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0000000000000000000000000000000000000000..efcf05096adb4bc4336a1ab06ba147107fac35d8 Binary files /dev/null and b/__pycache__/train_milk10k_effb2_dual_metadata.cpython-314.pyc differ diff --git a/milk10k_effb2_metadata/MILK10K_EFFB2_METADATA_CLI.md b/milk10k_effb2_metadata/MILK10K_EFFB2_METADATA_CLI.md index 9c467a783f5db02fc2938b239aafbb79ec441940..6ec8b106d6242b45f0feea9db0c872ececcd53af 100644 --- a/milk10k_effb2_metadata/MILK10K_EFFB2_METADATA_CLI.md +++ b/milk10k_effb2_metadata/MILK10K_EFFB2_METADATA_CLI.md @@ -78,6 +78,52 @@ python train_milk10k_effb2_dual_metadata.py \ --output-dir milk10k_effb2_gated_only ``` +## Image Fusion Options + +Keep the current final representation concat: + +```bash +--image-fusion concat +``` + +Try the global feature fusion ideas from `archs_to_try.md`: + +```bash +--image-fusion cross_attention +--image-fusion co_attention +--image-fusion low_rank_bilinear +--image-fusion adaptive_gate +--image-fusion moe +--image-fusion shared_private +``` + +`compact_bilinear` remains accepted as a backward-compatible alias for the low-rank projected product fusion. + +Recommended first F1-focused run: + +```bash +python train_milk10k_effb2_dual_metadata.py \ + --clinical-checkpoint best_effnetb2_ufes_clinical.pth \ + --dermoscopic-checkpoint efficientnet_b2_best_dermoscopic.pt \ + --image-fusion cross_attention \ + --loss ce_f1 \ + --f1-weight 0.5 \ + --f1-ignore-classes MAL_OTH \ + --output-dir milk10k_effb2_cross_attention_ce_f1_no_mal_oth +``` + +Swap `cross_attention` for `low_rank_bilinear`, `adaptive_gate`, or `moe` for the next ablations. + +Metadata fusion can be combined with every image fusion mode: + +```bash +--metadata-fusion concat +--metadata-fusion gated_concat +--metadata-fusion gated_only +``` + +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. + ## 3. Class Weight Only ```bash @@ -136,6 +182,26 @@ python train_milk10k_effb2_dual_metadata.py \ --output-dir milk10k_effb2_focal_sampler_p05 ``` +## 5b. F1-Priority Loss + +Optimize CE plus a differentiable soft macro-F1 auxiliary term: + +```bash +python train_milk10k_effb2_dual_metadata.py \ + --clinical-checkpoint best_effnetb2_ufes_clinical.pth \ + --dermoscopic-checkpoint efficientnet_b2_best_dermoscopic.pt \ + --loss ce_f1 \ + --f1-weight 0.5 \ + --f1-ignore-classes MAL_OTH \ + --output-dir milk10k_effb2_ce_f1_no_mal_oth +``` + +Downweight a class instead of fully ignoring it: + +```bash +--f1-class-weight MAL_OTH=0.1 +``` + ## 6. 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keeps the baseline; gated modes use metadata for channel gating.", ) + parser.add_argument( + "--image-fusion", + choices=[ + "concat", + "cross_attention", + "co_attention", + "compact_bilinear", + "low_rank_bilinear", + "adaptive_gate", + "moe", + "shared_private", + ], + default="concat", + help="Image representation fusion mode. concat keeps the baseline final fusion.", + ) parser.add_argument( "--metadata-gate-hidden-dim", type=int, @@ -85,11 +100,34 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--metadata-dim", type=int, default=64) parser.add_argument("--classifier-hidden-dim", type=int, default=512) parser.add_argument("--dropout", type=float, default=0.3) + parser.add_argument( + "--logit-fusion-mode", + choices=["single", "fixed"], + default="single", + help="single uses one fused classifier. fixed adds clinical/dermoscopic logits and mixes them with fixed weights.", + ) + parser.add_argument("--fusion-logit-weight", type=float, default=0.6) + parser.add_argument("--clinical-logit-weight", type=float, default=0.2) + parser.add_argument("--dermoscopic-logit-weight", type=float, default=0.2) 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("--loss", choices=["ce", "focal", "ldam"], default="ce") + 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) + parser.add_argument("--f1-weight", type=float, default=0.3) + parser.add_argument( + "--f1-ignore-classes", + nargs="*", + default=[], + help="Class names excluded from the soft macro-F1 auxiliary term, e.g. --f1-ignore-classes MAL_OTH.", + ) + parser.add_argument( + "--f1-class-weight", + action="append", + default=[], + help="Optional CLASS=VALUE override for the soft macro-F1 auxiliary term. Can be passed multiple times.", + ) parser.add_argument("--ldam-beta", type=float, default=0.9999) parser.add_argument("--ldam-max-margin", type=float, default=0.5) parser.add_argument("--ldam-drw-start-epoch", type=int, default=0) @@ -113,5 +151,25 @@ def parse_args() -> argparse.Namespace: action="store_true", help="Initialize backbones with ImageNet weights before loading any branch checkpoints. Enabled automatically when no branch checkpoints are passed.", ) + parser.add_argument( + "--selection-metric", + choices=["f1_macro", "dice_macro"], + default="f1_macro", + help="Validation metric used for best.pt checkpoint selection and LR scheduling.", + ) + parser.add_argument( + "--calibrate-bias", + action="store_true", + help="Tune per-class logit biases on validation predictions after training and save calibration.json.", + ) + parser.add_argument( + "--calibration-metric", + choices=["f1_macro", "dice_macro"], + default="dice_macro", + help="Metric optimized by post-hoc class-bias calibration.", + ) + parser.add_argument("--calibration-max-bias", type=float, default=1.5) + 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) return parser.parse_args() diff --git a/milk10k_effb2_metadata/engine.py b/milk10k_effb2_metadata/engine.py index a99ff221829611a452ec800c5ffec8ef45a46721..20df4ea6a286d16f98dfae953dfafca5a3f1cd61 100644 --- a/milk10k_effb2_metadata/engine.py +++ b/milk10k_effb2_metadata/engine.py @@ -15,7 +15,7 @@ from torch.amp import GradScaler, autocast from torch.utils.data import DataLoader from tqdm.auto import tqdm -from milk10k_effb2_metadata.metrics import move_batch +from milk10k_effb2_metadata.metrics import macro_dice_from_confusion_matrix, move_batch from milk10k_effb2_metadata.model_setup import build_optimizer from milk10k_effb2_metadata.models import DualEffB2MetadataClassifier, set_encoder_trainable from milk10k_effb2_metadata.training_utils import json_safe @@ -95,6 +95,7 @@ def run_epoch( zero_division=0, ) cm = confusion_matrix(y_true, y_pred, labels=labels) + stats["dice_macro"] = macro_dice_from_confusion_matrix(cm) for idx, class_name in enumerate(class_names): name = metric_name(class_name) row_total = int(cm[idx, :].sum()) @@ -167,6 +168,8 @@ def save_checkpoint( "model_state": model.state_dict(), "optimizer_state": optimizer.state_dict(), "best_val_f1_macro": best_val_f1, + "best_selection_metric": best_val_f1, + "selection_metric_name": args.selection_metric, "class_names": class_names, "label_to_idx": label_to_idx, "metadata_spec": metadata_spec, @@ -237,7 +240,8 @@ def train_phase( tail_class_indices=tail_class_indices, class_names=class_names, ) - scheduler.step(val_stats["f1_macro"]) + selection_metric = args.selection_metric + scheduler.step(val_stats[selection_metric]) row = { "phase": phase, "epoch": epoch, @@ -251,7 +255,8 @@ def train_phase( f"train_loss={train_stats['loss']:.4f} val_loss={val_stats['loss']:.4f} " f"train_bal_acc={train_stats['balanced_accuracy']:.4f} train_f1={train_stats['f1_macro']:.4f} " f"val_acc={val_stats['accuracy']:.4f} val_bal_acc={val_stats['balanced_accuracy']:.4f} " - f"val_f1={val_stats['f1_macro']:.4f} val_top3={val_stats['top3_accuracy']:.4f}" + f"val_f1={val_stats['f1_macro']:.4f} val_dice={val_stats.get('dice_macro', 0.0):.4f} " + f"val_top3={val_stats['top3_accuracy']:.4f}" ) if tail_class_indices: print( @@ -263,8 +268,8 @@ def train_phase( print(f" train {format_class_diagnostics(train_stats, class_name, class_names)}") print(f" val {format_class_diagnostics(val_stats, class_name, class_names)}") - if val_stats["f1_macro"] > best_val_f1: - best_val_f1 = val_stats["f1_macro"] + if val_stats[selection_metric] > best_val_f1: + best_val_f1 = val_stats[selection_metric] patience_count = 0 save_checkpoint( output_dir / "best.pt", @@ -280,7 +285,7 @@ def train_phase( ) print( f"Saved best checkpoint: phase={phase} epoch={epoch:03d} " - f"best_val_f1_macro={best_val_f1:.4f} path={output_dir / 'best.pt'}" + f"best_{selection_metric}={best_val_f1:.4f} path={output_dir / 'best.pt'}" ) else: patience_count += 1 diff --git a/milk10k_effb2_metadata/inference.py b/milk10k_effb2_metadata/inference.py index 5161a1a84c4904356be64f2d8a41b4d450a05860..b961b7ffd7521255919846ebdcc9baebaa7c201d 100644 --- a/milk10k_effb2_metadata/inference.py +++ b/milk10k_effb2_metadata/inference.py @@ -3,6 +3,7 @@ from __future__ import annotations import argparse +import json from pathlib import Path from typing import Any @@ -15,7 +16,7 @@ from tqdm.auto import tqdm from datasets import LABEL_COLUMNS, normalize_image_type from milk10k_effb2_metadata.data import METADATA_COLUMNS, make_transforms, metadata_vector, resolve_monet_columns -from milk10k_effb2_metadata.metrics import compute_metrics +from milk10k_effb2_metadata.metrics import apply_class_bias, compute_metrics from milk10k_effb2_metadata.models import DualEffB2MetadataClassifier from milk10k_effb2_metadata.training import json_safe @@ -47,7 +48,13 @@ class InferencePairedDataset(Dataset): def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Run inference with a MILK10k dual EffB2 metadata checkpoint.") - parser.add_argument("--checkpoint", type=Path, required=True, help="Path to best.pt from training.") + parser.add_argument("--checkpoint", type=Path, nargs="*", default=None, help="One or more checkpoint paths.") + parser.add_argument( + "--checkpoint-dir", + type=Path, + default=None, + help="Optional run directory. If it contains fold_*/best.pt those checkpoints are ensembled; otherwise uses best.pt in the directory.", + ) 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.") @@ -56,6 +63,14 @@ def parse_args() -> argparse.Namespace: parser.add_argument("--batch-size", type=int, default=16) parser.add_argument("--image-size", type=int, default=None, help="Defaults to checkpoint args image_size.") parser.add_argument("--num-workers", type=int, default=0) + parser.add_argument("--tta-flips", action="store_true", help="Average original, H-flip, V-flip, and HV-flip predictions.") + parser.add_argument( + "--calibration-file", + type=Path, + default=None, + help="Optional calibration.json override. By default calibration.json next to each checkpoint is loaded automatically.", + ) + parser.add_argument("--no-auto-calibration", action="store_true", help="Disable auto-loading calibration.json next to checkpoints.") parser.add_argument("--include-debug-columns", action="store_true", help="Include lesion/file IDs and predicted labels before class probabilities.") return parser.parse_args() @@ -133,6 +148,22 @@ def checkpoint_arg(checkpoint_args: dict[str, Any], key: str, default: Any) -> A return value +def resolve_checkpoint_paths(args: argparse.Namespace) -> list[Path]: + checkpoint_paths = [path.expanduser().resolve() for path in (args.checkpoint or [])] + if args.checkpoint_dir is not None: + checkpoint_dir = args.checkpoint_dir.expanduser().resolve() + fold_paths = sorted(path for path in checkpoint_dir.glob("fold_*/best.pt") if path.is_file()) + if fold_paths: + checkpoint_paths.extend(fold_paths) + else: + best_path = checkpoint_dir / "best.pt" + if best_path.is_file(): + checkpoint_paths.append(best_path) + if not checkpoint_paths: + raise ValueError("Pass --checkpoint, or pass --checkpoint-dir containing best.pt or fold_*/best.pt.") + return checkpoint_paths + + def build_model_from_checkpoint(checkpoint: dict[str, Any], metadata_dim: int, device: torch.device) -> DualEffB2MetadataClassifier: state = checkpoint["model_state"] checkpoint_args = checkpoint.get("args", {}) @@ -152,7 +183,12 @@ def build_model_from_checkpoint(checkpoint: dict[str, Any], metadata_dim: int, d backbone=checkpoint_arg(checkpoint_args, "backbone", "efficientnet_b2"), disable_metadata=checkpoint_arg(checkpoint_args, "disable_metadata", False), 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"), + 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), + dermoscopic_logit_weight=checkpoint_arg(checkpoint_args, "dermoscopic_logit_weight", 0.2), ).to(device) model.load_state_dict(state) model.eval() @@ -160,17 +196,54 @@ def build_model_from_checkpoint(checkpoint: dict[str, Any], metadata_dim: int, d @torch.no_grad() -def predict_dataframe(model: DualEffB2MetadataClassifier, loader: DataLoader, device: torch.device) -> np.ndarray: +def predict_dataframe(model: DualEffB2MetadataClassifier, loader: DataLoader, device: torch.device, tta_flips: bool = False) -> np.ndarray: probs_all = [] for batch in tqdm(loader, leave=False): clinical = batch["clinical"].to(device, non_blocking=True) dermoscopic = batch["dermoscopic"].to(device, non_blocking=True) metadata = batch["metadata"].to(device, non_blocking=True) - logits = model(clinical, dermoscopic, metadata) - probs_all.append(torch.softmax(logits, dim=1).cpu().numpy()) + views = [(clinical, dermoscopic)] + if tta_flips: + views.extend( + [ + (torch.flip(clinical, dims=(-1,)), torch.flip(dermoscopic, dims=(-1,))), + (torch.flip(clinical, dims=(-2,)), torch.flip(dermoscopic, dims=(-2,))), + (torch.flip(clinical, dims=(-2, -1)), torch.flip(dermoscopic, dims=(-2, -1))), + ] + ) + probs = None + for clinical_view, dermoscopic_view in views: + logits = model(clinical_view, dermoscopic_view, metadata) + view_prob = torch.softmax(logits, dim=1) + probs = view_prob if probs is None else probs + view_prob + probs_all.append((probs / len(views)).cpu().numpy()) return np.concatenate(probs_all) +def load_calibration_bias( + checkpoint_path: Path, + args: argparse.Namespace, + expected_class_names: list[str], +) -> np.ndarray | None: + if args.calibration_file is not None: + calibration_path = args.calibration_file.expanduser().resolve() + elif args.no_auto_calibration: + return None + else: + calibration_path = checkpoint_path.parent / "calibration.json" + if not calibration_path.exists(): + return None + with open(calibration_path, encoding="utf-8") as f: + payload = json.load(f) + class_names = payload.get("class_names", []) + if class_names != expected_class_names: + raise ValueError( + f"Calibration class_names mismatch for {calibration_path}: " + f"expected {expected_class_names}, got {class_names}" + ) + return np.asarray(payload["class_bias"], dtype=np.float32) + + def save_inference_outputs( df: pd.DataFrame, y_prob: np.ndarray, @@ -206,25 +279,41 @@ def save_inference_outputs( def main() -> None: args = parse_args() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - checkpoint = torch.load(args.checkpoint, map_location=device, weights_only=False) - metadata_spec = checkpoint["metadata_spec"] - class_names = checkpoint["class_names"] - checkpoint_args = checkpoint.get("args", {}) - image_size = args.image_size or int(checkpoint_args.get("image_size", 260)) - input_dir, metadata_csv, groundtruth_csv = resolve_input_paths(args) df = load_inference_dataframe(input_dir, metadata_csv, groundtruth_csv) - _, eval_transform = make_transforms(image_size) - dataset = InferencePairedDataset(df, metadata_spec, eval_transform) - loader = DataLoader( - dataset, - batch_size=args.batch_size, - num_workers=args.num_workers, - pin_memory=torch.cuda.is_available(), - shuffle=False, - ) - model = build_model_from_checkpoint(checkpoint, dataset.metadata.shape[1], device) - y_prob = predict_dataframe(model, loader, device) + checkpoint_paths = resolve_checkpoint_paths(args) + ensemble_probs = [] + class_names: list[str] | None = None + + for checkpoint_path in checkpoint_paths: + checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False) + checkpoint_class_names = checkpoint["class_names"] + if class_names is None: + class_names = checkpoint_class_names + elif checkpoint_class_names != class_names: + raise ValueError( + f"Checkpoint class_names mismatch: expected {class_names}, got {checkpoint_class_names} from {checkpoint_path}" + ) + checkpoint_args = checkpoint.get("args", {}) + image_size = args.image_size or int(checkpoint_args.get("image_size", 260)) + _, eval_transform = make_transforms(image_size) + dataset = InferencePairedDataset(df, checkpoint["metadata_spec"], eval_transform) + loader = DataLoader( + dataset, + batch_size=args.batch_size, + num_workers=args.num_workers, + pin_memory=torch.cuda.is_available(), + shuffle=False, + ) + model = build_model_from_checkpoint(checkpoint, dataset.metadata.shape[1], device) + y_prob = predict_dataframe(model, loader, device, tta_flips=args.tta_flips) + class_bias = load_calibration_bias(checkpoint_path, args, checkpoint_class_names) + if class_bias is not None: + y_prob = apply_class_bias(y_prob, class_bias) + ensemble_probs.append(y_prob) + + assert class_names is not None + y_prob = np.mean(ensemble_probs, axis=0) save_inference_outputs(df, y_prob, class_names, args.output, args.include_debug_columns) print(f"Saved predictions: {args.output}") diff --git a/milk10k_effb2_metadata/losses.py b/milk10k_effb2_metadata/losses.py index 8b6d627c534d71d99d855b29c65bc294b87c6303..a9f264a556310e3492c808a55e3091fda8b37a01 100644 --- a/milk10k_effb2_metadata/losses.py +++ b/milk10k_effb2_metadata/losses.py @@ -66,6 +66,56 @@ class LDAMLoss(nn.Module): return loss.mean() +class SoftMacroDiceLoss(nn.Module): + def __init__(self, eps: float = 1e-6) -> None: + super().__init__() + self.eps = eps + + def forward(self, logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + probs = torch.softmax(logits, dim=1) + one_hot = F.one_hot(labels, num_classes=logits.size(1)).to(dtype=probs.dtype) + intersection = (probs * one_hot).sum(dim=0) + denominator = probs.sum(dim=0) + one_hot.sum(dim=0) + dice = (2.0 * intersection + self.eps) / (denominator + self.eps) + return 1.0 - dice.mean() + + +class SoftMacroF1Loss(nn.Module): + def __init__(self, class_weights: torch.Tensor | None = None, eps: float = 1e-6) -> None: + super().__init__() + if class_weights is not None: + self.register_buffer("class_weights", class_weights.float()) + else: + self.class_weights = None + self.eps = eps + + def forward(self, logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + probs = torch.softmax(logits, dim=1) + one_hot = F.one_hot(labels, num_classes=logits.size(1)).to(dtype=probs.dtype) + tp = (probs * one_hot).sum(dim=0) + fp = (probs * (1.0 - one_hot)).sum(dim=0) + fn = ((1.0 - probs) * one_hot).sum(dim=0) + f1 = (2.0 * tp + self.eps) / (2.0 * tp + fp + fn + self.eps) + if self.class_weights is None: + return 1.0 - f1.mean() + weights = self.class_weights.to(device=logits.device, dtype=probs.dtype) + if weights.numel() != logits.size(1): + raise RuntimeError(f"Expected {logits.size(1)} F1 class weights, got {weights.numel()}.") + denominator = weights.sum().clamp_min(self.eps) + return 1.0 - (f1 * weights).sum() / denominator + + +class CompositeClassificationLoss(nn.Module): + def __init__(self, ce_loss: nn.Module, auxiliary_loss: nn.Module, auxiliary_weight: float) -> None: + super().__init__() + self.ce_loss = ce_loss + self.auxiliary_loss = auxiliary_loss + self.auxiliary_weight = auxiliary_weight + + def forward(self, logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + return self.ce_loss(logits, labels) + self.auxiliary_weight * self.auxiliary_loss(logits, labels) + + def effective_number_alpha(counts: torch.Tensor, beta: float) -> torch.Tensor: if beta <= 0.0: return torch.ones_like(counts) @@ -86,6 +136,33 @@ def class_count_tensor(train_df: pd.DataFrame, label_to_idx: dict[str, int], dev return torch.tensor(counts, dtype=torch.float32, device=device) +def resolve_label_name(label_to_idx: dict[str, int], name: str) -> str: + normalized = {label.upper(): label for label in label_to_idx} + key = name.strip().upper() + if key not in normalized: + raise ValueError(f"Unknown class name for F1 loss: {name!r}. Choices: {sorted(label_to_idx)}") + return normalized[key] + + +def f1_class_weight_tensor(label_to_idx: dict[str, int], args: argparse.Namespace, device: torch.device) -> torch.Tensor: + weights = torch.ones(len(label_to_idx), dtype=torch.float32, device=device) + for class_name in getattr(args, "f1_ignore_classes", []): + resolved = resolve_label_name(label_to_idx, class_name) + weights[label_to_idx[resolved]] = 0.0 + for item in getattr(args, "f1_class_weight", []): + if "=" not in item: + raise ValueError(f"--f1-class-weight expects CLASS=VALUE, got {item!r}.") + class_name, value = item.split("=", 1) + resolved = resolve_label_name(label_to_idx, class_name) + weight = float(value) + if weight < 0.0: + raise ValueError(f"--f1-class-weight must be non-negative, got {item!r}.") + weights[label_to_idx[resolved]] = weight + if float(weights.sum().item()) <= 0.0: + raise ValueError("F1 class weights sum to zero. Keep at least one class active for --loss ce_f1.") + return weights + + def build_loss(train_df: pd.DataFrame, label_to_idx: dict[str, int], args: argparse.Namespace, device: torch.device) -> nn.Module: if args.loss == "ldam": counts = class_count_tensor(train_df, label_to_idx, device) @@ -102,6 +179,11 @@ 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 args.loss == "focal": return FocalLoss(weight=weight, gamma=args.focal_gamma) - return nn.CrossEntropyLoss(weight=weight) + if args.loss == "ce_dice": + return CompositeClassificationLoss(ce_loss, SoftMacroDiceLoss(), args.dice_weight) + if args.loss == "ce_f1": + return CompositeClassificationLoss(ce_loss, SoftMacroF1Loss(f1_class_weight_tensor(label_to_idx, args, device)), args.f1_weight) + return ce_loss diff --git a/milk10k_effb2_metadata/metrics.py b/milk10k_effb2_metadata/metrics.py index e9c3f3d079aa08915f21fa758bf05891d861c1bd..350b8c2c12a305b4d908d8bd904a8fc484f83bfd 100644 --- a/milk10k_effb2_metadata/metrics.py +++ b/milk10k_effb2_metadata/metrics.py @@ -44,6 +44,17 @@ def predict(model: DualEffB2MetadataClassifier, loader: DataLoader, device: torc return np.concatenate(labels_all), np.concatenate(probs_all) +def macro_dice_from_confusion_matrix(cm: np.ndarray) -> float: + per_class = [] + for idx in range(cm.shape[0]): + tp = float(cm[idx, idx]) + fn = float(cm[idx, :].sum() - tp) + fp = float(cm[:, idx].sum() - tp) + denom = 2.0 * tp + fp + fn + per_class.append(0.0 if denom <= 0.0 else (2.0 * tp) / denom) + return float(np.mean(per_class)) if per_class else 0.0 + + def compute_metrics(y_true: np.ndarray, y_prob: np.ndarray, class_names: list[str]) -> tuple[dict[str, Any], pd.DataFrame, np.ndarray]: y_pred = y_prob.argmax(axis=1) labels = list(range(len(class_names))) @@ -94,6 +105,7 @@ def compute_metrics(y_true: np.ndarray, y_prob: np.ndarray, class_names: list[st "precision_weighted": float(precision_weighted), "recall_weighted": float(recall_weighted), "f1_weighted": float(f1_weighted), + "dice_macro": macro_dice_from_confusion_matrix(cm), "roc_auc_macro_ovr": safe_roc_auc(y_true_bin, y_prob, "macro"), "roc_auc_weighted_ovr": safe_roc_auc(y_true_bin, y_prob, "weighted"), "roc_auc_micro_ovr": safe_roc_auc(y_true_bin, y_prob, "micro"), @@ -142,3 +154,58 @@ def save_predictions( probability_df = pd.DataFrame(y_prob, columns=[f"prob_{name}" for name in class_names]) pd.concat([prediction_df, probability_df], axis=1).to_csv(output_dir / "val_predictions.csv", index=False) + +def apply_class_bias(y_prob: np.ndarray, bias: np.ndarray) -> np.ndarray: + log_prob = np.log(np.clip(y_prob, 1e-12, 1.0)) + adjusted = log_prob + bias[None, :] + adjusted -= adjusted.max(axis=1, keepdims=True) + exp_scores = np.exp(adjusted) + return exp_scores / exp_scores.sum(axis=1, keepdims=True) + + +def metric_value_from_probabilities( + y_true: np.ndarray, + y_prob: np.ndarray, + class_names: list[str], + metric_name: str, +) -> float: + metrics, _, _ = compute_metrics(y_true, y_prob, class_names) + return float(metrics[metric_name]) + + +def optimize_class_bias( + y_true: np.ndarray, + y_prob: np.ndarray, + class_names: list[str], + metric_name: str = "dice_macro", + max_bias: float = 1.5, + step: float = 0.25, + passes: int = 3, +) -> tuple[np.ndarray, float]: + bias = np.zeros(len(class_names), dtype=np.float32) + best_score = metric_value_from_probabilities(y_true, y_prob, class_names, metric_name) + current_step = step + + for _ in range(max(1, passes)): + deltas = np.arange(-max_bias, max_bias + current_step * 0.5, current_step, dtype=np.float32) + improved = False + for class_idx in range(len(class_names)): + best_class_bias = float(bias[class_idx]) + best_class_score = best_score + for delta in deltas: + trial_bias = bias.copy() + trial_bias[class_idx] = best_class_bias + float(delta) + trial_prob = apply_class_bias(y_prob, trial_bias) + score = metric_value_from_probabilities(y_true, trial_prob, class_names, metric_name) + if score > best_class_score + 1e-12: + best_class_score = score + best_class_bias = float(trial_bias[class_idx]) + if best_class_score > best_score + 1e-12: + bias[class_idx] = best_class_bias + best_score = best_class_score + improved = True + current_step = max(current_step / 2.0, 0.01) + if not improved: + break + + return bias, best_score diff --git a/milk10k_effb2_metadata/model_setup.py b/milk10k_effb2_metadata/model_setup.py index adaed11bb482bc9312d8a9d2b5bcd555936bea9e..7309cfdf20bf669152539e7926da2308bcb04118 100644 --- a/milk10k_effb2_metadata/model_setup.py +++ b/milk10k_effb2_metadata/model_setup.py @@ -112,11 +112,17 @@ def load_resume_checkpoint( checkpoint = torch.load(checkpoint_path, map_location=device, weights_only=False) model.load_state_dict(checkpoint["model_state"]) next_epoch = int(checkpoint.get("epoch", 0)) + 1 - best_val_f1 = float(checkpoint.get("best_val_f1_macro", float("-inf"))) + best_val_f1 = float( + checkpoint.get( + "best_selection_metric", + checkpoint.get("best_val_f1_macro", float("-inf")), + ) + ) phase = checkpoint.get("phase") + selection_metric_name = checkpoint.get("selection_metric_name", "f1_macro") print( f"Resumed checkpoint: {checkpoint_path}, phase={phase}, " - f"last_epoch={next_epoch - 1}, best_val_f1_macro={best_val_f1:.4f}" + f"last_epoch={next_epoch - 1}, best_{selection_metric_name}={best_val_f1:.4f}" ) print("Optimizer is re-created from current CLI LR settings.") return next_epoch, best_val_f1, str(phase) if phase is not None else None @@ -143,7 +149,12 @@ def build_model( backbone=args.backbone, disable_metadata=args.disable_metadata, metadata_fusion=args.metadata_fusion, + image_fusion=getattr(args, "image_fusion", "concat"), metadata_gate_hidden_dim=args.metadata_gate_hidden_dim, + logit_fusion_mode=args.logit_fusion_mode, + fusion_logit_weight=args.fusion_logit_weight, + clinical_logit_weight=args.clinical_logit_weight, + dermoscopic_logit_weight=args.dermoscopic_logit_weight, ).to(device) if args.resume_checkpoint is None: if args.clinical_checkpoint is not None: diff --git a/milk10k_effb2_metadata/models.py b/milk10k_effb2_metadata/models.py index f6d126347f16f6904eae70a9055005db53a23c81..4fc4c66853cd110a3e9b8cb48f89ad5d7e615c11 100644 --- a/milk10k_effb2_metadata/models.py +++ b/milk10k_effb2_metadata/models.py @@ -23,6 +23,35 @@ class ProjectionHead(nn.Module): return self.net(x) +class BranchClassifier(nn.Module): + def __init__(self, in_dim: int, num_classes: int, dropout: float) -> None: + super().__init__() + self.net = nn.Sequential( + nn.LayerNorm(in_dim), + nn.Dropout(dropout), + nn.Linear(in_dim, num_classes), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.net(x) + + +class GatedExpertClassifier(nn.Module): + def __init__(self, in_dim: int, hidden_dim: int, num_classes: int, dropout: float) -> None: + super().__init__() + self.net = nn.Sequential( + nn.LayerNorm(in_dim), + nn.Dropout(dropout), + nn.Linear(in_dim, hidden_dim), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, num_classes), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.net(x) + + class MetadataHead(nn.Module): def __init__(self, in_dim: int, out_dim: int, dropout: float) -> None: super().__init__() @@ -76,17 +105,40 @@ class DualEffB2MetadataClassifier(nn.Module): backbone: str = "efficientnet_b2", disable_metadata: bool = False, metadata_fusion: str = "concat", + image_fusion: str = "concat", metadata_gate_hidden_dim: int | None = None, + logit_fusion_mode: str = "single", + fusion_logit_weight: float = 0.6, + clinical_logit_weight: float = 0.2, + dermoscopic_logit_weight: float = 0.2, ) -> None: super().__init__() if metadata_fusion not in ("concat", "gated_concat", "gated_only"): raise ValueError(f"Unsupported metadata_fusion: {metadata_fusion}") + if image_fusion not in ( + "concat", + "cross_attention", + "co_attention", + "compact_bilinear", + "low_rank_bilinear", + "adaptive_gate", + "moe", + "shared_private", + ): + 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}") self.clinical_backbone_backend = clinical_backbone_backend self.dermoscopic_backbone_backend = dermoscopic_backbone_backend self.backbone = normalize_backbone_name(backbone) self.disable_metadata = disable_metadata self.metadata_dim = metadata_dim self.metadata_fusion = metadata_fusion + self.image_fusion = image_fusion + self.logit_fusion_mode = logit_fusion_mode + self.fusion_logit_weight = fusion_logit_weight + self.clinical_logit_weight = clinical_logit_weight + self.dermoscopic_logit_weight = dermoscopic_logit_weight self.clinical_encoder, clinical_feature_dim = build_feature_encoder( backbone, clinical_backbone_backend, @@ -115,18 +167,84 @@ class DualEffB2MetadataClassifier(nn.Module): gate_hidden_dim, dropout, ) - fused_dim = branch_dim * 2 - if metadata_fusion != "gated_only": - fused_dim += metadata_dim - self.classifier = nn.Sequential( - nn.LayerNorm(fused_dim), + metadata_output_dim = 0 if metadata_fusion == "gated_only" else metadata_dim + fused_dim = self._fusion_dim(branch_dim, metadata_output_dim, image_fusion) + heads = 4 if branch_dim % 4 == 0 else 1 + if image_fusion == "cross_attention": + self.cross_attention = nn.MultiheadAttention(branch_dim, heads, dropout=dropout, batch_first=True) + self.cross_attention_norm = nn.LayerNorm(branch_dim * 2) + elif image_fusion == "co_attention": + self.co_attention = nn.MultiheadAttention(branch_dim, heads, dropout=dropout, batch_first=True) + self.co_attention_norm = nn.LayerNorm(branch_dim * 4) + elif image_fusion in ("compact_bilinear", "low_rank_bilinear"): + self.bilinear_clinical = nn.Linear(branch_dim, branch_dim) + self.bilinear_dermoscopic = nn.Linear(branch_dim, branch_dim) + self.bilinear_norm = nn.LayerNorm(branch_dim) + elif image_fusion == "adaptive_gate": + gate_input_dim = branch_dim * 2 + metadata_output_dim + self.image_gate = nn.Sequential( + nn.LayerNorm(gate_input_dim), + nn.Linear(gate_input_dim, max(branch_dim, 64)), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(max(branch_dim, 64), branch_dim), + nn.Sigmoid(), + ) + elif image_fusion == "moe": + clinical_expert_dim = branch_dim + metadata_output_dim + dermoscopic_expert_dim = branch_dim + metadata_output_dim + joint_expert_dim = branch_dim * 2 + metadata_output_dim + router_dim = branch_dim * 2 + metadata_output_dim + self.clinical_expert = GatedExpertClassifier(clinical_expert_dim, classifier_hidden_dim, num_classes, dropout) + self.dermoscopic_expert = GatedExpertClassifier( + dermoscopic_expert_dim, + classifier_hidden_dim, + num_classes, + dropout, + ) + self.joint_expert = GatedExpertClassifier(joint_expert_dim, classifier_hidden_dim, num_classes, dropout) + self.expert_router = nn.Sequential( + nn.LayerNorm(router_dim), + nn.Dropout(dropout), + nn.Linear(router_dim, 3), + ) + elif image_fusion == "shared_private": + 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) + if logit_fusion_mode == "fixed": + self.clinical_classifier = BranchClassifier(branch_dim, num_classes, dropout) + self.dermoscopic_classifier = BranchClassifier(branch_dim, num_classes, dropout) + else: + self.clinical_classifier = None + self.dermoscopic_classifier = None + + @staticmethod + 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), - nn.Linear(fused_dim, classifier_hidden_dim), + nn.Linear(in_dim, hidden_dim), nn.GELU(), nn.Dropout(dropout), - nn.Linear(classifier_hidden_dim, num_classes), + nn.Linear(hidden_dim, num_classes), ) + @staticmethod + def _fusion_dim(branch_dim: int, metadata_dim: int, image_fusion: str) -> int: + if image_fusion in ("concat", "cross_attention"): + image_dim = branch_dim * 2 + elif image_fusion in ("compact_bilinear", "low_rank_bilinear", "adaptive_gate", "shared_private"): + image_dim = branch_dim * 3 + elif image_fusion == "co_attention": + image_dim = branch_dim * 4 + elif image_fusion == "moe": + image_dim = branch_dim * 2 + else: + raise ValueError(f"Unsupported image_fusion: {image_fusion}") + return image_dim + metadata_dim + def forward( self, clinical: torch.Tensor, @@ -155,15 +273,101 @@ class DualEffB2MetadataClassifier(nn.Module): dermoscopic_features = torch.flatten(dermoscopic_features, 1) clinical_repr = self.clinical_head(clinical_features) dermoscopic_repr = self.dermoscopic_head(dermoscopic_features) + metadata_repr = None if self.metadata_fusion == "gated_only": - fused = torch.cat([clinical_repr, dermoscopic_repr], dim=1) + metadata_repr = None else: if self.disable_metadata: metadata_repr = clinical_repr.new_zeros((clinical_repr.size(0), self.metadata_dim)) else: metadata_repr = self.metadata_head(metadata) - fused = torch.cat([clinical_repr, dermoscopic_repr, metadata_repr], dim=1) - return self.classifier(fused) + if self.image_fusion == "moe": + fusion_logits = self._moe_logits(clinical_repr, dermoscopic_repr, metadata_repr) + else: + 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 + 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 + ) + + def _append_metadata(self, features: torch.Tensor, metadata_repr: torch.Tensor | None) -> torch.Tensor: + if metadata_repr is None: + return features + return torch.cat([features, metadata_repr], dim=1) + + def _fused_features( + self, + clinical_features: torch.Tensor, + dermoscopic_features: torch.Tensor, + clinical_repr: torch.Tensor, + dermoscopic_repr: torch.Tensor, + metadata_repr: torch.Tensor | None, + ) -> torch.Tensor: + if self.image_fusion == "concat": + fused = torch.cat([clinical_repr, dermoscopic_repr], dim=1) + elif self.image_fusion == "cross_attention": + tokens = torch.stack([clinical_repr, dermoscopic_repr], dim=1) + attended, _ = self.cross_attention(tokens, tokens, tokens) + fused = self.cross_attention_norm(attended.reshape(attended.size(0), -1)) + elif self.image_fusion == "co_attention": + tokens = torch.stack([clinical_repr, dermoscopic_repr], dim=1) + attended, _ = self.co_attention(tokens, tokens, tokens) + updated = tokens + attended + fused = torch.cat( + [ + clinical_repr, + dermoscopic_repr, + updated[:, 0], + updated[:, 1], + ], + dim=1, + ) + fused = self.co_attention_norm(fused) + elif self.image_fusion in ("compact_bilinear", "low_rank_bilinear"): + bilinear = self.bilinear_clinical(clinical_repr) * self.bilinear_dermoscopic(dermoscopic_repr) + bilinear = self.bilinear_norm(bilinear) + fused = torch.cat([clinical_repr, dermoscopic_repr, bilinear], dim=1) + elif self.image_fusion == "adaptive_gate": + gate_input = torch.cat([clinical_repr, dermoscopic_repr], dim=1) + if metadata_repr is not None: + gate_input = torch.cat([gate_input, metadata_repr], dim=1) + gate = self.image_gate(gate_input) + gated = gate * clinical_repr + (1.0 - gate) * dermoscopic_repr + fused = torch.cat([gated, torch.abs(clinical_repr - dermoscopic_repr), clinical_repr * dermoscopic_repr], dim=1) + elif self.image_fusion == "shared_private": + clinical_shared = self.shared_head(clinical_features) + dermoscopic_shared = self.shared_head(dermoscopic_features) + shared = 0.5 * (clinical_shared + dermoscopic_shared) + fused = torch.cat([clinical_repr, dermoscopic_repr, shared], dim=1) + else: + raise ValueError(f"Unsupported image_fusion: {self.image_fusion}") + return self._append_metadata(fused, metadata_repr) + + def _moe_logits( + self, + clinical_repr: torch.Tensor, + dermoscopic_repr: torch.Tensor, + metadata_repr: torch.Tensor | None, + ) -> torch.Tensor: + clinical_input = self._append_metadata(clinical_repr, metadata_repr) + dermoscopic_input = self._append_metadata(dermoscopic_repr, metadata_repr) + joint_input = self._append_metadata(torch.cat([clinical_repr, dermoscopic_repr], dim=1), metadata_repr) + expert_logits = torch.stack( + [ + self.clinical_expert(clinical_input), + self.dermoscopic_expert(dermoscopic_input), + self.joint_expert(joint_input), + ], + dim=1, + ) + router_weights = torch.softmax(self.expert_router(joint_input), dim=1) + return (expert_logits * router_weights[:, :, None]).sum(dim=1) def encode_with_metadata_gate( self, diff --git a/milk10k_effb2_metadata/runner.py b/milk10k_effb2_metadata/runner.py index 4b39d6e160bc3dab5624ef6da0e572de33b72980..eda03cc8d9d46bcb51ce88ae0c62ea6de61bc063 100644 --- a/milk10k_effb2_metadata/runner.py +++ b/milk10k_effb2_metadata/runner.py @@ -19,7 +19,7 @@ from milk10k_effb2_metadata.data import ( ) from milk10k_effb2_metadata.engine import train_phase from milk10k_effb2_metadata.losses import build_loss -from milk10k_effb2_metadata.metrics import compute_metrics, predict, save_predictions +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.training_utils import json_safe, save_kfold_summary, save_run_config @@ -100,9 +100,14 @@ def run_training_split( print( f"Metadata mode: disable_metadata={args.disable_metadata}, " f"freeze_metadata_head={args.freeze_metadata_head}, metadata_lr={args.metadata_lr}, " - f"fusion={args.metadata_fusion}, gate_hidden_dim={args.metadata_gate_hidden_dim}" + 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}") + 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": + print(f"Soft-F1 class controls: ignore={args.f1_ignore_classes}, weights={args.f1_class_weight}") if args.loss == "ldam" and args.class_weight: print("Note: --class-weight is ignored for --loss ldam because LDAM+DRW uses effective-number alpha.") if tail_config is not None: @@ -170,10 +175,41 @@ def run_training_split( model.load_state_dict(checkpoint["model_state"]) y_true, y_prob = predict(model, val_loader, device) metrics, per_class_df, cm = compute_metrics(y_true, y_prob, class_names) - metrics = {"best_val_f1_macro": float(best_val_f1), **metrics} + 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, + **metrics, + } if tail_config is not None: metrics["best_val_tail_recall_macro"] = float(best_val_tail_recall) metrics["tail_class_names"] = tail_config["tail_class_names"] + if args.calibrate_bias: + class_bias, calibrated_score = optimize_class_bias( + y_true, + y_prob, + class_names, + metric_name=args.calibration_metric, + max_bias=args.calibration_max_bias, + step=args.calibration_step, + passes=args.calibration_passes, + ) + calibrated_prob = apply_class_bias(y_prob, class_bias) + calibrated_metrics, calibrated_per_class_df, calibrated_cm = compute_metrics(y_true, calibrated_prob, class_names) + calibration_payload = { + "metric": args.calibration_metric, + "optimized_score": float(calibrated_score), + "class_names": class_names, + "class_bias": [float(item) for item in class_bias.tolist()], + "metrics": calibrated_metrics, + } + with open(output_dir / "calibration.json", "w", encoding="utf-8") as f: + json.dump(json_safe(calibration_payload), f, indent=2) + calibrated_per_class_df.to_csv(output_dir / "per_class_metrics_calibrated.csv", index=False) + pd.DataFrame(calibrated_cm, index=class_names, columns=class_names).to_csv( + output_dir / "confusion_matrix_calibrated.csv" + ) + metrics["calibrated"] = calibrated_metrics with open(output_dir / "metrics.json", "w", encoding="utf-8") as f: json.dump(json_safe(metrics), f, indent=2) pd.DataFrame(cm, index=class_names, columns=class_names).to_csv(output_dir / "confusion_matrix.csv") diff --git a/milk10k_effb2_metadata/training_utils.py b/milk10k_effb2_metadata/training_utils.py index 96df26e95c0a775a4d5d85ced5c23db69a0e3687..83a29b0a597d6f31b863c18badb2dc0f8d1b70a9 100644 --- a/milk10k_effb2_metadata/training_utils.py +++ b/milk10k_effb2_metadata/training_utils.py @@ -33,7 +33,8 @@ def save_run_config( "train_size": len(train_df), "val_size": len(val_df), "fold": fold, - "fusion": args.metadata_fusion, + "metadata_fusion": args.metadata_fusion, + "image_fusion": getattr(args, "image_fusion", "concat"), "clinical_backbone": f"{clinical_backbone_backend} {args.backbone}", "dermoscopic_backbone": f"{dermoscopic_backbone_backend} {args.backbone}", } @@ -46,9 +47,11 @@ def save_kfold_summary(fold_metrics: list[dict[str, Any]], output_dir: Path) -> summary_keys = [ "best_val_f1_macro", + "best_selection_metric", "best_val_tail_recall_macro", "accuracy", "balanced_accuracy", + "dice_macro", "f1_macro", "roc_auc_macro_ovr", "top3_accuracy", diff --git a/tests/__pycache__/test_fusion_and_f1_loss.cpython-314.pyc b/tests/__pycache__/test_fusion_and_f1_loss.cpython-314.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4b9e8c365cae97d8dd1b008dd5f592051eab6535 Binary files /dev/null and b/tests/__pycache__/test_fusion_and_f1_loss.cpython-314.pyc differ diff --git a/tests/test_fusion_and_f1_loss.py b/tests/test_fusion_and_f1_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..025cf7b220d5d836f3a91938e26f330b663715ab --- /dev/null +++ b/tests/test_fusion_and_f1_loss.py @@ -0,0 +1,170 @@ +from __future__ import annotations + +import argparse +import tempfile +from pathlib import Path +import unittest +from unittest.mock import patch + +MISSING_DEPENDENCY: str | None = None + +try: + import torch + from torch import nn +except ModuleNotFoundError as exc: # pragma: no cover - local minimal env may omit ML deps. + MISSING_DEPENDENCY = exc.name + +if MISSING_DEPENDENCY is None: + try: + import numpy as np + import pandas as pd + 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 + except ModuleNotFoundError as exc: # pragma: no cover - local minimal env may omit ML deps. + MISSING_DEPENDENCY = exc.name + + +if MISSING_DEPENDENCY is not None: + class MissingDependencyTest(unittest.TestCase): + @unittest.skip(f"Missing ML test dependency: {MISSING_DEPENDENCY}") + def test_missing_dependency(self) -> None: + pass + + +if MISSING_DEPENDENCY is None: + class FakeEncoder(nn.Module): + def __init__(self, feature_dim: int) -> None: + super().__init__() + self.feature_dim = feature_dim + + def forward(self, images: torch.Tensor) -> torch.Tensor: + pooled = images.mean(dim=(2, 3)) + repeats = (self.feature_dim + pooled.size(1) - 1) // pooled.size(1) + return pooled.repeat(1, repeats)[:, : self.feature_dim] + + def features(self, images: torch.Tensor) -> torch.Tensor: + return self.forward(images).view(images.size(0), self.feature_dim, 1, 1) + + + def fake_build_feature_encoder(backbone: str, backbone_backend: str, imagenet_pretrained: bool): + feature_dim = 16 + return FakeEncoder(feature_dim), feature_dim + + + class FusionSmokeTest(unittest.TestCase): + def test_all_image_and_metadata_fusions_forward(self) -> None: + modes = [ + "concat", + "cross_attention", + "co_attention", + "compact_bilinear", + "low_rank_bilinear", + "adaptive_gate", + "moe", + "shared_private", + ] + metadata_modes = ["concat", "gated_concat", "gated_only"] + with patch("milk10k_effb2_metadata.models.build_feature_encoder", side_effect=fake_build_feature_encoder): + for mode in modes: + for metadata_mode in metadata_modes: + with self.subTest(mode=mode, metadata_mode=metadata_mode): + model = DualEffB2MetadataClassifier( + 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="torchvision", + dermoscopic_backbone_backend="torchvision", + backbone="efficientnet_b2", + metadata_fusion=metadata_mode, + image_fusion=mode, + ) + 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)) + + def test_expected_fused_dims(self) -> None: + branch_dim = 8 + metadata_dim = 6 + expected = { + "concat": 22, + "cross_attention": 22, + "co_attention": 38, + "compact_bilinear": 30, + "low_rank_bilinear": 30, + "adaptive_gate": 30, + "shared_private": 30, + "moe": 22, + } + for mode, expected_dim in expected.items(): + with self.subTest(mode=mode): + self.assertEqual(DualEffB2MetadataClassifier._fusion_dim(branch_dim, metadata_dim, mode), expected_dim) + gated_only_dim = expected_dim - metadata_dim + self.assertEqual(DualEffB2MetadataClassifier._fusion_dim(branch_dim, 0, mode), gated_only_dim) + + + class MetadataAugmentationTest(unittest.TestCase): + def test_image_transform_does_not_change_metadata(self) -> None: + with tempfile.TemporaryDirectory() as tmp_dir: + image_path = Path(tmp_dir) / "image.jpg" + Image.fromarray(np.full((8, 8, 3), 127, dtype=np.uint8)).save(image_path) + df = pd.DataFrame( + [ + { + "lesion_id": "L1", + "label": "BCC", + "clinical_path": str(image_path), + "dermoscopic_path": str(image_path), + "clinical_age_approx": 60, + "dermoscopic_age_approx": 60, + "clinical_skin_tone_class": 3, + "dermoscopic_skin_tone_class": 3, + "clinical_sex": "female", + "dermoscopic_sex": "female", + "clinical_site": "arm", + "dermoscopic_site": "arm", + } + ] + ) + metadata_spec = {"sex_values": ["female"], "site_values": ["arm"], "monet_columns": []} + + def noisy_transform(image): + return torch.rand(3, image.height, image.width) + + dataset = PairedMilk10kMetadataDataset(df, {"BCC": 0}, metadata_spec, noisy_transform) + first = dataset[0]["metadata"] + second = dataset[0]["metadata"] + self.assertTrue(torch.equal(first, second)) + + + class F1LossControlTest(unittest.TestCase): + def test_f1_class_controls_ignore_and_weight_classes(self) -> None: + label_to_idx = {"BCC": 0, "MAL_OTH": 1, "MEL": 2} + args = argparse.Namespace(f1_ignore_classes=["MAL_OTH"], f1_class_weight=["BCC=2.5"]) + weights = f1_class_weight_tensor(label_to_idx, args, torch.device("cpu")) + self.assertTrue(torch.equal(weights, torch.tensor([2.5, 0.0, 1.0]))) + + def test_soft_f1_ignores_zero_weight_class(self) -> None: + logits = torch.tensor([[4.0, 0.0, 0.0], [0.0, 4.0, 0.0], [0.0, 0.0, 4.0]]) + labels = torch.tensor([0, 1, 2]) + masked = SoftMacroF1Loss(torch.tensor([1.0, 0.0, 1.0]))(logits, labels) + probs = torch.softmax(logits, dim=1) + one_hot = torch.nn.functional.one_hot(labels, num_classes=3).float() + tp = (probs * one_hot).sum(dim=0) + fp = (probs * (1.0 - one_hot)).sum(dim=0) + fn = ((1.0 - probs) * one_hot).sum(dim=0) + f1 = (2.0 * tp + 1e-6) / (2.0 * tp + fp + fn + 1e-6) + manual = 1.0 - (f1[0] + f1[2]) / 2.0 + self.assertAlmostEqual(float(masked), float(manual), places=6) + + +if __name__ == "__main__": + unittest.main()