue if hasattr(criterion, "set_epoch"): criterion.set_epoch(epoch) - sampler = getattr(train_loader, "sampler", None) - if hasattr(sampler, "set_epoch"): - sampler.set_epoch(epoch) - if hasattr(sampler, "exposure_summary"): - print(f"Hybrid balance epoch {epoch:03d}: effective_class_counts={sampler.exposure_summary()}") train_stats = run_epoch( model, train_loader, @@ -322,27 +317,6 @@ def train_phase( f"best_val_tail_recall_macro={best_val_tail_recall:.4f} path={output_dir / 'tail_best.pt'}" ) - save_checkpoint( - output_dir / "last.pt", - model, - optimizer, - epoch, - phase, - best_val_f1, - class_names, - label_to_idx, - metadata_spec, - args, - { - "last_selection_metric": float(val_stats[selection_metric]), - "last_val_stats": val_stats, - }, - ) - print( - f"Saved last checkpoint: phase={phase} epoch={epoch:03d} " - f"{selection_metric}={val_stats[selection_metric]:.4f} path={output_dir / 'last.pt'}" - ) - if patience_count >= args.patience: print(f"Early stopping {phase} at epoch {epoch}") break diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/inference.py b/milk10k_effb2_metadata/milk10k_effb2_metadata/inference.py index f6a82a5620c58f152a2a23c94f3ca3b8be5fb928..e5b2253f1ac4f797a4860934dded4ec70a10f096 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/inference.py +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/inference.py @@ -15,16 +15,7 @@ from torch.utils.data import DataLoader, Dataset from tqdm.auto import tqdm from datasets import LABEL_COLUMNS, normalize_image_type -from milk10k_effb2_metadata.data import ( - DERMOSCOPIC_MASK_PATH_COLUMN, - METADATA_COLUMNS, - apply_dermoscopic_mask, - audit_dermoscopic_masks, - make_transforms, - metadata_vector, - print_mask_audit_summary, - resolve_monet_columns, -) +from milk10k_effb2_metadata.data import METADATA_COLUMNS, make_transforms, metadata_vector, resolve_monet_columns from milk10k_effb2_metadata.metrics import apply_class_bias, compute_metrics from milk10k_effb2_metadata.models import ( DualEffB2MetadataClassifier, @@ -44,9 +35,9 @@ class InferencePairedDataset(Dataset): def __len__(self) -> int: return len(self.df) - def _load_image(self, path: str, mask_path: str | Path | None = None) -> torch.Tensor: + def _load_image(self, path: str) -> torch.Tensor: with Image.open(path) as img: - image = apply_dermoscopic_mask(img, mask_path) + image = img.convert("RGB") if self.transform is not None: image = self.transform(image) return image @@ -55,10 +46,7 @@ class InferencePairedDataset(Dataset): row = self.df.iloc[idx] return { "clinical": self._load_image(row["clinical_path"]), - "dermoscopic": self._load_image( - row["dermoscopic_path"], - row.get(DERMOSCOPIC_MASK_PATH_COLUMN), - ), + "dermoscopic": self._load_image(row["dermoscopic_path"]), "metadata": torch.from_numpy(self.metadata[idx]), } @@ -75,18 +63,6 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--data-dir", type=Path, default=None, help="Directory containing MILK10k input/metadata files.") parser.add_argument("--input-dir", type=Path, default=None, help="Image root. Overrides --data-dir/MILK10k_Training_Input.") parser.add_argument("--metadata-csv", type=Path, default=None, help="Metadata CSV. Overrides --data-dir/MILK10k_Training_Metadata.csv.") - parser.add_argument( - "--dermoscopic-mask-dir", - type=Path, - default=None, - help="Optional directory containing _dermoscopic_mask.png files.", - ) - parser.add_argument( - "--min-dermoscopic-mask-ratio", - type=float, - default=0.01, - help="Fallback to the original dermoscopic image when mask foreground ratio is below this value.", - ) parser.add_argument("--groundtruth-csv", type=Path, default=None, help="Optional ground-truth CSV for metrics.") parser.add_argument("--output", type=Path, default=Path("test_predictions.csv")) parser.add_argument("--batch-size", type=int, default=16) @@ -222,7 +198,6 @@ def build_model_from_checkpoint(checkpoint: dict[str, Any], metadata_dim: int, d metadata_fusion=checkpoint_arg(checkpoint_args, "metadata_fusion", "concat"), image_fusion=checkpoint_arg(checkpoint_args, "image_fusion", "concat"), metadata_gate_hidden_dim=checkpoint_args.get("metadata_gate_hidden_dim"), - classifier_style=checkpoint_arg(checkpoint_args, "classifier_style", "legacy"), logit_fusion_mode=checkpoint_arg(checkpoint_args, "logit_fusion_mode", "single"), fusion_logit_weight=checkpoint_arg(checkpoint_args, "fusion_logit_weight", 0.6), clinical_logit_weight=checkpoint_arg(checkpoint_args, "clinical_logit_weight", 0.2), @@ -319,21 +294,6 @@ def main() -> None: device = torch.device("cuda" if torch.cuda.is_available() else "cpu") input_dir, metadata_csv, groundtruth_csv = resolve_input_paths(args) df = load_inference_dataframe(input_dir, metadata_csv, groundtruth_csv) - if not 0.0 <= args.min_dermoscopic_mask_ratio <= 1.0: - raise ValueError("--min-dermoscopic-mask-ratio must be between 0 and 1.") - if args.dermoscopic_mask_dir is not None: - args.dermoscopic_mask_dir = args.dermoscopic_mask_dir.expanduser().resolve() - df, mask_audit = audit_dermoscopic_masks( - df, - args.dermoscopic_mask_dir, - args.min_dermoscopic_mask_ratio, - mask_id_column="dermoscopic_isic_id", - mask_suffix="_mask.png", - ) - audit_output = args.output.with_name(f"{args.output.stem}.mask_audit.csv") - audit_output.parent.mkdir(parents=True, exist_ok=True) - mask_audit.to_csv(audit_output, index=False) - print_mask_audit_summary(mask_audit, args.min_dermoscopic_mask_ratio) checkpoint_paths = resolve_checkpoint_paths(args) ensemble_probs = [] class_names: list[str] | None = None diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/model_setup.py b/milk10k_effb2_metadata/milk10k_effb2_metadata/model_setup.py index 103c8313c68d37565b1da2418660c316ad636bec..6d556c9f707647fa2f747bb44640b2d63f94e750 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/model_setup.py +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/model_setup.py @@ -152,7 +152,6 @@ def build_model( metadata_fusion=args.metadata_fusion, image_fusion=getattr(args, "image_fusion", "concat"), metadata_gate_hidden_dim=args.metadata_gate_hidden_dim, - classifier_style=getattr(args, "classifier_style", "legacy"), logit_fusion_mode=args.logit_fusion_mode, fusion_logit_weight=args.fusion_logit_weight, clinical_logit_weight=args.clinical_logit_weight, diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/models.py b/milk10k_effb2_metadata/milk10k_effb2_metadata/models.py index e086c3c761a3ebd29582edb80dd8d8b5afc3b8e2..6ee30370cbba7b01fca972db8e8bebb302cc1087 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/models.py +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/models.py @@ -107,7 +107,6 @@ class DualEffB2MetadataClassifier(nn.Module): metadata_fusion: str = "concat", image_fusion: str = "concat", metadata_gate_hidden_dim: int | None = None, - classifier_style: str = "legacy", logit_fusion_mode: str = "single", fusion_logit_weight: float = 0.6, clinical_logit_weight: float = 0.2, @@ -129,8 +128,6 @@ class DualEffB2MetadataClassifier(nn.Module): raise ValueError(f"Unsupported image_fusion: {image_fusion}") if logit_fusion_mode not in ("single", "fixed"): raise ValueError(f"Unsupported logit_fusion_mode: {logit_fusion_mode}") - if classifier_style not in ("legacy", "simple"): - raise ValueError(f"Unsupported classifier_style: {classifier_style}") self.clinical_backbone_backend = clinical_backbone_backend self.dermoscopic_backbone_backend = dermoscopic_backbone_backend self.backbone = normalize_backbone_name(backbone) @@ -138,7 +135,6 @@ class DualEffB2MetadataClassifier(nn.Module): self.metadata_dim = metadata_dim self.metadata_fusion = metadata_fusion self.image_fusion = image_fusion - self.classifier_style = classifier_style self.logit_fusion_mode = logit_fusion_mode self.fusion_logit_weight = fusion_logit_weight self.clinical_logit_weight = clinical_logit_weight @@ -216,11 +212,7 @@ class DualEffB2MetadataClassifier(nn.Module): if clinical_feature_dim != dermoscopic_feature_dim: raise ValueError("shared_private image fusion requires matching branch feature dimensions.") self.shared_head = ProjectionHead(clinical_feature_dim, branch_dim, dropout) - self.classifier = ( - None - if image_fusion == "moe" - else self._classifier(fused_dim, classifier_hidden_dim, num_classes, dropout, classifier_style) - ) + self.classifier = None if image_fusion == "moe" else self._classifier(fused_dim, classifier_hidden_dim, num_classes, dropout) if logit_fusion_mode == "fixed": self.clinical_classifier = BranchClassifier(branch_dim, num_classes, dropout) self.dermoscopic_classifier = BranchClassifier(branch_dim, num_classes, dropout) @@ -229,20 +221,7 @@ class DualEffB2MetadataClassifier(nn.Module): self.dermoscopic_classifier = None @staticmethod - def _classifier( - in_dim: int, - hidden_dim: int, - num_classes: int, - dropout: float, - classifier_style: str, - ) -> nn.Sequential: - if classifier_style == "simple": - return nn.Sequential( - nn.Linear(in_dim, hidden_dim), - nn.ReLU(), - nn.Dropout(dropout), - nn.Linear(hidden_dim, num_classes), - ) + def _classifier(in_dim: int, hidden_dim: int, num_classes: int, dropout: float) -> nn.Sequential: return nn.Sequential( nn.LayerNorm(in_dim), nn.Dropout(dropout), @@ -422,8 +401,6 @@ class DualConvNeXtMetadataClassifier(DualEffB2MetadataClassifier): def normalize_backbone_name(name: str) -> str: name = name.lower().replace(" ", "").replace("_", "").replace("-", "") - if name in ("tfefficientnetv2b2", "efficientnetv2b2", "effnetv2b2", "effv2b2"): - return "tf_efficientnetv2_b2" if name in ("efficientnetb2", "effnetb2", "effb2"): return "efficientnet_b2" if name in ("efficientnetb1", "effnetb1", "effb1"): @@ -448,8 +425,6 @@ def default_image_size(backbone: str) -> int: backbone = normalize_backbone_name(backbone) if backbone == "efficientnet_b2": return 260 - if backbone == "tf_efficientnetv2_b2": - return 384 if backbone == "efficientnet_b1": return 240 if backbone == "convnext_base": @@ -503,8 +478,6 @@ def build_feature_encoder(backbone: str, backbone_backend: str, imagenet_pretrai return model, int(model.num_features) if backbone_backend == "torchvision": - if backbone == "tf_efficientnetv2_b2": - raise ValueError("tf_efficientnetv2_b2 is only available with --backbone-backend timm.") if backbone == "efficientnet_b2": from torchvision.models import efficientnet_b2, EfficientNet_B2_Weights weights = EfficientNet_B2_Weights.IMAGENET1K_V1 if imagenet_pretrained else None diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/runner.py b/milk10k_effb2_metadata/milk10k_effb2_metadata/runner.py index d2fb633cd21b5c5c4dfff062e0e0db7d2d0425ed..eda03cc8d9d46bcb51ce88ae0c62ea6de61bc063 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/runner.py +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/runner.py @@ -12,10 +12,8 @@ import torch from milk10k_effb2_metadata.data import ( fit_metadata_spec, - hybrid_balance_summary, kfold_splits, lesion_split, - load_paired_dataframe, make_loaders, metadata_vector, ) @@ -23,7 +21,6 @@ from milk10k_effb2_metadata.engine import train_phase from milk10k_effb2_metadata.losses import build_loss from milk10k_effb2_metadata.metrics import apply_class_bias, compute_metrics, optimize_class_bias, predict, save_predictions from milk10k_effb2_metadata.model_setup import build_model, load_resume_checkpoint -from milk10k_effb2_metadata.reporting import build_data_summary, save_data_summary, save_kfold_report, save_run_diagnostics from milk10k_effb2_metadata.training_utils import json_safe, save_kfold_summary, save_run_config @@ -48,94 +45,6 @@ def build_tail_tracking_config( } -def resolve_label_name(class_names: list[str], name: str) -> str: - normalized = {label.upper(): label for label in class_names} - key = name.strip().upper() - if key not in normalized: - raise ValueError(f"Unknown augmented class name: {name!r}. Choices: {class_names}") - return normalized[key] - - -def source_lesion_id(value: Any) -> str: - """Return the original lesion ID for a generated paired lesion ID.""" - return str(value).split("__sdpair_", 1)[0] - - -def load_augmented_subset( - base_df: pd.DataFrame, - class_names: list[str], - args: argparse.Namespace, -) -> pd.DataFrame: - augmented_data_dir = getattr(args, "augmented_data_dir", None) - if augmented_data_dir is None: - return pd.DataFrame(columns=base_df.columns) - augmented_dir = augmented_data_dir.expanduser().resolve() - augmented_df = load_paired_dataframe(augmented_dir) - base_lesion_ids = set(base_df["lesion_id"].astype(str)) - augmented_df = augmented_df[~augmented_df["lesion_id"].astype(str).isin(base_lesion_ids)].copy() - augmented_classes = getattr(args, "augmented_classes", []) - if augmented_classes: - allowed = {resolve_label_name(class_names, name) for name in augmented_classes} - augmented_df = augmented_df[augmented_df["label"].isin(allowed)].copy() - augmented_max_per_class = getattr(args, "augmented_max_per_class", 0) - if augmented_max_per_class < 0: - raise ValueError("--augmented-max-per-class must be >= 0.") - augmented_df["is_augmented"] = True - augmented_df["ignore_metadata"] = bool(getattr(args, "zero_augmented_metadata", False)) - return augmented_df - - -def append_augmented_train_rows( - base_df: pd.DataFrame, - train_df: pd.DataFrame, - val_df: pd.DataFrame, - class_names: list[str], - args: argparse.Namespace, -) -> pd.DataFrame: - augmented_df = load_augmented_subset(base_df, class_names, args) - if augmented_df.empty: - if getattr(args, "augmented_data_dir", None) is not None: - print("Augmented data: no extra rows selected.") - return train_df - train_source_ids = set(train_df["lesion_id"].astype(str).map(source_lesion_id)) - val_source_ids = set(val_df["lesion_id"].astype(str).map(source_lesion_id)) - augmented_df["source_lesion_id"] = augmented_df["lesion_id"].astype(str).map(source_lesion_id) - source_overlap = train_source_ids & val_source_ids - if source_overlap: - raise RuntimeError( - f"Source leakage already exists between train and validation: {len(source_overlap)} lesion IDs." - ) - selected = augmented_df["source_lesion_id"].isin(train_source_ids) - excluded_validation = augmented_df["source_lesion_id"].isin(val_source_ids) - unknown = ~(selected | excluded_validation) - if unknown.any(): - examples = augmented_df.loc[unknown, "lesion_id"].astype(str).head(5).tolist() - raise ValueError( - "Augmented lesions cannot be mapped to an original train/validation source. " - f"Examples: {examples}" - ) - excluded_count = int(excluded_validation.sum()) - augmented_df = augmented_df.loc[selected].copy() - augmented_max_per_class = getattr(args, "augmented_max_per_class", 0) - if augmented_max_per_class > 0 and not augmented_df.empty: - augmented_df = ( - augmented_df.sample(frac=1.0, random_state=args.seed) - .groupby("label", group_keys=False) - .head(augmented_max_per_class) - .sort_values(["label", "lesion_id"]) - .reset_index(drop=True) - ) - counts = augmented_df["label"].value_counts().sort_index().to_dict() - print( - "Source-safe augmented train append: " - f"rows={len(augmented_df)}, counts={counts}, " - f"excluded_validation_sources={excluded_count}, " - f"zero_metadata={getattr(args, 'zero_augmented_metadata', False)}, " - f"source={getattr(args, 'augmented_data_dir', None)}" - ) - return pd.concat([train_df, augmented_df], ignore_index=True, sort=False) - - def run_training_split( df: pd.DataFrame, train_df: pd.DataFrame, @@ -154,14 +63,6 @@ def run_training_split( split_dir.mkdir(exist_ok=True) train_df.to_csv(split_dir / "train.csv", index=False) val_df.to_csv(split_dir / "val.csv", index=False) - data_summary = build_data_summary(df, train_df, val_df, class_names) - if args.balance_mode == "hybrid": - data_summary["balance"] = hybrid_balance_summary( - [label_to_idx[label] for label in train_df["label"].tolist()], - {idx: label for label, idx in label_to_idx.items()}, - args, - ) - save_data_summary(output_dir, data_summary) metadata_spec = fit_metadata_spec(train_df) metadata_dim = len(metadata_vector(train_df.iloc[0], metadata_spec)) @@ -169,7 +70,6 @@ def run_training_split( output_dir, args, class_names, - label_to_idx, metadata_spec, train_df, val_df, @@ -203,12 +103,7 @@ def run_training_split( f"metadata_fusion={args.metadata_fusion}, image_fusion={getattr(args, 'image_fusion', 'concat')}, " f"gate_hidden_dim={args.metadata_gate_hidden_dim}" ) - print( - f"Loss: {args.loss}, class_weight={args.class_weight}, weighted_sampler={args.weighted_sampler}, " - f"balance_mode={args.balance_mode}" - ) - if args.balance_mode == "hybrid": - print(f"Hybrid balance plan: {data_summary['balance']}") + print(f"Loss: {args.loss}, class_weight={args.class_weight}, weighted_sampler={args.weighted_sampler}") if getattr(args, "image_fusion", "concat") == "moe" and args.logit_fusion_mode == "fixed": print("Note: --image-fusion moe already mixes expert logits; --logit-fusion-mode fixed adds extra branch logits.") if args.loss == "ce_f1": @@ -320,17 +215,6 @@ def run_training_split( pd.DataFrame(cm, index=class_names, columns=class_names).to_csv(output_dir / "confusion_matrix.csv") per_class_df.to_csv(output_dir / "per_class_metrics.csv", index=False) save_predictions(val_df, y_true, y_prob, class_names, output_dir) - save_run_diagnostics( - output_dir, - args, - data_summary, - metrics, - per_class_df, - cm, - y_prob, - class_names, - fold, - ) print( f"Done: best_val_f1_macro={best_val_f1:.4f}, " f"val_acc={metrics['accuracy']:.4f}, balanced_acc={metrics['balanced_accuracy']:.4f}, " @@ -349,32 +233,18 @@ def train_single_run( clinical_backbone_backend: str, dermoscopic_backbone_backend: str, ) -> dict[str, Any]: - df = df.copy() - df["is_augmented"] = False - df["ignore_metadata"] = False if args.synthetic_train_only: synthetic_mask = df["lesion_id"].astype(str).str.contains("__sdpair_", regex=False) real_df = df[~synthetic_mask].copy() synthetic_df = df[synthetic_mask].copy() train_df, val_df = lesion_split(real_df, args.val_size, args.seed) - train_sources = set(train_df["lesion_id"].astype(str)) - val_sources = set(val_df["lesion_id"].astype(str)) - synthetic_df["source_lesion_id"] = synthetic_df["lesion_id"].astype(str).map(source_lesion_id) - unknown_sources = ~synthetic_df["source_lesion_id"].isin(train_sources | val_sources) - if unknown_sources.any(): - examples = synthetic_df.loc[unknown_sources, "lesion_id"].astype(str).head(5).tolist() - raise ValueError(f"Synthetic lesions have unknown source IDs. Examples: {examples}") - safe_synthetic_df = synthetic_df[synthetic_df["source_lesion_id"].isin(train_sources)].copy() - excluded_count = int(synthetic_df["source_lesion_id"].isin(val_sources).sum()) - train_df = pd.concat([train_df, safe_synthetic_df], ignore_index=True, sort=False) + train_df = pd.concat([train_df, synthetic_df], ignore_index=True, sort=False) print( - f"Source-safe synthetic train-only split: real_train={len(train_df) - len(safe_synthetic_df)}, " - f"synthetic_train={len(safe_synthetic_df)}, excluded_validation_sources={excluded_count}, " - f"val_real={len(val_df)}" + f"Synthetic train-only split: real_train={len(train_df) - len(synthetic_df)}, " + f"synthetic_train={len(synthetic_df)}, val_real={len(val_df)}" ) else: train_df, val_df = lesion_split(df, args.val_size, args.seed) - train_df = append_augmented_train_rows(df, train_df, val_df, class_names, args) return run_training_split( df, train_df, @@ -398,13 +268,9 @@ def train_kfold( clinical_backbone_backend: str, dermoscopic_backbone_backend: str, ) -> list[dict[str, Any]]: - df = df.copy() - df["is_augmented"] = False - df["ignore_metadata"] = False fold_metrics = [] for fold_idx, (train_df, val_df) in enumerate(kfold_splits(df, args.k_folds, args.seed)): print(f"\nK-fold {fold_idx + 1}/{args.k_folds}") - train_df = append_augmented_train_rows(df, train_df, val_df, class_names, args) metrics = run_training_split( df, train_df, @@ -420,5 +286,4 @@ def train_kfold( ) fold_metrics.append({"fold": fold_idx, **metrics}) save_kfold_summary(fold_metrics, args.output_dir) - save_kfold_report(fold_metrics, args.output_dir) return fold_metrics diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/training.py b/milk10k_effb2_metadata/milk10k_effb2_metadata/training.py index 8c75ca81de924c7d853027323c2e939e21bde5ad..c04d11f5a74450e1e61c75fc9d97985a0606c7a3 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/training.py +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/training.py @@ -7,33 +7,17 @@ import argparse from milk10k_effb2_metadata.training_utils import json_safe -def validate_balance_args(args: argparse.Namespace) -> None: - if args.balance_mode == "hybrid" and args.weighted_sampler: - raise ValueError("--balance-mode hybrid cannot be combined with --weighted-sampler.") - if args.balance_head_ratio <= 0: - raise ValueError("--balance-head-ratio must be greater than 0.") - if args.balance_tail_floor < 0: - raise ValueError("--balance-tail-floor must be >= 0.") - if args.balance_min_source_count < 1: - raise ValueError("--balance-min-source-count must be at least 1.") - - def run(args: argparse.Namespace) -> None: import torch from datasets import resolve_data_dir, set_seed - from milk10k_effb2_metadata.data import ( - audit_dermoscopic_masks, - load_paired_dataframe, - print_mask_audit_summary, - ) + from milk10k_effb2_metadata.data import load_paired_dataframe from milk10k_effb2_metadata.model_setup import resolve_training_backbone_backends from milk10k_effb2_metadata.models import normalize_backbone_name, resolve_image_size from milk10k_effb2_metadata.runner import train_kfold, train_single_run if args.k_folds < 1: raise ValueError("--k-folds must be at least 1.") - validate_balance_args(args) set_seed(args.seed) data_dir = resolve_data_dir(args.data_dir) @@ -47,17 +31,6 @@ def run(args: argparse.Namespace) -> None: args.image_size = resolve_image_size(args.backbone, args.image_size) df = load_paired_dataframe(data_dir) - if not 0.0 <= args.min_dermoscopic_mask_ratio <= 1.0: - raise ValueError("--min-dermoscopic-mask-ratio must be between 0 and 1.") - if args.dermoscopic_mask_dir is not None: - args.dermoscopic_mask_dir = args.dermoscopic_mask_dir.expanduser().resolve() - df, mask_audit = audit_dermoscopic_masks( - df, - args.dermoscopic_mask_dir, - args.min_dermoscopic_mask_ratio, - ) - mask_audit.to_csv(args.output_dir / "dermoscopic_mask_audit.csv", index=False) - print_mask_audit_summary(mask_audit, args.min_dermoscopic_mask_ratio) class_names = sorted(df["label"].unique()) label_to_idx = {label: idx for idx, label in enumerate(class_names)} device = torch.device("cuda" if torch.cuda.is_available() else "cpu") diff --git a/milk10k_effb2_metadata/milk10k_effb2_metadata/training_utils.py b/milk10k_effb2_metadata/milk10k_effb2_metadata/training_utils.py index 0da6c59020b32dd17f09190061d0f346bbf9d706..b59702dec390c74651f5ce2989c0b8812b6b86c5 100644 --- a/milk10k_effb2_metadata/milk10k_effb2_metadata/training_utils.py +++ b/milk10k_effb2_metadata/milk10k_effb2_metadata/training_utils.py @@ -17,7 +17,6 @@ def save_run_config( output_dir: Path, args: argparse.Namespace, class_names: list[str], - label_to_idx: dict[str, int], metadata_spec: dict[str, Any], train_df: pd.DataFrame, val_df: pd.DataFrame, @@ -27,13 +26,9 @@ def save_run_config( ) -> None: import pandas as pd - from milk10k_effb2_metadata.reporting import collect_environment_info - payload = { "args": json_safe(vars(args)), - "environment": collect_environment_info(), "class_names": class_names, - "label_to_idx": label_to_idx, "metadata_spec": json_safe(metadata_spec), "model_type": "DualConvNeXtMetadataClassifier" if args.backbone == "convnext_base" else "DualEffB2MetadataClassifier", "train_size": len(train_df), @@ -43,15 +38,6 @@ def save_run_config( "image_fusion": getattr(args, "image_fusion", "concat"), "clinical_backbone": f"{clinical_backbone_backend} {args.backbone}", "dermoscopic_backbone": f"{dermoscopic_backbone_backend} {args.backbone}", - "paths": { - "output_dir": str(output_dir), - "data_dir": str(getattr(args, "data_dir", "")), - "dermoscopic_mask_dir": str(getattr(args, "dermoscopic_mask_dir", "")), - "clinical_checkpoint": str(getattr(args, "clinical_checkpoint", "")), - "dermoscopic_checkpoint": str(getattr(args, "dermoscopic_checkpoint", "")), - "resume_checkpoint": str(getattr(args, "resume_checkpoint", "")), - "augmented_data_dir": str(getattr(args, "augmented_data_dir", "")), - }, } with open(output_dir / "run_config.json", "w", encoding="utf-8") as f: json.dump(payload, f, indent=2) diff --git a/milk10k_effb2_metadata/model_setup.py b/milk10k_effb2_metadata/model_setup.py index 103c8313c68d37565b1da2418660c316ad636bec..a4b593ea271e0625db5bce320061f8b357f8167a 100644 --- a/milk10k_effb2_metadata/model_setup.py +++ b/milk10k_effb2_metadata/model_setup.py @@ -103,6 +103,7 @@ def load_resume_checkpoint( checkpoint_path: Path | None, model: DualEffB2MetadataClassifier, device: torch.device, + ema_model: torch.nn.Module | None = None, ) -> tuple[int, float, str | None]: if checkpoint_path is None: return 1, float("-inf"), None @@ -111,6 +112,8 @@ def load_resume_checkpoint( raise FileNotFoundError(f"Resume checkpoint not found: {checkpoint_path}") checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False) model.load_state_dict(checkpoint["model_state"]) + if ema_model is not None and "ema_model_state" in checkpoint: + ema_model.load_state_dict(checkpoint["ema_model_state"]) next_epoch = int(checkpoint.get("epoch", 0)) + 1 best_val_f1 = float( checkpoint.get( diff --git a/milk10k_effb2_metadata/models.py b/milk10k_effb2_metadata/models.py index e086c3c761a3ebd29582edb80dd8d8b5afc3b8e2..bba74aa841acab0da4098a4ff1f546181c3d05a0 100644 --- a/milk10k_effb2_metadata/models.py +++ b/milk10k_effb2_metadata/models.py @@ -227,6 +227,8 @@ class DualEffB2MetadataClassifier(nn.Module): else: self.clinical_classifier = None self.dermoscopic_classifier = None + + self.class_scales = nn.Parameter(torch.ones(num_classes)) @staticmethod def _classifier( @@ -308,14 +310,14 @@ class DualEffB2MetadataClassifier(nn.Module): fused = self._fused_features(clinical_features, dermoscopic_features, clinical_repr, dermoscopic_repr, metadata_repr) fusion_logits = self.classifier(fused) if self.logit_fusion_mode != "fixed": - return fusion_logits + return fusion_logits * self.class_scales clinical_logits = self.clinical_classifier(clinical_repr) dermoscopic_logits = self.dermoscopic_classifier(dermoscopic_repr) return ( self.fusion_logit_weight * fusion_logits + self.clinical_logit_weight * clinical_logits + self.dermoscopic_logit_weight * dermoscopic_logits - ) + ) * self.class_scales def _append_metadata(self, features: torch.Tensor, metadata_repr: torch.Tensor | None) -> torch.Tensor: if metadata_repr is None: diff --git a/milk10k_effb2_metadata/runner.py b/milk10k_effb2_metadata/runner.py index ac49a227ff7917bc6e580a30ad17ab4df2e604a9..313b09b13d4679dbacfe33da815f36520dd6e6dd 100644 --- a/milk10k_effb2_metadata/runner.py +++ b/milk10k_effb2_metadata/runner.py @@ -25,6 +25,89 @@ from milk10k_effb2_metadata.metrics import apply_class_bias, compute_metrics, op from milk10k_effb2_metadata.model_setup import build_model, load_resume_checkpoint from milk10k_effb2_metadata.reporting import build_data_summary, save_data_summary, save_kfold_report, save_run_diagnostics from milk10k_effb2_metadata.training_utils import json_safe, save_kfold_summary, save_run_config +import torch.nn.functional as F + +def train_lws_post_training( + model: DualEffB2MetadataClassifier, + train_loader: DataLoader, + criterion: nn.Module, + device: torch.device, + args: argparse.Namespace, + ema_model: nn.Module | None = None, +) -> None: + if args.lws_epochs <= 0: + return + + print(f"\nStarting LWS Post-Training for {args.lws_epochs} epochs...") + model.requires_grad_(False) + model.class_scales.requires_grad_(True) + + optimizer = torch.optim.Adam([model.class_scales], lr=args.head_lr) + + from milk10k_effb2_metadata.data import build_weighted_sampler + dataset = train_loader.dataset + # Force weighted sampler for LWS + lws_sampler = build_weighted_sampler(dataset, args) + lws_loader = DataLoader( + dataset, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=torch.cuda.is_available(), + sampler=lws_sampler, + ) + + model.train() + from milk10k_effb2_metadata.metrics import move_batch + for epoch in range(1, args.lws_epochs + 1): + total_loss = 0.0 + for batch in lws_loader: + clinical, dermoscopic, metadata, labels = move_batch(batch, device) + optimizer.zero_grad() + logits = model(clinical, dermoscopic, metadata) + loss = criterion(logits, labels) + loss.backward() + optimizer.step() + + model.class_scales.data.clamp_(0.75, 1.5) + + if ema_model is not None: + ema_model.update_parameters(model) + + total_loss += loss.item() + + scales_str = np.array2string(model.class_scales.detach().cpu().numpy(), precision=3, separator=',') + print(f"LWS Epoch {epoch}/{args.lws_epochs} - Loss: {total_loss / len(lws_loader):.4f} - Scales: {scales_str}") + +def fit_global_temperature( + model: nn.Module, + val_loader: DataLoader, + device: torch.device, +) -> float: + model.eval() + all_logits = [] + all_labels = [] + from milk10k_effb2_metadata.metrics import move_batch + with torch.no_grad(): + for batch in val_loader: + clinical, dermoscopic, metadata, labels = move_batch(batch, device) + logits = model(clinical, dermoscopic, metadata) + all_logits.append(logits) + all_labels.append(labels) + + all_logits = torch.cat(all_logits) + all_labels = torch.cat(all_labels) + + temperature = torch.nn.Parameter(torch.ones(1, device=device)) + optimizer = torch.optim.LBFGS([temperature], lr=0.01, max_iter=50) + + def eval_fn(): + optimizer.zero_grad() + loss = F.cross_entropy(all_logits / temperature, all_labels) + loss.backward() + return loss + + optimizer.step(eval_fn) + return float(temperature.item()) def build_tail_tracking_config( @@ -56,6 +139,11 @@ def resolve_label_name(class_names: list[str], name: str) -> str: return normalized[key] +def source_lesion_id(value: Any) -> str: + """Return the original lesion ID for a generated paired lesion ID.""" + return str(value).split("__sdpair_", 1)[0] + + def load_augmented_subset( base_df: pd.DataFrame, class_names: list[str], @@ -75,14 +163,6 @@ def load_augmented_subset( augmented_max_per_class = getattr(args, "augmented_max_per_class", 0) if augmented_max_per_class < 0: raise ValueError("--augmented-max-per-class must be >= 0.") - if augmented_max_per_class > 0 and not augmented_df.empty: - augmented_df = ( - augmented_df.sample(frac=1.0, random_state=args.seed) - .groupby("label", group_keys=False) - .head(augmented_max_per_class) - .sort_values(["label", "lesion_id"]) - .reset_index(drop=True) - ) augmented_df["is_augmented"] = True augmented_df["ignore_metadata"] = bool(getattr(args, "zero_augmented_metadata", False)) return augmented_df @@ -91,6 +171,7 @@ def load_augmented_subset( def append_augmented_train_rows( base_df: pd.DataFrame, train_df: pd.DataFrame, + val_df: pd.DataFrame, class_names: list[str], args: argparse.Namespace, ) -> pd.DataFrame: @@ -99,10 +180,39 @@ def append_augmented_train_rows( if getattr(args, "augmented_data_dir", None) is not None: print("Augmented data: no extra rows selected.") return train_df + train_source_ids = set(train_df["lesion_id"].astype(str).map(source_lesion_id)) + val_source_ids = set(val_df["lesion_id"].astype(str).map(source_lesion_id)) + augmented_df["source_lesion_id"] = augmented_df["lesion_id"].astype(str).map(source_lesion_id) + source_overlap = train_source_ids & val_source_ids + if source_overlap: + raise RuntimeError( + f"Source leakage already exists between train and validation: {len(source_overlap)} lesion IDs." + ) + selected = augmented_df["source_lesion_id"].isin(train_source_ids) + excluded_validation = augmented_df["source_lesion_id"].isin(val_source_ids) + unknown = ~(selected | excluded_validation) + if unknown.any(): + examples = augmented_df.loc[unknown, "lesion_id"].astype(str).head(5).tolist() + raise ValueError( + "Augmented lesions cannot be mapped to an original train/validation source. " + f"Examples: {examples}" + ) + excluded_count = int(excluded_validation.sum()) + augmented_df = augmented_df.loc[selected].copy() + augmented_max_per_class = getattr(args, "augmented_max_per_class", 0) + if augmented_max_per_class > 0 and not augmented_df.empty: + augmented_df = ( + augmented_df.sample(frac=1.0, random_state=args.seed) + .groupby("label", group_keys=False) + .head(augmented_max_per_class) + .sort_values(["label", "lesion_id"]) + .reset_index(drop=True) + ) counts = augmented_df["label"].value_counts().sort_index().to_dict() print( - "Augmented train append: " + "Source-safe augmented train append: " f"rows={len(augmented_df)}, counts={counts}, " + f"excluded_validation_sources={excluded_count}, " f"zero_metadata={getattr(args, 'zero_augmented_metadata', False)}, " f"source={getattr(args, 'augmented_data_dir', None)}" ) @@ -159,7 +269,13 @@ def run_training_split( clinical_backbone_backend, dermoscopic_backbone_backend, ) - resume_epoch, resume_best_val_f1, resume_phase = load_resume_checkpoint(args.resume_checkpoint, model, device) + + ema_model = None + if getattr(args, "ema", False): + from torch.optim.swa_utils import AveragedModel, get_ema_multi_avg_fn + ema_model = AveragedModel(model, multi_avg_fn=get_ema_multi_avg_fn(args.ema_decay)) + + resume_epoch, resume_best_val_f1, resume_phase = load_resume_checkpoint(args.resume_checkpoint, model, device, ema_model=ema_model) train_loader, val_loader = make_loaders(train_df, val_df, label_to_idx, metadata_spec, args) criterion = build_loss(train_df, label_to_idx, args, device) tail_config = build_tail_tracking_config(train_df, class_names, label_to_idx, args) @@ -225,6 +341,7 @@ def run_training_split( skip_freeze_until, **(tail_config or {}), best_val_tail_recall=best_tail_start, + ema_model=ema_model, ) epoch, best_val_f1, best_val_tail_recall = train_phase( "finetune", @@ -245,18 +362,36 @@ def run_training_split( skip_finetune_until, **(tail_config or {}), best_val_tail_recall=best_val_tail_recall, + ema_model=ema_model, ) best_path = output_dir / "best.pt" if best_path.exists(): checkpoint = torch.load(best_path, map_location=device, weights_only=False) model.load_state_dict(checkpoint["model_state"]) - y_true, y_prob = predict(model, val_loader, device) + if ema_model is not None and "ema_model_state" in checkpoint: + ema_model.load_state_dict(checkpoint["ema_model_state"]) + + eval_model = ema_model if ema_model is not None else model + + if args.lws_epochs > 0: + train_lws_post_training(model, train_loader, criterion, device, args, ema_model=ema_model) + # Re-save best checkpoint to include LWS scales + checkpoint["model_state"] = model.state_dict() + if ema_model is not None: + checkpoint["ema_model_state"] = ema_model.state_dict() + torch.save(checkpoint, best_path) + + opt_temp = fit_global_temperature(eval_model, val_loader, device) + print(f"Optimal Global Temperature (T) = {opt_temp:.4f}") + + y_true, y_prob = predict(eval_model, val_loader, device) metrics, per_class_df, cm = compute_metrics(y_true, y_prob, class_names) metrics = { "best_selection_metric": float(best_val_f1), "selection_metric_name": args.selection_metric, "best_val_f1_macro": float(best_val_f1) if args.selection_metric == "f1_macro" else None, + "global_temperature": opt_temp, **metrics, } if tail_config is not None: @@ -330,14 +465,24 @@ def train_single_run( real_df = df[~synthetic_mask].copy() synthetic_df = df[synthetic_mask].copy() train_df, val_df = lesion_split(real_df, args.val_size, args.seed) - train_df = pd.concat([train_df, synthetic_df], ignore_index=True, sort=False) + train_sources = set(train_df["lesion_id"].astype(str)) + val_sources = set(val_df["lesion_id"].astype(str)) + synthetic_df["source_lesion_id"] = synthetic_df["lesion_id"].astype(str).map(source_lesion_id) + unknown_sources = ~synthetic_df["source_lesion_id"].isin(train_sources | val_sources) + if unknown_sources.any(): + examples = synthetic_df.loc[unknown_sources, "lesion_id"].astype(str).head(5).tolist() + raise ValueError(f"Synthetic lesions have unknown source IDs. Examples: {examples}") + safe_synthetic_df = synthetic_df[synthetic_df["source_lesion_id"].isin(train_sources)].copy() + excluded_count = int(synthetic_df["source_lesion_id"].isin(val_sources).sum()) + train_df = pd.concat([train_df, safe_synthetic_df], ignore_index=True, sort=False) print( - f"Synthetic train-only split: real_train={len(train_df) - len(synthetic_df)}, " - f"synthetic_train={len(synthetic_df)}, val_real={len(val_df)}" + f"Source-safe synthetic train-only split: real_train={len(train_df) - len(safe_synthetic_df)}, " + f"synthetic_train={len(safe_synthetic_df)}, excluded_validation_sources={excluded_count}, " + f"val_real={len(val_df)}" ) else: train_df, val_df = lesion_split(df, args.val_size, args.seed) - train_df = append_augmented_train_rows(df, train_df, class_names, args) + train_df = append_augmented_train_rows(df, train_df, val_df, class_names, args) return run_training_split( df, train_df, @@ -367,7 +512,7 @@ def train_kfold( fold_metrics = [] for fold_idx, (train_df, val_df) in enumerate(kfold_splits(df, args.k_folds, args.seed)): print(f"\nK-fold {fold_idx + 1}/{args.k_folds}") - train_df = append_augmented_train_rows(df, train_df, class_names, args) + train_df = append_augmented_train_rows(df, train_df, val_df, class_names, args) metrics = run_training_split( df, train_df, diff --git a/milk10k_effb2_metadata/tests/__pycache__/test_fusion_and_f1_loss.cpython-310.pyc b/milk10k_effb2_metadata/tests/__pycache__/test_fusion_and_f1_loss.cpython-310.pyc index e14b6e64567988b3a30bd96897d9666d6a06f895..b567aeff7d6ea019e5668e089385c4b7e806865b 100644 Binary files a/milk10k_effb2_metadata/tests/__pycache__/test_fusion_and_f1_loss.cpython-310.pyc and b/milk10k_effb2_metadata/tests/__pycache__/test_fusion_and_f1_loss.cpython-310.pyc differ diff --git a/milk10k_effb2_metadata/tests/__pycache__/test_fusion_and_f1_loss.cpython-314.pyc b/milk10k_effb2_metadata/tests/__pycache__/test_fusion_and_f1_loss.cpython-314.pyc index 01f7d8ba66aada363501339b9747c2f27c1ebaa9..632842433b271112ef186bf07b3c2fdc64d6fc86 100644 Binary files a/milk10k_effb2_metadata/tests/__pycache__/test_fusion_and_f1_loss.cpython-314.pyc and b/milk10k_effb2_metadata/tests/__pycache__/test_fusion_and_f1_loss.cpython-314.pyc differ diff --git a/milk10k_effb2_metadata/tests/test_fusion_and_f1_loss.py b/milk10k_effb2_metadata/tests/test_fusion_and_f1_loss.py index 025cf7b220d5d836f3a91938e26f330b663715ab..c770808b54d90c96323ad91ef3092275cb76f356 100644 --- a/milk10k_effb2_metadata/tests/test_fusion_and_f1_loss.py +++ b/milk10k_effb2_metadata/tests/test_fusion_and_f1_loss.py @@ -21,7 +21,14 @@ if MISSING_DEPENDENCY is None: from PIL import Image from milk10k_effb2_metadata.data import PairedMilk10kMetadataDataset from milk10k_effb2_metadata.losses import SoftMacroF1Loss, f1_class_weight_tensor - from milk10k_effb2_metadata.models import DualEffB2MetadataClassifier + from milk10k_effb2_metadata.inference import build_model_from_checkpoint + from milk10k_effb2_metadata.models import ( + DualConvNeXtMetadataClassifier, + DualEffB2MetadataClassifier, + default_image_size, + model_class_for_backbone, + resolve_image_size, + ) except ModuleNotFoundError as exc: # pragma: no cover - local minimal env may omit ML deps. MISSING_DEPENDENCY = exc.name @@ -54,6 +61,76 @@ if MISSING_DEPENDENCY is None: class FusionSmokeTest(unittest.TestCase): + @staticmethod + def model_kwargs(backbone: str = "efficientnet_b2") -> dict: + return { + "num_classes": 4, + "metadata_input_dim": 5, + "branch_dim": 8, + "metadata_dim": 6, + "classifier_hidden_dim": 12, + "dropout": 0.0, + "imagenet_pretrained": False, + "clinical_backbone_backend": "timm", + "dermoscopic_backbone_backend": "timm", + "backbone": backbone, + } + + def test_dedicated_convnext_forward_and_selection(self) -> None: + with patch("milk10k_effb2_metadata.models.build_feature_encoder", side_effect=fake_build_feature_encoder): + model = DualConvNeXtMetadataClassifier(**self.model_kwargs("convnext_base")) + logits = model( + torch.randn(2, 3, 8, 8), + torch.randn(2, 3, 8, 8), + torch.randn(2, 5), + ) + self.assertEqual(tuple(logits.shape), (2, 4)) + self.assertIs(model_class_for_backbone("convnext_base"), DualConvNeXtMetadataClassifier) + self.assertIs(model_class_for_backbone("efficientnet_b2"), DualEffB2MetadataClassifier) + + def test_backbone_default_image_sizes(self) -> None: + self.assertEqual(default_image_size("convnext_base"), 384) + self.assertEqual(default_image_size("efficientnet_b2"), 260) + self.assertEqual(default_image_size("efficientnet_b1"), 240) + self.assertEqual(default_image_size("resnet50"), 224) + self.assertEqual(resolve_image_size("convnext_base", 320), 320) + + def test_checkpoint_reconstructs_dedicated_and_legacy_models(self) -> None: + checkpoint_args = { + "branch_dim": 8, + "metadata_dim": 6, + "classifier_hidden_dim": 12, + "dropout": 0.0, + "metadata_fusion": "concat", + "image_fusion": "concat", + "logit_fusion_mode": "single", + } + with patch("milk10k_effb2_metadata.models.build_feature_encoder", side_effect=fake_build_feature_encoder): + convnext = DualConvNeXtMetadataClassifier(**self.model_kwargs("convnext_base")) + efficientnet = DualEffB2MetadataClassifier(**self.model_kwargs("efficientnet_b2")) + with patch("milk10k_effb2_metadata.inference.infer_backend_from_model_state", return_value="timm"): + loaded_convnext = build_model_from_checkpoint( + { + "model_state": convnext.state_dict(), + "model_type": "DualConvNeXtMetadataClassifier", + "class_names": ["A", "B", "C", "D"], + "args": {**checkpoint_args, "backbone": "convnext_base"}, + }, + metadata_dim=5, + device=torch.device("cpu"), + ) + loaded_legacy = build_model_from_checkpoint( + { + "model_state": efficientnet.state_dict(), + "class_names": ["A", "B", "C", "D"], + "args": {**checkpoint_args, "backbone": "efficientnet_b2"}, + }, + metadata_dim=5, + device=torch.device("cpu"), + ) + self.assertIsInstance(loaded_convnext, DualConvNeXtMetadataClassifier) + self.assertIs(type(loaded_legacy), DualEffB2MetadataClassifier) + def test_all_image_and_metadata_fusions_forward(self) -> None: modes = [ "concat",