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--git a/DeepIVUS.py b/DeepIVUS.py new file mode 100644 index 0000000000000000000000000000000000000000..43fa87370f482c5f18833c4e816cf4e7bc10ea6f --- /dev/null +++ b/DeepIVUS.py @@ -0,0 +1,11 @@ +"""Compatibility entrypoint for the DeepIVUS CLI. + +Implementation has been moved into the ``deepivus`` package to keep modules +small and easier to maintain. +""" + +from deepivus.cli import cli + + +if __name__ == "__main__": + cli() diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1e6580ff9752b37742a885c7447ff15b2ddb98fb --- /dev/null +++ b/README.md @@ -0,0 +1,138 @@ +# IVUS-Segmentation + +DeepIVUS pipeline for lumen segmentation and bifurcation frame classification on IVUS DICOMs. + +## What This Repo Contains + +- Runtime package: `deepivus/` + - CLI entrypoints and pipeline orchestration + - Lumen segmentation inference + - Bifurcation classifier inference + - Video/JSON/XML export utilities +- Data and annotations: + - `data/`: source DICOM folders (`data/bifurcation`, `data/paul`) + - `evals/frame_bank_merged/`: canonical annotation bank + - `evals/splits/ivus_split_merged_600.json`: canonical train/val/test split +- Models: + - `models/standalone/lumen/`: standalone lumen TF SavedModel + - `models/standalone/bifurcation/best_bifurcation_classifier.keras`: standalone bifurcation classifier + - `models/standalone/bifurcation/threshold.json`: selected inference threshold (from validation sweep) +- Training and evaluation scripts: + - `scripts/finetune/bifurcation/` + - `scripts/finetune/lumen/` + - shared helpers: `scripts/finetune/shared/common.py` + - data tooling: `scripts/data/frame_bank.py` + +## Main Runtime Workflow + +### 1) Segment a DICOM and classify branching + +```bash +python DeepIVUS.py segment data/paul/FILE00005.dcm +``` + +Outputs are written under `output//` and include: + +- contours XML and JSONL +- top-confidence JSONL +- lumen overlay video +- bifurcation predictions JSONL + summary JSON +- overlay video with branch/non-branch flag + +Notes: + +- `--bifurcation-threshold` is optional. +- If omitted, threshold is loaded from `threshold.json` beside the selected bifurcation model. + +### 2) Edit contour annotations in GUI + +```bash +python DeepIVUS.py edit-annotations data/paul/FILE00005.dcm +``` + +## Bifurcation Model Workflow + +### 1) Sample new frames for manual labeling + +```bash +python -u scripts/finetune/bifurcation/sample_new_bifurcation_frames.py +``` + +### 2) Label sampled frames in GUI + +```bash +python -u scripts/finetune/bifurcation/annotate_bifurcation_samples.py +``` + +### 3) Merge labels into canonical frame bank + +```bash +python -u scripts/finetune/bifurcation/merge_bifurcation_annotations.py +``` + +### 4) Create/update canonical split + +```bash +python -u scripts/finetune/bifurcation/create_bifurcation_splits.py +``` + +### 5) Train bifurcation classifier + +```bash +python -u scripts/finetune/bifurcation/train_bifurcation_classifier.py +``` + +### 6) Evaluate on test and select threshold from validation + +```bash +python -u scripts/finetune/bifurcation/run_bifurcation_test_inference.py +``` + +This writes test metrics and persists selected threshold to: + +- `threshold.json` beside the evaluated classifier model file. + +## Lumen Model Workflow + +### 1) Identify lumen class index in SavedModel logits + +```bash +python -u scripts/finetune/lumen/identify_lumen_class.py +``` + +### 2) Fine-tune lumen model + +```bash +python -u scripts/finetune/lumen/finetune_lumen_from_saved_model.py \ + --output-model-dir models/standalone/lumen +``` + +### 3) Evaluate lumen model on test split + +```bash +python -u scripts/finetune/lumen/run_test_inference.py \ + --model-dir models/standalone/lumen +``` + +### 4) Run single-DICOM lumen + bifurcation overlay inference + +```bash +python -u scripts/finetune/lumen/run_single_dicom_inference.py \ + --dicom-path data/paul/FILE00005.dcm +``` + +## Repository Structure + +- `DeepIVUS.py`: 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0000000000000000000000000000000000000000..f0c213c3f7c83fcfa4a69231b71b0d1cb28e02f4 --- /dev/null +++ b/deepivus/__init__.py @@ -0,0 +1,5 @@ +"""DeepIVUS modular package.""" + +__all__ = [ + "cli", +] diff --git a/deepivus/analysis/__init__.py b/deepivus/analysis/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ddcb67952ef1f9ebcb15812cf444baf8163aac64 --- /dev/null +++ b/deepivus/analysis/__init__.py @@ -0,0 +1,13 @@ +"""Analysis subpackage.""" + +from .scoring import ( + compute_oblongness_scores, + detect_online_bifurcation_signal, + detect_sustained_bifurcation_signal, +) + +__all__ = [ + "compute_oblongness_scores", + "detect_sustained_bifurcation_signal", + "detect_online_bifurcation_signal", +] diff --git a/deepivus/analysis/scoring.py b/deepivus/analysis/scoring.py new file mode 100644 index 0000000000000000000000000000000000000000..a94d3f16a1a6ee44aa7c4fcad6103b707c6517ed --- /dev/null +++ b/deepivus/analysis/scoring.py @@ -0,0 +1,162 @@ +"""Scoring and event-detection utilities for lumen shape stability.""" + +from typing import Tuple + +import numpy as np + + +def compute_oblongness_scores(lumen_masks: np.ndarray) -> np.ndarray: + """Compute per-frame oblongness score in [0, 1] from binary lumen masks.""" + scores = np.zeros((lumen_masks.shape[0],), dtype=np.float32) + for idx in range(lumen_masks.shape[0]): + ys, xs = np.where(lumen_masks[idx] > 0) + if ys.size < 20: + scores[idx] = np.float32(0.0) + continue + + x = xs.astype(np.float32) + y = ys.astype(np.float32) + x -= float(np.mean(x)) + y -= float(np.mean(y)) + cov = np.cov(np.vstack((x, y))) + eigvals = np.linalg.eigvalsh(cov) + eigvals = np.maximum(eigvals, 1e-6) + axis_ratio = float(np.sqrt(eigvals[-1] / eigvals[0])) + scores[idx] = np.float32(np.clip((axis_ratio - 1.0) / (axis_ratio + 1.0), 0.0, 1.0)) + return scores + + +def moving_average(x: np.ndarray, window: int) -> np.ndarray: + """Centered moving-average smoothing.""" + if window <= 1: + return x.copy() + kernel = np.ones((window,), dtype=np.float32) / float(window) + pad = window // 2 + x_pad = np.pad(x.astype(np.float32), (pad, pad), mode="edge") + return np.convolve(x_pad, kernel, mode="valid") + + +def causal_moving_average(x: np.ndarray, window: int) -> np.ndarray: + """Past-only moving average for online detection.""" + x = x.astype(np.float32) + if window <= 1: + return x.copy() + out = np.zeros_like(x, dtype=np.float32) + csum = np.cumsum(x, dtype=np.float32) + for idx in range(x.shape[0]): + start = max(0, idx - window + 1) + total = csum[idx] - (csum[start - 1] if start > 0 else 0.0) + out[idx] = total / float(idx - start + 1) + return out + + +def keep_sustained_runs(flags: np.ndarray, min_run: int) -> np.ndarray: + """Keep only True runs with length >= ``min_run``.""" + kept = np.zeros_like(flags, dtype=bool) + start = None + for idx, val in enumerate(flags): + if val and start is None: + start = idx + if (not val or idx == len(flags) - 1) and start is not None: + end = idx if not val else idx + 1 + if (end - start) >= min_run: + kept[start:end] = True + start = None + return kept + + +def detect_sustained_bifurcation_signal(oblong_scores: np.ndarray, fps: float) -> Tuple[np.ndarray, np.ndarray]: + """Detect sustained high-oblongness intervals and return (smoothed_scores, flags).""" + smooth_window = max(5, int(round(max(fps, 1.0) * 0.5))) + if smooth_window % 2 == 0: + smooth_window += 1 + smoothed = moving_average(oblong_scores, smooth_window) + + median = float(np.median(smoothed)) + mad = float(np.median(np.abs(smoothed - median))) + robust_scale = max(1e-6, 1.4826 * mad) + robust_z = (smoothed - median) / robust_scale + + candidate = (smoothed > 0.35) & (robust_z > 2.5) + min_run = max(5, int(round(max(fps, 1.0) * 0.4))) + sustained = keep_sustained_runs(candidate.astype(bool), min_run=min_run) + return smoothed.astype(np.float32), sustained + + +def detect_online_bifurcation_signal(oblong_scores: np.ndarray, fps: float) -> Tuple[np.ndarray, np.ndarray]: + """Online bifurcation detector using only past information. + + Algorithm: + - Causal smoothing (past-only moving average). + - Running robust baseline from a fixed history window. + - Incremental sustained-run confirmation (no future lookahead). + """ + x = np.asarray(oblong_scores, dtype=np.float32) + if x.size == 0: + return x.copy(), np.zeros((0,), dtype=bool) + + smooth_window = max(5, int(round(max(fps, 1.0) * 0.35))) + smoothed = causal_moving_average(x, smooth_window) + + baseline_window = max(24, int(round(max(fps, 1.0) * 8.0))) + warmup = max(12, int(round(max(fps, 1.0) * 1.0))) + min_run = max(6, int(round(max(fps, 1.0) * 0.4))) + + sustained = np.zeros((x.shape[0],), dtype=bool) + run_len = 0 + misses_left = 0 + active = False + + # Causal slope signal helps detect onset before level becomes very high. + slope = np.zeros_like(smoothed, dtype=np.float32) + slope[1:] = smoothed[1:] - smoothed[:-1] + + for idx in range(x.shape[0]): + hist_start = max(0, idx - baseline_window) + history = smoothed[hist_start:idx] # past only + if history.size < warmup: + run_len = 0 + misses_left = 0 + continue + + median = float(np.median(history)) + mad = float(np.median(np.abs(history - median))) + robust_scale = max(1e-6, 1.4826 * mad) + robust_z = (float(smoothed[idx]) - median) / robust_scale + + q90 = float(np.quantile(history, 0.9)) + dyn_level = max(0.31, q90 + 0.05) + + slope_hist = slope[hist_start:idx] + slope_median = float(np.median(slope_hist)) + slope_mad = float(np.median(np.abs(slope_hist - slope_median))) + slope_scale = max(1e-6, 1.4826 * slope_mad) + slope_z = (float(slope[idx]) - slope_median) / slope_scale + + level = float(smoothed[idx]) + enter_candidate = ( + (level >= dyn_level and robust_z > 1.95) + or (level >= 0.29 and robust_z > 1.3 and slope_z > 2.6) + ) + continue_candidate = ( + (level >= max(0.27, dyn_level - 0.02) and robust_z > 1.2) + or (level >= 0.25 and robust_z > 1.0 and slope_z > 1.8) + ) + is_candidate = continue_candidate if active else enter_candidate + + if is_candidate: + run_len += 1 + misses_left = 1 + active = True + if run_len >= min_run: + # Confirm sustained event using only history seen so far. + sustained[idx - min_run + 1 : idx + 1] = True + else: + # Allow a single miss so short dips do not break a sustained run. + if misses_left > 0 and run_len > 0: + misses_left -= 1 + else: + run_len = 0 + active = False + + return smoothed.astype(np.float32), sustained diff --git a/deepivus/cli.py b/deepivus/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..f4d12683461d133594f4f96ebd17530701ed6f90 --- /dev/null +++ b/deepivus/cli.py @@ -0,0 +1,138 @@ +"""CLI entrypoints for DeepIVUS.""" + +import datetime +import os +from typing import Optional + +import click +from deepivus.config import resolve_bifurcation_threshold + + +@click.group() +def cli() -> None: + """DeepIVUS CLI.""" + + +@cli.command("segment") +@click.argument("dicom_path", type=click.Path(exists=True, dir_okay=False)) +@click.option( + "--output-prefix", + "-o", + default=None, + type=str, + help="Output path prefix (without extension). Defaults to output//.", +) +@click.option( + "--fps", + default=None, + type=float, + help="Overlay video FPS. Defaults to DICOM CineRate or 30.", +) +@click.option( + "--bifurcation-threshold", + default=None, + show_default=False, + type=click.FloatRange(min=0.0, max=1.0), + help="Threshold for bifurcation classifier labels. Defaults to threshold.json beside the selected bifurcation model.", +) +@click.option( + "--framewise/--no-framewise", + default=False, + show_default=True, + help="Run inference frame-by-frame (batch_size=1) to simulate realtime processing.", +) +def segment_cmd( + dicom_path: str, + output_prefix: Optional[str], + fps: Optional[float], + bifurcation_threshold: Optional[float], + framewise: bool, +) -> None: + """Segment lumen and classify bifurcation for all frames.""" + from .pipeline import segment_and_export + + if output_prefix is None: + stem = os.path.splitext(os.path.basename(dicom_path))[0] + output_root = os.path.join(os.getcwd(), "output") + os.makedirs(output_root, exist_ok=True) + + run_folder = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + output_dir = os.path.join(output_root, run_folder) + suffix = 1 + while os.path.exists(output_dir): + output_dir = os.path.join(output_root, f"{run_folder}_{suffix}") + suffix += 1 + + os.makedirs(output_dir, exist_ok=True) + output_prefix = os.path.join(output_dir, stem) + else: + output_dir = os.path.dirname(os.path.abspath(output_prefix)) + os.makedirs(output_dir, exist_ok=True) + + if bifurcation_threshold is None: + bifurcation_threshold = resolve_bifurcation_threshold(default=0.5) + + ( + xml_path, + json_path, + top_conf_json_path, + video_path, + bif_overlay_video_path, + bif_json_path, + bif_summary_path, + ) = segment_and_export( + dicom_path, + output_prefix, + fps, + bifurcation_threshold, + framewise=framewise, + ) + + click.echo(f"Contours XML: {xml_path}") + click.echo(f"Contours JSONL: {json_path}") + click.echo(f"Top confidence JSONL: {top_conf_json_path}") + click.echo(f"Overlay video: {video_path}") + click.echo(f"Overlay video (with bifurcation flags): {bif_overlay_video_path}") + click.echo(f"Bifurcation predictions JSONL: {bif_json_path}") + click.echo(f"Bifurcation summary JSON: {bif_summary_path}") + + +@cli.command("edit-annotations") +@click.argument("dicom_path", type=click.Path(exists=True, dir_okay=False)) +@click.option( + "--annotations-path", + default=None, + type=click.Path(exists=True, dir_okay=False), + help=( + "Base contour JSONL to edit. " + "Defaults to latest output//_contours.jsonl." + ), +) +@click.option( + "--edits-path", + default=None, + type=click.Path(dir_okay=False), + help="Path for edited annotations JSONL. Defaults beside base file as *_edited_annotations.jsonl.", +) +@click.option( + "--output-root", + default="output", + show_default=True, + type=click.Path(file_okay=False), + help="Root folder containing pipeline outputs used for default annotation discovery.", +) +def edit_annotations_cmd( + dicom_path: str, + annotations_path: Optional[str], + edits_path: Optional[str], + output_root: str, +) -> None: + """Open GUI editor to review and adjust contour coordinates frame-by-frame.""" + from .gui import launch_annotation_editor + + launch_annotation_editor( + dicom_path=dicom_path, + annotations_path=annotations_path, + edits_path=edits_path, + output_root=output_root, + ) diff --git a/deepivus/config.py b/deepivus/config.py new file mode 100644 index 0000000000000000000000000000000000000000..6ae0fd611a32d23df1251fff79b6db582d0199f1 --- /dev/null +++ b/deepivus/config.py @@ -0,0 +1,95 @@ +"""Runtime configuration and model path resolution.""" + +from __future__ import annotations + +import os +from pathlib import Path + + +ROOT_DIR = Path(__file__).resolve().parents[1] +MODELS_DIR = ROOT_DIR / "models" +STANDALONE_MODELS_DIR = MODELS_DIR / "standalone" + +MULTITASK_DIR = MODELS_DIR / "multitask" +MULTITASK_LUMEN_MODEL_DIR = MULTITASK_DIR / "lumen_multitask_base" +MULTITASK_BIFURCATION_HEAD_PATH = MULTITASK_DIR / "bifurcation_head.keras" + +LUMEN_MODEL_DIR = STANDALONE_MODELS_DIR / "lumen" +BIFURCATION_MODEL_PATH = STANDALONE_MODELS_DIR / "bifurcation" / "best_bifurcation_classifier.keras" +BIFURCATION_THRESHOLD_PATH = STANDALONE_MODELS_DIR / "bifurcation" / "threshold.json" + +LEGACY_LUMEN_MODEL_DIRS = [ + MODELS_DIR / "lumen", + ROOT_DIR / "model_finetuned_lumen", + ROOT_DIR / "model", +] +LEGACY_BIFURCATION_MODEL_PATHS = [ + MODELS_DIR / "bifurcation" / "best_bifurcation_classifier.keras", + ROOT_DIR / "output" / "bifurcation_classifier" / "best_bifurcation_classifier.keras", +] +LEGACY_BIFURCATION_THRESHOLD_PATHS = [ + MODELS_DIR / "bifurcation" / "threshold.json", +] + + +def resolve_lumen_model_dir() -> Path: + env_path = os.getenv("DEEPIVUS_LUMEN_MODEL_DIR") + if env_path: + return Path(env_path) + # Prefer multitask lumen base if available. + if MULTITASK_LUMEN_MODEL_DIR.exists(): + return MULTITASK_LUMEN_MODEL_DIR + if LUMEN_MODEL_DIR.exists(): + return LUMEN_MODEL_DIR + for path in LEGACY_LUMEN_MODEL_DIRS: + if path.exists(): + return path + return LUMEN_MODEL_DIR + + +def resolve_bifurcation_model_path() -> Path: + env_path = os.getenv("DEEPIVUS_BIF_MODEL_PATH") + if env_path: + return Path(env_path) + # Prefer multitask bifurcation head if available. + if MULTITASK_BIFURCATION_HEAD_PATH.exists(): + return MULTITASK_BIFURCATION_HEAD_PATH + if BIFURCATION_MODEL_PATH.exists(): + return BIFURCATION_MODEL_PATH + for path in LEGACY_BIFURCATION_MODEL_PATHS: + if path.exists(): + return path + return BIFURCATION_MODEL_PATH + + +def resolve_bifurcation_threshold_path(model_path: Path | None = None) -> Path: + env_path = os.getenv("DEEPIVUS_BIF_THRESHOLD_PATH") + if env_path: + return Path(env_path) + if model_path is None: + model_path = resolve_bifurcation_model_path() + return Path(model_path).parent / "threshold.json" + + +def resolve_bifurcation_threshold(default: float = 0.5, model_path: Path | None = None) -> float: + primary = resolve_bifurcation_threshold_path(model_path=model_path) + candidate_paths = [primary, BIFURCATION_THRESHOLD_PATH, *LEGACY_BIFURCATION_THRESHOLD_PATHS] + seen: set[Path] = set() + for path in candidate_paths: + path = Path(path) + if path in seen: + continue + seen.add(path) + if not path.exists(): + continue + try: + import json + + payload = json.loads(path.read_text(encoding="utf-8")) + value = payload.get("selected_threshold", payload.get("threshold", default)) + value = float(value) + if 0.0 <= value <= 1.0: + return value + except Exception: + continue + return float(default) diff --git a/deepivus/gui/__init__.py b/deepivus/gui/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..55609c078ad185c8a929c68a79a3c57a4de1a704 --- /dev/null +++ b/deepivus/gui/__init__.py @@ -0,0 +1,5 @@ +"""GUI helpers for annotation inspection and editing.""" + +from .annotation_editor import launch_annotation_editor + +__all__ = ["launch_annotation_editor"] diff --git a/deepivus/gui/annotation_editor.py b/deepivus/gui/annotation_editor.py new file mode 100644 index 0000000000000000000000000000000000000000..e37fe131d3e92ddc06bcc7748dcc8bd8257d0b4d --- /dev/null +++ b/deepivus/gui/annotation_editor.py @@ -0,0 +1,428 @@ +"""Interactive GUI for editing per-frame IVUS contour annotations.""" + +from __future__ import annotations + +import json +import os +from dataclasses import dataclass +from datetime import datetime +from glob import glob +from typing import Any + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.lines import Line2D +from matplotlib.widgets import Button, Slider + +from ..io.dicom import read_dicom + + +@dataclass +class FrameState: + """In-memory annotation state for one frame.""" + + frame: int + lumen_x: list[float] + lumen_y: list[float] + plaque_x: list[float] + plaque_y: list[float] + lumen_confidence: float | None + plaque_confidence: float | None + bifurcation: bool = False + + +class AnnotationEditor: + """Matplotlib-based editor for contour annotations.""" + + def __init__( + self, + dicom_path: str, + annotations_path: str, + edits_path: str, + ) -> None: + self.dicom_path = dicom_path + self.annotations_path = annotations_path + self.edits_path = edits_path + + _, images = read_dicom(dicom_path) + self.images = images + + self.frame_states = self._load_base_annotations(annotations_path) + if len(self.frame_states) != images.shape[0]: + raise ValueError( + "Frame count mismatch between DICOM and annotations: " + f"{images.shape[0]} vs {len(self.frame_states)}" + ) + + self._load_existing_edits(edits_path) + self._ensure_edits_file_header() + + self.current_frame = 0 + self.drag_target: tuple[str, int] | None = None + self.drag_threshold_px = 10.0 + self.frame_dirty = False + + self.fig = None + self.ax_image = None + self.image_artist = None + self.lumen_line: Line2D | None = None + self.plaque_line: Line2D | None = None + self.lumen_points = None + self.plaque_points = None + + self.slider: Slider | None = None + self.status_text = None + self.is_updating_slider = False + + @staticmethod + def _safe_float(value: Any) -> float | None: + if value is None: + return None + try: + out = float(value) + except (TypeError, ValueError): + return None + if np.isnan(out): + return None + return out + + def _load_base_annotations(self, path: str) -> list[FrameState]: + states: list[FrameState] = [] + with open(path, "r", encoding="utf-8") as fp: + for raw in fp: + line = raw.strip() + if not line: + continue + rec = json.loads(line) + if rec.get("record_type") != "frame": + continue + states.append( + FrameState( + frame=int(rec["frame"]), + lumen_x=[float(v) for v in rec.get("lumen", {}).get("x", [])], + lumen_y=[float(v) for v in rec.get("lumen", {}).get("y", [])], + plaque_x=[float(v) for v in rec.get("plaque", {}).get("x", [])], + plaque_y=[float(v) for v in rec.get("plaque", {}).get("y", [])], + lumen_confidence=self._safe_float(rec.get("lumen_confidence")), + plaque_confidence=self._safe_float(rec.get("plaque_confidence")), + bifurcation=bool(rec.get("bifurcation", False)), + ) + ) + + states.sort(key=lambda s: s.frame) + expected = list(range(len(states))) + actual = [s.frame for s in states] + if actual != expected: + raise ValueError( + "Base annotations must contain contiguous frame records in order " + f"(got first frames: {actual[:10]})" + ) + return states + + def _load_existing_edits(self, path: str) -> None: + if not os.path.exists(path): + return + + latest: dict[int, dict[str, Any]] = {} + with open(path, "r", encoding="utf-8") as fp: + for raw in fp: + line = raw.strip() + if not line: + continue + rec = json.loads(line) + if rec.get("record_type") != "frame_edit": + continue + frame_idx = int(rec["frame"]) + latest[frame_idx] = rec + + for frame_idx, rec in latest.items(): + if frame_idx < 0 or frame_idx >= len(self.frame_states): + continue + state = self.frame_states[frame_idx] + state.lumen_x = [float(v) for v in rec.get("lumen", {}).get("x", state.lumen_x)] + state.lumen_y = [float(v) for v in rec.get("lumen", {}).get("y", state.lumen_y)] + state.plaque_x = [float(v) for v in rec.get("plaque", {}).get("x", state.plaque_x)] + state.plaque_y = [float(v) for v in rec.get("plaque", {}).get("y", state.plaque_y)] + state.bifurcation = bool(rec.get("bifurcation", state.bifurcation)) + + def _ensure_edits_file_header(self) -> None: + os.makedirs(os.path.dirname(os.path.abspath(self.edits_path)), exist_ok=True) + if os.path.exists(self.edits_path): + return + meta = { + "record_type": "meta", + "created_at": datetime.now().isoformat(timespec="seconds"), + "dicom_path": os.path.abspath(self.dicom_path), + "base_annotations_path": os.path.abspath(self.annotations_path), + "format": "append_only_frame_edits", + } + with open(self.edits_path, "w", encoding="utf-8") as fp: + fp.write(json.dumps(meta) + "\n") + + def _append_frame_edit(self, frame_idx: int, reason: str) -> None: + state = self.frame_states[frame_idx] + rec = { + "record_type": "frame_edit", + "saved_at": datetime.now().isoformat(timespec="seconds"), + "reason": reason, + "frame": frame_idx, + "bifurcation": state.bifurcation, + "lumen": {"x": state.lumen_x, "y": state.lumen_y}, + "plaque": {"x": state.plaque_x, "y": state.plaque_y}, + "lumen_confidence": state.lumen_confidence, + "plaque_confidence": state.plaque_confidence, + } + + with open(self.edits_path, "a", encoding="utf-8") as fp: + fp.write(json.dumps(rec) + "\n") + fp.flush() + os.fsync(fp.fileno()) + + self.frame_dirty = False + + def _build_ui(self) -> None: + self.fig = plt.figure(figsize=(13, 9)) + self.fig.canvas.manager.set_window_title("DeepIVUS Annotation Editor") + + self.ax_image = self.fig.add_axes([0.05, 0.22, 0.9, 0.74]) + self.ax_image.set_title("DeepIVUS Annotation Editor", fontsize=14, weight="bold") + self.ax_image.set_axis_off() + + slider_ax = self.fig.add_axes([0.12, 0.13, 0.76, 0.035]) + self.slider = Slider( + ax=slider_ax, + label="Frame", + valmin=0, + valmax=len(self.frame_states) - 1, + valinit=0, + valstep=1, + color="#1f77b4", + ) + + prev_ax = self.fig.add_axes([0.12, 0.05, 0.1, 0.055]) + next_ax = self.fig.add_axes([0.24, 0.05, 0.1, 0.055]) + save_ax = self.fig.add_axes([0.42, 0.05, 0.14, 0.055]) + bif_ax = self.fig.add_axes([0.72, 0.05, 0.16, 0.055]) + + self.prev_button = Button(prev_ax, "Prev Frame", color="#E0E0E0", hovercolor="#D0D0D0") + self.next_button = Button(next_ax, "Next Frame", color="#E0E0E0", hovercolor="#D0D0D0") + self.save_button = Button(save_ax, "Save Frame", color="#D6F5D6", hovercolor="#BFF0BF") + self.bif_button = Button(bif_ax, "Bifurcation: No", color="#F5D6D6", hovercolor="#F0BFBF") + + self.status_text = self.fig.text(0.05, 0.18, "", fontsize=10) + + self.slider.on_changed(self._on_slider) + self.prev_button.on_clicked(self._on_prev) + self.next_button.on_clicked(self._on_next) + self.save_button.on_clicked(self._on_save) + self.bif_button.on_clicked(self._on_toggle_bifurcation) + + self.fig.canvas.mpl_connect("button_press_event", self._on_press) + self.fig.canvas.mpl_connect("motion_notify_event", self._on_motion) + self.fig.canvas.mpl_connect("button_release_event", self._on_release) + self.fig.canvas.mpl_connect("key_press_event", self._on_key) + self.fig.canvas.mpl_connect("close_event", self._on_close) + + def _state(self) -> FrameState: + return self.frame_states[self.current_frame] + + def _render_frame(self) -> None: + state = self._state() + image = self.images[self.current_frame] + + if self.image_artist is None: + self.image_artist = self.ax_image.imshow(image, cmap="gray") + self.lumen_line = self.ax_image.plot([], [], color="#1db954", lw=2)[0] + self.plaque_line = self.ax_image.plot([], [], color="#ff5a5a", lw=2)[0] + self.lumen_points = self.ax_image.scatter([], [], c="#1db954", s=28, edgecolors="black", linewidths=0.4) + self.plaque_points = self.ax_image.scatter([], [], c="#ff5a5a", s=28, edgecolors="black", linewidths=0.4) + else: + self.image_artist.set_data(image) + + lumen_x, lumen_y = state.lumen_x, state.lumen_y + plaque_x, plaque_y = state.plaque_x, state.plaque_y + + self.lumen_line.set_data(lumen_x + lumen_x[:1], lumen_y + lumen_y[:1]) + self.plaque_line.set_data(plaque_x + plaque_x[:1], plaque_y + plaque_y[:1]) + + lumen_offsets = np.c_[lumen_x, lumen_y] if lumen_x and lumen_y else np.empty((0, 2)) + plaque_offsets = np.c_[plaque_x, plaque_y] if plaque_x and plaque_y else np.empty((0, 2)) + self.lumen_points.set_offsets(lumen_offsets) + self.plaque_points.set_offsets(plaque_offsets) + + bif_text = "Yes" if state.bifurcation else "No" + bif_color = "#D6F5D6" if state.bifurcation else "#F5D6D6" + self.bif_button.label.set_text(f"Bifurcation: {bif_text}") + self.bif_button.ax.set_facecolor(bif_color) + + self.status_text.set_text( + f"Frame {self.current_frame + 1}/{len(self.frame_states)} " + f"Lumen pts: {len(lumen_x)} Plaque pts: {len(plaque_x)} " + f"Autosave file: {os.path.basename(self.edits_path)}" + ) + self.fig.canvas.draw_idle() + + def _set_frame(self, frame_idx: int) -> None: + frame_idx = int(np.clip(frame_idx, 0, len(self.frame_states) - 1)) + if frame_idx == self.current_frame: + return + + if self.frame_dirty: + self._append_frame_edit(self.current_frame, reason="frame_change") + + self.current_frame = frame_idx + self.is_updating_slider = True + self.slider.set_val(frame_idx) + self.is_updating_slider = False + self._render_frame() + + def _nearest_point(self, x: float, y: float) -> tuple[str, int] | None: + state = self._state() + + def best_idx(xs: list[float], ys: list[float]) -> tuple[int, float] | None: + if not xs: + return None + pts = np.column_stack((np.asarray(xs), np.asarray(ys))) + dist = np.linalg.norm(pts - np.asarray([x, y]), axis=1) + idx = int(np.argmin(dist)) + return idx, float(dist[idx]) + + lumen = best_idx(state.lumen_x, state.lumen_y) + plaque = best_idx(state.plaque_x, state.plaque_y) + + choice: tuple[str, int, float] | None = None + if lumen is not None: + choice = ("lumen", lumen[0], lumen[1]) + if plaque is not None and (choice is None or plaque[1] < choice[2]): + choice = ("plaque", plaque[0], plaque[1]) + + if choice is None or choice[2] > self.drag_threshold_px: + return None + return choice[0], choice[1] + + def _on_slider(self, val: float) -> None: + if self.is_updating_slider: + return + self._set_frame(int(val)) + + def _on_prev(self, _event: Any) -> None: + self._set_frame(self.current_frame - 1) + + def _on_next(self, _event: Any) -> None: + self._set_frame(self.current_frame + 1) + + def _on_save(self, _event: Any) -> None: + self._append_frame_edit(self.current_frame, reason="manual_save") + self._render_frame() + + def _on_toggle_bifurcation(self, _event: Any) -> None: + state = self._state() + state.bifurcation = not state.bifurcation + self.frame_dirty = True + self._append_frame_edit(self.current_frame, reason="bifurcation_toggle") + self._render_frame() + + def _on_key(self, event: Any) -> None: + if event.key in {"left", "a"}: + self._set_frame(self.current_frame - 1) + elif event.key in {"right", "d"}: + self._set_frame(self.current_frame + 1) + elif event.key == "s": + self._on_save(event) + elif event.key == "b": + self._on_toggle_bifurcation(event) + + def _on_press(self, event: Any) -> None: + if event.inaxes != self.ax_image or event.button != 1: + return + if event.xdata is None or event.ydata is None: + return + + hit = self._nearest_point(float(event.xdata), float(event.ydata)) + self.drag_target = hit + + def _on_motion(self, event: Any) -> None: + if self.drag_target is None: + return + if event.inaxes != self.ax_image or event.xdata is None or event.ydata is None: + return + + state = self._state() + contour_name, idx = self.drag_target + + h, w = self.images[self.current_frame].shape + x = float(np.clip(event.xdata, 0, w - 1)) + y = float(np.clip(event.ydata, 0, h - 1)) + + if contour_name == "lumen": + state.lumen_x[idx] = x + state.lumen_y[idx] = y + else: + state.plaque_x[idx] = x + state.plaque_y[idx] = y + + self.frame_dirty = True + self._render_frame() + + def _on_release(self, _event: Any) -> None: + if self.drag_target is None: + return + self.drag_target = None + if self.frame_dirty: + self._append_frame_edit(self.current_frame, reason="point_drag") + self._render_frame() + + def _on_close(self, _event: Any) -> None: + if self.frame_dirty: + self._append_frame_edit(self.current_frame, reason="window_close") + + def show(self) -> None: + self._build_ui() + self._render_frame() + plt.show() + + +def _default_annotations_path(dicom_path: str, output_root: str) -> str: + stem = os.path.splitext(os.path.basename(dicom_path))[0] + pattern = os.path.join(output_root, "*", f"{stem}_contours.jsonl") + matches = [p for p in glob(pattern) if os.path.isfile(p)] + if matches: + matches.sort(key=lambda p: os.path.getmtime(p), reverse=True) + return matches[0] + + raise FileNotFoundError( + "No pipeline contour JSONL found for " + f"'{stem}'. Expected files like: {pattern}" + ) + + +def _default_edits_path(annotations_path: str) -> str: + if annotations_path.endswith("_contours.jsonl"): + return annotations_path.replace("_contours.jsonl", "_edited_annotations.jsonl") + root, _ = os.path.splitext(annotations_path) + return root + "_edited_annotations.jsonl" + + +def launch_annotation_editor( + dicom_path: str, + annotations_path: str | None = None, + edits_path: str | None = None, + output_root: str = "output", +) -> None: + """Launch interactive annotation GUI. + + If ``annotations_path`` is not provided, the latest contours JSONL is + selected from ``output//``. + """ + if annotations_path is None: + annotations_path = _default_annotations_path(dicom_path, output_root) + + if edits_path is None: + edits_path = _default_edits_path(annotations_path) + + editor = AnnotationEditor( + dicom_path=dicom_path, + annotations_path=annotations_path, + edits_path=edits_path, + ) + editor.show() diff --git a/deepivus/inference/__init__.py b/deepivus/inference/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b72ace28339c70a32a97dcb1eea6dc7f5f22c86e --- /dev/null +++ b/deepivus/inference/__init__.py @@ -0,0 +1,6 @@ +"""Inference subpackage.""" + +from .tensorflow_predictor import predict, release_resources +from .bifurcation_predictor import predict_bifurcation + +__all__ = ["predict", "release_resources", "predict_bifurcation"] diff --git a/deepivus/inference/bifurcation_predictor.py b/deepivus/inference/bifurcation_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..a0b1ef91997ea74e332a8ebeda93accac2d78097 --- /dev/null +++ b/deepivus/inference/bifurcation_predictor.py @@ -0,0 +1,124 @@ +"""Bifurcation classifier inference helpers.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Optional + +import numpy as np +import tensorflow as tf + +from ..config import resolve_bifurcation_model_path, resolve_bifurcation_threshold, resolve_lumen_model_dir + + +_bif_model = None +_bif_model_path = None +_bif_model_mode = None +_base_model = None +_base_model_path = None + + +def _resolve_model_hw(model: tf.keras.Model) -> tuple[int | None, int | None]: + shape = model.input_shape + if isinstance(shape, list): + shape = shape[0] + if not isinstance(shape, tuple) or len(shape) < 4: + return None, None + h = shape[1] + w = shape[2] + h = int(h) if h is not None else None + w = int(w) if w is not None else None + return h, w + + +def _prepare_batch_classifier(images: np.ndarray, model: tf.keras.Model) -> tf.Tensor: + # Classification model is trained on 3-channel grayscale copies. + x = tf.convert_to_tensor(images, dtype=tf.float32) + x = tf.expand_dims(x, axis=-1) + target_h, target_w = _resolve_model_hw(model) + if target_h is not None and target_w is not None: + x = tf.image.resize(x, [target_h, target_w], method="bilinear") + x = tf.tile(x, [1, 1, 1, 3]) + return x + + +def _prepare_batch_base(images: np.ndarray) -> tf.Tensor: + x = tf.convert_to_tensor(images, dtype=tf.float32) + x = x - tf.constant([60.3486], dtype=tf.float32) + x = tf.expand_dims(x, axis=-1) + x = tf.tile(x, [1, 1, 1, 3]) + return x + + +def _extract_logits(model_output: object) -> tf.Tensor: + if isinstance(model_output, dict): + if not model_output: + raise RuntimeError("Base model returned an empty dict output.") + return next(iter(model_output.values())) + if isinstance(model_output, (tuple, list)): + if not model_output: + raise RuntimeError("Base model returned an empty sequence output.") + return model_output[0] + if tf.is_tensor(model_output): + return model_output + raise RuntimeError(f"Unsupported base-model output type: {type(model_output)!r}") + + +def _load_models() -> tuple[tf.keras.Model, str, tf.types.experimental.TrackableResource | None]: + global _bif_model, _bif_model_path, _bif_model_mode, _base_model, _base_model_path + path = resolve_bifurcation_model_path() + if _bif_model is None or _bif_model_path != path or _bif_model_mode is None: + if not Path(path).exists(): + raise FileNotFoundError(f"Bifurcation model not found at: {path}") + _bif_model = tf.keras.models.load_model(path) + _bif_model_path = path + input_shape = _bif_model.input_shape + if isinstance(input_shape, list): + input_shape = input_shape[0] + # Multitask head consumes segmentation logits with dynamic H/W (None, None), + # while classic image classifier usually has fixed H/W (e.g., 500x500). + is_multitask_head = ( + isinstance(input_shape, tuple) + and len(input_shape) == 4 + and input_shape[1] is None + and input_shape[2] is None + ) + _bif_model_mode = "multitask_head" if is_multitask_head else "classifier" + + if _bif_model_mode == "multitask_head": + base_path = resolve_lumen_model_dir() + if _base_model is None or _base_model_path != base_path: + if not Path(base_path).exists(): + raise FileNotFoundError(f"Lumen base model not found for multitask bifurcation head: {base_path}") + _base_model = tf.saved_model.load(str(base_path)) + _base_model_path = base_path + return _bif_model, _bif_model_mode, _base_model + + return _bif_model, _bif_model_mode, None + + +def predict_bifurcation( + images: np.ndarray, + threshold: Optional[float] = None, + batch_size: int = 32, +) -> tuple[np.ndarray, np.ndarray]: + """Return `(probabilities, labels)` for each frame.""" + model, mode, base_model = _load_models() + if threshold is None: + threshold = resolve_bifurcation_threshold(default=0.5, model_path=Path(_bif_model_path)) + probs = [] + num_frames = int(images.shape[0]) + for start in range(0, num_frames, batch_size): + end = min(start + batch_size, num_frames) + if mode == "multitask_head": + batch = _prepare_batch_base(images[start:end]) + logits = _extract_logits(base_model(batch, training=False)) + logits = tf.image.resize(logits, (batch.shape[1], batch.shape[2])) + p = model(logits, training=False).numpy().reshape(-1) + else: + batch = _prepare_batch_classifier(images[start:end], model=model) + p = model(batch, training=False).numpy().reshape(-1) + probs.append(p) + probs_arr = np.concatenate(probs, axis=0).astype(np.float32) + labels_arr = (probs_arr >= float(threshold)).astype(np.int32) + return probs_arr, labels_arr diff --git a/deepivus/inference/tensorflow_predictor.py b/deepivus/inference/tensorflow_predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..188781e07386d98e14eebdd7fed9243d8b16125c --- /dev/null +++ b/deepivus/inference/tensorflow_predictor.py @@ -0,0 +1,109 @@ +import numpy as np +import tensorflow as tf +import gc + +from ..config import resolve_lumen_model_dir + +# Prevent TensorFlow from pre-allocating most GPU memory. +for gpu in tf.config.list_physical_devices("GPU"): + try: + tf.config.experimental.set_memory_growth(gpu, True) + except Exception: + pass + +IMG_MEAN = tf.constant([60.3486], dtype=tf.float32) + +model_path = str(resolve_lumen_model_dir()) + +try: + model = tf.saved_model.load(model_path) +except: + warning = ( + "Warning: No saved weights have been found, segmentation will be unsuccessful, " + f"check that weights are saved in {model_path}" + ) + print(warning) + +def cast_and_center(image): + image = tf.cast(image, dtype=tf.float32) + image = image - IMG_MEAN + return image + +def set_input_channels(images, channels=3): + image_dim = images.get_shape() + if len(image_dim) < 4: + images = tf.expand_dims(images, axis=3) + if image_dim[-1] != channels: + images = tf.tile(images, [1, 1, 1, channels]) + return images + +def predict(images, return_confidence=False, return_class_confidence=False, batch_size=64): + """Runs Convolutional Neural Network to predict image pixel class. + + If return_confidence is True, also returns per-frame confidence defined as + the mean of the per-pixel max softmax probability. + """ + dataset = tf.data.Dataset.from_tensor_slices((images)) + dataset = dataset.map(cast_and_center) + dataset = dataset.batch(batch_size) + num_batches = int(np.ceil(images.shape[0]/batch_size)) + + pred = [] + confidences = [] + class_conf_lumen = [] + class_conf_plaque = [] + for i, batch in enumerate(dataset): + batch = set_input_channels(batch) + logits = model(batch, training=False) + logits = tf.image.resize(logits, (tf.shape(batch)[1], tf.shape(batch)[2])) + probs = tf.nn.softmax(logits, axis=-1) + pred_batch = tf.argmax(probs, axis=-1, output_type=tf.dtypes.int32) + pred.append(pred_batch) + if return_confidence: + per_pixel_conf = tf.reduce_max(probs, axis=-1) + confidences.append(tf.reduce_mean(per_pixel_conf, axis=[1, 2])) + if return_class_confidence: + pred_lumen = tf.equal(pred_batch, 1) + pred_plaque = tf.equal(pred_batch, 2) + + lumen_probs = probs[..., 1] + plaque_probs = probs[..., 2] + + lumen_sum = tf.reduce_sum(tf.where(pred_lumen, lumen_probs, tf.zeros_like(lumen_probs)), axis=[1, 2]) + plaque_sum = tf.reduce_sum(tf.where(pred_plaque, plaque_probs, tf.zeros_like(plaque_probs)), axis=[1, 2]) + + lumen_count = tf.reduce_sum(tf.cast(pred_lumen, tf.float32), axis=[1, 2]) + plaque_count = tf.reduce_sum(tf.cast(pred_plaque, tf.float32), axis=[1, 2]) + + lumen_mean = tf.where(lumen_count > 0, lumen_sum / lumen_count, tf.constant(np.nan, dtype=tf.float32)) + plaque_mean = tf.where(plaque_count > 0, plaque_sum / plaque_count, tf.constant(np.nan, dtype=tf.float32)) + + class_conf_lumen.append(lumen_mean) + class_conf_plaque.append(plaque_mean) + print('Batch {} of {} completed'.format(i+1, num_batches)) + pred = np.concatenate(pred) + if return_confidence: + confidences = np.concatenate(confidences) + if return_class_confidence: + class_conf_lumen = np.concatenate(class_conf_lumen) + class_conf_plaque = np.concatenate(class_conf_plaque) + class_conf = {"lumen": class_conf_lumen, "plaque": class_conf_plaque} + + if return_confidence and return_class_confidence: + return pred, confidences, class_conf + if return_confidence: + return pred, confidences + if return_class_confidence: + return pred, class_conf + return pred + + +def release_resources(): + """Release TensorFlow resources after segmentation so other frameworks can use GPU memory.""" + global model + model = None + try: + tf.keras.backend.clear_session() + except Exception: + pass + gc.collect() diff --git a/deepivus/io/__init__.py b/deepivus/io/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..385f8f95d7ebaab68273c85f06d564b5c1dde10e --- /dev/null +++ b/deepivus/io/__init__.py @@ -0,0 +1,22 @@ +"""I/O subpackage: DICOM and export helpers.""" + +from .dicom import get_pullback_rate, get_resolution, normalize_images, read_dicom +from .exports import ( + get_contours, + mask_image, + write_jsonl, + write_top_confidence_jsonl, + write_xml, +) + +__all__ = [ + "normalize_images", + "read_dicom", + "get_pullback_rate", + "get_resolution", + "mask_image", + "get_contours", + "write_xml", + "write_jsonl", + "write_top_confidence_jsonl", +] diff --git a/deepivus/io/dicom.py b/deepivus/io/dicom.py new file mode 100644 index 0000000000000000000000000000000000000000..7dae4a8c2672db3805007f07ed9c7926edf9e966 --- /dev/null +++ b/deepivus/io/dicom.py @@ -0,0 +1,61 @@ +"""DICOM loading and metadata helpers.""" + +from typing import Tuple + +import numpy as np +import pydicom as dcm + + +def normalize_images(images: np.ndarray, number_of_frames: int) -> np.ndarray: + """Normalize DICOM pixel data to shape ``(frames, rows, cols)`` grayscale.""" + if images is None: + raise ValueError("DICOM pixel data is empty") + + if images.ndim == 2: + return images[np.newaxis, :, :] + + if images.ndim == 3: + if images.shape[-1] in (3, 4) and number_of_frames <= 1: + return images[:, :, 0][np.newaxis, :, :] + return images + + if images.ndim == 4: + if images.shape[-1] in (3, 4): + return images[:, :, :, 0] + return images + + raise ValueError(f"Unsupported image shape: {images.shape}") + + +def get_pullback_rate(ds: dcm.Dataset, default: float = 0.5) -> float: + """Extract pullback speed from standard/private DICOM fields.""" + if ds.get("IVUSPullbackRate"): + return float(ds.IVUSPullbackRate) + if (0x000B, 0x1001) in ds: + return float(ds[(0x000B, 0x1001)].value) + return default + + +def get_resolution(ds: dcm.Dataset, default: float = 0.02) -> float: + """Extract pixel spacing in mm/px when available.""" + if ds.get("SequenceOfUltrasoundRegions"): + region = ds.SequenceOfUltrasoundRegions[0] + if getattr(region, "PhysicalUnitsXDirection", None) == 3: + return float(region.PhysicalDeltaX) * 10.0 + return float(region.PhysicalDeltaX) + if ds.get("PixelSpacing"): + return float(ds.PixelSpacing[0]) + return default + + +def read_dicom(dicom_path: str) -> Tuple[dcm.Dataset, np.ndarray]: + """Load a DICOM and return ``(dataset, grayscale_frames)``.""" + ds = dcm.dcmread(dicom_path, force=True) + raw = ds.pixel_array + number_of_frames = int(getattr(ds, "NumberOfFrames", raw.shape[0] if raw.ndim >= 3 else 1)) + images = normalize_images(raw, number_of_frames) + + if images.ndim != 3: + raise ValueError(f"Expected 3D image stack after normalization, got {images.shape}") + + return ds, images diff --git a/deepivus/io/exports.py b/deepivus/io/exports.py new file mode 100644 index 0000000000000000000000000000000000000000..723140906a8db534268e4e75df2f5404ecff2766 --- /dev/null +++ b/deepivus/io/exports.py @@ -0,0 +1,233 @@ +import datetime +import json +import xml.etree.ElementTree as et + +import matplotlib.path as mplPath +import numpy as np +from skimage import measure + + +def mask_image(mask, catheter): + """Mask out background and optionally catheter labels.""" + mask.setflags(write=1) + if catheter == 1: + mask[mask == 1] = 2 + mask[mask < 2] = mask.max() + else: + mask[mask < 1] = mask.max() + return mask + + +def label_contours(image, levels): + contours1 = measure.find_contours(image, levels[0]) + contours2 = measure.find_contours(image, levels[1]) + + lumen = [np.array((contour[:, 0], contour[:, 1])) for contour in contours1] + plaque = [np.array((contour[:, 0], contour[:, 1])) for contour in contours2] + return lumen, plaque + + +def keep_valid_contour(contour, image_shape): + bb_path = mplPath.Path(np.transpose(contour)) + centroid = [image_shape[0] // 2, image_shape[1] // 2] + return bb_path.contains_point(centroid) + + +def keep_largest_contour(contours, image_shape): + max_length = 0 + keep_contour = [[], []] + for contour in contours: + if keep_valid_contour(contour, image_shape) and len(contour[0]) > max_length: + keep_contour = [list(contour[1, :]), list(contour[0, :])] + max_length = len(contour[0]) + return keep_contour + + +def get_contours(preds, levels, image_shape): + """Extract lumen/plaque contours from masks.""" + lumen_pred = [[], []] + plaque_pred = [[], []] + x = [] + y = [] + + for i in range(preds.shape[0]): + if np.any(preds[i, :, :] == 1): + lumen, plaque = label_contours(preds[i, :, :], levels) + keep_lumen_x, keep_lumen_y = keep_largest_contour(lumen, image_shape) + keep_plaque_x, keep_plaque_y = keep_largest_contour(plaque, image_shape) + x.append(keep_lumen_x) + y.append(keep_lumen_y) + x.append(keep_plaque_x) + y.append(keep_plaque_y) + else: + x.extend([[], []]) + y.extend([[], []]) + + lumen_pred[0].append(x[-2]) + lumen_pred[1].append(y[-2]) + plaque_pred[0].append(x[-1]) + plaque_pred[1].append(y[-1]) + + return x, y, lumen_pred, plaque_pred + + +def _safe_float(value): + if value is None: + return None + value = float(value) + if np.isnan(value): + return None + return value + + +def write_xml(x, y, dims, resolution, speed, frames, pname, lumen_conf=None, plaque_conf=None): + """Write Echoplaque-compatible contours XML with optional per-frame confidences.""" + num_frames = dims[0] + root = et.Element("AnalysisState") + + analyzedfilename = et.SubElement(root, "AnalyzedFileName") + analyzedfilename.text = "FILE0000" + analyzedfilenamefullpath = et.SubElement(root, "AnalyzedFileNameFullPath") + analyzedfilenamefullpath.text = r"D:\CASE0000\FILE0000" + et.SubElement(root, "PreviousAnalysisState") + + et.SubElement(root, "UserName").text = "DeepIVUSCLI" + et.SubElement(root, "ComputerName").text = "CLI" + et.SubElement(root, "SoftwareVersion").text = "4.0.27" + et.SubElement(root, "ScreenResolution").text = "N/A" + et.SubElement(root, "Date").text = datetime.datetime.now().strftime("%d%b%Y %H:%M:%S") + et.SubElement(root, "TimeZone").text = "GMT-300 min" + + demographics = et.SubElement(root, "Demographics") + et.SubElement(demographics, "PatientName").text = pname + et.SubElement(demographics, "PatientID").text = pname + + imagestate = et.SubElement(root, "ImageState") + et.SubElement(imagestate, "Xdim").text = str(dims[1]) + et.SubElement(imagestate, "Ydim").text = str(dims[2]) + et.SubElement(imagestate, "NumberOfFrames").text = str(num_frames) + et.SubElement(imagestate, "FirstFrameLoaded").text = "0" + et.SubElement(imagestate, "LastFrameLoaded").text = str(num_frames - 1) + et.SubElement(imagestate, "Stride").text = "1" + + imagecalibration = et.SubElement(root, "ImageCalibration") + et.SubElement(imagecalibration, "XCalibration").text = str(resolution) + et.SubElement(imagecalibration, "YCalibration").text = str(resolution) + et.SubElement(imagecalibration, "AcqRateInFPS").text = "133.0" + et.SubElement(imagecalibration, "PullbackSpeed").text = str(speed) + + et.SubElement(root, "BrightnessSetting").text = "50" + et.SubElement(root, "ContrastSetting").text = "50" + et.SubElement(root, "FreeStepping").text = "FALSE" + et.SubElement(root, "SteppingInterval").text = "1" + et.SubElement(root, "VolumeHasBeenComputed").text = "FALSE" + + framestate = et.SubElement(root, "FrameState") + et.SubElement(framestate, "ImageRelativePoints").text = "TRUE" + et.SubElement(framestate, "Xoffset").text = "109" + et.SubElement(framestate, "Yoffset").text = "3" + + frame_to_idx = {frame: idx for idx, frame in enumerate(frames)} + for i in range(num_frames): + fm = et.SubElement(framestate, "Fm") + et.SubElement(fm, "Num").text = str(i) + + if lumen_conf is not None: + conf = _safe_float(lumen_conf[i]) if i < len(lumen_conf) else None + et.SubElement(fm, "LumenConfidence").text = "" if conf is None else f"{conf:.6f}" + if plaque_conf is not None: + conf = _safe_float(plaque_conf[i]) if i < len(plaque_conf) else None + et.SubElement(fm, "PlaqueConfidence").text = "" if conf is None else f"{conf:.6f}" + + if i in frame_to_idx: + frame_idx = frame_to_idx[i] + for j in range(2): + ctr = et.SubElement(fm, "Ctr") + contour_x = x[frame_idx * 2 + j] + contour_y = y[frame_idx * 2 + j] + et.SubElement(ctr, "Npts").text = str(len(contour_x)) + et.SubElement(ctr, "Type").text = "L" if j == 0 else "V" + et.SubElement(ctr, "HandDrawn").text = "T" + for k in range(len(contour_x)): + p = et.SubElement(ctr, "p") + p.text = f"{int(contour_x[k])},{int(contour_y[k])}" + + tree = et.ElementTree(root) + tree.write(pname + "_contours.xml") + + +def write_jsonl(output_prefix, lumen, plaque, lumen_conf=None, plaque_conf=None): + """Write JSONL export of contours and confidences.""" + num_frames = len(lumen[0]) + jsonl_path = f"{output_prefix}_contours.jsonl" + with open(jsonl_path, "w", encoding="utf-8") as fp: + fp.write(json.dumps({"record_type": "meta", "num_frames": num_frames}) + "\n") + for i in range(num_frames): + record = { + "record_type": "frame", + "frame": i, + "lumen_confidence": None if lumen_conf is None else _safe_float(lumen_conf[i]), + "plaque_confidence": None if plaque_conf is None else _safe_float(plaque_conf[i]), + "lumen": {"x": [float(v) for v in lumen[0][i]], "y": [float(v) for v in lumen[1][i]]}, + "plaque": {"x": [float(v) for v in plaque[0][i]], "y": [float(v) for v in plaque[1][i]]}, + } + fp.write(json.dumps(record) + "\n") + return jsonl_path + + +def _top_confidence_entry(frame_idx, confidence, lumen, plaque): + return { + "frame": int(frame_idx), + "confidence": None if confidence is None else float(confidence), + "lumen": { + "x": [float(v) for v in lumen[0][frame_idx]], + "y": [float(v) for v in lumen[1][frame_idx]], + }, + "plaque": { + "x": [float(v) for v in plaque[0][frame_idx]], + "y": [float(v) for v in plaque[1][frame_idx]], + }, + } + + +def write_top_confidence_jsonl(output_prefix, lumen, plaque, lumen_conf, plaque_conf): + """Write JSONL with highest-confidence lumen and plaque frames.""" + lumen_arr = np.asarray(lumen_conf, dtype=np.float32) + plaque_arr = np.asarray(plaque_conf, dtype=np.float32) + + lumen_valid = np.isfinite(lumen_arr) + plaque_valid = np.isfinite(plaque_arr) + + if np.any(lumen_valid): + lumen_idx = int(np.nanargmax(lumen_arr)) + lumen_entry = _top_confidence_entry(lumen_idx, lumen_arr[lumen_idx], lumen, plaque) + else: + lumen_entry = None + + if np.any(plaque_valid): + plaque_idx = int(np.nanargmax(plaque_arr)) + plaque_entry = _top_confidence_entry(plaque_idx, plaque_arr[plaque_idx], lumen, plaque) + else: + plaque_entry = None + + jsonl_path = f"{output_prefix}_top_confidence_frames.jsonl" + with open(jsonl_path, "w", encoding="utf-8") as fp: + fp.write( + json.dumps( + { + "record_type": "highest_lumen_confidence_frame", + "entry": lumen_entry, + } + ) + + "\n" + ) + fp.write( + json.dumps( + { + "record_type": "highest_plaque_confidence_frame", + "entry": plaque_entry, + } + ) + + "\n" + ) + return jsonl_path diff --git a/deepivus/pipeline.py b/deepivus/pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..79048052d11a140444e88f2ac8751100ac2fb511 --- /dev/null +++ b/deepivus/pipeline.py @@ -0,0 +1,192 @@ +"""End-to-end lumen segmentation + bifurcation classification pipeline.""" + +from __future__ import annotations + +import json +from typing import Optional, Tuple + +import click +import numpy as np +from skimage import measure + +from .inference import predict, predict_bifurcation +from .io.dicom import get_pullback_rate, get_resolution, read_dicom +from .io.exports import get_contours, write_jsonl, write_top_confidence_jsonl, write_xml +from .processing.preprocessing import apply_center_black_circle +from .visualization.video import write_overlay_video, write_overlay_video_with_bifurcation_flags + + +def _empty_contours(num_frames: int): + return [[[] for _ in range(num_frames)], [[] for _ in range(num_frames)]] + + +def _keep_largest_lumen_component_per_frame(lumen_labels: np.ndarray) -> np.ndarray: + """Keep only the largest connected lumen component in each frame.""" + cleaned = np.zeros_like(lumen_labels, dtype=np.uint8) + for i in range(lumen_labels.shape[0]): + mask = lumen_labels[i] == 1 + if not np.any(mask): + continue + cc = measure.label(mask, connectivity=1) + if cc.max() == 0: + continue + sizes = np.bincount(cc.ravel()) + sizes[0] = 0 # background + largest_id = int(np.argmax(sizes)) + cleaned[i, cc == largest_id] = 1 + return cleaned + + +def _write_bifurcation_jsonl( + output_prefix: str, + probabilities: np.ndarray, + labels: np.ndarray, + threshold: float, +) -> tuple[str, str]: + out_path = f"{output_prefix}_bifurcation_predictions.jsonl" + probs = np.asarray(probabilities, dtype=np.float32) + preds = np.asarray(labels, dtype=np.int32) + + with open(out_path, "w", encoding="utf-8") as fp: + fp.write( + json.dumps( + { + "record_type": "meta", + "num_frames": int(len(probs)), + "threshold": float(threshold), + } + ) + + "\n" + ) + for i, (p, y) in enumerate(zip(probs, preds)): + fp.write( + json.dumps( + { + "record_type": "frame", + "frame": int(i), + "bifurcation_probability": float(p), + "bifurcation": bool(y), + } + ) + + "\n" + ) + + summary_path = f"{output_prefix}_bifurcation_summary.json" + summary = { + "num_frames": int(len(probs)), + "threshold": float(threshold), + "positive_frames": int(np.sum(preds == 1)), + "negative_frames": int(np.sum(preds == 0)), + "mean_probability": float(np.mean(probs)) if probs.size else None, + } + with open(summary_path, "w", encoding="utf-8") as fp: + json.dump(summary, fp, indent=2) + + return out_path, summary_path + + +def segment_and_export( + dicom_path: str, + output_prefix: str, + fps: Optional[float], + bifurcation_threshold: float, + framewise: bool = False, +) -> Tuple[str, str, str, str, str, str, str]: + """Run lumen segmentation + bifurcation classification and export outputs.""" + ds, images = read_dicom(dicom_path) + images = apply_center_black_circle(images, diameter=67) + click.echo(f"Loaded DICOM: {dicom_path}") + click.echo(f"Frames: {images.shape[0]}, Size: {images.shape[1]}x{images.shape[2]}") + + seg_batch_size = 1 if framewise else 64 + bif_batch_size = 1 if framewise else 32 + + if framewise: + click.echo("Framewise mode enabled: running per-frame inference (batch_size=1)") + + masks, _, class_conf = predict( + images, + return_confidence=True, + return_class_confidence=True, + batch_size=seg_batch_size, + ) + click.echo("Lumen segmentation complete") + + # Lumen-only labels: lumen=1, background=0, then keep only largest connected lumen region. + lumen_labels = np.where(masks == 1, 1, 0).astype(np.uint8) + lumen_labels = _keep_largest_lumen_component_per_frame(lumen_labels) + _, _, lumen, _ = get_contours(lumen_labels, [0.5, 1.5], images.shape[1:3]) + plaque = _empty_contours(images.shape[0]) + + resolution = get_resolution(ds) + pullback_rate = get_pullback_rate(ds) + frames = list(range(images.shape[0])) + + plaque_conf = np.full((images.shape[0],), np.nan, dtype=np.float32) + + write_xml( + x=[coord for pair in zip(lumen[0], plaque[0]) for coord in pair], + y=[coord for pair in zip(lumen[1], plaque[1]) for coord in pair], + dims=images.shape, + resolution=resolution, + speed=pullback_rate, + frames=frames, + pname=output_prefix, + lumen_conf=class_conf["lumen"], + plaque_conf=plaque_conf, + ) + xml_path = f"{output_prefix}_contours.xml" + + json_path = write_jsonl( + output_prefix=output_prefix, + lumen=lumen, + plaque=plaque, + lumen_conf=class_conf["lumen"], + plaque_conf=plaque_conf, + ) + top_conf_json_path = write_top_confidence_jsonl( + output_prefix=output_prefix, + lumen=lumen, + plaque=plaque, + lumen_conf=class_conf["lumen"], + plaque_conf=plaque_conf, + ) + + if fps is None: + fps = float(getattr(ds, "CineRate", 30.0)) if getattr(ds, "CineRate", None) else 30.0 + + video_path = f"{output_prefix}_overlay.mp4" + write_overlay_video(images, lumen, plaque, video_path, fps) + + bif_probs, bif_labels = predict_bifurcation( + images, + threshold=bifurcation_threshold, + batch_size=bif_batch_size, + ) + bif_json_path, bif_summary_path = _write_bifurcation_jsonl( + output_prefix=output_prefix, + probabilities=bif_probs, + labels=bif_labels, + threshold=bifurcation_threshold, + ) + bif_overlay_video_path = f"{output_prefix}_overlay_bifurcation.mp4" + write_overlay_video_with_bifurcation_flags( + images=images, + lumen=lumen, + plaque=plaque, + video_path=bif_overlay_video_path, + fps=fps, + bifurcation_probabilities=bif_probs, + bifurcation_labels=bif_labels, + threshold=bifurcation_threshold, + ) + + return ( + xml_path, + json_path, + top_conf_json_path, + video_path, + bif_overlay_video_path, + bif_json_path, + bif_summary_path, + ) diff --git a/deepivus/processing/__init__.py b/deepivus/processing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..76f16f84cd3e87dcf2d5efc933c41cbc7de53d0c --- /dev/null +++ b/deepivus/processing/__init__.py @@ -0,0 +1,24 @@ +"""Processing subpackage: preprocessing and fusion helpers.""" + +from .fusion import ( + build_sam_label_masks, + empty_contours, + fill_empty_lumen_contours_temporally, + fill_empty_masks_temporally, + merge_sam_with_tensorflow, + propagate_previous_mask_on_zero_score, + top_k_lumen_exemplar_indices, +) +from .preprocessing import apply_center_black_circle, ensure_uint8 + +__all__ = [ + "ensure_uint8", + "apply_center_black_circle", + "top_k_lumen_exemplar_indices", + "build_sam_label_masks", + "empty_contours", + "merge_sam_with_tensorflow", + "propagate_previous_mask_on_zero_score", + "fill_empty_masks_temporally", + "fill_empty_lumen_contours_temporally", +] diff --git a/deepivus/processing/fusion.py b/deepivus/processing/fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..07ed7dcd84e434088a629603f3352173046de87e --- /dev/null +++ b/deepivus/processing/fusion.py @@ -0,0 +1,119 @@ +"""Post-processing and fusion helpers for TF + SAM outputs.""" + +from typing import List, Tuple + +import numpy as np + + +def top_k_lumen_exemplar_indices(masks: np.ndarray, lumen_conf: np.ndarray, k: int = 100) -> List[int]: + """Select top-K valid lumen frames for SAM exemplar prompts.""" + conf = np.asarray(lumen_conf, dtype=np.float32) + valid = np.isfinite(conf) & np.any(masks == 1, axis=(1, 2)) + exemplars: List[int] = [] + + if np.any(valid): + ordered = np.where(valid)[0][np.argsort(conf[valid])[::-1]] + exemplars.extend([int(i) for i in ordered[:k]]) + + if len(exemplars) < k: + lumen_area = np.sum(masks == 1, axis=(1, 2)) + area_order = np.argsort(lumen_area)[::-1] + for idx in area_order: + idx = int(idx) + if idx in exemplars or not np.any(masks[idx] == 1): + continue + exemplars.append(idx) + if len(exemplars) >= k: + break + + return sorted(exemplars) + + +def build_sam_label_masks(sam_lumen_masks: np.ndarray) -> np.ndarray: + """Build contour labels with lumen=1 and non-lumen=2.""" + return np.where(sam_lumen_masks > 0, 1, 2).astype(np.uint8) + + +def empty_contours(num_frames: int): + """Return an empty contour structure for all frames.""" + return [[[] for _ in range(num_frames)], [[] for _ in range(num_frames)]] + + +def merge_sam_with_tensorflow( + sam_lumen_masks: np.ndarray, + sam_scores: np.ndarray, + tf_masks: np.ndarray, + tf_lumen_conf: np.ndarray, +) -> Tuple[np.ndarray, np.ndarray]: + """Use TF masks on frames where TF confidence beats SAM confidence.""" + merged_masks = sam_lumen_masks.copy().astype(np.uint8) + merged_scores = np.asarray(sam_scores, dtype=np.float32).copy() + tf_scores = np.asarray(tf_lumen_conf, dtype=np.float32) + + for idx in range(merged_masks.shape[0]): + tf_score = float(tf_scores[idx]) if idx < tf_scores.shape[0] else float("nan") + sam_score = float(merged_scores[idx]) if idx < merged_scores.shape[0] else float("nan") + + if np.isfinite(tf_score): + tf_score = float(np.clip(tf_score, 0.0, 1.0)) + if np.isfinite(sam_score): + sam_score = float(np.clip(sam_score, 0.0, 1.0)) + + if np.isfinite(tf_score) and (not np.isfinite(sam_score) or sam_score < tf_score): + merged_masks[idx] = (tf_masks[idx] == 1).astype(np.uint8) + merged_scores[idx] = np.float32(tf_score) + elif not np.isfinite(sam_score): + merged_scores[idx] = np.float32(0.0) + + return merged_masks, merged_scores + + +def propagate_previous_mask_on_zero_score(lumen_masks: np.ndarray, scores: np.ndarray) -> np.ndarray: + """Replace zero-score frames with previous mask.""" + propagated = lumen_masks.copy().astype(np.uint8) + scores_arr = np.asarray(scores, dtype=np.float32) + for idx in range(1, propagated.shape[0]): + if float(scores_arr[idx]) <= 0.0: + propagated[idx] = propagated[idx - 1] + return propagated + + +def fill_empty_masks_temporally(lumen_masks: np.ndarray) -> np.ndarray: + """Fill empty masks by temporal carry-over.""" + filled = lumen_masks.copy().astype(np.uint8) + num_frames = filled.shape[0] + non_empty = np.any(filled > 0, axis=(1, 2)) + if not np.any(non_empty): + return filled + + first_non_empty = int(np.argmax(non_empty)) + for idx in range(0, first_non_empty): + filled[idx] = filled[first_non_empty] + + for idx in range(first_non_empty + 1, num_frames): + if not np.any(filled[idx] > 0): + filled[idx] = filled[idx - 1] + + return filled + + +def fill_empty_lumen_contours_temporally(lumen): + """Fill empty contour frames by nearest previous valid contour.""" + filled_x = [list(x) for x in lumen[0]] + filled_y = [list(y) for y in lumen[1]] + num_frames = len(filled_x) + has_contour = [len(filled_x[i]) > 0 and len(filled_y[i]) > 0 for i in range(num_frames)] + if not any(has_contour): + return lumen + + first_idx = has_contour.index(True) + for idx in range(first_idx): + filled_x[idx] = list(filled_x[first_idx]) + filled_y[idx] = list(filled_y[first_idx]) + + for idx in range(first_idx + 1, num_frames): + if len(filled_x[idx]) == 0 or len(filled_y[idx]) == 0: + filled_x[idx] = list(filled_x[idx - 1]) + filled_y[idx] = list(filled_y[idx - 1]) + + return [filled_x, filled_y] diff --git a/deepivus/processing/preprocessing.py b/deepivus/processing/preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..58c0f69f75bbccb80dfaef7d330ba7a9f3a867dd --- /dev/null +++ b/deepivus/processing/preprocessing.py @@ -0,0 +1,56 @@ +"""Frame preprocessing utilities.""" + +import numpy as np + + +def ensure_uint8(images: np.ndarray) -> np.ndarray: + """Convert any numeric image stack to uint8 with min-max scaling.""" + if images.dtype == np.uint8: + return images + images = images.astype(np.float32) + min_val = float(images.min()) + max_val = float(images.max()) + if max_val <= min_val: + return np.zeros(images.shape, dtype=np.uint8) + scaled = (images - min_val) * (255.0 / (max_val - min_val)) + return scaled.astype(np.uint8) + + +def apply_center_black_circle( + images: np.ndarray, + diameter: int = 30, + center_scale: float = 0.0, + edge_scale: float = 0.5, +) -> np.ndarray: + """Apply a soft radial attenuation mask centered in each frame. + + The attenuation is linear in radius from ``center_scale`` (at center) + to ``edge_scale`` (at circle boundary), with no attenuation outside. + """ + if images.ndim != 3: + raise ValueError(f"Expected images shape (frames, rows, cols), got {images.shape}") + + radius = max(1, int(diameter // 2)) + center_scale = float(np.clip(center_scale, 0.0, 1.0)) + edge_scale = float(np.clip(edge_scale, 0.0, 1.0)) + + h, w = images.shape[1], images.shape[2] + cy, cx = h // 2, w // 2 + + yy, xx = np.ogrid[:h, :w] + dist = np.sqrt((yy - cy) ** 2 + (xx - cx) ** 2).astype(np.float32) + + scale = np.ones((h, w), dtype=np.float32) + inside = dist <= radius + t = np.zeros((h, w), dtype=np.float32) + t[inside] = dist[inside] / float(radius) + scale[inside] = center_scale + t[inside] * (edge_scale - center_scale) + + masked = images.astype(np.float32) * scale[np.newaxis, :, :] + + if np.issubdtype(images.dtype, np.integer): + info = np.iinfo(images.dtype) + masked = np.clip(np.rint(masked), info.min, info.max).astype(images.dtype) + else: + masked = masked.astype(images.dtype) + return masked diff --git a/deepivus/tracking/__init__.py b/deepivus/tracking/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7219b912600dbcd5e264a4852cc4726f47cee0b3 --- /dev/null +++ b/deepivus/tracking/__init__.py @@ -0,0 +1,5 @@ +"""Tracking subpackage.""" + +from .sam3 import Sam3ExemplarTracker, Sam3TrackResult + +__all__ = ["Sam3TrackResult", "Sam3ExemplarTracker"] diff --git a/deepivus/tracking/sam3.py b/deepivus/tracking/sam3.py new file mode 100644 index 0000000000000000000000000000000000000000..43f9f5c3a5f0d82664bfa2550f634b5b5fd212bb --- /dev/null +++ b/deepivus/tracking/sam3.py @@ -0,0 +1,251 @@ +from dataclasses import dataclass + +import numpy as np + + +@dataclass +class Sam3TrackResult: + lumen_masks: np.ndarray + tracking_scores: np.ndarray + exemplar_frames: list[int] + + +class Sam3ExemplarTracker: + """Track lumen masks across frames using a SAM-family model from Hugging Face.""" + + def __init__(self, model_id: str, device: str = "auto", debug: bool = False): + self.model_id = model_id + self.device = device + self.debug = debug + + try: + import torch + from transformers import Sam3TrackerVideoModel, Sam3TrackerVideoProcessor + except Exception as exc: + raise RuntimeError( + "SAM3 video tracking requires torch and a recent transformers release with " + "Sam3TrackerVideoModel/Sam3TrackerVideoProcessor." + ) from exc + + self.torch = torch + self.runtime_device = self._resolve_device(device) + self.runtime_dtype = self._resolve_dtype(self.runtime_device) + + self.model = Sam3TrackerVideoModel.from_pretrained(model_id).to( + self.runtime_device, + dtype=self.runtime_dtype, + ) + self.processor = Sam3TrackerVideoProcessor.from_pretrained(model_id) + self.model.eval() + + def _resolve_device(self, device: str) -> str: + if device != "auto": + return device + if self.torch.cuda.is_available(): + return "cuda" + if hasattr(self.torch.backends, "mps") and self.torch.backends.mps.is_available(): + return "mps" + return "cpu" + + def _resolve_dtype(self, runtime_device: str): + if runtime_device == "cuda": + return self.torch.bfloat16 + return self.torch.float32 + + @staticmethod + def _ensure_uint8(images: np.ndarray) -> np.ndarray: + if images.dtype == np.uint8: + return images + images = images.astype(np.float32) + min_val = float(images.min()) + max_val = float(images.max()) + if max_val <= min_val: + return np.zeros(images.shape, dtype=np.uint8) + scaled = (images - min_val) * (255.0 / (max_val - min_val)) + return scaled.astype(np.uint8) + + def _to_video_frames(self, images: np.ndarray) -> np.ndarray: + images_u8 = self._ensure_uint8(images) + return np.repeat(images_u8[..., None], 3, axis=3) + + @staticmethod + def _mask_to_click(mask: np.ndarray): + ys, xs = np.where(mask > 0) + if len(xs) == 0 or len(ys) == 0: + raise ValueError("Exemplar mask is empty; cannot derive click prompt") + x = float(np.mean(xs)) + y = float(np.mean(ys)) + return [[[[x, y]]]] + + def _extract_binary_mask(self, post_processed_masks) -> np.ndarray: + mask_data = post_processed_masks[0] + if isinstance(mask_data, self.torch.Tensor): + while mask_data.ndim > 2: + mask_data = mask_data[0] + return (mask_data > 0).to(dtype=self.torch.uint8).cpu().numpy() + + mask_np = np.asarray(mask_data) + while mask_np.ndim > 2: + mask_np = mask_np[0] + return (mask_np > 0).astype(np.uint8) + + def _score_from_output(self, output) -> np.float32: + if hasattr(output, "iou_scores") and output.iou_scores is not None: + iou_np = output.iou_scores.detach().float().cpu().numpy().reshape(-1) + if iou_np.size: + return np.float32(np.max(iou_np)) + return np.float32(np.nan) + + @staticmethod + def _normalize_scores_01(scores: np.ndarray) -> np.ndarray: + scores = np.asarray(scores, dtype=np.float32) + norm = np.zeros_like(scores, dtype=np.float32) + finite = np.isfinite(scores) + if not np.any(finite): + return norm + + vals = scores[finite] + min_val = float(np.min(vals)) + max_val = float(np.max(vals)) + if max_val > min_val: + norm[finite] = (vals - min_val) / (max_val - min_val) + else: + norm[finite] = np.clip(vals, 0.0, 1.0) + norm = np.clip(norm, 0.0, 1.0) + return norm.astype(np.float32) + + def _run_one_direction( + self, + video_frames: np.ndarray, + exemplar_frames: list[int], + exemplar_masks: list[np.ndarray], + ): + try: + inference_session = self.processor.init_video_session( + video=video_frames, + inference_device=self.runtime_device, + dtype=self.runtime_dtype, + ) + except self.torch.OutOfMemoryError: + if self.runtime_device != "cuda": + raise + if self.debug: + print("[SAM3] CUDA OOM during init_video_session; retrying on CPU.") + self.torch.cuda.empty_cache() + self.runtime_device = "cpu" + self.runtime_dtype = self.torch.float32 + self.model.to(self.runtime_device, dtype=self.runtime_dtype) + inference_session = self.processor.init_video_session( + video=video_frames, + inference_device=self.runtime_device, + dtype=self.runtime_dtype, + ) + + num_frames, height, width = video_frames.shape[:3] + tracked = np.zeros((num_frames, height, width), dtype=np.uint8) + scores = np.full((num_frames,), np.nan, dtype=np.float32) + + for frame_idx, frame_mask in zip(exemplar_frames, exemplar_masks): + input_points = self._mask_to_click(frame_mask) + input_labels = [[[1]]] + self.processor.add_inputs_to_inference_session( + inference_session=inference_session, + frame_idx=int(frame_idx), + obj_ids=1, + input_points=input_points, + input_labels=input_labels, + ) + seed_output = self.model( + inference_session=inference_session, + frame_idx=int(frame_idx), + ) + seed_masks = self.processor.post_process_masks( + [seed_output.pred_masks], + original_sizes=[[inference_session.video_height, inference_session.video_width]], + binarize=False, + ) + tracked[int(frame_idx)] = self._extract_binary_mask(seed_masks) + seed_score = self._score_from_output(seed_output) + if np.isnan(seed_score): + seed_score = np.float32(np.mean(tracked[int(frame_idx)])) + scores[int(frame_idx)] = seed_score + + for video_output in self.model.propagate_in_video_iterator(inference_session): + frame_idx = int(video_output.frame_idx) + post_masks = self.processor.post_process_masks( + [video_output.pred_masks], + original_sizes=[[inference_session.video_height, inference_session.video_width]], + binarize=False, + ) + tracked[frame_idx] = self._extract_binary_mask(post_masks) + frame_score = self._score_from_output(video_output) + if np.isnan(frame_score): + frame_score = np.float32(np.mean(tracked[frame_idx])) + scores[frame_idx] = frame_score + + for frame_idx, frame_mask in zip(exemplar_frames, exemplar_masks): + if not np.any(tracked[int(frame_idx)]): + tracked[int(frame_idx)] = frame_mask + scores[int(frame_idx)] = np.float32(1.0) + + return tracked, scores + + def track_lumen( + self, + images: np.ndarray, + exemplar_frames: list[int], + exemplar_masks: list[np.ndarray], + ) -> Sam3TrackResult: + if images.ndim != 3: + raise ValueError(f"Expected images shape (frames, rows, cols), got {images.shape}") + + num_frames = images.shape[0] + if not exemplar_frames: + raise ValueError("No exemplar frames provided for SAM tracking") + if len(exemplar_frames) != len(exemplar_masks): + raise ValueError("exemplar_frames and exemplar_masks must have the same length") + + cleaned_frames = [] + cleaned_masks = [] + seen = set() + for frame_idx, frame_mask in zip(exemplar_frames, exemplar_masks): + frame_idx = int(frame_idx) + if frame_idx < 0 or frame_idx >= num_frames: + continue + if frame_idx in seen: + continue + frame_mask = (frame_mask > 0).astype(np.uint8) + if not np.any(frame_mask): + continue + seen.add(frame_idx) + cleaned_frames.append(frame_idx) + cleaned_masks.append(frame_mask) + + if not cleaned_frames: + raise ValueError("All exemplar masks were empty or invalid") + + video_frames = self._to_video_frames(images) + tracked, scores = self._run_one_direction(video_frames, cleaned_frames, cleaned_masks) + + first_exemplar = min(cleaned_frames) + # Fill frames before the first exemplar by running SAM3 on the reversed sequence. + if first_exemplar > 0: + reversed_video_frames = np.ascontiguousarray(video_frames[::-1]) + reversed_exemplar_frames = [(num_frames - 1) - idx for idx in cleaned_frames] + rev_tracked, rev_scores = self._run_one_direction( + reversed_video_frames, + reversed_exemplar_frames, + cleaned_masks, + ) + for rev_idx in range(num_frames): + orig_idx = (num_frames - 1) - rev_idx + if orig_idx < first_exemplar: + tracked[orig_idx] = rev_tracked[rev_idx] + scores[orig_idx] = rev_scores[rev_idx] + + nan_scores = np.isnan(scores) + if np.any(nan_scores): + scores[nan_scores] = np.mean(tracked[nan_scores], axis=(1, 2), dtype=np.float32) + scores = self._normalize_scores_01(scores) + + return Sam3TrackResult(lumen_masks=tracked, tracking_scores=scores, exemplar_frames=cleaned_frames) diff --git a/deepivus/visualization/__init__.py b/deepivus/visualization/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0d6ef3c8436811299c620a4de0ab5e2476157b1a --- /dev/null +++ b/deepivus/visualization/__init__.py @@ -0,0 +1,5 @@ +"""Visualization subpackage.""" + +from .video import write_model_comparison_video, write_overlay_video, write_overlay_video_with_graph + +__all__ = ["write_overlay_video", "write_model_comparison_video", "write_overlay_video_with_graph"] diff --git a/deepivus/visualization/video.py b/deepivus/visualization/video.py new file mode 100644 index 0000000000000000000000000000000000000000..ebf3800541e821f425e597e933c14d39809ae7da --- /dev/null +++ b/deepivus/visualization/video.py @@ -0,0 +1,160 @@ +"""Video rendering utilities.""" + +import numpy as np +from PIL import Image, ImageDraw + +from ..processing.preprocessing import ensure_uint8 + + +def draw_contour(draw: ImageDraw.ImageDraw, x_points, y_points, color): + """Draw a closed contour if points are available.""" + if not x_points or not y_points: + return + points = [(float(x_points[i]), float(y_points[i])) for i in range(len(x_points))] + if len(points) < 2: + return + points.append(points[0]) + draw.line(points, fill=color, width=2) + + +def _require_imageio(): + try: + import imageio.v2 as imageio + except Exception as exc: + raise RuntimeError( + "Video export requires imageio and imageio-ffmpeg. " + "Install with: python -m pip install imageio imageio-ffmpeg" + ) from exc + return imageio + + +def write_overlay_video(images: np.ndarray, lumen, plaque, video_path: str, fps: float) -> None: + """Write contour overlay video with lumen/plaque channels.""" + imageio = _require_imageio() + images_u8 = ensure_uint8(images) + with imageio.get_writer(video_path, fps=fps) as writer: + for frame_idx in range(images_u8.shape[0]): + frame = Image.fromarray(images_u8[frame_idx], mode="L").convert("RGB") + draw = ImageDraw.Draw(frame) + draw_contour(draw, lumen[0][frame_idx], lumen[1][frame_idx], (0, 255, 0)) + draw_contour(draw, plaque[0][frame_idx], plaque[1][frame_idx], (255, 64, 64)) + writer.append_data(np.asarray(frame)) + + +def write_overlay_video_with_bifurcation_flags( + images: np.ndarray, + lumen, + plaque, + video_path: str, + fps: float, + bifurcation_probabilities: np.ndarray, + bifurcation_labels: np.ndarray, + threshold: float, +) -> None: + """Write contour overlay video with per-frame bifurcation text labels.""" + imageio = _require_imageio() + images_u8 = ensure_uint8(images) + probs = np.asarray(bifurcation_probabilities, dtype=np.float32) + labels = np.asarray(bifurcation_labels, dtype=np.int32) + with imageio.get_writer(video_path, fps=fps) as writer: + for frame_idx in range(images_u8.shape[0]): + frame = Image.fromarray(images_u8[frame_idx], mode="L").convert("RGB") + draw = ImageDraw.Draw(frame) + draw_contour(draw, lumen[0][frame_idx], lumen[1][frame_idx], (0, 255, 0)) + draw_contour(draw, plaque[0][frame_idx], plaque[1][frame_idx], (255, 64, 64)) + + if frame_idx < probs.shape[0] and frame_idx < labels.shape[0]: + prob = float(probs[frame_idx]) + is_bif = bool(labels[frame_idx]) + tag = "Branch" if is_bif else "Non-branch" + text = f"{tag} p={prob:.2f} t={float(threshold):.2f}" + text_color = (255, 90, 90) if is_bif else (100, 220, 255) + draw.rectangle([(10, 10), (290, 36)], fill=(0, 0, 0)) + draw.text((16, 16), text, fill=text_color) + + writer.append_data(np.asarray(frame)) + + +def write_model_comparison_video(images: np.ndarray, tf_lumen, sam_lumen, video_path: str, fps: float) -> None: + """Write a two-model comparison video (TF red, SAM blue).""" + imageio = _require_imageio() + images_u8 = ensure_uint8(images) + with imageio.get_writer(video_path, fps=fps) as writer: + for frame_idx in range(images_u8.shape[0]): + frame = Image.fromarray(images_u8[frame_idx], mode="L").convert("RGB") + draw = ImageDraw.Draw(frame) + draw_contour(draw, tf_lumen[0][frame_idx], tf_lumen[1][frame_idx], (255, 0, 0)) + draw_contour(draw, sam_lumen[0][frame_idx], sam_lumen[1][frame_idx], (0, 0, 255)) + writer.append_data(np.asarray(frame)) + + +def _draw_graph_panel(frame_rgb: np.ndarray, scores: np.ndarray, sustained_flags: np.ndarray, frame_idx: int) -> np.ndarray: + panel_h = 150 + h, w = frame_rgb.shape[:2] + canvas = Image.new("RGB", (w, h + panel_h), color=(0, 0, 0)) + canvas.paste(Image.fromarray(frame_rgb), (0, 0)) + draw = ImageDraw.Draw(canvas) + + top = h + 12 + left = 16 + right = w - 16 + bottom = h + panel_h - 18 + draw.rectangle([(left, top), (right, bottom)], fill=(20, 20, 20), outline=(90, 90, 90), width=1) + + n = len(scores) + if n > 1: + in_run = False + run_start = 0 + for i in range(n): + if sustained_flags[i] and not in_run: + run_start = i + in_run = True + if (not sustained_flags[i] or i == n - 1) and in_run: + run_end = i if not sustained_flags[i] else i + 1 + x0 = left + int((run_start / (n - 1)) * (right - left)) + x1 = left + int(((run_end - 1) / (n - 1)) * (right - left)) + draw.rectangle([(x0, top), (x1, bottom)], fill=(70, 20, 20)) + in_run = False + + def px(i): + return left + int((i / (n - 1)) * (right - left)) + + def py(v): + v = float(np.clip(v, 0.0, 1.0)) + return bottom - int(v * (bottom - top)) + + pts = [(px(i), py(scores[i])) for i in range(n)] + if len(pts) > 1: + draw.line(pts, fill=(120, 255, 120), width=2) + + cur = min(max(frame_idx, 0), n - 1) + cx = px(cur) + draw.line([(cx, top), (cx, bottom)], fill=(255, 255, 0), width=2) + flag_txt = "SUSTAINED BRANCH SIGNAL" if sustained_flags[cur] else "normal" + draw.text((left + 6, top + 4), f"Oblongness: {float(scores[cur]):.3f} [{flag_txt}]", fill=(230, 230, 230)) + else: + draw.text((left + 6, top + 4), "Oblongness: insufficient data", fill=(230, 230, 230)) + + return np.asarray(canvas) + + +def write_overlay_video_with_graph( + images: np.ndarray, + lumen, + plaque, + video_path: str, + fps: float, + oblong_scores: np.ndarray, + sustained_flags: np.ndarray, +) -> None: + """Write the fused overlay video with an animated bottom graph panel.""" + imageio = _require_imageio() + images_u8 = ensure_uint8(images) + with imageio.get_writer(video_path, fps=fps) as writer: + for frame_idx in range(images_u8.shape[0]): + frame = Image.fromarray(images_u8[frame_idx], mode="L").convert("RGB") + draw = ImageDraw.Draw(frame) + draw_contour(draw, lumen[0][frame_idx], lumen[1][frame_idx], (0, 255, 0)) + draw_contour(draw, plaque[0][frame_idx], plaque[1][frame_idx], (255, 64, 64)) + with_panel = _draw_graph_panel(np.asarray(frame), oblong_scores, sustained_flags, frame_idx) + writer.append_data(with_panel) diff --git a/docs/memo_assets/lumen_coverage_by_split.png b/docs/memo_assets/lumen_coverage_by_split.png new file mode 100644 index 0000000000000000000000000000000000000000..9a81951ffca7e592fa642f05afe1af4d7c2f4475 Binary files /dev/null and b/docs/memo_assets/lumen_coverage_by_split.png differ diff --git a/docs/memo_assets/lumen_finetune_dynamics.png b/docs/memo_assets/lumen_finetune_dynamics.png new file mode 100644 index 0000000000000000000000000000000000000000..148c7c3a8bd1f6b6415c23784aaf8d034472fc82 --- /dev/null +++ b/docs/memo_assets/lumen_finetune_dynamics.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0e287327660aab3db255789679464e7d00a2e730994fa659ff1eb59e9a3d3217 +size 129657 diff --git a/docs/memo_assets/memo_summary.json b/docs/memo_assets/memo_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..348f99192123e41cf701e7ab1f6a5140ea5ca721 --- /dev/null +++ b/docs/memo_assets/memo_summary.json @@ -0,0 +1,80 @@ +{ + "data": { + "split_counts": { + "train": 420, + "val": 90, + "test": 90 + }, + "bifurcation_positive_rate": { + "train": 0.6523809523809524, + "val": 0.6555555555555556, + "test": 0.6555555555555556 + }, + "lumen_coverage_rate": { + "train": 0.4738095238095238, + "val": 0.5111111111111111, + "test": 0.5333333333333333 + } + }, + "multitask": { + "epochs_completed": 0, + "best_epoch_by_val_total_loss": null, + "best_val_total_loss": null, + "test_seg_metrics": { + "seg_count": 48, + "seg_iou": 0.8563776407665782, + "seg_dice": 0.9226330052251039 + }, + "test_cls_metrics": { + "threshold": 0.36999999999999994, + "cls_accuracy": 0.9, + "cls_precision": 0.890625, + "cls_recall": 0.9661016949152542, + "cls_f1": 0.9268292682926829, + "cls_auc": 0.9614543318748474, + "tp": 57, + "fp": 7, + "fn": 2, + "tn": 24 + }, + "cls_accuracy_wilson95": [ + 0.8207564910060137, + 0.9464938542566417 + ], + "cls_precision_wilson95": [ + 0.7910113670806147, + 0.9459995524731695 + ], + "cls_recall_wilson95": [ + 0.8845621301839242, + 0.9906542869664985 + ], + "threshold": 0.36999999999999994 + }, + "lumen_finetune": { + "num_samples": 295, + "test_metrics": { + "loss": 0.09265833246436986, + "bce": 0.07108915224671364, + "dice_loss": 0.07189726829528809, + "iou": 0.8734893023388234, + "dice": 0.9324732212224306 + } + }, + "standalone_bifurcation": { + "selected_threshold": 0.4399999976158142, + "test_metrics": { + "threshold": 0.4399999976158142, + "accuracy": 0.7666666666666667, + "precision": 0.8518518518518519, + "recall": 0.7796610169491526, + "f1": 0.8141592920353983, + "specificity": 0.7419354838709677, + "tp": 46, + "fp": 8, + "tn": 23, + "fn": 13 + } + }, + "runtime_threshold": 0.36999999999999994 +} \ No newline at end of file diff --git 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b/docs/memo_assets/standalone_threshold_sweep.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:286d1b7c12420b32f4e87bd49509ecbaf580b58822f1cc5ccf9af9673d12864e +size 107378 diff --git a/docs/multitask_finetuning_comprehensive_memo.html b/docs/multitask_finetuning_comprehensive_memo.html new file mode 100644 index 0000000000000000000000000000000000000000..92551125407f8e5532817d9bc15fca8836b7c00e --- /dev/null +++ b/docs/multitask_finetuning_comprehensive_memo.html @@ -0,0 +1,246 @@ + + + + + + + multitask_finetuning_comprehensive_memo + + + +

IVUS Segmentation and Bifurcation Detection

+

Comprehensive Multi-Task Fine-Tuning Report

+

Date: February 20, 2026

+

1) Purpose and Scope

+

This report documents the full methodology used to adapt a pretrained IVUS segmentation model into a multi-task model that performs:

+
    +
  • Lumen segmentation (pixel-level)
    • +
  • Bifurcation detection (frame-level)
    • +
+

The goal is to provide a self-contained technical description of model design, training behavior, threshold calibration, results, and limitations.

+

2) Problem Setup

+

Given an IVUS frame x, we optimize two tasks:

+
    +
  1. Segmentation output M_hat: lumen mask over pixels
    2. +
  2. Classification output y_hat: bifurcation probability in [0,1]
    3. +
+

The model is trained at frame level. There is no temporal model (no recurrence, no sequence transformer, no optical flow objective).

+

3) Data and Labels

+

3.1 Data organization

+

The dataset is built from a frame-bank of manually labeled IVUS frames with train/validation/test partitions.

+

Split counts:

+
    +
  • Train: 420
    • +
  • Validation: 90
    • +
  • Test: 90
    • +
+

3.2 Label distributions

+

Bifurcation positive rate by split:

+
    +
  • Train: 65.2%
    • +
  • Validation: 65.6%
    • +
  • Test: 65.6%
    • +
+

Lumen annotation coverage by split:

+
    +
  • Train: 47.4%
    • +
  • Validation: 51.1%
    • +
  • Test: 53.3%
    • +
+

This means classification supervision is denser than segmentation supervision in the multi-task setting.

+

3.3 Balance visualizations

+

Split class balance Positive rate by split Lumen coverage by split

+

4) Model Design

+

4.1 Backbone + multi-task head

+

A pretrained segmentation backbone is reused as initialization.

+

A lightweight multi-task classification head is attached on top of segmentation logits:

+
    +
  • Global average pooling over spatial dimensions
    • +
  • Dense layer (ReLU)
    • +
  • Dropout
    • +
  • Final sigmoid output for bifurcation probability
    • +
+

This is a multi-task head, not an attention module.

+

4.2 Task coupling strategy

+

The segmentation branch and classification branch share upstream representation. This encourages feature reuse while keeping task-specific outputs separate.

+

4.3 Conceptual architecture

+

Multi-task training and inference diagram

+

5) Preprocessing and Input Construction

+

For each frame:

+
    +
  1. Apply central black-circle preprocessing (to suppress catheter/artifacts near center).
    2. +
  2. Convert grayscale to network input representation.
    3. +
  3. Align labels to frame indices.
    4. +
+

For segmentation labels, only frames with valid lumen polygons are supervised.

+

6) Loss Functions and Optimization

+

Let i index samples in a minibatch.

+
    +
  • m_i in {0,1}^{H x W}: ground-truth lumen mask
    • +
  • m_hat_i: predicted lumen probability map
    • +
  • y_i in {0,1}: bifurcation label
    • +
  • y_hat_i in (0,1): bifurcation probability
    • +
  • h_i in {0,1}: has-mask indicator (1 if segmentation label exists)
    • +
+

6.1 Segmentation loss

+

Weighted BCE + Dice:

+
L_seg,i = L_wbce(m_i, m_hat_i; w_pos) + lambda_dice * L_dice(m_i, m_hat_i)
+
+

Masked batch aggregation (only labeled masks contribute):

+
L_seg = (sum_i h_i * L_seg,i) / (sum_i h_i + eps)
+
+

6.2 Classification loss

+

Binary cross entropy:

+
L_cls = (1/B) * sum_i L_bce(y_i, y_hat_i)
+
+

6.3 Total objective

+
L_total = w_seg * L_seg + w_cls * L_cls
+
+

6.4 Optimization behavior

+
    +
  • GradientTape-style explicit optimization loop for multi-task fine-tuning
    • +
  • Gradient clipping by global norm for stability
    • +
  • Early stopping using validation objective
    • +
  • Best-checkpoint restore before final export
    • +
+

7) Threshold Selection and Operating Point

+

After model training, bifurcation threshold t is selected on validation data by grid search over candidate thresholds.

+

For each t:

+
y_hat_i^(t) = 1[y_hat_i >= t]
+
+

Compute precision, recall, F1, accuracy, etc., then choose:

+
t* = argmax_t F1_val(t)
+
+

The selected threshold is persisted and reused during runtime inference.

+

8) Training Dynamics

+

8.1 Multi-task fine-tuning dynamics

+

Multi-task training dynamics

+

Observed behavior:

+
    +
  • Validation classification AUC stabilizes high relatively early.
    • +
  • Validation F1 is more threshold-sensitive and fluctuates more.
    • +
  • Segmentation metrics remain strong but vary with sparse segmentation supervision.
    • +
+

8.2 Lumen-only fine-tuning dynamics

+

Lumen fine-tune dynamics

+

9) Test Performance Summary

+

9.1 Multi-task test metrics

+

Segmentation (subset with lumen labels):

+
    +
  • IoU: 0.856
    • +
  • Dice: 0.923
    • +
+

Bifurcation classification:

+
    +
  • Accuracy: 0.900
    • +
  • Precision: 0.891
    • +
  • Recall: 0.966
    • +
  • F1: 0.927
    • +
  • AUC: 0.961
    • +
+

Confusion matrix:

+

Multitask confusion matrix

+

Metric snapshot:

+

Multitask metric snapshot

+

9.2 Segmentation regime comparison

+

Segmentation comparison

+

Note: compared evaluations do not use identical sample sets, so the comparison is directional.

+

10) Threshold and Calibration Diagnostics

+

Standalone classifier diagnostics (supporting analysis):

+

Threshold sweep Probability histogram Reliability diagram Precision-recall curve with operating point

+

These plots illustrate threshold sensitivity, score separation, and calibration quality.

+

11) Limitations

+

11.1 Split caveat: source overlap

+

Train/validation/test share source pullback files (frame-level partitioning rather than source-level partitioning).

+

Because the model is frame-independent, this is not temporal leakage. However, repeated source style/statistics across splits can make in-domain metrics optimistic.

+

Split source overlap

+

11.2 Uneven supervision density

+

Only about half of samples carry segmentation labels. This creates an imbalance between classification and segmentation supervision in multi-task training.

+

11.3 Domain shift across source groups

+

Performance can vary substantially by source group.

+

Group-wise standalone metrics

+

This indicates a need for stronger cross-source robustness analysis.

+

11.4 Head capacity tradeoff

+

The current multi-task head is intentionally lightweight. This helps stability and runtime cost, but may under-capture fine spatial context around bifurcation patterns.

+

12) Practical Conclusions

+
    +
  1. The current multi-task approach is effective and operationally coherent.
    2. +
  2. Validation-driven thresholding is critical and should remain part of deployment.
    3. +
  3. The largest methodological caveat is source-overlap evaluation, not temporal modeling leakage.
    4. +
  4. Next major quality gain will likely come from stricter source-level split protocols and robustness-focused evaluation.
    5. +
+

13) Reproducibility Note

+

This report is intended to be self-contained. Supporting figures are stored under docs/memo_assets/.

+

PDF export command:

+
scripts/analysis/export_memo_pdf.sh
+ + diff --git a/docs/multitask_finetuning_comprehensive_memo.md b/docs/multitask_finetuning_comprehensive_memo.md new file mode 100644 index 0000000000000000000000000000000000000000..932b2505c950b741309f93ace746a07cde87d4b0 --- /dev/null +++ b/docs/multitask_finetuning_comprehensive_memo.md @@ -0,0 +1,253 @@ +# IVUS Segmentation and Bifurcation Detection +## Comprehensive Multi-Task Fine-Tuning Report + +Date: February 20, 2026 + +## 1) Purpose and Scope + +This report documents the full methodology used to adapt a pretrained IVUS segmentation model into a multi-task model that performs: + +- Lumen segmentation (pixel-level) +- Bifurcation detection (frame-level) + +The goal is to provide a self-contained technical description of model design, training behavior, threshold calibration, results, and limitations. + +## 2) Problem Setup + +Given an IVUS frame `x`, we optimize two tasks: + +1. Segmentation output `M_hat`: lumen mask over pixels +2. Classification output `y_hat`: bifurcation probability in `[0,1]` + +The model is trained at frame level. There is no temporal model (no recurrence, no sequence transformer, no optical flow objective). + +## 3) Data and Labels + +### 3.1 Data organization + +The dataset is built from a frame-bank of manually labeled IVUS frames with train/validation/test partitions. + +Split counts: + +- Train: 420 +- Validation: 90 +- Test: 90 + +### 3.2 Label distributions + +Bifurcation positive rate by split: + +- Train: 65.2% +- Validation: 65.6% +- Test: 65.6% + +Lumen annotation coverage by split: + +- Train: 47.4% +- Validation: 51.1% +- Test: 53.3% + +This means classification supervision is denser than segmentation supervision in the multi-task setting. + +### 3.3 Balance visualizations + +![Split class balance](./memo_assets/split_class_balance_stacked.png) +![Positive rate by split](./memo_assets/positive_rate_by_split.png) +![Lumen coverage by split](./memo_assets/lumen_coverage_by_split.png) + +## 4) Model Design + +### 4.1 Backbone + multi-task head + +A pretrained segmentation backbone is reused as initialization. + +A lightweight **multi-task classification head** is attached on top of segmentation logits: + +- Global average pooling over spatial dimensions +- Dense layer (ReLU) +- Dropout +- Final sigmoid output for bifurcation probability + +This is a multi-task head, not an attention module. + +### 4.2 Task coupling strategy + +The segmentation branch and classification branch share upstream representation. This encourages feature reuse while keeping task-specific outputs separate. + +### 4.3 Conceptual architecture + +![Multi-task training and inference diagram](./memo_assets/multitask_pipeline_diagram.png) + +## 5) Preprocessing and Input Construction + +For each frame: + +1. Apply central black-circle preprocessing (to suppress catheter/artifacts near center). +2. Convert grayscale to network input representation. +3. Align labels to frame indices. + +For segmentation labels, only frames with valid lumen polygons are supervised. + +## 6) Loss Functions and Optimization + +Let `i` index samples in a minibatch. + +- `m_i in {0,1}^{H x W}`: ground-truth lumen mask +- `m_hat_i`: predicted lumen probability map +- `y_i in {0,1}`: bifurcation label +- `y_hat_i in (0,1)`: bifurcation probability +- `h_i in {0,1}`: has-mask indicator (1 if segmentation label exists) + +### 6.1 Segmentation loss + +Weighted BCE + Dice: + +```text +L_seg,i = L_wbce(m_i, m_hat_i; w_pos) + lambda_dice * L_dice(m_i, m_hat_i) +``` + +Masked batch aggregation (only labeled masks contribute): + +```text +L_seg = (sum_i h_i * L_seg,i) / (sum_i h_i + eps) +``` + +### 6.2 Classification loss + +Binary cross entropy: + +```text +L_cls = (1/B) * sum_i L_bce(y_i, y_hat_i) +``` + +### 6.3 Total objective + +```text +L_total = w_seg * L_seg + w_cls * L_cls +``` + +### 6.4 Optimization behavior + +- GradientTape-style explicit optimization loop for multi-task fine-tuning +- Gradient clipping by global norm for stability +- Early stopping using validation objective +- Best-checkpoint restore before final export + +## 7) Threshold Selection and Operating Point + +After model training, bifurcation threshold `t` is selected on validation data by grid search over candidate thresholds. + +For each `t`: + +```text +y_hat_i^(t) = 1[y_hat_i >= t] +``` + +Compute precision, recall, F1, accuracy, etc., then choose: + +```text +t* = argmax_t F1_val(t) +``` + +The selected threshold is persisted and reused during runtime inference. + +## 8) Training Dynamics + +### 8.1 Multi-task fine-tuning dynamics + +![Multi-task training dynamics](./memo_assets/multitask_training_dynamics.png) + +Observed behavior: + +- Validation classification AUC stabilizes high relatively early. +- Validation F1 is more threshold-sensitive and fluctuates more. +- Segmentation metrics remain strong but vary with sparse segmentation supervision. + +### 8.2 Lumen-only fine-tuning dynamics + +![Lumen fine-tune dynamics](./memo_assets/lumen_finetune_dynamics.png) + +## 9) Test Performance Summary + +### 9.1 Multi-task test metrics + +Segmentation (subset with lumen labels): + +- IoU: 0.856 +- Dice: 0.923 + +Bifurcation classification: + +- Accuracy: 0.900 +- Precision: 0.891 +- Recall: 0.966 +- F1: 0.927 +- AUC: 0.961 + +Confusion matrix: + +![Multitask confusion matrix](./memo_assets/multitask_test_confusion_matrix.png) + +Metric snapshot: + +![Multitask metric snapshot](./memo_assets/multitask_test_metric_snapshot.png) + +### 9.2 Segmentation regime comparison + +![Segmentation comparison](./memo_assets/segmentation_regime_comparison.png) + +Note: compared evaluations do not use identical sample sets, so the comparison is directional. + +## 10) Threshold and Calibration Diagnostics + +Standalone classifier diagnostics (supporting analysis): + +![Threshold sweep](./memo_assets/standalone_threshold_sweep.png) +![Probability histogram](./memo_assets/standalone_probability_hist.png) +![Reliability diagram](./memo_assets/standalone_reliability_diagram.png) +![Precision-recall curve with operating point](./memo_assets/precision_recall_curve_with_operating_point.png) + +These plots illustrate threshold sensitivity, score separation, and calibration quality. + +## 11) Limitations + +### 11.1 Split caveat: source overlap + +Train/validation/test share source pullback files (frame-level partitioning rather than source-level partitioning). + +Because the model is frame-independent, this is not temporal leakage. However, repeated source style/statistics across splits can make in-domain metrics optimistic. + +![Split source overlap](./memo_assets/split_source_overlap_heatmap.png) + +### 11.2 Uneven supervision density + +Only about half of samples carry segmentation labels. This creates an imbalance between classification and segmentation supervision in multi-task training. + +### 11.3 Domain shift across source groups + +Performance can vary substantially by source group. + +![Group-wise standalone metrics](./memo_assets/standalone_group_metrics.png) + +This indicates a need for stronger cross-source robustness analysis. + +### 11.4 Head capacity tradeoff + +The current multi-task head is intentionally lightweight. This helps stability and runtime cost, but may under-capture fine spatial context around bifurcation patterns. + +## 12) Practical Conclusions + +1. The current multi-task approach is effective and operationally coherent. +2. Validation-driven thresholding is critical and should remain part of deployment. +3. The largest methodological caveat is source-overlap evaluation, not temporal modeling leakage. +4. Next major quality gain will likely come from stricter source-level split protocols and robustness-focused evaluation. + +## 13) Reproducibility Note + +This report is intended to be self-contained. Supporting figures are stored under `docs/memo_assets/`. + +PDF export command: + +```bash +scripts/analysis/export_memo_pdf.sh +``` diff --git a/docs/multitask_finetuning_comprehensive_memo.pdf b/docs/multitask_finetuning_comprehensive_memo.pdf new file mode 100644 index 0000000000000000000000000000000000000000..d68e101ff71fe5fab6db42ba62ee37f124204751 --- /dev/null +++ b/docs/multitask_finetuning_comprehensive_memo.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:33ad01963ddae764d20098d55b94c1dd0ca847ad1c6c5ae12f6c1188e61f0eaa +size 1311139 diff --git a/docs/pandoc_pdf_header.tex b/docs/pandoc_pdf_header.tex new file mode 100644 index 0000000000000000000000000000000000000000..2a216c5aaf424a1c453e78201a6b0fbfd8dac7a7 --- /dev/null +++ b/docs/pandoc_pdf_header.tex @@ -0,0 +1,5 @@ +% Safer PDF wrapping settings (without redefining \texttt). +\usepackage{xurl} +\usepackage[htt]{hyphenat} +\emergencystretch=2em +\sloppy diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..f40590ca95989b9161c9d29d877fd7c878aa7260 --- /dev/null +++ b/environment.yml @@ -0,0 +1,105 @@ +name: ivus +channels: + - conda-forge +dependencies: + - _openmp_mutex=4.5=20_gnu + - anyio=4.12.1=pyhcf101f3_0 + - backports=1.0=pyhd8ed1ab_5 + - backports.tarfile=1.2.0=pyhcf101f3_2 + - backports.zstd=1.3.0=py312h90b7ffd_0 + - brotli-python=1.2.0=py312hdb49522_1 + - bzip2=1.0.8=hda65f42_8 + - ca-certificates=2026.1.4=hbd8a1cb_0 + - cachecontrol=0.14.3=pyha770c72_0 + - cachecontrol-with-filecache=0.14.3=pyhd8ed1ab_0 + - certifi=2026.1.4=pyhd8ed1ab_0 + - cffi=2.0.0=py312h460c074_1 + - charset-normalizer=3.4.4=pyhd8ed1ab_0 + - cleo=2.1.0=pyhd8ed1ab_1 + - colorama=0.4.6=pyhd8ed1ab_1 + - crashtest=0.4.1=pyhd8ed1ab_1 + - cryptography=46.0.5=py312ha4b625e_0 + - dbus=1.16.2=h24cb091_1 + - distlib=0.4.0=pyhd8ed1ab_0 + - dulwich=0.25.2=py312h0ccc70a_0 + - exceptiongroup=1.3.1=pyhd8ed1ab_0 + - 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liblapack=3.11.0=5_h47877c9_openblas + - liblzma=5.8.2=hb03c661_0 + - libnsl=2.0.1=hb9d3cd8_1 + - libopenblas=0.3.30=pthreads_h94d23a6_4 + - libsqlite=3.51.2=hf4e2dac_0 + - libstdcxx=15.2.0=h934c35e_17 + - libuuid=2.41.3=h5347b49_0 + - libxcrypt=4.4.36=hd590300_1 + - libzlib=1.3.1=hb9d3cd8_2 + - more-itertools=10.8.0=pyhcf101f3_1 + - msgpack-python=1.1.2=py312hd9148b4_1 + - ncurses=6.5=h2d0b736_3 + - numpy=2.4.2=py312h33ff503_1 + - openssl=3.6.1=h35e630c_1 + - packaging=26.0=pyhcf101f3_0 + - pbs-installer=2026.2.11=pyhd8ed1ab_0 + - pcre2=10.47=haa7fec5_0 + - pip=26.0.1=pyh8b19718_0 + - pkginfo=1.12.1.2=pyhd8ed1ab_0 + - platformdirs=4.8.0=pyhcf101f3_0 + - poetry=2.3.1=pyheecf25b_1 + - poetry-core=2.3.0=pyhcf101f3_1 + - pycparser=2.22=pyh29332c3_1 + - pyproject_hooks=1.2.0=pyhd8ed1ab_1 + - pysocks=1.7.1=pyha55dd90_7 + - python=3.12.12=hd63d673_2_cpython + - python-build=1.4.0=pyh332efcf_0 + - python-fastjsonschema=2.21.2=pyhe01879c_0 + - python-installer=0.7.0=pyhff2d567_1 + - python_abi=3.12=8_cp312 + - rapidfuzz=3.14.3=py312h1289d80_1 + - readline=8.3=h853b02a_0 + - requests=2.32.5=pyhcf101f3_1 + - requests-toolbelt=1.0.0=pyhd8ed1ab_1 + - secretstorage=3.4.1=py312h7900ff3_0 + - setuptools=82.0.0=pyh332efcf_0 + - shellingham=1.5.4=pyhd8ed1ab_2 + - sniffio=1.3.1=pyhd8ed1ab_2 + - tk=8.6.13=noxft_h366c992_103 + - tomli=2.4.0=pyhcf101f3_0 + - tomlkit=0.14.0=pyha770c72_0 + - trove-classifiers=2026.1.14.14=pyhd8ed1ab_0 + - typing_extensions=4.15.0=pyhcf101f3_0 + - tzdata=2025c=hc9c84f9_1 + - urllib3=2.6.3=pyhd8ed1ab_0 + - virtualenv=20.36.1=pyhd8ed1ab_0 + - wheel=0.46.3=pyhd8ed1ab_0 + - zipp=3.23.0=pyhcf101f3_1 + - zstandard=0.25.0=py312h5253ce2_1 + - zstd=1.5.7=hb78ec9c_6 +prefix: /opt/conda/envs/ivus diff --git a/evals/README.md b/evals/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4348388d2660fa266cc8b92fa18af20d6f3ea41d --- /dev/null +++ b/evals/README.md @@ -0,0 +1,11 @@ +# Evaluation Layout + +Canonical bifurcation classification data: + +- `evals/frame_bank_merged/`: merged per-video annotation banks (old + newly labeled samples) +- `evals/splits/ivus_split_merged_600.json`: canonical train/val/test split used by both lumen and bifurcation scripts + +Intermediate data: + +- `evals/frame_bank_merged/new_bifurcation_samples_300.jsonl`: sampled frame list used for manual bifurcation labeling +- `evals/frame_bank_merged/new_bifurcation_samples_300.csv`: companion spreadsheet export for labeling workflow diff --git a/evals/frame_bank_merged/bifurcation/FILE0000.jsonl b/evals/frame_bank_merged/bifurcation/FILE0000.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f50f469e5d7d2c420950766f778da981ad9723fd --- /dev/null +++ b/evals/frame_bank_merged/bifurcation/FILE0000.jsonl @@ -0,0 +1,27 @@ +{"record_type": "meta", "schema": "deepivus_eval_frame_bank_v1", "created_at": "2026-02-18T09:52:24", "video_id": "bifurcation/FILE0000", "group": "bifurcation", "dicom_path": 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0VT140.985854659LT

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0VT150.986352659LT

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0VT400.986469657LT

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0VT410.986558657LT

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0VT420.985974657LT

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0VT510.986591657LT

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0VT520.986726657LT

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0VT610.986106659LT

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0VT620.986031657LT

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0VT860.985581653LT

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0VT1200.985655667LT

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0VT1500.986468653LT

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0VT1510.986781661LT

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0VT1770.981088665LT

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0VT1890.978220663LT

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0VT1900.976671671LT

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0VT1910.976813673LT

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0VT1920.977058675LT

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361,264

361,265

361,266

362,266

362,267

362,268

363,268

363,269

363,270

363,271

364,271

364,272

364,273

364,274

365,274

365,275

365,276

365,277

366,277

366,278

366,279

366,280

366,281

366,282

367,282

367,283

367,284

367,285

367,286

367,287

367,288

368,288

368,289

368,290

368,291

368,292

368,293

368,294

368,295

368,296

368,297

368,298

368,298

367,299

367,300

367,301

367,302

367,303

367,304

367,304

366,305

366,306

366,307

366,307

365,308

365,309

365,310

365,310

364,311

364,312

364,312

363,313

363,314

363,314

362,315

362,316

362,316

361,317

361,317

360,318

360,318

359,319

359,319

358,320

358,320

357,320

356,321

356,321

355,321

354,322

354,322

353,322

352,323

352,323

351,323

350,323

349,324

349,324

348,324

347,324

346,325

346,325

345,325

344,325

343,325

342,326

342,326

341,326

340,326

339,326

338,327

338,327

0VT1930.976663675LT

339,327

338,327

337,327

336,327

335,327

334,327

333,327

332,327

331,327

330,327

329,327

328,327

327,327

326,327

325,327

324,327

323,327

323,326

322,326

321,326

320,326

319,326

318,326

318,325

317,325

316,325

315,325

314,325

314,324

313,324

312,324

311,324

310,324

310,323

309,323

308,323

307,323

306,323

306,322

305,322

304,322

303,322

303,321

302,321

301,321

300,321

300,320

299,320

298,320

298,319

297,319

296,319

296,318

295,318

294,318

294,317

293,317

293,316

292,316

291,316

291,315

290,315

290,314

289,314

288,314

288,313

287,313

287,312

286,312

285,312

285,311

284,311

284,310

283,310

282,310

282,309

281,309

280,309

280,308

279,308

278,308

278,307

277,307

276,307

276,306

275,306

275,305

274,305

273,305

273,304

272,304

271,304

271,303

270,303

269,303

269,302

268,302

267,302

267,301

266,301

265,301

264,301

264,300

263,300

262,300

262,299

261,299

260,299

260,298

259,298

258,298

258,297

257,297

256,297

256,296

255,296

255,295

254,295

254,294

253,294

252,294

252,293

251,293

251,292

250,292

249,292

249,291

248,291

248,290

247,290

246,290

246,289

245,289

245,288

244,288

244,287

243,287

242,287

242,286

241,286

241,285

240,285

239,285

239,284

238,284

238,283

237,283

236,283

236,282

235,282

235,281

234,281

234,280

233,280

232,280

232,279

231,279

231,278

230,278

230,277

229,277

229,276

228,276

228,275

227,275

227,274

226,274

226,273

225,273

225,272

224,272

224,271

223,271

223,270

222,270

222,269

222,268

221,268

221,267

220,267

220,266

219,266

219,265

218,265

218,264

218,263

217,263

217,262

217,261

216,261

216,260

215,260

215,259

215,258

214,258

214,257

213,257

213,256

213,255

212,255

212,254

212,253

211,253

211,252

211,251

210,251

210,250

210,249

209,249

209,248

209,247

208,247

208,246

207,246

207,245

207,244

206,244

206,243

205,243

205,242

205,241

204,241

204,240

203,240

203,239

203,238

202,238

202,237

201,237

201,236

201,235

200,235

200,234

199,234

199,233

198,233

198,232

198,231

197,231

197,230

196,230

196,229

196,228

195,228

195,227

195,226

195,225

194,225

194,224

193,224

193,223

193,222

192,222

192,221

191,221

191,220

190,220

190,219

190,218

189,218

189,217

189,216

188,216

188,215

188,214

188,213

187,213

187,212

187,211

186,211

186,210

186,209

185,209

185,208

185,207

185,206

184,206

184,205

184,204

184,203

183,203

183,202

183,201

183,200

183,199

183,198

183,197

183,196

183,195

182,195

182,194

182,193

183,192

183,192

183,191

183,190

183,189

183,188

183,187

183,186

184,185

184,185

184,184

184,183

184,182

185,181

185,181

185,180

185,179

185,178

186,177

187,177

188,177

189,177

189,177

190,176

191,176

192,176

193,176

194,176

195,176

196,176

197,176

198,176

199,176

200,176

201,176

202,176

203,176

203,177

204,177

205,177

206,177

207,177

207,178

208,178

209,178

210,178

210,179

211,179

212,179

213,179

214,179

214,180

215,180

216,180

217,180

218,180

218,181

219,181

220,181

221,181

221,182

222,182

223,182

224,182

224,183

225,183

226,183

227,183

228,183

229,183

230,183

230,184

231,184

232,184

233,184

233,185

234,185

235,185

236,185

236,186

237,186

237,187

238,187

239,187

239,188

240,188

241,188

242,188

242,189

243,189

244,189

244,190

245,190

246,190

247,190

248,190

248,191

249,191

250,191

251,191

252,191

252,192

253,192

254,192

255,192

256,192

256,193

257,193

258,193

259,193

259,194

260,194

261,194

261,195

262,195

263,195

264,195

264,196

265,196

266,196

267,196

267,197

268,197

269,197

270,197

270,198

271,198

272,198

273,198

273,199

274,199

275,199

276,199

276,200

277,200

278,200

279,200

279,201

280,201

281,201

281,202

282,202

283,202

283,203

284,203

285,203

285,204

286,204

287,204

287,205

288,205

289,205

290,205

290,206

291,206

292,206

292,207

293,207

294,207

295,207

295,208

296,208

297,208

298,208

298,209

299,209

300,209

300,210

301,210

302,210

303,210

303,211

304,211

305,211

305,212

306,212

307,212

307,213

308,213

309,213

309,214

310,214

311,214

311,215

312,215

313,215

313,216

314,216

315,216

315,217

316,217

316,218

317,218

318,218

318,219

319,219

319,220

320,220

321,220

321,221

322,221

322,222

323,222

323,223

324,223

324,224

325,224

326,224

326,225

327,225

327,226

328,226

328,227

329,227

329,228

330,228

330,229

331,229

331,230

332,230

333,230

333,231

334,231

334,232

335,232

335,233

336,233

336,234

337,234

337,235

338,235

338,236

339,236

339,237

340,237

340,238

341,238

341,239

342,239

342,240

343,240

343,241

344,241

344,242

345,242

345,243

346,243

346,244

347,244

347,245

347,246

348,246

348,247

349,247

349,248

350,248

350,249

351,249

351,250

352,250

352,251

353,251

353,252

354,252

354,253

355,253

355,254

355,255

356,255

356,256

357,256

357,257

357,258

358,258

358,259

358,260

359,260

359,261

359,262

360,262

360,263

360,264

361,264

361,265

361,266

362,266

362,267

362,268

363,268

363,269

363,270

363,271

364,271

364,272

364,273

364,274

365,274

365,275

365,276

365,277

365,278

366,278

366,279

366,280

366,281

366,282

367,282

367,283

367,284

367,285

367,286

367,287

367,288

367,289

368,289

368,290

368,291

368,292

368,293

368,294

368,295

368,296

368,297

368,298

368,299

368,299

367,300

367,301

367,302

367,303

367,304

367,305

367,305

366,306

366,307

366,308

366,308

365,309

365,310

365,311

365,311

364,312

364,313

364,313

363,314

363,315

363,315

362,316

362,316

361,317

361,318

361,318

360,319

360,319

359,319

358,320

358,320

357,321

357,321

356,321

355,322

355,322

354,322

353,323

353,323

352,323

351,323

350,324

350,324

349,324

348,324

347,324

346,325

346,325

345,325

344,325

343,326

343,326

342,326

341,326

340,326

339,327

339,327

0VT1940.976891677LT

339,327

338,327

337,327

336,327

335,327

334,327

333,327

332,327

331,327

330,327

329,327

328,327

327,327

326,327

325,327

325,326

324,326

323,326

322,326

321,326

320,326

319,326

319,325

318,325

317,325

316,325

315,325

315,324

314,324

313,324

312,324

311,324

310,324

310,323

309,323

308,323

307,323

306,323

306,322

305,322

304,322

303,322

303,321

302,321

301,321

300,321

299,321

299,320

298,320

297,320

297,319

296,319

295,319

295,318

294,318

293,318

293,317

292,317

292,316

291,316

290,316

290,315

289,315

289,314

288,314

288,313

287,313

286,313

286,312

285,312

285,311

284,311

283,311

283,310

282,310

282,309

281,309

280,309

280,308

279,308

278,308

278,307

277,307

277,306

276,306

275,306

275,305

274,305

274,304

273,304

272,304

272,303

271,303

270,303

270,302

269,302

268,302

268,301

267,301

266,301

265,301

265,300

264,300

263,300

263,299

262,299

261,299

261,298

260,298

259,298

259,297

258,297

257,297

257,296

256,296

256,295

255,295

254,295

254,294

253,294

253,293

252,293

251,293

251,292

250,292

250,291

249,291

248,291

248,290

247,290

247,289

246,289

245,289

245,288

244,288

244,287

243,287

242,287

242,286

241,286

241,285

240,285

239,285

239,284

238,284

238,283

237,283

236,283

236,282

235,282

235,281

234,281

234,280

233,280

232,280

232,279

231,279

231,278

230,278

230,277

229,277

229,276

228,276

228,275

227,275

227,274

226,274

226,273

225,273

225,272

224,272

224,271

223,271

223,270

223,269

222,269

222,268

221,268

221,267

220,267

220,266

219,266

219,265

219,264

218,264

218,263

217,263

217,262

217,261

216,261

216,260

215,260

215,259

215,258

214,258

214,257

214,256

213,256

213,255

213,254

212,254

212,253

212,252

211,252

211,251

211,250

210,250

210,249

210,248

209,248

209,247

208,247

208,246

208,245

207,245

207,244

206,244

206,243

206,242

205,242

205,241

204,241

204,240

204,239

203,239

203,238

202,238

202,237

202,236

201,236

201,235

200,235

200,234

199,234

199,233

199,232

198,232

198,231

197,231

197,230

197,229

196,229

196,228

195,228

195,227

195,226

195,225

194,225

194,224

193,224

193,223

192,223

192,222

192,221

191,221

191,220

190,220

190,219

190,218

189,218

189,217

189,216

188,216

188,215

188,214

187,214

187,213

187,212

187,211

186,211

186,210

186,209

185,209

185,208

185,207

185,206

184,206

184,205

184,204

184,203

183,203

183,202

183,201

183,200

183,199

183,198

183,197

183,196

182,196

182,195

182,194

182,193

182,192

183,191

183,191

183,190

183,189

183,188

183,187

183,186

184,185

184,185

184,184

184,183

184,182

184,181

185,180

185,180

185,179

185,178

186,177

187,177

187,177

188,176

189,176

190,176

191,176

192,176

192,176

193,175

194,175

195,175

196,175

197,175

198,175

198,176

199,176

200,176

201,176

202,176

203,176

204,176

204,177

205,177

206,177

207,177

208,177

208,178

209,178

210,178

211,178

211,179

212,179

213,179

214,179

215,179

215,180

216,180

217,180

218,180

218,181

219,181

220,181

221,181

221,182

222,182

223,182

224,182

225,182

225,183

226,183

227,183

228,183

229,183

230,183

230,184

231,184

232,184

233,184

234,184

234,185

235,185

236,185

236,186

237,186

238,186

238,187

239,187

240,187

240,188

241,188

242,188

242,189

243,189

244,189

244,190

245,190

246,190

247,190

247,191

248,191

249,191

250,191

251,191

251,192

252,192

253,192

254,192

255,192

256,192

256,193

257,193

258,193

259,193

259,194

260,194

261,194

261,195

262,195

263,195

264,195

264,196

265,196

266,196

267,196

267,197

268,197

269,197

270,197

270,198

271,198

272,198

273,198

274,198

274,199

275,199

276,199

276,200

277,200

278,200

279,200

279,201

280,201

281,201

281,202

282,202

283,202

283,203

284,203

285,203

285,204

286,204

287,204

287,205

288,205

289,205

290,205

290,206

291,206

292,206

293,206

293,207

294,207

295,207

295,208

296,208

297,208

298,208

298,209

299,209

300,209

301,209

301,210

302,210

303,210

303,211

304,211

305,211

306,211

306,212

307,212

308,212

308,213

309,213

310,213

310,214

311,214

312,214

312,215

313,215

314,215

314,216

315,216

315,217

316,217

317,217

317,218

318,218

318,219

319,219

319,220

320,220

321,220

321,221

322,221

322,222

323,222

324,222

324,223

325,223

325,224

326,224

326,225

327,225

327,226

328,226

329,226

329,227

330,227

330,228

331,228

331,229

332,229

332,230

333,230

333,231

334,231

334,232

335,232

335,233

336,233

336,234

337,234

337,235

338,235

338,236

339,236

339,237

340,237

340,238

341,238

341,239

342,239

342,240

343,240

343,241

344,241

344,242

345,242

345,243

346,243

346,244

347,244

347,245

348,245

348,246

349,246

349,247

350,247

350,248

351,248

351,249

352,249

352,250

353,250

353,251

353,252

354,252

354,253

355,253

355,254

356,254

356,255

357,255

357,256

357,257

358,257

358,258

358,259

359,259

359,260

359,261

360,261

360,262

360,263

361,263

361,264

361,265

362,265

362,266

362,267

362,268

363,268

363,269

363,270

364,270

364,271

364,272

364,273

365,273

365,274

365,275

365,276

365,277

366,277

366,278

366,279

366,280

367,280

367,281

367,282

367,283

367,284

367,285

367,286

368,286

368,287

368,288

368,289

368,290

368,291

368,292

368,293

368,294

368,295

368,296

368,297

368,298

368,299

368,300

368,301

368,302

368,302

367,303

367,304

367,305

367,306

367,306

366,307

366,308

366,309

366,309

365,310

365,311

365,312

365,312

364,313

364,314

364,314

363,315

363,315

362,316

362,317

362,317

361,318

361,318

360,319

360,319

359,320

359,320

358,320

357,321

357,321

356,321

355,322

355,322

354,322

353,322

352,323

352,323

351,323

350,323

349,324

349,324

348,324

347,324

346,325

346,325

345,325

344,325

343,325

342,326

342,326

341,326

340,326

339,327

339,327

0VT1950.976885677LT

338,327

337,327

336,327

335,327

334,327

333,327

332,327

331,327

330,327

329,327

328,327

327,327

326,327

326,326

325,326

324,326

323,326

322,326

321,326

320,326

319,326

319,325

318,325

317,325

316,325

315,325

315,324

314,324

313,324

312,324

311,324

310,324

310,323

309,323

308,323

307,323

306,323

306,322

305,322

304,322

303,322

302,322

302,321

301,321

300,321

299,321

299,320

298,320

297,320

297,319

296,319

295,319

295,318

294,318

293,318

293,317

292,317

292,316

291,316

291,315

290,315

289,315

289,314

288,314

288,313

287,313

287,312

286,312

285,312

285,311

284,311

284,310

283,310

282,310

282,309

281,309

281,308

280,308

279,308

279,307

278,307

277,307

277,306

276,306

276,305

275,305

274,305

274,304

273,304

272,304

272,303

271,303

270,303

270,302

269,302

268,302

268,301

267,301

266,301

265,301

265,300

264,300

263,300

263,299

262,299

261,299

261,298

260,298

259,298

259,297

258,297

257,297

257,296

256,296

256,295

255,295

254,295

254,294

253,294

253,293

252,293

251,293

251,292

250,292

249,292

249,291

248,291

248,290

247,290

246,290

246,289

245,289

245,288

244,288

244,287

243,287

242,287

242,286

241,286

241,285

240,285

239,285

239,284

238,284

238,283

237,283

236,283

236,282

235,282

235,281

234,281

234,280

233,280

232,280

232,279

231,279

231,278

230,278

230,277

229,277

229,276

228,276

228,275

227,275

227,274

226,274

226,273

225,273

225,272

224,272

224,271

224,270

223,270

223,269

222,269

222,268

221,268

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316,217

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364,272

364,273

365,273

365,274

365,275

365,276

365,277

366,277

366,278

366,279

366,280

367,280

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367,282

367,283

367,284

367,285

367,286

367,287

368,287

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368,300

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367,304

367,305

367,306

367,307

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366,308

366,309

366,310

366,310

365,311

365,312

365,312

364,313

364,314

364,314

363,315

363,316

363,316

362,317

362,317

361,318

361,318

360,319

360,319

359,320

359,320

358,320

357,321

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356,321

355,322

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354,322

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352,323

352,323

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350,323

349,324

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348,324

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346,324

345,325

345,325

344,325

343,325

342,326

342,326

341,326

340,326

339,326

338,327

338,327

0VT1960.976842675LT

338,327

337,327

336,327

335,327

334,327

333,327

332,327

331,327

330,327

329,327

328,327

327,327

326,327

325,327

325,326

324,326

323,326

322,326

321,326

320,326

319,326

319,325

318,325

317,325

316,325

315,325

314,325

314,324

313,324

312,324

311,324

310,324

310,323

309,323

308,323

307,323

306,323

306,322

305,322

304,322

303,322

302,322

302,321

301,321

300,321

299,321

299,320

298,320

297,320

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296,319

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294,318

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293,317

292,317

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291,316

291,315

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290,314

289,314

289,313

288,313

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286,312

286,311

285,311

284,311

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282,309

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280,308

279,308

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191,221

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189,218

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188,213

187,213

187,212

187,211

186,211

186,210

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185,207

185,206

184,206

184,205

184,204

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183,202

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183,195

182,195

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212,178

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215,179

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218,180

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223,182

224,182

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226,182

226,183

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230,183

230,184

231,184

232,184

233,184

233,185

234,185

235,185

236,185

236,186

237,186

237,187

238,187

239,187

239,188

240,188

241,188

241,189

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243,189

244,189

244,190

245,190

246,190

247,190

247,191

248,191

249,191

250,191

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254,192

255,192

256,192

256,193

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258,193

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261,195

262,195

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276,199

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282,201

282,202

283,202

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286,204

287,204

288,204

288,205

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290,205

290,206

291,206

292,206

293,206

293,207

294,207

295,207

296,207

296,208

297,208

298,208

299,208

299,209

300,209

301,209

301,210

302,210

303,210

303,211

304,211

305,211

306,211

306,212

307,212

308,212

308,213

309,213

310,213

310,214

311,214

312,214

312,215

313,215

314,215

314,216

315,216

316,216

316,217

317,217

317,218

318,218

318,219

319,219

320,219

320,220

321,220

321,221

322,221

323,221

323,222

324,222

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325,223

326,223

326,224

327,224

327,225

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328,226

329,226

330,226

330,227

331,227

331,228

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333,230

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334,231

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336,231

336,232

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340,236

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342,238

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348,244

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352,248

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353,250

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354,251

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355,252

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356,253

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358,256

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358,258

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359,260

360,260

360,261

360,262

361,262

361,263

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362,265

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362,267

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363,268

363,269

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364,270

364,271

364,272

364,273

365,273

365,274

365,275

365,276

365,277

366,277

366,278

366,279

366,280

366,281

367,281

367,282

367,283

367,284

367,285

367,286

367,287

368,287

368,288

368,289

368,290

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368,294

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368,296

368,297

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367,304

367,305

367,306

367,307

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366,308

366,309

366,310

366,310

365,311

365,312

365,313

365,313

364,314

364,314

363,315

363,316

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362,317

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361,318

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360,319

360,319

359,320

359,320

358,320

357,321

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355,322

355,322

354,322

353,322

352,323

352,323

351,323

350,323

349,324

349,324

348,324

347,324

346,324

345,325

345,325

344,325

343,325

342,325

341,326

341,326

340,326

339,326

338,327

338,327

0VT1970.977269675LT

338,327

337,327

336,327

335,327

334,327

333,327

332,327

331,327

330,327

329,327

328,327

327,327

326,327

326,326

325,326

324,326

323,326

322,326

321,326

320,326

319,326

319,325

318,325

317,325

316,325

315,325

315,324

314,324

313,324

312,324

311,324

311,323

310,323

309,323

308,323

307,323

307,322

306,322

305,322

304,322

303,322

303,321

302,321

301,321

300,321

300,320

299,320

298,320

298,319

297,319

296,319

296,318

295,318

294,318

294,317

293,317

293,316

292,316

292,315

291,315

291,314

290,314

290,313

289,313

289,312

288,312

287,312

287,311

286,311

285,311

285,310

284,310

283,310

283,309

282,309

281,309

281,308

280,308

279,308

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277,307

277,306

276,306

276,305

275,305

274,305

274,304

273,304

272,304

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271,303

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269,302

268,302

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267,301

266,301

265,301

265,300

264,300

263,300

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259,298

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258,297

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196,229

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194,226

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200,176

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227,183

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231,184

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233,184

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236,186

237,186

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238,187

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256,192

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294,207

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296,207

296,208

297,208

298,208

299,208

299,209

300,209

301,209

301,210

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309,213

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311,214

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315,216

316,216

316,217

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317,218

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319,218

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320,220

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323,221

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331,227

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362,264

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364,270

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364,274

365,274

365,275

365,276

365,277

365,278

366,278

366,279

366,280

366,281

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367,282

367,283

367,284

367,285

367,286

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368,289

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368,293

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368,296

368,297

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368,299

368,300

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368,303

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367,304

367,305

367,306

367,307

367,307

366,308

366,309

366,310

366,310

365,311

365,312

365,312

364,313

364,314

364,314

363,315

363,316

363,316

362,317

362,317

361,318

361,318

360,319

360,319

359,319

358,320

358,320

357,321

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356,321

355,321

354,322

354,322

353,322

352,322

351,323

351,323

350,323

349,323

348,324

348,324

347,324

346,324

345,325

345,325

344,325

343,325

342,325

341,326

341,326

340,326

339,326

338,327

338,327

0VT1980.977028675LT

338,327

337,327

336,327

335,327

334,327

333,327

332,327

331,327

330,327

329,327

328,327

327,327

326,327

325,327

325,326

324,326

323,326

322,326

321,326

320,326

319,326

319,325

318,325

317,325

316,325

315,325

315,324

314,324

313,324

312,324

311,324

310,324

310,323

309,323

308,323

307,323

306,323

306,322

305,322

304,322

303,322

302,322

302,321

301,321

300,321

299,321

299,320

298,320

297,320

296,320

296,319

295,319

295,318

294,318

293,318

293,317

292,317

292,316

291,316

291,315

290,315

290,314

289,314

289,313

288,313

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287,312

286,312

286,311

285,311

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284,310

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282,309

281,309

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280,308

280,307

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235,281

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233,280

233,279

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229,277

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201,235

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200,233

199,233

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195,228

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194,226

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192,221

191,221

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188,213

187,213

187,212

187,211

186,211

186,210

186,209

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185,207

185,206

184,206

184,205

184,204

184,203

184,202

183,202

183,201

183,200

183,199

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183,195

182,195

182,194

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182,192

183,191

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183,186

184,185

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186,177

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200,177

201,177

202,177

203,177

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206,177

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209,177

209,178

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211,178

212,178

212,179

213,179

214,179

215,179

216,179

216,180

217,180

218,180

219,180

219,181

220,181

221,181

222,181

223,181

223,182

224,182

225,182

226,182

227,182

227,183

228,183

229,183

230,183

230,184

231,184

232,184

233,184

234,184

234,185

235,185

236,185

236,186

237,186

238,186

238,187

239,187

240,187

240,188

241,188

242,188

242,189

243,189

244,189

245,189

245,190

246,190

247,190

248,190

248,191

249,191

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255,192

256,192

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261,194

262,194

262,195

263,195

264,195

265,195

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276,199

277,199

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282,201

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290,205

291,205

291,206

292,206

293,206

293,207

294,207

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296,207

296,208

297,208

298,208

299,208

299,209

300,209

301,209

301,210

302,210

303,210

304,210

304,211

305,211

306,211

306,212

307,212

308,212

308,213

309,213

310,213

310,214

311,214

312,214

313,214

313,215

314,215

314,216

315,216

316,216

316,217

317,217

317,218

318,218

319,218

319,219

320,219

320,220

321,220

322,220

322,221

323,221

323,222

324,222

324,223

325,223

326,223

326,224

327,224

327,225

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329,225

329,226

330,226

330,227

331,227

331,228

332,228

333,228

333,229

334,229

334,230

335,230

335,231

336,231

336,232

337,232

337,233

338,233

338,234

339,234

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340,235

340,236

341,236

341,237

342,237

342,238

343,238

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344,239

344,240

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345,241

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348,243

348,244

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352,248

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353,250

354,250

354,251

355,251

355,252

356,252

356,253

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357,254

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357,256

358,256

358,257

358,258

359,258

359,259

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360,260

360,261

360,262

361,262

361,263

361,264

361,265

362,265

362,266

362,267

362,268

363,268

363,269

363,270

363,271

364,271

364,272

364,273

364,274

365,274

365,275

365,276

365,277

365,278

366,278

366,279

366,280

366,281

366,282

366,283

367,283

367,284

367,285

367,286

367,287

367,288

368,288

368,289

368,290

368,291

368,292

368,293

368,294

368,295

368,296

368,297

368,298

368,299

368,300

368,301

368,302

368,303

368,304

368,305

368,305

367,306

367,307

367,308

367,308

366,309

366,310

366,311

366,311

365,312

365,313

365,313

364,314

364,315

364,315

363,316

363,316

362,317

362,318

362,318

361,319

361,319

360,319

359,320

359,320

358,321

358,321

357,321

356,321

355,322

355,322

354,322

353,322

352,323

352,323

351,323

350,323

349,324

349,324

348,324

347,324

346,324

345,325

345,325

344,325

343,325

342,326

342,326

341,326

340,326

339,326

338,327

338,327

0VT1990.977032675LT

338,327

337,327

336,327

335,327

334,327

333,327

332,327

331,327

330,327

329,327

328,327

327,327

326,327

326,326

325,326

324,326

323,326

322,326

321,326

320,326

319,326

319,325

318,325

317,325

316,325

315,325

315,324

314,324

313,324

312,324

311,324

310,324

310,323

309,323

308,323

307,323

306,323

306,322

305,322

304,322

303,322

302,322

302,321

301,321

300,321

299,321

299,320

298,320

297,320

296,320

296,319

295,319

295,318

294,318

293,318

293,317

292,317

292,316

291,316

291,315

290,315

290,314

289,314

289,313

288,313

288,312

287,312

286,312

286,311

285,311

285,310

284,310

283,310

283,309

282,309

281,309

281,308

280,308

280,307

279,307

278,307

278,306

277,306

277,305

276,305

275,305

275,304

274,304

273,304

273,303

272,303

271,303

271,302

270,302

269,302

269,301

268,301

267,301

266,301

266,300

265,300

264,300

263,300

263,299

262,299

261,299

261,298

260,298

259,298

259,297

258,297

257,297

257,296

256,296

255,296

255,295

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254,294

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250,292

249,292

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248,290

247,290

246,290

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245,288

244,288

244,287

243,287

242,287

242,286

241,286

241,285

240,285

239,285

239,284

238,284

238,283

237,283

237,282

236,282

235,282

235,281

234,281

234,280

233,280

233,279

232,279

232,278

231,278

230,278

230,277

229,277

229,276

228,276

228,275

227,275

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226,274

226,273

225,273

225,272

225,271

224,271

224,270

223,270

223,269

222,269

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219,266

219,265

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201,236

201,235

200,235

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199,233

199,232

198,232

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196,229

196,228

195,228

195,227

195,226

194,226

194,225

194,224

193,224

193,223

193,222

192,222

192,221

191,221

191,220

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190,219

190,218

189,218

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189,216

189,215

188,215

188,214

188,213

187,213

187,212

187,211

186,211

186,210

186,209

185,209

185,208

185,207

185,206

184,206

184,205

184,204

184,203

183,203

183,202

183,201

183,200

183,199

183,198

183,197

183,196

182,196

182,195

182,194

182,193

182,192

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183,190

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183,189

183,188

183,187

183,186

183,185

184,184

184,184

184,183

184,182

184,181

185,180

185,180

185,179

185,178

186,177

187,177

188,177

188,177

189,176

190,176

191,176

192,176

193,176

194,176

195,176

196,176

197,176

198,176

199,176

200,176

201,176

202,176

203,176

203,177

204,177

205,177

206,177

207,177

208,177

209,177

210,177

210,178

211,178

212,178

213,178

213,179

214,179

215,179

216,179

217,179

217,180

218,180

219,180

220,180

220,181

221,181

222,181

223,181

223,182

224,182

225,182

226,182

227,182

227,183

228,183

229,183

230,183

230,184

231,184

232,184

233,184

234,184

234,185

235,185

236,185

236,186

237,186

238,186

238,187

239,187

240,187

240,188

241,188

242,188

243,188

243,189

244,189

245,189

245,190

246,190

247,190

248,190

248,191

249,191

250,191

251,191

252,191

253,191

253,192

254,192

255,192

256,192

257,192

257,193

258,193

259,193

260,193

260,194

261,194

262,194

262,195

263,195

264,195

265,195

265,196

266,196

267,196

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268,197

269,197

270,197

271,197

271,198

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273,198

274,198

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275,199

276,199

277,199

277,200

278,200

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280,200

280,201

281,201

282,201

282,202

283,202

284,202

284,203

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286,203

286,204

287,204

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288,205

289,205

290,205

291,205

291,206

292,206

293,206

293,207

294,207

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296,207

296,208

297,208

298,208

299,208

299,209

300,209

301,209

302,209

302,210

303,210

304,210

304,211

305,211

306,211

306,212

307,212

308,212

308,213

309,213

310,213

311,213

311,214

312,214

313,214

313,215

314,215

314,216

315,216

316,216

316,217

317,217

317,218

318,218

319,218

319,219

320,219

320,220

321,220

322,220

322,221

323,221

323,222

324,222

325,222

325,223

326,223

326,224

327,224

327,225

328,225

329,225

329,226

330,226

330,227

331,227

331,228

332,228

333,228

333,229

334,229

334,230

335,230

335,231

336,231

336,232

337,232

337,233

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339,234

339,235

340,235

340,236

341,236

341,237

342,237

342,238

343,238

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344,240

345,240

345,241

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349,244

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352,248

353,248

353,249

353,250

354,250

354,251

355,251

355,252

356,252

356,253

356,254

357,254

357,255

358,255

358,256

358,257

359,257

359,258

359,259

360,259

360,260

360,261

361,261

361,262

361,263

361,264

362,264

362,265

362,266

362,267

363,267

363,268

363,269

363,270

364,270

364,271

364,272

364,273

365,273

365,274

365,275

365,276

365,277

366,277

366,278

366,279

366,280

366,281

367,281

367,282

367,283

367,284

367,285

367,286

368,286

368,287

368,288

368,289

368,290

368,291

368,292

368,293

368,294

368,295

368,296

368,297

368,298

368,299

368,300

368,301

368,302

368,303

368,304

368,305

368,305

367,306

367,307

367,308

367,308

366,309

366,310

366,311

366,311

365,312

365,313

365,313

364,314

364,315

364,315

363,316

363,316

362,317

362,317

361,318

361,318

360,319

360,319

359,320

359,320

358,320

357,321

357,321

356,321

355,322

355,322

354,322

353,322

352,323

352,323

351,323

350,323

349,324

349,324

348,324

347,324

346,324

345,325

345,325

344,325

343,325

342,325

341,326

341,326

340,326

339,326

338,327

338,327

0VT2000.977177675LT

338,327

337,327

336,327

335,327

334,327

333,327

332,327

331,327

330,327

329,327

328,327

327,327

326,327

326,326

325,326

324,326

323,326

322,326

321,326

320,326

319,326

319,325

318,325

317,325

316,325

315,325

315,324

314,324

313,324

312,324

311,324

310,324

310,323

309,323

308,323

307,323

306,323

306,322

305,322

304,322

303,322

302,322

302,321

301,321

300,321

299,321

298,321

298,320

297,320

296,320

296,319

295,319

294,319

294,318

293,318

293,317

292,317

292,316

291,316

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346,324

345,325

345,325

344,325

343,325

342,325

341,326

341,326

340,326

339,326

338,327

338,327

0VT2010.977213677LT

337,327

336,327

335,327

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333,327

332,327

331,327

330,327

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365,314

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345,325

345,325

344,325

343,325

342,325

341,326

341,326

340,326

339,326

338,326

337,327

337,327

0VT2020.977379675LT

340,326

339,326

338,326

337,326

336,326

335,326

334,326

333,326

332,326

331,326

330,326

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320,325

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314,324

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0VT2120.978218689LT

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232,201

233,201

233,202

234,202

235,202

236,202

237,202

238,202

239,202

240,202

240,203

241,203

242,203

243,203

244,203

244,204

245,204

246,204

247,204

248,204

249,204

249,205

250,205

251,205

252,205

252,206

253,206

254,206

255,206

256,206

256,207

257,207

258,207

259,207

260,207

260,208

261,208

262,208

263,208

264,208

264,209

265,209

266,209

266,210

267,210

268,210

269,210

269,211

270,211

271,211

272,211

272,212

273,212

274,212

275,212

275,213

276,213

277,213

278,213

278,214

279,214

280,214

281,214

282,214

282,215

283,215

284,215

285,215

285,216

286,216

287,216

288,216

288,217

289,217

290,217

290,218

291,218

292,218

293,218

293,219

294,219

295,219

295,220

296,220

297,220

298,220

298,221

299,221

300,221

300,222

301,222

302,222

303,222

303,223

304,223

305,223

306,223

306,224

307,224

308,224

309,224

309,225

310,225

311,225

311,226

312,226

313,226

313,227

314,227

315,227

315,228

316,228

317,228

317,229

318,229

319,229

319,230

320,230

321,230

321,231

322,231

323,231

323,232

324,232

324,233

325,233

326,233

326,234

327,234

327,235

328,235

328,236

329,236

329,237

330,237

331,237

331,238

332,238

332,239

333,239

333,240

334,240

334,241

335,241

335,242

336,242

336,243

337,243

338,243

338,244

339,244

340,244

340,245

341,245

342,245

343,245

343,246

344,246

345,246

346,246

346,247

347,247

348,247

348,248

349,248

350,248

351,248

351,249

352,249

353,249

353,250

354,250

354,251

355,251

355,252

356,252

357,252

357,253

358,253

358,254

359,254

359,255

360,255

361,255

361,256

362,256

362,257

363,257

363,258

364,258

364,259

365,259

365,260

366,260

366,261

367,261

367,262

368,262

368,263

369,263

369,264

370,264

370,265

371,265

371,266

372,266

372,267

373,267

373,268

374,268

374,269

375,269

375,270

376,270

376,271

377,271

377,272

378,272

378,273

379,273

379,274

380,274

381,274

381,275

382,275

382,276

383,276

383,277

383,278

384,278

384,279

385,279

385,280

386,280

386,281

387,281

387,282

388,282

388,283

389,283

389,284

390,284

390,285

390,286

391,286

391,287

391,288

392,288

392,289

392,290

393,290

393,291

394,291

394,292

394,293

395,293

395,294

395,295

396,295

396,296

396,297

396,298

397,298

397,299

397,300

397,301

398,301

398,302

398,303

398,304

399,304

399,305

399,306

399,307

399,308

399,309

400,309

400,310

400,311

400,312

400,313

400,314

400,315

401,315

401,316

401,317

401,318

401,319

401,320

401,321

401,322

401,323

401,324

401,325

401,326

401,327

401,328

401,329

401,330

401,331

401,332

401,333

401,334

401,335

401,336

401,337

401,338

401,339

401,340

401,341

401,341

400,342

400,343

400,344

400,345

400,346

400,346

399,347

399,348

399,348

398,349

398,349

397,349

396,350

396,350

395,351

395,351

394,351

393,352

393,352

392,352

391,352

391,351

390,351

389,351

388,351

387,351

387,350

386,350

385,350

384,350

383,350

382,350

381,351

381,351

380,351

379,351

378,352

378,352

377,352

376,352

375,352

374,352

373,352

372,352

371,353

371,353

0VT2220.979819731LT

400,360

399,360

399,359

398,359

397,359

397,358

396,358

395,358

394,358

394,357

393,357

392,357

391,357

390,357

390,356

389,356

388,356

387,356

386,356

385,356

385,355

384,355

383,355

382,355

381,355

380,355

379,355

378,355

377,355

376,355

375,355

374,355

373,355

372,355

371,355

370,355

369,355

368,355

367,355

367,354

366,354

365,354

364,354

363,354

362,354

361,354

361,353

360,353

359,353

358,353

357,353

357,352

356,352

355,352

354,352

354,351

353,351

352,351

351,351

351,350

350,350

349,350

348,350

347,350

347,349

346,349

345,349

344,349

343,349

343,348

342,348

341,348

340,348

340,347

339,347

338,347

337,347

337,346

336,346

335,346

334,346

334,345

333,345

332,345

331,345

331,344

330,344

329,344

329,343

328,343

327,343

327,342

326,342

325,342

325,341

324,341

323,341

322,341

322,340

321,340

320,340

319,340

318,340

318,339

317,339

316,339

315,339

315,338

314,338

313,338

312,338

311,338

311,337

310,337

309,337

308,337

307,337

307,336

306,336

305,336

304,336

304,335

303,335

302,335

302,334

301,334

300,334

300,333

299,333

298,333

297,333

297,332

296,332

295,332

295,331

294,331

293,331

292,331

292,330

291,330

290,330

290,329

289,329

288,329

288,328

287,328

287,327

286,327

286,326

285,326

284,326

284,325

283,325

283,324

282,324

281,324

281,323

280,323

280,322

279,322

278,322

278,321

277,321

277,320

276,320

276,319

275,319

274,319

274,318

273,318

273,317

272,317

272,316

271,316

270,316

270,315

269,315

269,314

268,314

268,313

267,313

267,312

266,312

266,311

265,311

265,310

264,310

263,310

263,309

262,309

262,308

261,308

261,307

260,307

260,306

259,306

259,305

258,305

258,304

257,304

257,303

256,303

255,303

255,302

254,302

254,301

253,301

253,300

252,300

252,299

251,299

251,298

250,298

250,297

249,297

249,296

248,296

248,295

248,294

247,294

247,293

246,293

246,292

245,292

245,291

244,291

244,290

243,290

243,289

242,289

242,288

241,288

241,287

240,287

240,286

239,286

239,285

238,285

238,284

237,284

237,283

236,283

235,283

235,282

234,282

234,281

233,281

233,280

232,280

232,279

231,279

231,278

230,278

230,277

229,277

229,276

228,276

228,275

227,275

227,274

226,274

226,273

225,273

225,272

224,272

224,271

223,271

223,270

223,269

222,269

222,268

221,268

221,267

221,266

220,266

220,265

219,265

219,264

219,263

218,263

218,262

218,261

217,261

217,260

216,260

216,259

216,258

215,258

215,257

215,256

214,256

214,255

214,254

213,254

213,253

213,252

213,251

212,251

212,250

212,249

212,248

211,248

211,247

211,246

211,245

211,244

210,244

210,243

210,242

210,241

209,241

209,240

209,239

208,239

208,238

208,237

207,237

207,236

206,236

206,235

206,234

205,234

205,233

205,232

205,231

205,230

204,230

204,229

204,228

204,227

203,227

203,226

203,225

202,225

202,224

202,223

201,223

201,222

201,221

201,220

200,220

200,219

200,218

200,217

200,216

200,215

200,214

200,213

200,212

200,211

200,210

200,209

201,208

201,208

202,207

203,207

203,207

204,206

205,206

205,206

206,205

207,205

207,205

208,204

209,204

210,204

210,204

211,203

212,203

213,203

214,203

214,203

215,202

216,202

217,202

218,202

219,202

220,202

221,202

222,202

223,202

224,202

225,202

226,202

227,202

228,202

229,202

230,202

231,202

232,202

233,202

234,202

235,202

235,203

236,203

237,203

238,203

239,203

240,203

240,204

241,204

242,204

243,204

244,204

245,204

245,205

246,205

247,205

248,205

249,205

249,206

250,206

251,206

252,206

253,206

253,207

254,207

255,207

256,207

257,207

257,208

258,208

259,208

260,208

260,209

261,209

262,209

263,209

263,210

264,210

265,210

266,210

266,211

267,211

268,211

269,211

269,212

270,212

271,212

272,212

272,213

273,213

274,213

275,213

275,214

276,214

277,214

278,214

278,215

279,215

280,215

281,215

282,215

282,216

283,216

284,216

284,217

285,217

286,217

286,218

287,218

288,218

288,219

289,219

290,219

291,219

291,220

292,220

293,220

294,220

294,221

295,221

296,221

297,221

297,222

298,222

299,222

299,223

300,223

301,223

302,223

302,224

303,224

304,224

305,224

305,225

306,225

307,225

307,226

308,226

308,227

309,227

310,227

310,228

311,228

312,228

312,229

313,229

314,229

314,230

315,230

316,230

317,230

317,231

318,231

319,231

319,232

320,232

321,232

321,233

322,233

323,233

323,234

324,234

325,234

325,235

326,235

327,235

327,236

328,236

328,237

329,237

330,237

330,238

331,238

331,239

332,239

333,239

333,240

334,240

334,241

335,241

336,241

336,242

337,242

337,243

338,243

339,243

339,244

340,244

340,245

341,245

342,245

342,246

343,246

344,246

344,247

345,247

346,247

346,248

347,248

347,249

348,249

349,249

349,250

350,250

351,250

351,251

352,251

352,252

353,252

354,252

354,253

355,253

355,254

356,254

356,255

357,255

357,256

358,256

359,256

359,257

360,257

360,258

361,258

362,258

362,259

363,259

363,260

364,260

364,261

365,261

365,262

366,262

366,263

367,263

368,263

368,264

369,264

369,265

370,265

370,266

371,266

372,266

372,267

373,267

373,268

374,268

374,269

375,269

375,270

376,270

376,271

377,271

378,271

378,272

379,272

379,273

380,273

380,274

381,274

381,275

382,275

382,276

383,276

383,277

384,277

384,278

385,278

385,279

386,279

386,280

387,280

387,281

388,281

388,282

389,282

389,283

390,283

390,284

390,285

391,285

391,286

392,286

392,287

393,287

393,288

393,289

394,289

394,290

395,290

395,291

396,291

396,292

396,293

397,293

397,294

397,295

398,295

398,296

398,297

399,297

399,298

399,299

400,299

400,300

400,301

400,302

401,302

401,303

401,304

401,305

402,305

402,306

402,307

402,308

403,308

403,309

403,310

403,311

404,311

404,312

404,313

404,314

404,315

405,315

405,316

405,317

405,318

405,319

406,319

406,320

406,321

406,322

406,323

406,324

406,325

406,326

406,327

406,328

407,328

407,329

407,330

407,331

407,332

407,333

407,334

407,335

407,336

407,336

406,337

406,338

406,339

406,340

406,341

406,342

406,343

406,344

406,345

406,345

405,346

405,347

405,348

405,349

405,349

404,350

404,351

404,352

404,352

403,353

403,354

403,354

402,355

402,356

402,357

402,357

401,358

401,358

400,359

400,360

400,360

0VT2230.972255737LT

384,360

383,360

383,359

382,359

381,359

380,359

379,359

378,359

377,359

376,359

375,359

374,359

373,359

372,359

372,358

371,358

370,358

369,358

368,358

367,358

366,358

366,357

365,357

364,357

363,357

362,357

362,356

361,356

360,356

359,356

358,356

358,355

357,355

356,355

355,355

354,355

354,354

353,354

352,354

351,354

351,353

350,353

350,352

349,352

348,352

348,351

347,351

346,351

346,350

345,350

344,350

343,350

343,349

342,349

341,349

340,349

340,348

339,348

338,348

337,348

337,347

336,347

335,347

334,347

334,346

333,346

332,346

331,346

330,346

330,345

329,345

328,345

328,344

327,344

326,344

326,343

325,343

324,343

323,343

323,342

322,342

321,342

320,342

320,341

319,341

318,341

317,341

317,340

316,340

315,340

314,340

313,340

313,339

312,339

311,339

310,339

309,339

309,338

308,338

307,338

306,338

305,338

305,337

304,337

303,337

303,336

302,336

301,336

301,335

300,335

299,335

299,334

298,334

297,334

296,334

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295,333

294,333

294,332

293,332

292,332

292,331

291,331

291,330

290,330

289,330

289,329

288,329

287,329

287,328

286,328

286,327

285,327

284,327

284,326

283,326

283,325

282,325

281,325

281,324

280,324

280,323

279,323

278,323

278,322

277,322

277,321

276,321

276,320

275,320

275,319

274,319

274,318

273,318

272,318

272,317

271,317

271,316

270,316

270,315

269,315

269,314

268,314

267,314

267,313

266,313

266,312

265,312

265,311

264,311

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263,310

263,309

262,309

262,308

261,308

260,308

260,307

259,307

259,306

258,306

258,305

257,305

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256,304

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255,302

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249,297

249,296

248,296

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247,295

247,294

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246,292

245,292

245,291

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243,290

243,289

242,289

242,288

241,288

241,287

240,287

240,286

239,286

239,285

238,285

238,284

237,284

237,283

236,283

236,282

235,282

234,282

234,281

233,281

233,280

232,280

232,279

231,279

231,278

230,278

230,277

229,277

229,276

228,276

228,275

227,275

227,274

226,274

226,273

225,273

225,272

224,272

224,271

223,271

223,270

223,269

222,269

222,268

221,268

221,267

220,267

220,266

220,265

219,265

219,264

219,263

218,263

218,262

217,262

217,261

217,260

216,260

216,259

216,258

215,258

215,257

215,256

214,256

214,255

214,254

214,253

213,253

213,252

213,251

212,251

212,250

212,249

211,249

211,248

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211,246

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210,245

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209,240

209,239

208,239

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207,238

207,237

207,236

206,236

206,235

206,234

205,234

205,233

205,232

205,231

204,231

204,230

204,229

204,228

204,227

203,227

203,226

203,225

202,225

202,224

202,223

202,222

201,222

201,221

201,220

201,219

201,218

200,218

200,217

200,216

200,215

200,214

200,213

200,212

200,211

200,210

201,209

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202,208

202,208

203,207

204,207

204,207

205,206

206,206

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208,205

209,205

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210,204

211,204

212,204

213,204

214,204

214,204

215,203

216,203

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218,203

219,203

220,203

221,203

222,203

223,203

223,203

224,202

225,202

226,202

226,203

227,203

228,203

229,203

230,203

231,203

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233,203

234,203

235,203

236,203

236,204

237,204

238,204

239,204

240,204

241,204

241,205

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243,205

244,205

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245,206

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247,206

248,206

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249,207

250,207

251,207

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254,208

255,208

256,208

257,208

257,209

258,209

259,209

260,209

260,210

261,210

262,210

263,210

264,210

264,211

265,211

266,211

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267,212

268,212

269,212

270,212

270,213

271,213

272,213

273,213

274,213

274,214

275,214

276,214

277,214

277,215

278,215

279,215

280,215

280,216

281,216

282,216

282,217

283,217

284,217

284,218

285,218

286,218

286,219

287,219

288,219

288,220

289,220

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290,221

291,221

292,221

293,221

293,222

294,222

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296,222

296,223

297,223

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299,223

299,224

300,224

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301,225

302,225

303,225

303,226

304,226

305,226

305,227

306,227

307,227

307,228

308,228

309,228

310,228

310,229

311,229

312,229

312,230

313,230

314,230

314,231

315,231

316,231

316,232

317,232

318,232

318,233

319,233

320,233

320,234

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322,235

323,235

324,235

324,236

325,236

325,237

326,237

327,237

327,238

328,238

329,238

329,239

330,239

331,239

331,240

332,240

333,240

333,241

334,241

335,241

335,242

336,242

336,243

337,243

338,243

338,244

339,244

339,245

340,245

341,245

341,246

342,246

343,246

343,247

344,247

345,247

345,248

346,248

347,248

347,249

348,249

349,249

349,250

350,250

350,251

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0VT2310.958681799LT

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0VT2320.966471799LT

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0VT2340.971896803LT

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0VT2410.957290839LT

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0VT2500.974027883LT

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331,272

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333,274

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421,318

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429,331

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429,333

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430,335

430,336

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430,338

431,338

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431,340

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429,361

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428,362

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424,365

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417,369

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415,371

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402,383

401,384

401,384

400,384

399,385

399,385

398,386

398,386

397,386

396,386

395,387

395,387

0VT2610.957232815LT

386,380

385,380

384,380

383,380

382,380

382,379

381,379

380,379

380,378

379,378

378,378

377,378

377,377

376,377

375,377

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374,376

373,376

372,376

372,375

371,375

371,374

370,374

369,374

368,374

368,373

367,373

367,372

366,372

365,372

365,371

364,371

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363,370

362,370

361,370

361,369

360,369

359,369

359,368

358,368

358,367

357,367

357,366

356,366

355,366

355,365

354,365

353,365

353,364

352,364

351,364

350,364

350,363

349,363

348,363

347,363

347,362

346,362

345,362

344,362

343,362

342,362

342,361

341,361

340,361

339,361

338,361

337,361

336,361

335,361

334,361

333,361

333,360

332,360

331,360

330,360

329,360

328,360

327,360

326,360

326,359

325,359

324,359

323,359

322,359

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320,358

319,358

318,358

317,358

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316,357

315,357

314,357

313,357

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287,350

286,350

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200,227

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233,220

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235,219

236,219

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238,219

239,219

240,219

241,219

242,219

243,219

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248,219

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251,219

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254,220

255,220

256,220

257,220

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260,221

261,221

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263,222

263,223

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266,224

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268,225

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272,229

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274,230

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275,231

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276,232

276,233

277,233

277,234

278,234

279,234

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280,235

281,235

281,236

282,236

283,236

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284,237

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286,238

286,239

287,239

287,240

288,240

289,240

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290,241

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291,242

291,243

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293,244

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297,247

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316,259

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317,261

318,261

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319,262

319,263

320,263

320,264

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330,272

330,273

331,273

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332,274

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333,275

333,276

333,277

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335,278

335,279

336,279

336,280

337,280

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351,297

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416,315

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423,323

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391,378

390,378

389,379

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386,380

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0VT2620.949401867LT

431,388

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0VT2820.975304561LT

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0VT2970.976613549LT

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0VT2980.975722551LT

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0VT2990.975272551LT

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0VT3000.975023549LT

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0VT3010.974516547LT

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0VT3020.974849547LT

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0VT3220.973226521LT

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286,370

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0VT3230.973380521LT

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0VT3240.973002521LT

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0VT3830.984929515LT

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0VT4030.986801523LT

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0VT4040.986351523LT

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0VT4050.986757523LT

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0VT4520.980451551LT

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0VT4530.979401561LT

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250,264

249,264

248,265

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242,266

242,266

0VT4890.966166325LT

241,266

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284,248

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250,264

249,265

249,265

248,265

247,265

246,265

245,265

244,265

243,265

242,265

241,266

241,266

0VT4900.966558323LT

249,265

248,265

247,265

246,265

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241,265

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296,222

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296,224

296,225

296,226

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281,251

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257,262

256,262

255,262

254,263

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0VT4910.966640323LT

242,266

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281,251

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251,264

250,264

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249,265

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244,265

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242,266

242,266

0VT4920.967561321LT

249,265

248,265

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241,265

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239,265

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279,253

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276,254

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263,260

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258,262

257,262

256,262

255,263

255,263

254,263

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252,264

252,264

251,264

250,264

249,265

249,265

0VT4930.967309323LT

247,265

246,265

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244,265

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242,265

241,265

240,265

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0VT4940.966497323LT

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0VT4950.966098323LT

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0VT4960.964443325LT

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0VT4970.965564325LT

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0VT4980.964554325LT

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0VT4990.963288327LT

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0VT5000.963646325LT

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251,264

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243,266

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0VT5010.966699325LT

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255,263

254,263

253,264

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252,264

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250,265

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249,265

248,265

247,266

247,266

0VT5020.967426325LT

248,266

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246,266

245,266

244,266

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242,266

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240,266

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297,232

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297,234

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296,235

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286,249

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281,252

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265,260

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263,261

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256,263

256,263

255,263

254,263

253,264

253,264

252,264

251,265

251,265

250,265

249,265

248,266

248,266

0VT5030.968101331LT

250,266

249,266

248,266

247,266

246,266

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244,266

243,266

242,266

241,266

240,266

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231,266

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229,266

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224,264

223,264

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214,255

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+[project] +name = "deepivus" +version = "0.0.1" +description = "Package for IVUS Segmentation" +authors = [ + {name = "Aditya Mangalampalli"} +] +requires-python = ">=3.12,<3.15" +dependencies = [ + "click (>=8.3.1,<9.0.0)", + "imageio (>=2.37.2,<3.0.0)", + "imageio-ffmpeg (>=0.6.0,<0.7.0)", + "numpy (>=2.4.2,<3.0.0)", + "pydicom (>=3.0.1,<4.0.0)", + "scikit-image (>=0.26.0,<0.27.0)", + "scipy (>=1.17.0,<2.0.0)", + "tensorflow (>=2.20.0,<3.0.0)", + "matplotlib (>=3.10.8,<4.0.0)", + "tqdm (>=4.67.3,<5.0.0)", + "setuptools (==70.3.0)", + "tensorboard (>=2.20.0,<3.0.0)" +] + +[project.scripts] +deepivus = "deepivus.cli:cli" + + +[build-system] +requires = ["poetry-core>=2.0.0,<3.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/scripts/analysis/export_memo_pdf.sh b/scripts/analysis/export_memo_pdf.sh new file mode 100644 index 0000000000000000000000000000000000000000..13e8be77f5c16bcc02b66202f2cd4ba217d0404b --- /dev/null +++ b/scripts/analysis/export_memo_pdf.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash +set -euo pipefail + +INPUT="docs/multitask_finetuning_comprehensive_memo.md" +OUTPUT="docs/multitask_finetuning_comprehensive_memo.pdf" + +pandoc "$INPUT" \ + -o "$OUTPUT" \ + --from gfm \ + --resource-path=.:docs \ + --include-in-header=docs/pandoc_pdf_header.tex + +echo "Wrote $OUTPUT" diff --git a/scripts/analysis/generate_project_memo_assets.py b/scripts/analysis/generate_project_memo_assets.py new file mode 100644 index 0000000000000000000000000000000000000000..774b88c68e043ba65218a11b20b0146252e0e7fe --- /dev/null +++ b/scripts/analysis/generate_project_memo_assets.py @@ -0,0 +1,344 @@ +#!/usr/bin/env python3 +"""Generate figures and numeric summaries for the project fine-tuning memo.""" + +from __future__ import annotations + +import json +import math +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np + +ROOT = Path(__file__).resolve().parents[2] +ASSET_DIR = ROOT / "docs" / "memo_assets" +ASSET_DIR.mkdir(parents=True, exist_ok=True) + + +# ---------------------------- Helpers ---------------------------- + +def read_json(path: Path) -> dict: + with path.open("r", encoding="utf-8") as f: + return json.load(f) + + +def read_json_first(paths: list[Path], required: bool = True) -> dict: + for path in paths: + if path.exists(): + return read_json(path) + if required: + raise FileNotFoundError(f"None of the candidate files exist: {paths}") + return {} + + +def wilson_interval(successes: int, n: int, z: float = 1.96) -> tuple[float, float]: + if n <= 0: + return float("nan"), float("nan") + p = successes / n + denom = 1.0 + (z * z / n) + center = (p + (z * z / (2 * n))) / denom + half = (z / denom) * math.sqrt((p * (1 - p) / n) + (z * z / (4 * n * n))) + return max(0.0, center - half), min(1.0, center + half) + + +def ensure_style(): + plt.rcParams.update( + { + "font.size": 10, + "axes.grid": True, + "grid.alpha": 0.22, + "figure.facecolor": "white", + "axes.facecolor": "#fafafa", + } + ) + + +# ---------------------------- Load inputs ---------------------------- + +multitask_summary = read_json_first( + [ + ROOT / "models" / "multitask" / "multitask_summary.json", + ROOT / "output" / "training_outputs" / "multitask_summary.json", + ], + required=False, +) +multitask_test = read_json_first( + [ + ROOT / "output" / "multitask_test_inference.json", + ROOT / "output" / "training_outputs" / "multitask_test_inference.json", + ] +) +lumen_summary = read_json_first( + [ + ROOT / "models" / "standalone" / "lumen" / "finetune_summary.json", + ROOT / "output" / "training_outputs" / "lumen_finetune_summary.json", + ] +) +standalone_test = read_json_first( + [ + ROOT / "output" / "bifurcation_classifier" / "test_inference.json", + ROOT / "output" / "training_outputs" / "bifurcation_classifier" / "test_inference.json", + ] +) +threshold_payload = read_json_first( + [ + ROOT / "models" / "multitask" / "threshold.json", + ROOT / "models" / "standalone" / "bifurcation" / "threshold.json", + ROOT / "models" / "bifurcation" / "threshold.json", + ROOT / "output" / "bifurcation_classifier" / "threshold.json", + ] +) +dataset_balance = read_json_first( + [ + ROOT / "output" / "dataset_balance" / "dataset_balance_summary.json", + ROOT / "docs" / "memo_assets" / "dataset_balance_summary.json", + ] +) + + +# ---------------------------- Figure 1: Training curves (multitask) ---------------------------- + +ensure_style() +history = multitask_summary.get("history", []) +if history: + epochs = [int(r["epoch"]) for r in history] + train_total = [float(r.get("total_loss", np.nan)) for r in history] + val_total = [float(r.get("val_total_loss", np.nan)) for r in history] + val_dice = [float(r.get("val_seg_dice", np.nan)) for r in history] + val_iou = [float(r.get("val_seg_iou", np.nan)) for r in history] + val_f1 = [float(r.get("val_cls_f1", np.nan)) for r in history] + val_auc = [float(r.get("val_cls_auc", np.nan)) for r in history] + + fig, axes = plt.subplots(2, 2, figsize=(12, 8)) + + axes[0, 0].plot(epochs, train_total, label="train total loss", color="#1f77b4") + axes[0, 0].plot(epochs, val_total, label="val total loss", color="#d62728") + axes[0, 0].set_title("Multitask Total Loss") + axes[0, 0].set_xlabel("Epoch") + axes[0, 0].set_ylabel("Loss") + axes[0, 0].legend() + + axes[0, 1].plot(epochs, val_dice, label="val seg dice", color="#2ca02c") + axes[0, 1].plot(epochs, val_iou, label="val seg IoU", color="#17becf") + axes[0, 1].set_title("Validation Segmentation Metrics") + axes[0, 1].set_xlabel("Epoch") + axes[0, 1].set_ylabel("Score") + axes[0, 1].set_ylim(0.75, 0.97) + axes[0, 1].legend() + + axes[1, 0].plot(epochs, val_f1, label="val cls F1", color="#9467bd") + axes[1, 0].plot(epochs, val_auc, label="val cls AUC", color="#ff7f0e") + axes[1, 0].set_title("Validation Classification Metrics") + axes[1, 0].set_xlabel("Epoch") + axes[1, 0].set_ylabel("Score") + axes[1, 0].set_ylim(0.0, 1.0) + axes[1, 0].legend() + + # Stability plot: train seg/cls losses (if available) + train_seg = [float(r.get("seg_loss", np.nan)) for r in history] + train_cls = [float(r.get("cls_loss", np.nan)) for r in history] + axes[1, 1].plot(epochs, train_seg, label="train seg loss", color="#8c564b") + axes[1, 1].plot(epochs, train_cls, label="train cls loss", color="#e377c2") + axes[1, 1].set_title("Train Task Loss Components") + axes[1, 1].set_xlabel("Epoch") + axes[1, 1].set_ylabel("Loss") + axes[1, 1].legend() + + fig.suptitle("Multitask Fine-tuning Dynamics", fontsize=13) + fig.tight_layout() + fig.savefig(ASSET_DIR / "multitask_training_dynamics.png", dpi=220) + plt.close(fig) + + +# ---------------------------- Figure 2: Lumen fine-tune curves ---------------------------- + +lh = lumen_summary.get("history", []) +le = [int(float(r.get("epoch", i + 1))) for i, r in enumerate(lh)] +tr_loss = [float(r.get("train_loss", np.nan)) for r in lh] +val_loss = [float(r.get("val_loss", np.nan)) for r in lh] +val_dice_l = [float(r.get("val_dice", np.nan)) for r in lh] +val_iou_l = [float(r.get("val_iou", np.nan)) for r in lh] + +fig, axes = plt.subplots(1, 2, figsize=(11, 4.2)) +axes[0].plot(le, tr_loss, label="train loss", color="#1f77b4") +axes[0].plot(le, val_loss, label="val loss", color="#d62728") +axes[0].set_title("Lumen Fine-tune Loss") +axes[0].set_xlabel("Epoch") +axes[0].set_ylabel("Loss") +axes[0].legend() + +axes[1].plot(le, val_dice_l, label="val dice", color="#2ca02c") +axes[1].plot(le, val_iou_l, label="val IoU", color="#17becf") +axes[1].set_title("Lumen Fine-tune Validation Metrics") +axes[1].set_xlabel("Epoch") +axes[1].set_ylabel("Score") +axes[1].set_ylim(0.82, 0.97) +axes[1].legend() + +fig.tight_layout() +fig.savefig(ASSET_DIR / "lumen_finetune_dynamics.png", dpi=220) +plt.close(fig) + + +# ---------------------------- Figure 3: Standalone threshold sweep ---------------------------- + +sweep = standalone_test.get("threshold_selection", {}).get("val_threshold_sweep", []) +if sweep: + ths = np.array([float(r["threshold"]) for r in sweep], dtype=float) + f1s = np.array([float(r.get("f1", np.nan)) for r in sweep], dtype=float) + recs = np.array([float(r.get("recall", np.nan)) for r in sweep], dtype=float) + precs = np.array([float(r.get("precision", np.nan)) for r in sweep], dtype=float) + + best_t = float(standalone_test.get("selected_threshold", ths[int(np.nanargmax(f1s))])) + best_i = int(np.argmin(np.abs(ths - best_t))) + + fig, ax = plt.subplots(figsize=(8, 4.8)) + ax.plot(ths, f1s, label="F1", color="#1f77b4", linewidth=2) + ax.plot(ths, recs, label="Recall", color="#2ca02c", alpha=0.75) + ax.plot(ths, precs, label="Precision", color="#d62728", alpha=0.75) + ax.scatter([ths[best_i]], [f1s[best_i]], color="black", s=60, zorder=3, label=f"Selected t={ths[best_i]:.3f}") + ax.axvline(ths[best_i], linestyle="--", color="black", linewidth=1) + ax.set_title("Validation Threshold Sweep (Standalone Bifurcation Model)") + ax.set_xlabel("Threshold") + ax.set_ylabel("Score") + ax.set_ylim(0.0, 1.0) + ax.legend(loc="best") + fig.tight_layout() + fig.savefig(ASSET_DIR / "standalone_threshold_sweep.png", dpi=220) + plt.close(fig) + + +# ---------------------------- Figure 4: Probability distributions ---------------------------- + +pred_rows = standalone_test.get("predictions", []) +if pred_rows: + y_true = np.array([int(r["label"]) for r in pred_rows], dtype=int) + y_prob = np.array([float(r["probability"]) for r in pred_rows], dtype=float) + th = float(standalone_test.get("selected_threshold", 0.5)) + + fig, ax = plt.subplots(figsize=(8, 4.6)) + ax.hist(y_prob[y_true == 0], bins=24, alpha=0.6, label="True non-bifurcation", color="#1f77b4") + ax.hist(y_prob[y_true == 1], bins=24, alpha=0.6, label="True bifurcation", color="#d62728") + ax.axvline(th, color="black", linestyle="--", linewidth=1.3, label=f"Operating threshold={th:.3f}") + ax.set_title("Predicted Probability Distributions (Standalone Test Set)") + ax.set_xlabel("Predicted bifurcation probability") + ax.set_ylabel("Count") + ax.legend() + fig.tight_layout() + fig.savefig(ASSET_DIR / "standalone_probability_hist.png", dpi=220) + plt.close(fig) + + +# ---------------------------- Figure 5: Pipeline diagram ---------------------------- + +fig, ax = plt.subplots(figsize=(13, 5.8)) +ax.axis("off") + +# Draw boxes manually +boxes = { + "data": (0.03, 0.62, 0.2, 0.25, "Frame-bank JSONL + DICOM\n(train/val/test split)"), + "pre": (0.28, 0.62, 0.2, 0.25, "Preprocess\n(center black circle,\n3-channel replication)"), + "base": (0.53, 0.62, 0.2, 0.25, "Pretrained lumen base\n(SavedModel logits)"), + "seg": (0.78, 0.72, 0.19, 0.14, "Segmentation branch\n(BCE + Dice)") , + "cls": (0.78, 0.52, 0.19, 0.14, "Classification head\n(GAP + Dense + Sigmoid)") , + "loss": (0.53, 0.18, 0.26, 0.22, "Total loss\n= w_seg * seg_loss\n+ w_cls * cls_loss\n(mask seg loss by has_mask)"), + "eval": (0.84, 0.18, 0.13, 0.22, "Val threshold sweep\n→ threshold.json\n→ runtime inference"), +} + +for _, (x, y, w, h, txt) in boxes.items(): + rect = plt.Rectangle((x, y), w, h, facecolor="#f7f7f7", edgecolor="#333333", linewidth=1.2) + ax.add_patch(rect) + ax.text(x + w / 2, y + h / 2, txt, ha="center", va="center", fontsize=10) + + +def arrow(x0, y0, x1, y1): + ax.annotate("", xy=(x1, y1), xytext=(x0, y0), arrowprops=dict(arrowstyle="->", linewidth=1.4, color="#333333")) + +arrow(0.23, 0.745, 0.28, 0.745) +arrow(0.48, 0.745, 0.53, 0.745) +arrow(0.73, 0.745, 0.78, 0.79) +arrow(0.73, 0.71, 0.78, 0.59) +arrow(0.875, 0.72, 0.66, 0.40) +arrow(0.875, 0.52, 0.66, 0.40) +arrow(0.79, 0.29, 0.84, 0.29) + +ax.set_title("Training and Inference Design: Pretrained Base + Multitask Head", fontsize=13) +fig.tight_layout() +fig.savefig(ASSET_DIR / "multitask_pipeline_diagram.png", dpi=220) +plt.close(fig) + + +# ---------------------------- Figure 6: Metric comparison ---------------------------- + +mt = multitask_test["bifurcation_metrics"] +ms = multitask_test["segmentation_metrics"] +lumen_test = lumen_summary["final_test_metrics"] + +labels = ["Lumen IoU", "Lumen Dice", "Bif Acc", "Bif F1", "Bif AUC"] +values = [ + float(ms.get("seg_iou", np.nan)), + float(ms.get("seg_dice", np.nan)), + float(mt.get("cls_accuracy", np.nan)), + float(mt.get("cls_f1", np.nan)), + float(mt.get("cls_auc", np.nan)), +] + +fig, ax = plt.subplots(figsize=(8.2, 4.4)) +bars = ax.bar(labels, values, color=["#17becf", "#2ca02c", "#1f77b4", "#9467bd", "#ff7f0e"]) +ax.set_ylim(0.0, 1.0) +ax.set_ylabel("Score") +ax.set_title("Multitask Test Metrics Snapshot") +for b, v in zip(bars, values): + ax.text(b.get_x() + b.get_width() / 2, v + 0.015, f"{v:.3f}", ha="center", va="bottom", fontsize=9) +fig.tight_layout() +fig.savefig(ASSET_DIR / "multitask_test_metric_snapshot.png", dpi=220) +plt.close(fig) + + +# ---------------------------- Summary JSON ---------------------------- + +cls = multitask_test["bifurcation_metrics"] +seg = multitask_test["segmentation_metrics"] +n_cls = int(cls["tp"] + cls["fp"] + cls["fn"] + cls["tn"]) +acc_ci = wilson_interval(int(cls["tp"] + cls["tn"]), n_cls) +rec_ci = wilson_interval(int(cls["tp"]), int(cls["tp"] + cls["fn"])) +prec_ci = wilson_interval(int(cls["tp"]), int(cls["tp"] + cls["fp"])) + +best_epoch = None +if history: + best_epoch = min(history, key=lambda r: float(r.get("val_total_loss", np.inf))).get("epoch") + +summary = { + "data": { + "split_counts": dataset_balance.get("counts", {}), + "bifurcation_positive_rate": dataset_balance.get("bifurcation_positive_rate", {}), + "lumen_coverage_rate": dataset_balance.get("lumen_coverage_rate", {}), + }, + "multitask": { + "epochs_completed": len(history), + "best_epoch_by_val_total_loss": best_epoch, + "best_val_total_loss": multitask_summary.get("best_val_total_loss"), + "test_seg_metrics": seg, + "test_cls_metrics": cls, + "cls_accuracy_wilson95": acc_ci, + "cls_precision_wilson95": prec_ci, + "cls_recall_wilson95": rec_ci, + "threshold": multitask_test.get("threshold_info", {}).get("selected_threshold"), + }, + "lumen_finetune": { + "num_samples": lumen_summary.get("num_samples"), + "test_metrics": lumen_summary.get("final_test_metrics", {}), + }, + "standalone_bifurcation": { + "selected_threshold": standalone_test.get("selected_threshold"), + "test_metrics": standalone_test.get("metrics", {}), + }, + "runtime_threshold": threshold_payload.get("selected_threshold"), +} + +with (ASSET_DIR / "memo_summary.json").open("w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + +print("Saved assets to", ASSET_DIR) +for p in sorted(ASSET_DIR.glob("*")): + print(p.name) diff --git a/scripts/analysis/plot_data_scale_vs_accuracy.py b/scripts/analysis/plot_data_scale_vs_accuracy.py new file mode 100644 index 0000000000000000000000000000000000000000..8f1bccc12bf711d6943571064f3434bb1e84ab67 --- /dev/null +++ b/scripts/analysis/plot_data_scale_vs_accuracy.py @@ -0,0 +1,245 @@ +#!/usr/bin/env python3 +"""Plot data scale vs accuracy from observed runs, with optional rough extrapolation. + +This script intentionally separates observed points from extrapolated points. +Use extrapolation only for presentation drafts, not model selection. +""" + +from __future__ import annotations + +import argparse +import json +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable + +import matplotlib.pyplot as plt +import numpy as np + + +@dataclass +class Point: + task: str + metric: str + data_size: int + value: float + source: str + + +def _read_json(path: Path) -> dict: + with path.open("r", encoding="utf-8") as f: + return json.load(f) + + +def collect_default_points(root: Path) -> list[Point]: + points: list[Point] = [] + + # Lumen fine-tune run (~295 labeled frames) + lumen_summary = root / "models/standalone/lumen/finetune_summary.json" + if lumen_summary.exists(): + d = _read_json(lumen_summary) + n = int(d.get("num_samples", 0)) + m = d.get("final_test_metrics", {}) + if n > 0 and "dice" in m: + points.append(Point("lumen", "dice", n, float(m["dice"]), str(lumen_summary))) + if n > 0 and "iou" in m: + points.append(Point("lumen", "iou", n, float(m["iou"]), str(lumen_summary))) + + # Multitask run (split built from merged_600) + multitask_summary = root / "models/multitask/multitask_summary.json" + if multitask_summary.exists(): + d = _read_json(multitask_summary) + split_json = d.get("split_json", "") + total_n = 600 if str(split_json).endswith("_600.json") else int(d.get("num_train", 0)) + tm = d.get("test_metrics", {}) + if total_n > 0 and "seg_dice" in tm: + points.append(Point("lumen", "dice", total_n, float(tm["seg_dice"]), str(multitask_summary))) + if total_n > 0 and "seg_iou" in tm: + points.append(Point("lumen", "iou", total_n, float(tm["seg_iou"]), str(multitask_summary))) + if total_n > 0 and "cls_f1" in tm: + points.append(Point("bifurcation", "f1", total_n, float(tm["cls_f1"]), str(multitask_summary))) + + # Standalone bifurcation classifier run (split built from merged_600) + bif_summary = root / "output/training_outputs/bifurcation_classifier/training_summary.json" + if bif_summary.exists(): + d = _read_json(bif_summary) + total_n = 600 if "merged600" in str(d.get("tensorboard_run_dir", "")) else int(d.get("num_train", 0)) + tm = d.get("test_metrics", {}) + if total_n > 0 and "accuracy" in tm: + points.append(Point("bifurcation", "accuracy", total_n, float(tm["accuracy"]), str(bif_summary))) + + return points + + +def fit_log_linear(x: np.ndarray, y: np.ndarray, x_new: np.ndarray) -> np.ndarray: + # y = a + b*log(x) ; stable for small point count + X = np.vstack([np.ones_like(x), np.log(x)]).T + coef, *_ = np.linalg.lstsq(X, y, rcond=None) + return coef[0] + coef[1] * np.log(x_new) + + +def illustrative_upward_curve( + x_end: int, + y_start: float, + y_end: float, + k: float, + num: int = 120, +) -> tuple[np.ndarray, np.ndarray]: + """Create a smooth, monotonic, saturating upward curve from x=0 to x=x_end.""" + x = np.linspace(0.0, float(x_end), num=num) + t = x / max(float(x_end), 1.0) + denom = 1.0 - np.exp(-k) + growth = (1.0 - np.exp(-k * t)) / max(denom, 1e-9) + y = y_start + (y_end - y_start) * growth + return x, y + + +def projected_points_from_curve( + x_end: int, + y_start: float, + y_end: float, + k: float, + n_points: int, +) -> tuple[np.ndarray, np.ndarray]: + n_points = max(int(n_points), 2) + x = np.linspace(max(1.0, x_end / n_points), float(x_end), num=n_points) + t = x / max(float(x_end), 1.0) + denom = 1.0 - np.exp(-k) + growth = (1.0 - np.exp(-k * t)) / max(denom, 1e-9) + y = y_start + (y_end - y_start) * growth + return x, y + + +def group_points(points: Iterable[Point]) -> dict[tuple[str, str], list[Point]]: + grouped: dict[tuple[str, str], list[Point]] = {} + for p in points: + grouped.setdefault((p.task, p.metric), []).append(p) + for key in grouped: + grouped[key] = sorted(grouped[key], key=lambda z: z.data_size) + return grouped + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--root", type=Path, default=Path("."), help="repo root") + parser.add_argument("--output", type=Path, default=Path("output/data_scale_vs_accuracy.png")) + parser.add_argument("--extrapolate-to", type=int, nargs="*", default=[800, 1000, 1500]) + parser.add_argument("--no-extrapolation", action="store_true") + parser.add_argument( + "--illustrative-lumen-prior", + action="store_true", + help="Draw an illustrative upward curve from n=0 to --lumen-current-n for lumen metrics.", + ) + parser.add_argument("--lumen-current-n", type=int, default=300) + parser.add_argument("--lumen-start", type=float, default=0.60) + parser.add_argument("--lumen-curve-k", type=float, default=3.0) + parser.add_argument("--projected-points", type=int, default=4) + parser.add_argument("--bif-current-n", type=int, default=600) + parser.add_argument("--bif-start", type=float, default=0.55) + parser.add_argument("--bif-curve-k", type=float, default=2.4) + parser.add_argument("--show", action="store_true") + args = parser.parse_args() + + points = collect_default_points(args.root) + if not points: + raise SystemExit("No points found. Check expected summary JSON files.") + + grouped = group_points(points) + + fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharex=True) + task_to_ax = {"lumen": axes[0], "bifurcation": axes[1]} + + for (task, metric), pts in grouped.items(): + ax = task_to_ax.get(task) + if ax is None: + continue + + x = np.array([p.data_size for p in pts], dtype=float) + y = np.array([p.value for p in pts], dtype=float) + ax.plot(x, y, "o-", label=f"{metric} (observed)") + + if args.illustrative_lumen_prior and task == "lumen": + anchor_idx = int(np.argmin(np.abs(x - float(args.lumen_current_n)))) + y_end = float(y[anchor_idx]) + x_curve, y_curve = illustrative_upward_curve( + x_end=args.lumen_current_n, + y_start=args.lumen_start, + y_end=y_end, + k=args.lumen_curve_k, + ) + ax.plot(x_curve, y_curve, ":", linewidth=2, label=f"{metric} (lumen trend)") + xp, yp = projected_points_from_curve( + x_end=args.lumen_current_n, + y_start=args.lumen_start, + y_end=y_end, + k=args.lumen_curve_k, + n_points=args.projected_points, + ) + ax.plot(xp, yp, "s-", linewidth=1.3, markersize=5, label=f"{metric} (projected points)") + + if args.illustrative_lumen_prior and task == "bifurcation": + anchor_idx = int(np.argmin(np.abs(x - float(args.bif_current_n)))) + y_end = float(y[anchor_idx]) + x_curve, y_curve = illustrative_upward_curve( + x_end=args.bif_current_n, + y_start=args.bif_start, + y_end=y_end, + k=args.bif_curve_k, + ) + ax.plot(x_curve, y_curve, ":", linewidth=2, label=f"{metric} (bifurcation trend)") + xp, yp = projected_points_from_curve( + x_end=args.bif_current_n, + y_start=args.bif_start, + y_end=y_end, + k=args.bif_curve_k, + n_points=args.projected_points, + ) + ax.plot(xp, yp, "s-", linewidth=1.3, markersize=5, label=f"{metric} (projected points)") + + if not args.no_extrapolation and len(pts) >= 2 and args.extrapolate_to: + x_new = np.array(sorted(set(args.extrapolate_to)), dtype=float) + x_new = x_new[x_new > x.max()] + if x_new.size: + y_new = fit_log_linear(x, y, x_new) + y_new = np.clip(y_new, 0.0, 1.0) + ax.plot(x_new, y_new, "x--", label=f"{metric} (rough extrapolation)") + + ax.set_title(task) + ax.set_xlabel("Labeled data size") + ax.set_ylabel("Metric") + ax.set_ylim(0.5, 1.0) + ax.grid(True, alpha=0.25) + ax.legend() + + fig.suptitle("Data Scale vs Accuracy (Observed + Optional Rough Extrapolation)") + fig.tight_layout() + + args.output.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(args.output, dpi=180) + + # Also write a tiny audit trail for reproducibility. + txt = args.output.with_suffix(".txt") + with txt.open("w", encoding="utf-8") as f: + f.write("Observed points:\n") + for p in points: + f.write(f"- task={p.task} metric={p.metric} n={p.data_size} value={p.value:.6f} source={p.source}\n") + if not args.no_extrapolation: + f.write("\nExtrapolation model: y = a + b*log(n) per task+metric (least squares).\n") + f.write("Use extrapolated points only as a draft figure, not as evidence.\n") + if args.illustrative_lumen_prior: + f.write( + "\nIllustrative lumen prior: monotonic saturating curve from n=0 " + f"to n={args.lumen_current_n}, anchored to nearest observed lumen point.\n" + ) + f.write("Projected point count per metric: " + f"{args.projected_points} (lumen to n={args.lumen_current_n}, bifurcation to n={args.bif_current_n}).\n") + + print(f"Saved: {args.output}") + print(f"Saved: {txt}") + + if args.show: + plt.show() + + +if __name__ == "__main__": + main() diff --git a/scripts/analysis/render_three_stage_bifurcation_frame.py b/scripts/analysis/render_three_stage_bifurcation_frame.py new file mode 100644 index 0000000000000000000000000000000000000000..6ebc28a9b2a88f6ba3047f6779d5e86df6041d35 --- /dev/null +++ b/scripts/analysis/render_three_stage_bifurcation_frame.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python3 +"""Render a 3-stage graphic for one bifurcation frame. + +Panels: +1) Raw grayscale frame +2) Segmentation overlay +3) Segmentation overlay + bifurcation prediction +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +from skimage import measure + +REPO_ROOT = Path(__file__).resolve().parents[2] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from deepivus.inference import predict, predict_bifurcation +from deepivus.io.dicom import read_dicom +from deepivus.processing.preprocessing import apply_center_black_circle + + +def _pick_frame_index(images: np.ndarray, threshold: float, requested_frame: int | None) -> tuple[int, float, int]: + probs, labels = predict_bifurcation(images, threshold=threshold, batch_size=32) + if requested_frame is not None: + if requested_frame < 0 or requested_frame >= len(images): + raise ValueError(f"--frame-index {requested_frame} is out of range [0, {len(images)-1}]") + idx = requested_frame + else: + idx = int(np.argmax(probs)) + return idx, float(probs[idx]), int(labels[idx]) + + +def _render_three_panel( + frame: np.ndarray, + lumen_mask: np.ndarray, + bif_prob: float, + bif_label: int, + threshold: float, + out_path: Path, + title_prefix: str, +) -> None: + frame_f = frame.astype(np.float32) + mask = (lumen_mask == 1).astype(np.uint8) + + fig, axes = plt.subplots(3, 1, figsize=(8, 12)) + + axes[0].imshow(frame_f, cmap="gray") + axes[0].set_title(f"{title_prefix} - Raw Frame") + axes[0].axis("off") + + axes[1].imshow(frame_f, cmap="gray") + axes[1].imshow(np.ma.masked_where(mask == 0, mask), cmap="spring", alpha=0.35, vmin=0, vmax=1) + axes[1].contour(mask, levels=[0.5], colors=["yellow"], linewidths=1.2) + axes[1].set_title(f"{title_prefix} - Segmentation") + axes[1].axis("off") + + axes[2].imshow(frame_f, cmap="gray") + axes[2].imshow(np.ma.masked_where(mask == 0, mask), cmap="spring", alpha=0.35, vmin=0, vmax=1) + axes[2].contour(mask, levels=[0.5], colors=["yellow"], linewidths=1.2) + tag = "BIFURCATION" if bif_label == 1 else "NON-BIFURCATION" + color = "#d62728" if bif_label == 1 else "#2ca02c" + axes[2].set_title( + f"{title_prefix} - Segmentation + Bifurcation ({tag})\n" + f"prob={bif_prob:.3f}, threshold={threshold:.3f}", + color=color, + ) + axes[2].axis("off") + + fig.tight_layout() + out_path.parent.mkdir(parents=True, exist_ok=True) + fig.savefig(out_path, dpi=220) + plt.close(fig) + + +def _keep_largest_component(mask: np.ndarray) -> np.ndarray: + binary = mask == 1 + if not np.any(binary): + return np.zeros_like(mask, dtype=np.uint8) + cc = measure.label(binary, connectivity=1) + if cc.max() == 0: + return np.zeros_like(mask, dtype=np.uint8) + sizes = np.bincount(cc.ravel()) + sizes[0] = 0 + keep_id = int(np.argmax(sizes)) + out = np.zeros_like(mask, dtype=np.uint8) + out[cc == keep_id] = 1 + return out + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--dicom-path", type=Path, required=True) + parser.add_argument("--output", type=Path, default=Path("output/bifurcation_three_stage.png")) + parser.add_argument("--frame-index", type=int, default=None, help="Optional explicit frame index.") + parser.add_argument("--threshold", type=float, default=0.5, help="Bifurcation threshold.") + parser.add_argument("--diameter", type=int, default=67, help="Center-black-circle preprocessing diameter.") + args = parser.parse_args() + + _, images = read_dicom(str(args.dicom_path)) + images = apply_center_black_circle(images, diameter=args.diameter) + + idx, bif_prob, bif_label = _pick_frame_index(images, threshold=args.threshold, requested_frame=args.frame_index) + one = images[idx : idx + 1] + seg_pred = predict(one, return_confidence=False, return_class_confidence=False, batch_size=1) + mask = _keep_largest_component(seg_pred[0]) + + _render_three_panel( + frame=images[idx], + lumen_mask=mask, + bif_prob=bif_prob, + bif_label=bif_label, + threshold=float(args.threshold), + out_path=args.output, + title_prefix=f"Frame {idx}", + ) + print(f"Saved: {args.output}") + print(f"Frame index: {idx}") + print(f"Bifurcation probability: {bif_prob:.6f}") + print(f"Predicted label: {bif_label}") + + +if __name__ == "__main__": + main() diff --git a/scripts/data/frame_bank.py b/scripts/data/frame_bank.py new file mode 100644 index 0000000000000000000000000000000000000000..0e34554366a12891a80b90c770487f012e8a518c --- /dev/null +++ b/scripts/data/frame_bank.py @@ -0,0 +1,701 @@ +#!/usr/bin/env python3 +"""Evaluation frame-bank workflow for manual annotation. + +This pathway is intentionally outside the core library pipeline. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import sys +import tempfile +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path +from typing import Any + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.backend_bases import MouseButton +from matplotlib.lines import Line2D +from matplotlib.widgets import Button, RadioButtons, Slider + +ROOT = Path(__file__).resolve().parents[2] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from deepivus.io.dicom import read_dicom + +GROUPS = ("bifurcation", "paul") + + +@dataclass +class FrameState: + frame: int + lumen_x: list[float] + lumen_y: list[float] + lumen_confidence: float | None + bifurcation: bool + + +def _now_iso() -> str: + return datetime.now().isoformat(timespec="seconds") + + +def _safe_float(value: Any) -> float | None: + if value is None: + return None + try: + out = float(value) + except (TypeError, ValueError): + return None + if np.isnan(out): + return None + return out + + +def _video_id(group: str, dicom_path: Path) -> str: + return f"{group}/{dicom_path.stem}" + + +def _stable_video_seed(video_id: str, base_seed: int) -> int: + digest = hashlib.sha256(video_id.encode("utf-8")).hexdigest()[:16] + return (int(digest, 16) ^ int(base_seed)) & 0xFFFFFFFF + + +def _read_jsonl(path: Path) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + if not path.exists(): + return rows + with path.open("r", encoding="utf-8") as fp: + for raw in fp: + line = raw.strip() + if not line: + continue + rows.append(json.loads(line)) + return rows + + +def _write_jsonl_atomic(path: Path, rows: list[dict[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", dir=path.parent, delete=False) as tmp: + tmp_path = Path(tmp.name) + for rec in rows: + tmp.write(json.dumps(rec) + "\n") + tmp.flush() + os.fsync(tmp.fileno()) + os.replace(tmp_path, path) + + +def _choose_frames(num_frames: int, frames_per_video: int, seed: int) -> list[int]: + k = min(frames_per_video, num_frames) + rng = np.random.default_rng(seed) + picks = rng.choice(num_frames, size=k, replace=False) + return sorted(int(v) for v in picks) + + +def _build_bank_file( + dicom_path: Path, + group: str, + out_root: Path, + frames_per_video: int, + base_seed: int, + force: bool, +) -> Path: + video_id = _video_id(group, dicom_path) + out_dir = out_root / group + out_dir.mkdir(parents=True, exist_ok=True) + out_file = out_dir / f"{dicom_path.stem}.jsonl" + + if out_file.exists() and not force: + return out_file + + _, images = read_dicom(str(dicom_path)) + seed = _stable_video_seed(video_id, base_seed) + sampled = _choose_frames(images.shape[0], frames_per_video, seed) + + default_bif = group == "bifurcation" + + with out_file.open("w", encoding="utf-8") as fp: + meta = { + "record_type": "meta", + "schema": "deepivus_eval_frame_bank_v1", + "created_at": _now_iso(), + "video_id": video_id, + "group": group, + "dicom_path": str(dicom_path.resolve()), + "seed": seed, + "requested_frames_per_video": int(frames_per_video), + "sampled_frames": sampled, + "num_sampled_frames": len(sampled), + "default_bifurcation": default_bif, + } + fp.write(json.dumps(meta) + "\n") + for i, frame_idx in enumerate(sampled): + rec = { + "record_type": "frame", + "sample_order": i, + "frame": int(frame_idx), + "bifurcation": default_bif, + "lumen_confidence": None, + "lumen": {"x": [], "y": []}, + "annotated_at": None, + "updated_at": None, + } + fp.write(json.dumps(rec) + "\n") + + return out_file + + +def build_all_banks(data_root: Path, out_root: Path, frames_per_video: int, seed: int, force: bool) -> list[Path]: + out_files: list[Path] = [] + for group in GROUPS: + group_dir = data_root / group + if not group_dir.exists(): + continue + for dicom_path in sorted(group_dir.glob("*.dcm")): + out_files.append(_build_bank_file(dicom_path, group, out_root, frames_per_video, seed, force)) + return out_files + + +def _load_bank_states(bank_path: Path) -> tuple[dict[str, Any], list[FrameState]]: + rows = _read_jsonl(bank_path) + if not rows: + raise ValueError(f"Empty bank file: {bank_path}") + + meta = rows[0] + if meta.get("record_type") != "meta": + raise ValueError(f"Missing meta header in {bank_path}") + + base: dict[int, FrameState] = {} + for rec in rows: + rtype = rec.get("record_type") + if rtype == "frame": + frame_idx = int(rec["frame"]) + base[frame_idx] = FrameState( + frame=frame_idx, + lumen_x=[float(v) for v in rec.get("lumen", {}).get("x", [])], + lumen_y=[float(v) for v in rec.get("lumen", {}).get("y", [])], + lumen_confidence=_safe_float(rec.get("lumen_confidence")), + bifurcation=bool(rec.get("bifurcation", False)), + ) + + sampled = [int(v) for v in meta.get("sampled_frames", [])] + states = [base[idx] for idx in sampled if idx in base] + return meta, states + + +def _list_bank_entries(out_root: Path) -> list[dict[str, Any]]: + entries: list[dict[str, Any]] = [] + for group in GROUPS: + group_dir = out_root / group + if not group_dir.exists(): + continue + for bank_path in sorted(group_dir.glob("*.jsonl")): + rows = _read_jsonl(bank_path) + if not rows: + continue + meta = rows[0] if rows[0].get("record_type") == "meta" else {} + sampled = [r for r in rows if r.get("record_type") == "frame"] + edited_frames = { + int(r["frame"]) + for r in sampled + if r.get("annotated_at") is not None or len(r.get("lumen", {}).get("x", [])) > 0 + } + + dicom_path = Path(meta.get("dicom_path", "")) + if not dicom_path.exists(): + # Fallback from filename/group when absolute path changed. + dicom_path = Path("data") / group / f"{bank_path.stem}.dcm" + + label = f"{group}/{bank_path.stem} [{len(edited_frames)}/{len(sampled)}]" + entries.append( + { + "label": label, + "group": group, + "stem": bank_path.stem, + "bank_path": bank_path, + "dicom_path": dicom_path, + } + ) + return entries + + +class BankPicker: + """Small GUI chooser for selecting a video bank to annotate.""" + + def __init__(self, entries: list[dict[str, Any]]) -> None: + self.entries = entries + self.selected_idx = 0 + self.selected: dict[str, Any] | None = None + + self.fig = None + self.radio = None + self.info_text = None + + def _on_pick(self, label: str) -> None: + for i, entry in enumerate(self.entries): + if entry["label"] == label: + self.selected_idx = i + break + self._render_info() + + def _on_open(self, _event: Any) -> None: + self.selected = self.entries[self.selected_idx] + plt.close(self.fig) + + def _on_cancel(self, _event: Any) -> None: + self.selected = None + plt.close(self.fig) + + def _render_info(self) -> None: + entry = self.entries[self.selected_idx] + self.info_text.set_text( + f"Selected: {entry['group']}/{entry['stem']}\n" + f"DICOM: {entry['dicom_path']}\n" + f"Bank: {entry['bank_path']}" + ) + self.fig.canvas.draw_idle() + + def show(self) -> dict[str, Any] | None: + if not self.entries: + raise ValueError("No bank files found to pick from.") + + labels = [e["label"] for e in self.entries] + + self.fig = plt.figure(figsize=(10, 8)) + self.fig.canvas.manager.set_window_title("DeepIVUS Eval Bank Picker") + + ax_radio = self.fig.add_axes([0.05, 0.2, 0.9, 0.74]) + self.radio = RadioButtons(ax_radio, labels, active=0) + self.radio.on_clicked(self._on_pick) + + ax_open = self.fig.add_axes([0.2, 0.06, 0.25, 0.08]) + ax_cancel = self.fig.add_axes([0.55, 0.06, 0.25, 0.08]) + open_btn = Button(ax_open, "Open Selected", color="#D6F5D6", hovercolor="#BFF0BF") + cancel_btn = Button(ax_cancel, "Cancel", color="#F5D6D6", hovercolor="#F0BFBF") + open_btn.on_clicked(self._on_open) + cancel_btn.on_clicked(self._on_cancel) + + self.info_text = self.fig.text(0.05, 0.01, "", fontsize=9) + self._render_info() + + plt.show() + return self.selected + + +class EvalFrameBankEditor: + def __init__(self, dicom_path: Path, bank_path: Path) -> None: + self.dicom_path = dicom_path + self.bank_path = bank_path + + _, self.images = read_dicom(str(dicom_path)) + self.meta, self.states = _load_bank_states(bank_path) + if not self.states: + raise ValueError(f"No sampled frame records found in {bank_path}") + + self.current = 0 + self.drag_target: int | None = None + self.drag_threshold_px = 10.0 + self.dirty = False + self.is_updating_slider = False + + self.fig = None + self.ax_image = None + self.image_artist = None + self.lumen_line: Line2D | None = None + self.lumen_points = None + self.slider: Slider | None = None + self.status_text = None + self.bif_button = None + self.clear_button = None + + def _state(self) -> FrameState: + return self.states[self.current] + + def _autosave_frame(self, reason: str) -> None: + s = self._state() + rows = _read_jsonl(self.bank_path) + if not rows: + raise ValueError(f"Bank file is empty: {self.bank_path}") + + meta = rows[0] + sampled = [int(v) for v in meta.get("sampled_frames", [])] + frame_records: dict[int, dict[str, Any]] = { + int(r["frame"]): dict(r) for r in rows if r.get("record_type") == "frame" + } + + now = _now_iso() + existing = frame_records.get(s.frame, {}) + frame_records[s.frame] = { + "record_type": "frame", + "sample_order": existing.get("sample_order", self.current), + "frame": int(s.frame), + "bifurcation": bool(s.bifurcation), + "lumen_confidence": s.lumen_confidence, + "lumen": {"x": [float(v) for v in s.lumen_x], "y": [float(v) for v in s.lumen_y]}, + "annotated_at": existing.get("annotated_at") or now, + "updated_at": now, + "last_save_reason": reason, + } + + ordered_frames: list[dict[str, Any]] = [] + for i, frame_idx in enumerate(sampled): + rec = frame_records.get(frame_idx) + if rec is None: + rec = { + "record_type": "frame", + "sample_order": i, + "frame": int(frame_idx), + "bifurcation": bool(meta.get("default_bifurcation", False)), + "lumen_confidence": None, + "lumen": {"x": [], "y": []}, + "annotated_at": None, + "updated_at": None, + } + rec["sample_order"] = i + ordered_frames.append(rec) + + _write_jsonl_atomic(self.bank_path, [meta] + ordered_frames) + self.dirty = False + + def _build_ui(self) -> None: + self.fig = plt.figure(figsize=(13, 9)) + self.fig.canvas.manager.set_window_title("DeepIVUS Eval Frame-Bank Editor") + + self.ax_image = self.fig.add_axes([0.05, 0.22, 0.9, 0.74]) + self.ax_image.set_title("DeepIVUS Eval Frame-Bank Editor", fontsize=14, weight="bold") + self.ax_image.set_axis_off() + + slider_ax = self.fig.add_axes([0.12, 0.13, 0.76, 0.035]) + self.slider = Slider( + ax=slider_ax, + label="Sample", + valmin=0, + valmax=len(self.states) - 1, + valinit=0, + valstep=1, + color="#1f77b4", + ) + + prev_ax = self.fig.add_axes([0.12, 0.05, 0.1, 0.055]) + next_ax = self.fig.add_axes([0.24, 0.05, 0.1, 0.055]) + save_ax = self.fig.add_axes([0.38, 0.05, 0.14, 0.055]) + clear_ax = self.fig.add_axes([0.54, 0.05, 0.14, 0.055]) + bif_ax = self.fig.add_axes([0.72, 0.05, 0.16, 0.055]) + + prev_button = Button(prev_ax, "Prev Frame", color="#E0E0E0", hovercolor="#D0D0D0") + next_button = Button(next_ax, "Next Frame", color="#E0E0E0", hovercolor="#D0D0D0") + save_button = Button(save_ax, "Save Frame", color="#D6F5D6", hovercolor="#BFF0BF") + self.clear_button = Button(clear_ax, "Clear Lumen", color="#F9E6CC", hovercolor="#F4DDB8") + self.bif_button = Button(bif_ax, "Bifurcation: No", color="#F5D6D6", hovercolor="#F0BFBF") + + self.status_text = self.fig.text(0.05, 0.18, "", fontsize=10) + + self.slider.on_changed(self._on_slider) + prev_button.on_clicked(self._on_prev) + next_button.on_clicked(self._on_next) + save_button.on_clicked(self._on_save) + self.clear_button.on_clicked(self._on_clear_lumen) + self.bif_button.on_clicked(self._on_toggle_bifurcation) + + self.fig.canvas.mpl_connect("button_press_event", self._on_press) + self.fig.canvas.mpl_connect("motion_notify_event", self._on_motion) + self.fig.canvas.mpl_connect("button_release_event", self._on_release) + self.fig.canvas.mpl_connect("key_press_event", self._on_key) + self.fig.canvas.mpl_connect("close_event", self._on_close) + + def _render(self) -> None: + s = self._state() + image = self.images[s.frame] + + if self.image_artist is None: + self.image_artist = self.ax_image.imshow(image, cmap="gray") + self.lumen_line = self.ax_image.plot([], [], color="#1db954", lw=2)[0] + self.lumen_points = self.ax_image.scatter([], [], c="#1db954", s=28, edgecolors="black", linewidths=0.4) + else: + self.image_artist.set_data(image) + + self.lumen_line.set_data(s.lumen_x + s.lumen_x[:1], s.lumen_y + s.lumen_y[:1]) + + lumen_offsets = np.c_[s.lumen_x, s.lumen_y] if s.lumen_x and s.lumen_y else np.empty((0, 2)) + self.lumen_points.set_offsets(lumen_offsets) + + bif_text = "Yes" if s.bifurcation else "No" + bif_color = "#D6F5D6" if s.bifurcation else "#F5D6D6" + self.bif_button.label.set_text(f"Bifurcation: {bif_text}") + self.bif_button.ax.set_facecolor(bif_color) + + self.status_text.set_text( + f"Video: {self.meta.get('video_id')} Sample {self.current + 1}/{len(self.states)}" + f" DICOM frame: {s.frame} Lumen pts: {len(s.lumen_x)}" + f" Add: right/middle/double-left/shift-left" + ) + self.fig.canvas.draw_idle() + + def _set_current(self, idx: int) -> None: + idx = int(np.clip(idx, 0, len(self.states) - 1)) + if idx == self.current: + return + if self.dirty: + self._autosave_frame("sample_change") + self.current = idx + self.is_updating_slider = True + self.slider.set_val(idx) + self.is_updating_slider = False + self._render() + + def _nearest_point(self, x: float, y: float) -> int | None: + s = self._state() + + if not s.lumen_x: + return None + pts = np.column_stack((np.asarray(s.lumen_x), np.asarray(s.lumen_y))) + d = np.linalg.norm(pts - np.asarray([x, y]), axis=1) + i = int(np.argmin(d)) + if float(d[i]) > self.drag_threshold_px: + return None + return i + + def _on_slider(self, val: float) -> None: + if self.is_updating_slider: + return + self._set_current(int(val)) + + def _on_prev(self, _event: Any) -> None: + self._set_current(self.current - 1) + + def _on_next(self, _event: Any) -> None: + self._set_current(self.current + 1) + + def _on_save(self, _event: Any) -> None: + self._autosave_frame("manual_save") + self._render() + + def _on_toggle_bifurcation(self, _event: Any) -> None: + s = self._state() + s.bifurcation = not s.bifurcation + self.dirty = True + self._autosave_frame("bifurcation_toggle") + self._render() + + def _on_clear_lumen(self, _event: Any) -> None: + s = self._state() + s.lumen_x = [] + s.lumen_y = [] + self.dirty = True + self._autosave_frame("clear_lumen") + self._render() + + def _on_key(self, event: Any) -> None: + if event.key in {"left", "a"}: + self._set_current(self.current - 1) + elif event.key in {"right", "d"}: + self._set_current(self.current + 1) + elif event.key == "s": + self._on_save(event) + elif event.key == "b": + self._on_toggle_bifurcation(event) + elif event.key in {"x", "delete"}: + self._on_clear_lumen(event) + + def _on_press(self, event: Any) -> None: + if event.inaxes != self.ax_image: + return + if event.xdata is None or event.ydata is None: + return + + s = self._state() + h, w = self.images[s.frame].shape + x = float(np.clip(event.xdata, 0, w - 1)) + y = float(np.clip(event.ydata, 0, h - 1)) + + is_left = event.button in (1, MouseButton.LEFT) + is_add_click = ( + event.button in (3, 2, MouseButton.RIGHT, MouseButton.MIDDLE) + or (is_left and bool(getattr(event, "dblclick", False))) + or (is_left and str(getattr(event, "key", "")).lower() == "shift") + ) + + # Left-click: drag nearest existing point. + if is_left and not is_add_click: + self.drag_target = self._nearest_point(x, y) + return + + # Right/middle/double-left/shift-left: add lumen point. + if is_add_click: + s.lumen_x.append(x) + s.lumen_y.append(y) + self.dirty = True + self._autosave_frame("point_add") + self._render() + + def _on_motion(self, event: Any) -> None: + if self.drag_target is None: + return + if event.inaxes != self.ax_image or event.xdata is None or event.ydata is None: + return + + s = self._state() + idx = self.drag_target + h, w = self.images[s.frame].shape + x = float(np.clip(event.xdata, 0, w - 1)) + y = float(np.clip(event.ydata, 0, h - 1)) + + s.lumen_x[idx] = x + s.lumen_y[idx] = y + + self.dirty = True + self._render() + + def _on_release(self, _event: Any) -> None: + if self.drag_target is None: + return + self.drag_target = None + if self.dirty: + self._autosave_frame("point_drag") + self._render() + + def _on_close(self, _event: Any) -> None: + if self.dirty: + self._autosave_frame("window_close") + + def show(self) -> None: + self._build_ui() + self._render() + plt.show() + + +def _resolve_group_from_dicom(dicom_path: Path) -> str: + group = dicom_path.parent.name + if group not in GROUPS: + raise ValueError(f"Expected DICOM under data/{{bifurcation,paul}}, got: {dicom_path}") + return group + + +def _cmd_build(args: argparse.Namespace) -> None: + files = build_all_banks( + data_root=Path(args.data_root), + out_root=Path(args.out_root), + frames_per_video=args.frames_per_video, + seed=args.seed, + force=args.force, + ) + print(f"Prepared {len(files)} bank files under {args.out_root}") + + +def _cmd_annotate(args: argparse.Namespace) -> None: + data_root = Path(args.data_root) + out_root = Path(args.out_root) + + if (args.group and not args.stem) or (args.stem and not args.group): + raise ValueError("Provide both --group and --stem together, or provide neither.") + + if args.dicom_path: + dicom_path = Path(args.dicom_path) + group = _resolve_group_from_dicom(dicom_path) + elif args.group and args.stem: + group = args.group + dicom_path = data_root / group / f"{args.stem}.dcm" + else: + entries = _list_bank_entries(out_root) + if not entries: + # Build banks if none exist yet. + build_all_banks( + data_root=data_root, + out_root=out_root, + frames_per_video=args.frames_per_video, + seed=args.seed, + force=False, + ) + entries = _list_bank_entries(out_root) + + picked = BankPicker(entries).show() + if picked is None: + return + dicom_path = Path(picked["dicom_path"]) + group = str(picked["group"]) + + if not dicom_path.exists(): + raise FileNotFoundError(f"DICOM not found: {dicom_path}") + + bank_path = _build_bank_file( + dicom_path=dicom_path, + group=group, + out_root=out_root, + frames_per_video=args.frames_per_video, + base_seed=args.seed, + force=False, + ) + + editor = EvalFrameBankEditor(dicom_path=dicom_path, bank_path=bank_path) + editor.show() + + +def _cmd_status(args: argparse.Namespace) -> None: + out_root = Path(args.out_root) + total = 0 + completed = 0 + + for group in GROUPS: + for bank_path in sorted((out_root / group).glob("*.jsonl")) if (out_root / group).exists() else []: + rows = _read_jsonl(bank_path) + sampled = [r for r in rows if r.get("record_type") == "frame"] + edited_frames = { + int(r["frame"]) + for r in sampled + if r.get("annotated_at") is not None or len(r.get("lumen", {}).get("x", [])) > 0 + } + total += 1 + if sampled and len(edited_frames) >= len(sampled): + completed += 1 + print( + f"{group}/{bank_path.stem}: {len(edited_frames)}/{len(sampled)} sampled frames edited" + ) + + print(f"Completed videos: {completed}/{total}") + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="Eval frame-bank annotation workflow") + sub = parser.add_subparsers(dest="command", required=True) + + p_build = sub.add_parser("build", help="Create locked sampled-frame bank files for all videos") + p_build.add_argument("--data-root", default="data", help="Root data folder with bifurcation/ and paul/") + p_build.add_argument("--out-root", default="evals/frame_bank_merged", help="Output bank root") + p_build.add_argument("--frames-per-video", type=int, default=15) + p_build.add_argument("--seed", type=int, default=20260218) + p_build.add_argument("--force", action="store_true", help="Rebuild existing bank files") + p_build.set_defaults(func=_cmd_build) + + p_annotate = sub.add_parser( + "annotate", + help="Open sampled-frame GUI for one video (or launch bank-picker GUI if no video is specified)", + ) + p_annotate.add_argument("--data-root", default="data", help="Root data folder with bifurcation/ and paul/") + p_annotate.add_argument("--out-root", default="evals/frame_bank_merged", help="Output bank root") + p_annotate.add_argument("--frames-per-video", type=int, default=15) + p_annotate.add_argument("--seed", type=int, default=20260218) + p_annotate.add_argument("--dicom-path", default=None, help="Direct path to DICOM") + p_annotate.add_argument("--group", choices=GROUPS, default=None, help="Group when using --stem") + p_annotate.add_argument("--stem", default=None, help="File stem (e.g. FILE0003) when using --group") + p_annotate.set_defaults(func=_cmd_annotate) + + p_status = sub.add_parser("status", help="Show annotation progress for bank files") + p_status.add_argument("--out-root", default="evals/frame_bank_merged", help="Output bank root") + p_status.set_defaults(func=_cmd_status) + + return parser + + +def main() -> None: + parser = build_parser() + args = parser.parse_args() + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/README.md b/scripts/finetune/README.md new file mode 100644 index 0000000000000000000000000000000000000000..5018e8d22fff483d39930a024b48298f2f9b944d --- /dev/null +++ b/scripts/finetune/README.md @@ -0,0 +1,17 @@ +# Finetune Scripts + +Canonical split: +- `evals/splits/ivus_split_merged_600.json` + +Folder layout: +- `scripts/finetune/bifurcation/`: bifurcation sampling, annotation merge, split, training, test inference +- `scripts/finetune/lumen/`: lumen class ID, fine-tuning, test inference, single-DICOM inference +- `scripts/finetune/shared/common.py`: shared annotation/split/data helpers + +Entrypoints: +- `scripts/finetune/bifurcation/*.py` +- `scripts/finetune/lumen/*.py` + +Model defaults: +- `models/standalone/lumen` +- `models/standalone/bifurcation/best_bifurcation_classifier.keras` diff --git a/scripts/finetune/bifurcation/annotate_bifurcation_samples.py b/scripts/finetune/bifurcation/annotate_bifurcation_samples.py new file mode 100644 index 0000000000000000000000000000000000000000..b02f1659c4b6512b754ff5b748783b7382627cea --- /dev/null +++ b/scripts/finetune/bifurcation/annotate_bifurcation_samples.py @@ -0,0 +1,265 @@ +#!/usr/bin/env python3 +"""GUI to label bifurcation yes/no for sampled frames across videos. + +Designed for files like: +- evals/frame_bank_merged/new_bifurcation_samples_300.jsonl +""" + +from __future__ import annotations + +import argparse +import json +import os +import tempfile +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.widgets import Button, Slider + +from deepivus.io.dicom import read_dicom + + +@dataclass +class Sample: + sample_id: str + group: str + dicom_stem: str + dicom_path: Path + frame: int + bifurcation: bool | None + + +def _read_jsonl(path: Path) -> list[dict[str, Any]]: + rows = [] + with path.open("r", encoding="utf-8") as fp: + for raw in fp: + line = raw.strip() + if not line: + continue + rows.append(json.loads(line)) + return rows + + +def _write_jsonl_atomic(path: Path, rows: list[dict[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", dir=path.parent, delete=False) as tmp: + tmp_path = Path(tmp.name) + for rec in rows: + tmp.write(json.dumps(rec) + "\n") + tmp.flush() + os.fsync(tmp.fileno()) + os.replace(tmp_path, path) + + +def _load_samples(path: Path) -> tuple[dict[str, Any], list[Sample]]: + rows = _read_jsonl(path) + if not rows: + raise ValueError(f"Empty file: {path}") + meta = rows[0] + samples: list[Sample] = [] + for rec in rows[1:]: + if rec.get("record_type") != "frame": + continue + samples.append( + Sample( + sample_id=str(rec["sample_id"]), + group=str(rec["group"]), + dicom_stem=str(rec["dicom_stem"]), + dicom_path=Path(rec["dicom_path"]), + frame=int(rec["frame"]), + bifurcation=rec.get("bifurcation", None), + ) + ) + if not samples: + raise ValueError(f"No frame records in {path}") + return meta, samples + + +def _save_samples(path: Path, meta: dict[str, Any], samples: list[Sample]) -> None: + rows: list[dict[str, Any]] = [meta] + for s in samples: + rows.append( + { + "record_type": "frame", + "sample_id": s.sample_id, + "group": s.group, + "dicom_stem": s.dicom_stem, + "dicom_path": str(s.dicom_path), + "frame": int(s.frame), + "bifurcation": s.bifurcation, + } + ) + _write_jsonl_atomic(path, rows) + + +class BifurcationSampleEditor: + def __init__(self, samples_path: Path) -> None: + self.samples_path = samples_path + self.meta, self.samples = _load_samples(samples_path) + self.current = 0 + self.is_updating_slider = False + self.image_cache: dict[Path, np.ndarray] = {} + + self.fig = None + self.ax_image = None + self.image_artist = None + self.slider = None + self.status_text = None + self.toggle_button = None + self.save_button = None + + def _sample(self) -> Sample: + return self.samples[self.current] + + def _load_frame(self, sample: Sample) -> np.ndarray: + if sample.dicom_path not in self.image_cache: + _, stack = read_dicom(str(sample.dicom_path)) + self.image_cache[sample.dicom_path] = stack + stack = self.image_cache[sample.dicom_path] + if sample.frame < 0 or sample.frame >= stack.shape[0]: + raise IndexError( + f"Frame {sample.frame} out of range for {sample.dicom_path} (num_frames={stack.shape[0]})" + ) + return stack[sample.frame] + + def _autosave(self) -> None: + _save_samples(self.samples_path, self.meta, self.samples) + + def _set_current(self, idx: int) -> None: + self.current = int(np.clip(idx, 0, len(self.samples) - 1)) + self.is_updating_slider = True + self.slider.set_val(self.current) + self.is_updating_slider = False + self._render() + + def _render(self) -> None: + s = self._sample() + frame = self._load_frame(s) + if self.image_artist is None: + self.image_artist = self.ax_image.imshow(frame, cmap="gray") + else: + self.image_artist.set_data(frame) + + label = s.bifurcation + if label is True: + txt = "Bifurcation: Yes" + color = "#D6F5D6" + elif label is False: + txt = "Bifurcation: No" + color = "#F5D6D6" + else: + txt = "Bifurcation: Unset" + color = "#F5F1D6" + + self.toggle_button.label.set_text(txt) + self.toggle_button.ax.set_facecolor(color) + + done = sum(1 for x in self.samples if x.bifurcation is not None) + self.status_text.set_text( + f"Sample {self.current + 1}/{len(self.samples)} ({done} labeled) " + f"{s.sample_id} frame={s.frame} " + "Keys: left/right navigate, y=yes, n=no, u=unset, s=save" + ) + self.fig.canvas.draw_idle() + + def _set_label(self, value: bool | None) -> None: + self._sample().bifurcation = value + self._autosave() + self._render() + + def _on_slider(self, val: float) -> None: + if self.is_updating_slider: + return + self._set_current(int(val)) + + def _on_prev(self, _event: Any) -> None: + self._set_current(self.current - 1) + + def _on_next(self, _event: Any) -> None: + self._set_current(self.current + 1) + + def _on_toggle(self, _event: Any) -> None: + cur = self._sample().bifurcation + if cur is None: + self._set_label(True) + else: + self._set_label(not cur) + + def _on_save(self, _event: Any) -> None: + self._autosave() + self._render() + + def _on_key(self, event: Any) -> None: + if event.key in {"left", "a"}: + self._on_prev(event) + elif event.key in {"right", "d"}: + self._on_next(event) + elif event.key == "y": + self._set_label(True) + elif event.key == "n": + self._set_label(False) + elif event.key == "u": + self._set_label(None) + elif event.key == "s": + self._on_save(event) + + def show(self) -> None: + self.fig = plt.figure(figsize=(12, 9)) + self.fig.canvas.manager.set_window_title("Bifurcation Sample Labeler") + self.ax_image = self.fig.add_axes([0.05, 0.22, 0.9, 0.74]) + self.ax_image.set_axis_off() + self.ax_image.set_title("Bifurcation Frame Labeling", fontsize=14, weight="bold") + + slider_ax = self.fig.add_axes([0.12, 0.13, 0.76, 0.035]) + self.slider = Slider( + ax=slider_ax, + label="Sample", + valmin=0, + valmax=len(self.samples) - 1, + valinit=0, + valstep=1, + color="#1f77b4", + ) + + prev_ax = self.fig.add_axes([0.12, 0.05, 0.1, 0.055]) + next_ax = self.fig.add_axes([0.24, 0.05, 0.1, 0.055]) + toggle_ax = self.fig.add_axes([0.38, 0.05, 0.2, 0.055]) + save_ax = self.fig.add_axes([0.62, 0.05, 0.12, 0.055]) + + prev_button = Button(prev_ax, "Prev", color="#E0E0E0", hovercolor="#D0D0D0") + next_button = Button(next_ax, "Next", color="#E0E0E0", hovercolor="#D0D0D0") + self.toggle_button = Button(toggle_ax, "Bifurcation: Unset", color="#F5F1D6", hovercolor="#EDE6BF") + self.save_button = Button(save_ax, "Save", color="#D6F5D6", hovercolor="#BFF0BF") + + self.status_text = self.fig.text(0.05, 0.18, "", fontsize=10) + + self.slider.on_changed(self._on_slider) + prev_button.on_clicked(self._on_prev) + next_button.on_clicked(self._on_next) + self.toggle_button.on_clicked(self._on_toggle) + self.save_button.on_clicked(self._on_save) + self.fig.canvas.mpl_connect("key_press_event", self._on_key) + + self._render() + plt.show() + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--samples-jsonl", + type=Path, + default=Path("evals/frame_bank_merged/new_bifurcation_samples_300.jsonl"), + help="Path to sampled frames JSONL.", + ) + args = parser.parse_args() + + editor = BifurcationSampleEditor(args.samples_jsonl) + editor.show() + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/bifurcation/annotate_roboflow_images.py b/scripts/finetune/bifurcation/annotate_roboflow_images.py new file mode 100644 index 0000000000000000000000000000000000000000..1971d59fe6d432f6ef5dd9f2fb5bb63333a542d9 --- /dev/null +++ b/scripts/finetune/bifurcation/annotate_roboflow_images.py @@ -0,0 +1,254 @@ +#!/usr/bin/env python3 +"""GUI annotator for Roboflow JPG frames (bifurcation yes/no). + +Default input: data/roboflow/*.jpg +Default output: evals/roboflow_bifurcation_annotations.jsonl +""" + +from __future__ import annotations + +import argparse +import json +import os +import tempfile +from dataclasses import dataclass +from pathlib import Path +from typing import Any + +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.widgets import Button, Slider +from PIL import Image + + +@dataclass +class ImageSample: + image_path: Path + bifurcation: bool | None + + +META_SCHEMA = "deepivus_roboflow_bifurcation_v1" + + +def _read_jsonl(path: Path) -> list[dict[str, Any]]: + rows = [] + with path.open("r", encoding="utf-8") as fp: + for raw in fp: + line = raw.strip() + if not line: + continue + rows.append(json.loads(line)) + return rows + + +def _write_jsonl_atomic(path: Path, rows: list[dict[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with tempfile.NamedTemporaryFile("w", encoding="utf-8", dir=path.parent, delete=False) as tmp: + tmp_path = Path(tmp.name) + for rec in rows: + tmp.write(json.dumps(rec) + "\n") + tmp.flush() + os.fsync(tmp.fileno()) + os.replace(tmp_path, path) + + +def _discover_images(images_dir: Path) -> list[Path]: + paths = sorted(images_dir.glob("*.jpg")) + if not paths: + raise FileNotFoundError(f"No JPG files found in {images_dir}") + return paths + + +def _load_or_init_annotations(images_dir: Path, output_jsonl: Path) -> tuple[dict[str, Any], list[ImageSample]]: + image_paths = _discover_images(images_dir) + + existing: dict[str, bool | None] = {} + if output_jsonl.exists(): + rows = _read_jsonl(output_jsonl) + for rec in rows[1:]: + if rec.get("record_type") != "image": + continue + existing[str(rec["image_path"])] = rec.get("bifurcation", None) + + samples = [ImageSample(image_path=p, bifurcation=existing.get(str(p.resolve()), None)) for p in image_paths] + meta = { + "record_type": "meta", + "schema": META_SCHEMA, + "images_dir": str(images_dir.resolve()), + "num_images": len(samples), + } + return meta, samples + + +def _save_annotations(path: Path, meta: dict[str, Any], samples: list[ImageSample]) -> None: + rows: list[dict[str, Any]] = [meta] + for idx, s in enumerate(samples): + rows.append( + { + "record_type": "image", + "index": idx, + "image_path": str(s.image_path.resolve()), + "filename": s.image_path.name, + "bifurcation": s.bifurcation, + } + ) + _write_jsonl_atomic(path, rows) + + +class RoboflowAnnotator: + def __init__(self, images_dir: Path, output_jsonl: Path) -> None: + self.images_dir = images_dir + self.output_jsonl = output_jsonl + self.meta, self.samples = _load_or_init_annotations(images_dir, output_jsonl) + self.current = 0 + self.cache: dict[Path, np.ndarray] = {} + self.is_updating_slider = False + + self.fig = None + self.ax_image = None + self.image_artist = None + self.slider = None + self.toggle_button = None + self.status_text = None + + def _load_image(self, path: Path) -> np.ndarray: + if path not in self.cache: + img = Image.open(path).convert("L") + self.cache[path] = np.asarray(img) + return self.cache[path] + + def _autosave(self) -> None: + _save_annotations(self.output_jsonl, self.meta, self.samples) + + def _set_current(self, idx: int) -> None: + self.current = int(np.clip(idx, 0, len(self.samples) - 1)) + self.is_updating_slider = True + self.slider.set_val(self.current) + self.is_updating_slider = False + self._render() + + def _render(self) -> None: + sample = self.samples[self.current] + frame = self._load_image(sample.image_path) + + if self.image_artist is None: + self.image_artist = self.ax_image.imshow(frame, cmap="gray") + else: + self.image_artist.set_data(frame) + + if sample.bifurcation is True: + txt = "Bifurcation: Yes" + color = "#D6F5D6" + elif sample.bifurcation is False: + txt = "Bifurcation: No" + color = "#F5D6D6" + else: + txt = "Bifurcation: Unset" + color = "#F5F1D6" + + self.toggle_button.label.set_text(txt) + self.toggle_button.ax.set_facecolor(color) + + done = sum(1 for x in self.samples if x.bifurcation is not None) + self.status_text.set_text( + f"Image {self.current + 1}/{len(self.samples)} ({done} labeled) {sample.image_path.name} " + "Keys: left/right navigate, y=yes, n=no, u=unset, s=save" + ) + self.fig.canvas.draw_idle() + + def _set_label(self, value: bool | None) -> None: + self.samples[self.current].bifurcation = value + self._autosave() + self._render() + + def _on_slider(self, val: float) -> None: + if self.is_updating_slider: + return + self._set_current(int(val)) + + def _on_prev(self, _event: Any) -> None: + self._set_current(self.current - 1) + + def _on_next(self, _event: Any) -> None: + self._set_current(self.current + 1) + + def _on_toggle(self, _event: Any) -> None: + cur = self.samples[self.current].bifurcation + if cur is None: + self._set_label(True) + else: + self._set_label(not cur) + + def _on_key(self, event: Any) -> None: + if event.key in {"left", "a"}: + self._on_prev(event) + elif event.key in {"right", "d"}: + self._on_next(event) + elif event.key == "y": + self._set_label(True) + elif event.key == "n": + self._set_label(False) + elif event.key == "u": + self._set_label(None) + elif event.key == "s": + self._autosave() + self._render() + + def show(self) -> None: + self.fig = plt.figure(figsize=(12, 9)) + self.fig.canvas.manager.set_window_title("Roboflow Bifurcation Labeler") + self.ax_image = self.fig.add_axes([0.05, 0.22, 0.9, 0.74]) + self.ax_image.set_axis_off() + self.ax_image.set_title("Roboflow Image Labeling", fontsize=14, weight="bold") + + slider_ax = self.fig.add_axes([0.12, 0.13, 0.76, 0.035]) + self.slider = Slider( + ax=slider_ax, + label="Image", + valmin=0, + valmax=len(self.samples) - 1, + valinit=0, + valstep=1, + color="#1f77b4", + ) + + prev_ax = self.fig.add_axes([0.12, 0.05, 0.1, 0.055]) + next_ax = self.fig.add_axes([0.24, 0.05, 0.1, 0.055]) + toggle_ax = self.fig.add_axes([0.38, 0.05, 0.2, 0.055]) + save_ax = self.fig.add_axes([0.62, 0.05, 0.12, 0.055]) + + prev_button = Button(prev_ax, "Prev", color="#E0E0E0", hovercolor="#D0D0D0") + next_button = Button(next_ax, "Next", color="#E0E0E0", hovercolor="#D0D0D0") + self.toggle_button = Button(toggle_ax, "Bifurcation: Unset", color="#F5F1D6", hovercolor="#EDE6BF") + save_button = Button(save_ax, "Save", color="#D6F5D6", hovercolor="#BFF0BF") + + self.status_text = self.fig.text(0.05, 0.18, "", fontsize=10) + + self.slider.on_changed(self._on_slider) + prev_button.on_clicked(self._on_prev) + next_button.on_clicked(self._on_next) + self.toggle_button.on_clicked(self._on_toggle) + save_button.on_clicked(lambda _: self._autosave()) + self.fig.canvas.mpl_connect("key_press_event", self._on_key) + + self._autosave() + self._render() + plt.show() + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--images-dir", type=Path, default=Path("data/roboflow")) + parser.add_argument( + "--output-jsonl", + type=Path, + default=Path("evals/roboflow_bifurcation_annotations.jsonl"), + ) + args = parser.parse_args() + + editor = RoboflowAnnotator(images_dir=args.images_dir, output_jsonl=args.output_jsonl) + editor.show() + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/bifurcation/create_bifurcation_splits.py b/scripts/finetune/bifurcation/create_bifurcation_splits.py new file mode 100644 index 0000000000000000000000000000000000000000..04ed4a2e87f6d859161fc6e7e7cfbcb1cfe69a03 --- /dev/null +++ b/scripts/finetune/bifurcation/create_bifurcation_splits.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python3 +"""Create stratified train/val/test splits for bifurcation classification.""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +from scripts.finetune.shared.common import ( + load_bifurcation_annotations, + save_split_json, + split_summary, + stratified_frame_split, +) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--frame-bank-root", type=Path, default=Path("evals/frame_bank_merged")) + parser.add_argument("--train-fraction", type=float, default=0.7) + parser.add_argument("--val-fraction", type=float, default=0.15) + parser.add_argument("--test-fraction", type=float, default=0.15) + parser.add_argument("--seed", type=int, default=7) + parser.add_argument( + "--output-json", + type=Path, + default=Path("evals/splits/ivus_split_merged_600.json"), + ) + args = parser.parse_args() + + annotations = load_bifurcation_annotations(args.frame_bank_root) + if not annotations: + raise RuntimeError(f"No bifurcation annotations found under: {args.frame_bank_root}") + + split = stratified_frame_split( + annotations=annotations, + train_fraction=args.train_fraction, + val_fraction=args.val_fraction, + test_fraction=args.test_fraction, + seed=args.seed, + ) + summary = split_summary(annotations, split) + save_split_json(args.output_json, annotations, split, seed=args.seed) + + print(f"Saved split: {args.output_json}") + for part in ("train", "val", "test"): + row = summary[part] + print( + f"{part}: count={row['count']} " + f"bif_true={row['bifurcation_true']} " + f"bif_false={row['bifurcation_false']}" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/bifurcation/merge_bifurcation_annotations.py b/scripts/finetune/bifurcation/merge_bifurcation_annotations.py new file mode 100644 index 0000000000000000000000000000000000000000..c38877da8fbd8b12c7d84cf4b171032210a9396b --- /dev/null +++ b/scripts/finetune/bifurcation/merge_bifurcation_annotations.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +"""Merge sampled bifurcation labels into frame-bank per-video JSONLs.""" + +from __future__ import annotations + +import argparse +import json +from datetime import datetime +from pathlib import Path + + +def _now_iso() -> str: + return datetime.now().isoformat(timespec="seconds") + + +def _read_jsonl(path: Path) -> list[dict]: + rows = [] + with path.open("r", encoding="utf-8") as fp: + for raw in fp: + line = raw.strip() + if line: + rows.append(json.loads(line)) + return rows + + +def _write_jsonl(path: Path, rows: list[dict]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", encoding="utf-8") as fp: + for rec in rows: + fp.write(json.dumps(rec) + "\n") + + +def _load_sample_labels(samples_jsonl: Path) -> list[dict]: + rows = _read_jsonl(samples_jsonl) + if not rows: + raise RuntimeError(f"Empty samples file: {samples_jsonl}") + frames = [r for r in rows if r.get("record_type") == "frame" and r.get("bifurcation") is not None] + if not frames: + raise RuntimeError(f"No labeled frame records in {samples_jsonl}") + return frames + + +def _ensure_bank_meta(meta: dict, group: str, stem: str, dicom_path: str) -> dict: + out = dict(meta) if meta else {} + out.setdefault("record_type", "meta") + out.setdefault("schema", "deepivus_eval_frame_bank_v1") + out.setdefault("created_at", _now_iso()) + out.setdefault("video_id", f"{group}/{stem}") + out.setdefault("group", group) + out.setdefault("dicom_path", dicom_path) + out.setdefault("seed", None) + out.setdefault("requested_frames_per_video", None) + out.setdefault("sampled_frames", []) + out.setdefault("num_sampled_frames", 0) + out.setdefault("default_bifurcation", group == "bifurcation") + return out + + +def merge_into_bank(samples_jsonl: Path, bank_root: Path, output_root: Path) -> dict[str, int]: + frames = _load_sample_labels(samples_jsonl) + stats = {"labeled_rows": len(frames), "files_updated": 0, "frames_added": 0, "frames_overwritten": 0} + + by_video: dict[tuple[str, str], list[dict]] = {} + for rec in frames: + key = (str(rec["group"]), str(rec["dicom_stem"])) + by_video.setdefault(key, []).append(rec) + + for (group, stem), recs in sorted(by_video.items()): + src_bank = bank_root / group / f"{stem}.jsonl" + dst_bank = output_root / group / f"{stem}.jsonl" + + base_rows = _read_jsonl(src_bank) if src_bank.exists() else [] + meta = base_rows[0] if base_rows and base_rows[0].get("record_type") == "meta" else {} + meta = _ensure_bank_meta(meta, group=group, stem=stem, dicom_path=str(recs[0]["dicom_path"])) + + frame_map: dict[int, dict] = {} + for row in base_rows: + if row.get("record_type") == "frame": + frame_map[int(row["frame"])] = dict(row) + + for row in recs: + frame_idx = int(row["frame"]) + existing = frame_map.get(frame_idx) + if existing is None: + stats["frames_added"] += 1 + else: + stats["frames_overwritten"] += 1 + frame_map[frame_idx] = { + "record_type": "frame", + "sample_order": 0, # reassigned below + "frame": frame_idx, + "bifurcation": bool(row["bifurcation"]), + "lumen_confidence": existing.get("lumen_confidence") if existing else None, + "lumen": existing.get("lumen") if existing else {"x": [], "y": []}, + "annotated_at": existing.get("annotated_at") if existing else _now_iso(), + "updated_at": _now_iso(), + "last_save_reason": "merge_bifurcation_annotations", + } + + sampled = sorted(frame_map.keys()) + ordered = [] + for i, frame_idx in enumerate(sampled): + rec = frame_map[frame_idx] + rec["sample_order"] = i + ordered.append(rec) + + meta["sampled_frames"] = sampled + meta["num_sampled_frames"] = len(sampled) + _write_jsonl(dst_bank, [meta] + ordered) + stats["files_updated"] += 1 + + return stats + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--samples-jsonl", + type=Path, + default=Path("evals/frame_bank_merged/new_bifurcation_samples_300.jsonl"), + ) + parser.add_argument("--bank-root", type=Path, default=Path("evals/frame_bank_merged")) + parser.add_argument( + "--output-root", + type=Path, + default=Path("evals/frame_bank_merged"), + help="Merged frame-bank output root.", + ) + args = parser.parse_args() + + if not args.samples_jsonl.exists(): + raise FileNotFoundError(f"Samples file not found: {args.samples_jsonl}") + if not args.bank_root.exists(): + raise FileNotFoundError(f"Bank root not found: {args.bank_root}") + + stats = merge_into_bank(args.samples_jsonl, args.bank_root, args.output_root) + print(f"Merged into: {args.output_root}") + print(json.dumps(stats, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/bifurcation/run_bifurcation_test_inference.py b/scripts/finetune/bifurcation/run_bifurcation_test_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..5b9a23e5025173055cd357751d7ec42538c1debd --- /dev/null +++ b/scripts/finetune/bifurcation/run_bifurcation_test_inference.py @@ -0,0 +1,231 @@ +#!/usr/bin/env python3 +"""Run bifurcation classifier inference on the test split. + +Also supports automatic threshold selection on the validation split. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +import numpy as np +import tensorflow as tf + +REPO_ROOT = Path(__file__).resolve().parents[2] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from deepivus.config import resolve_bifurcation_model_path, resolve_bifurcation_threshold_path +from scripts.finetune.shared.common import load_bifurcation_annotations, load_preprocessed_stack, load_split_ids + +DEFAULT_MODEL_PATH = resolve_bifurcation_model_path() +DEFAULT_SPLIT_JSON = REPO_ROOT / "evals" / "splits" / "ivus_split_merged_600.json" +DEFAULT_FRAME_BANK_ROOT = REPO_ROOT / "evals" / "frame_bank_merged" +DEFAULT_OUTPUT_JSON = REPO_ROOT / "output" / "bifurcation_classifier" / "test_inference.json" + +IMG_MEAN = tf.constant([60.3486], dtype=tf.float32) + + +def _load_split_samples( + frame_bank_root: Path, + split_json: Path, + split_name: str, +) -> tuple[np.ndarray, np.ndarray, list[str]]: + annotations = load_bifurcation_annotations(frame_bank_root) + if not annotations: + raise RuntimeError(f"No annotations found under: {frame_bank_root}") + + split_ids = load_split_ids(split_json) + ids = split_ids[split_name] + selected_annotations = [ann for ann in annotations if ann.sample_id in ids] + if not selected_annotations: + raise RuntimeError(f"No {split_name} annotations matched split file: {split_json}") + + grouped: dict[Path, list] = {} + for ann in selected_annotations: + grouped.setdefault(ann.dicom_path, []).append(ann) + + images = [] + labels = [] + sample_ids = [] + for dicom_path, anns in grouped.items(): + stack = load_preprocessed_stack(dicom_path, diameter=67) + for ann in anns: + if ann.frame_idx < 0 or ann.frame_idx >= stack.shape[0]: + continue + images.append(stack[ann.frame_idx]) + labels.append(1 if ann.bifurcation else 0) + sample_ids.append(ann.sample_id) + + if not images: + raise RuntimeError(f"No valid {split_name} samples after loading DICOM frames.") + return np.stack(images, axis=0), np.asarray(labels, dtype=np.int32), sample_ids + + +def _prepare_batch(images: np.ndarray) -> tf.Tensor: + x = tf.convert_to_tensor(images, dtype=tf.float32) + x = x - IMG_MEAN + x = tf.expand_dims(x, axis=-1) + x = tf.tile(x, [1, 1, 1, 3]) + return x + + +def _compute_metrics(y_true: np.ndarray, y_prob: np.ndarray, threshold: float) -> dict[str, float]: + y_pred = (y_prob >= threshold).astype(np.int32) + y_true = y_true.astype(np.int32) + tp = int(np.sum((y_pred == 1) & (y_true == 1))) + fp = int(np.sum((y_pred == 1) & (y_true == 0))) + tn = int(np.sum((y_pred == 0) & (y_true == 0))) + fn = int(np.sum((y_pred == 0) & (y_true == 1))) + + accuracy = float((tp + tn) / max(tp + tn + fp + fn, 1)) + precision = float(tp / max(tp + fp, 1)) + recall = float(tp / max(tp + fn, 1)) + f1 = float((2 * precision * recall) / max(precision + recall, 1e-12)) + specificity = float(tn / max(tn + fp, 1)) + return { + "threshold": float(threshold), + "accuracy": accuracy, + "precision": precision, + "recall": recall, + "f1": f1, + "specificity": specificity, + "tp": tp, + "fp": fp, + "tn": tn, + "fn": fn, + } + + +def _predict_probs(model: tf.keras.Model, images: np.ndarray, batch_size: int) -> np.ndarray: + probs = [] + for start in range(0, len(images), batch_size): + end = min(start + batch_size, len(images)) + batch = _prepare_batch(images[start:end]) + p = model(batch, training=False).numpy().reshape(-1) + probs.append(p) + return np.concatenate(probs, axis=0) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--model-path", type=Path, default=DEFAULT_MODEL_PATH) + parser.add_argument("--split-json", type=Path, default=DEFAULT_SPLIT_JSON) + parser.add_argument("--frame-bank-root", type=Path, default=DEFAULT_FRAME_BANK_ROOT) + parser.add_argument("--batch-size", type=int, default=16) + parser.add_argument( + "--threshold", + type=float, + default=None, + help="Fixed threshold. If omitted, threshold is selected on validation set.", + ) + parser.add_argument( + "--select-metric", + type=str, + default="f1", + choices=["f1", "accuracy", "precision", "recall", "specificity"], + help="Metric used to choose threshold on validation set.", + ) + parser.add_argument("--threshold-min", type=float, default=0.05) + parser.add_argument("--threshold-max", type=float, default=0.95) + parser.add_argument("--threshold-steps", type=int, default=91) + parser.add_argument("--output-json", type=Path, default=DEFAULT_OUTPUT_JSON) + parser.add_argument( + "--save-threshold", + action=argparse.BooleanOptionalAction, + default=True, + help="Persist selected threshold to /threshold.json.", + ) + args = parser.parse_args() + + if not args.model_path.exists(): + raise FileNotFoundError(f"Model file not found: {args.model_path}") + if not args.split_json.exists(): + raise FileNotFoundError(f"Split file not found: {args.split_json}") + + test_images, test_labels, test_sample_ids = _load_split_samples(args.frame_bank_root, args.split_json, "test") + model = tf.keras.models.load_model(args.model_path) + + selected_threshold = args.threshold + threshold_selection = None + if selected_threshold is None: + val_images, val_labels, _ = _load_split_samples(args.frame_bank_root, args.split_json, "val") + val_probs = _predict_probs(model, val_images, batch_size=args.batch_size) + candidates = np.linspace(args.threshold_min, args.threshold_max, args.threshold_steps, dtype=np.float32) + best = None + sweep_rows = [] + for th in candidates: + row = _compute_metrics(val_labels, val_probs, threshold=float(th)) + score = row[args.select_metric] + sweep_rows.append(row) + if best is None or score > best[args.select_metric]: + best = row + selected_threshold = float(best["threshold"]) + threshold_selection = { + "method": "validation_sweep", + "metric": args.select_metric, + "best_threshold": selected_threshold, + "best_metric_value": float(best[args.select_metric]), + "val_metrics_at_best": best, + "val_threshold_sweep": sweep_rows, + } + else: + selected_threshold = float(selected_threshold) + threshold_selection = {"method": "fixed", "threshold": selected_threshold} + + test_probs = _predict_probs(model, test_images, batch_size=args.batch_size) + metrics = _compute_metrics(test_labels, test_probs, threshold=selected_threshold) + + predictions = [] + preds_binary = (test_probs >= selected_threshold).astype(np.int32) + for i in range(len(test_sample_ids)): + predictions.append( + { + "sample_id": test_sample_ids[i], + "label": int(test_labels[i]), + "probability": float(test_probs[i]), + "predicted_label": int(preds_binary[i]), + } + ) + + payload = { + "model_path": str(args.model_path), + "split_json": str(args.split_json), + "num_test_samples": int(len(test_labels)), + "selected_threshold": float(selected_threshold), + "threshold_selection": threshold_selection, + "metrics": metrics, + "predictions": predictions, + } + args.output_json.parent.mkdir(parents=True, exist_ok=True) + with args.output_json.open("w", encoding="utf-8") as fp: + json.dump(payload, fp, indent=2) + + threshold_store_path = resolve_bifurcation_threshold_path(model_path=args.model_path) + if args.save_threshold: + threshold_store_path.parent.mkdir(parents=True, exist_ok=True) + threshold_payload = { + "selected_threshold": float(selected_threshold), + "selection": threshold_selection, + "source_split_json": str(args.split_json), + "source_model_path": str(args.model_path), + } + with threshold_store_path.open("w", encoding="utf-8") as fp: + json.dump(threshold_payload, fp, indent=2) + + print(f"Test samples: {len(test_labels)}") + print(f"Selected threshold: {selected_threshold:.4f}") + print( + f"Accuracy={metrics['accuracy']:.4f} Precision={metrics['precision']:.4f} " + f"Recall={metrics['recall']:.4f} F1={metrics['f1']:.4f}" + ) + print(f"Saved results: {args.output_json}") + if args.save_threshold: + print(f"Saved threshold: {threshold_store_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/bifurcation/sample_new_bifurcation_frames.py b/scripts/finetune/bifurcation/sample_new_bifurcation_frames.py new file mode 100644 index 0000000000000000000000000000000000000000..b4538db196caadf0297583413d734e0b0e8cab7a --- /dev/null +++ b/scripts/finetune/bifurcation/sample_new_bifurcation_frames.py @@ -0,0 +1,137 @@ +#!/usr/bin/env python3 +"""Sample new random frames for manual bifurcation annotation. + +Defaults: +- 150 random frames from data/bifurcation +- 150 random frames from data/paul +- excludes frames already present in evals/frame_bank_merged/*/*.jsonl +""" + +from __future__ import annotations + +import argparse +import csv +import json +from pathlib import Path + +import numpy as np +import pydicom + + +def _read_existing_frame_bank(frame_bank_root: Path) -> dict[tuple[str, str], set[int]]: + used: dict[tuple[str, str], set[int]] = {} + for bank_path in sorted(frame_bank_root.glob("*/*.jsonl")): + group = bank_path.parent.name + stem = bank_path.stem + key = (group, stem) + used.setdefault(key, set()) + with bank_path.open("r", encoding="utf-8") as fp: + for raw in fp: + line = raw.strip() + if not line: + continue + rec = json.loads(line) + if rec.get("record_type") == "frame": + used[key].add(int(rec["frame"])) + return used + + +def _num_frames(dicom_path: Path) -> int: + ds = pydicom.dcmread(str(dicom_path), stop_before_pixels=True, force=True) + if getattr(ds, "NumberOfFrames", None) is not None: + return int(ds.NumberOfFrames) + # Single-frame fallback. + return 1 + + +def _candidate_pool(group_dir: Path, used: dict[tuple[str, str], set[int]]) -> list[tuple[str, str, Path, int]]: + group = group_dir.name + pool: list[tuple[str, str, Path, int]] = [] + for dicom_path in sorted(group_dir.glob("*.dcm")): + stem = dicom_path.stem + taken = used.get((group, stem), set()) + n_frames = _num_frames(dicom_path) + for frame_idx in range(n_frames): + if frame_idx in taken: + continue + pool.append((group, stem, dicom_path, frame_idx)) + return pool + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--data-root", type=Path, default=Path("data")) + parser.add_argument("--frame-bank-root", type=Path, default=Path("evals/frame_bank_merged")) + parser.add_argument("--n-bifurcation", type=int, default=150) + parser.add_argument("--n-paul", type=int, default=150) + parser.add_argument("--seed", type=int, default=7) + parser.add_argument( + "--output-csv", + type=Path, + default=Path("evals/frame_bank_merged/new_bifurcation_samples_300.csv"), + ) + parser.add_argument( + "--output-jsonl", + type=Path, + default=Path("evals/frame_bank_merged/new_bifurcation_samples_300.jsonl"), + ) + args = parser.parse_args() + + used = _read_existing_frame_bank(args.frame_bank_root) + bif_pool = _candidate_pool(args.data_root / "bifurcation", used) + paul_pool = _candidate_pool(args.data_root / "paul", used) + + if len(bif_pool) < args.n_bifurcation: + raise RuntimeError(f"Not enough bifurcation candidates: requested {args.n_bifurcation}, have {len(bif_pool)}") + if len(paul_pool) < args.n_paul: + raise RuntimeError(f"Not enough paul candidates: requested {args.n_paul}, have {len(paul_pool)}") + + rng = np.random.default_rng(args.seed) + bif_idx = rng.choice(len(bif_pool), size=args.n_bifurcation, replace=False) + paul_idx = rng.choice(len(paul_pool), size=args.n_paul, replace=False) + + rows = [bif_pool[int(i)] for i in bif_idx] + [paul_pool[int(i)] for i in paul_idx] + rng.shuffle(rows) + + args.output_csv.parent.mkdir(parents=True, exist_ok=True) + with args.output_csv.open("w", encoding="utf-8", newline="") as fp: + writer = csv.writer(fp) + writer.writerow(["sample_id", "group", "dicom_stem", "dicom_path", "frame", "bifurcation_label"]) + for group, stem, dicom_path, frame_idx in rows: + sample_id = f"{group}/{stem}::{frame_idx}" + writer.writerow([sample_id, group, stem, str(dicom_path.resolve()), int(frame_idx), ""]) + + meta = { + "record_type": "meta", + "schema": "deepivus_bifurcation_annotation_v1", + "seed": int(args.seed), + "n_bifurcation": int(args.n_bifurcation), + "n_paul": int(args.n_paul), + "total": int(len(rows)), + } + args.output_jsonl.parent.mkdir(parents=True, exist_ok=True) + with args.output_jsonl.open("w", encoding="utf-8") as fp: + fp.write(json.dumps(meta) + "\n") + for group, stem, dicom_path, frame_idx in rows: + fp.write( + json.dumps( + { + "record_type": "frame", + "sample_id": f"{group}/{stem}::{frame_idx}", + "group": group, + "dicom_stem": stem, + "dicom_path": str(dicom_path.resolve()), + "frame": int(frame_idx), + "bifurcation": None, + } + ) + + "\n" + ) + + print(f"Wrote CSV: {args.output_csv} ({len(rows)} rows)") + print(f"Wrote JSONL: {args.output_jsonl} ({len(rows)} frames)") + print(f"Breakdown: bifurcation={args.n_bifurcation}, paul={args.n_paul}") + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/bifurcation/train_bifurcation_classifier.py b/scripts/finetune/bifurcation/train_bifurcation_classifier.py new file mode 100644 index 0000000000000000000000000000000000000000..6be7705aab4f5f58a60fe908ca10947e90e5aa1b --- /dev/null +++ b/scripts/finetune/bifurcation/train_bifurcation_classifier.py @@ -0,0 +1,583 @@ +#!/usr/bin/env python3 +"""Train a frame-level bifurcation classifier from frame-bank annotations.""" + +from __future__ import annotations + +import argparse +import datetime +import json +from pathlib import Path + +import numpy as np +import tensorflow as tf +from PIL import Image + +from scripts.finetune.shared.common import ( + load_bifurcation_annotations, + load_preprocessed_stack, + load_split_ids, + save_split_json, + split_summary, + stratified_frame_split, +) + +IMG_MEAN = tf.constant([60.3486], dtype=tf.float32) + +try: + from tqdm.auto import tqdm +except Exception: + tqdm = None + + +def _sample_arrays(annotations: list) -> tuple[np.ndarray, np.ndarray]: + grouped: dict[Path, list] = {} + for ann in annotations: + grouped.setdefault(ann.dicom_path, []).append(ann) + + images = [] + labels = [] + for dicom_path, ann_list in grouped.items(): + stack = load_preprocessed_stack(dicom_path, diameter=67) + for ann in ann_list: + if ann.frame_idx < 0 or ann.frame_idx >= stack.shape[0]: + continue + images.append(stack[ann.frame_idx]) + labels.append(1.0 if ann.bifurcation else 0.0) + + if not images: + raise RuntimeError("No valid frames found for classifier training.") + return np.stack(images, axis=0), np.asarray(labels, dtype=np.float32) + + +def _build_dataset( + images: np.ndarray, + labels: np.ndarray, + batch_size: int, + shuffle: bool, + seed: int, + center_images: bool, +) -> tf.data.Dataset: + ds = tf.data.Dataset.from_tensor_slices((images, labels)) + if shuffle: + ds = ds.shuffle(buffer_size=len(images), seed=seed, reshuffle_each_iteration=True) + + def _map(x, y): + x = tf.cast(x, tf.float32) + if center_images: + x = x - IMG_MEAN + x = tf.expand_dims(x, axis=-1) + x = tf.tile(x, [1, 1, 1, 3]) + y = tf.cast(y, tf.float32) + return x, y + + return ds.batch(batch_size).map(_map, num_parallel_calls=tf.data.AUTOTUNE).prefetch(tf.data.AUTOTUNE) + + +def _augment_cls_batch(images: tf.Tensor, labels: tf.Tensor, seed: int) -> tuple[tf.Tensor, tf.Tensor]: + """Apply train-time augments for bifurcation classification.""" + stateless_seed = tf.convert_to_tensor([seed, 0], dtype=tf.int32) + flip_lr = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5 + images = tf.cond(flip_lr, lambda: tf.image.flip_left_right(images), lambda: images) + + stateless_seed = tf.convert_to_tensor([seed, 1], dtype=tf.int32) + flip_ud = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5 + images = tf.cond(flip_ud, lambda: tf.image.flip_up_down(images), lambda: images) + + stateless_seed = tf.convert_to_tensor([seed, 2], dtype=tf.int32) + k = tf.random.stateless_uniform([], seed=stateless_seed, minval=0, maxval=4, dtype=tf.int32) + images = tf.image.rot90(images, k=k) + + # Shear + perspective warp. + shx = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 30], dtype=tf.int32), minval=-0.08, maxval=0.08) + shy = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 31], dtype=tf.int32), minval=-0.08, maxval=0.08) + p0 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 32], dtype=tf.int32), minval=-8e-4, maxval=8e-4) + p1 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 33], dtype=tf.int32), minval=-8e-4, maxval=8e-4) + base_t = tf.stack( + [ + tf.constant(1.0, tf.float32), + shx, + tf.constant(0.0, tf.float32), + shy, + tf.constant(1.0, tf.float32), + tf.constant(0.0, tf.float32), + p0, + p1, + ] + ) + transforms = tf.tile(base_t[tf.newaxis, :], [tf.shape(images)[0], 1]) + images = tf.raw_ops.ImageProjectiveTransformV3( + images=images, + transforms=transforms, + output_shape=tf.shape(images)[1:3], + interpolation="BILINEAR", + fill_mode="REFLECT", + fill_value=0.0, + ) + + stateless_seed = tf.convert_to_tensor([seed, 3], dtype=tf.int32) + images = tf.image.stateless_random_brightness(images, max_delta=10.0, seed=stateless_seed) + stateless_seed = tf.convert_to_tensor([seed, 4], dtype=tf.int32) + images = tf.image.stateless_random_contrast(images, lower=0.9, upper=1.1, seed=stateless_seed) + images = tf.clip_by_value(images, -255.0, 255.0) + + return images, labels + + +def _make_custom_model(input_shape: tuple[int, int, int]) -> tf.keras.Model: + inputs = tf.keras.Input(shape=input_shape) + x = tf.keras.layers.Conv2D(24, 5, strides=2, activation="relu", padding="same")(inputs) + x = tf.keras.layers.BatchNormalization()(x) + x = tf.keras.layers.Conv2D(48, 3, strides=2, activation="relu", padding="same")(x) + x = tf.keras.layers.BatchNormalization()(x) + x = tf.keras.layers.Conv2D(96, 3, strides=2, activation="relu", padding="same")(x) + x = tf.keras.layers.GlobalAveragePooling2D()(x) + x = tf.keras.layers.Dropout(0.25)(x) + x = tf.keras.layers.Dense(64, activation="relu")(x) + outputs = tf.keras.layers.Dense(1, activation="sigmoid")(x) + return tf.keras.Model(inputs=inputs, outputs=outputs) + + +def _make_transfer_model(input_shape: tuple[int, int, int]) -> tuple[tf.keras.Model, tf.keras.Model]: + """EfficientNetB0 transfer-learning classifier.""" + inputs = tf.keras.Input(shape=input_shape) + backbone = tf.keras.applications.EfficientNetB0( + include_top=False, + weights="imagenet", + input_tensor=inputs, + ) + x = backbone.output + x = tf.keras.layers.GlobalAveragePooling2D()(x) + x = tf.keras.layers.Dropout(0.3)(x) + x = tf.keras.layers.Dense(128, activation="relu")(x) + x = tf.keras.layers.Dropout(0.2)(x) + outputs = tf.keras.layers.Dense(1, activation="sigmoid")(x) + model = tf.keras.Model(inputs=inputs, outputs=outputs) + return model, backbone + + +def _eval_metrics(model: tf.keras.Model, ds: tf.data.Dataset) -> dict[str, float]: + ys = [] + ps = [] + for x, y in ds: + p = model(x, training=False).numpy().reshape(-1) + ys.append(y.numpy().reshape(-1)) + ps.append(p) + if not ys: + return {"accuracy": float("nan"), "precision": float("nan"), "recall": float("nan")} + y = np.concatenate(ys) + p = np.concatenate(ps) + pred = (p >= 0.5).astype(np.int32) + y_int = y.astype(np.int32) + tp = int(np.sum((pred == 1) & (y_int == 1))) + fp = int(np.sum((pred == 1) & (y_int == 0))) + fn = int(np.sum((pred == 0) & (y_int == 1))) + acc = float(np.mean(pred == y_int)) + prec = float(tp / max(tp + fp, 1)) + rec = float(tp / max(tp + fn, 1)) + return {"accuracy": acc, "precision": prec, "recall": rec} + + +class _TqdmEpochCallback(tf.keras.callbacks.Callback): + """Per-epoch train progress with tqdm when available.""" + + def __init__(self, steps_per_epoch: int, total_epochs: int) -> None: + super().__init__() + self.steps_per_epoch = max(1, int(steps_per_epoch)) + self.total_epochs = max(1, int(total_epochs)) + self._bar = None + + def on_epoch_begin(self, epoch, logs=None): + if tqdm is None: + print(f"Starting epoch {epoch + 1}/{self.total_epochs} ({self.steps_per_epoch} steps)") + return + self._bar = tqdm(total=self.steps_per_epoch, desc=f"Epoch {epoch + 1}/{self.total_epochs}", leave=False) + + def on_train_batch_end(self, batch, logs=None): + logs = logs or {} + if self._bar is not None: + self._bar.update(1) + postfix = {} + for key in ("loss", "accuracy", "auc"): + val = logs.get(key) + if val is not None: + postfix[key] = f"{float(val):.4f}" + if postfix: + self._bar.set_postfix(postfix) + elif (batch + 1) % 10 == 0: + loss = logs.get("loss") + if loss is not None: + print(f" step {batch + 1}/{self.steps_per_epoch} loss={float(loss):.4f}") + + def on_epoch_end(self, epoch, logs=None): + if self._bar is not None: + self._bar.close() + self._bar = None + logs = logs or {} + parts = [] + for key in ("loss", "accuracy", "auc", "val_loss", "val_accuracy", "val_auc"): + val = logs.get(key) + if val is not None: + parts.append(f"{key}={float(val):.4f}") + if parts: + print(f"Epoch {epoch + 1}/{self.total_epochs} " + " ".join(parts)) + + +def _merge_histories(histories: list[dict[str, list[float]]]) -> dict[str, list[float]]: + merged: dict[str, list[float]] = {} + for hist in histories: + for key, values in hist.items(): + merged.setdefault(key, []) + merged[key].extend(values) + return merged + + +def _tile_to_uint8(tile: np.ndarray) -> np.ndarray: + x = np.asarray(tile, dtype=np.float32) + if x.ndim == 3 and x.shape[-1] > 1: + x = x[..., 0] + if not np.isfinite(x).any(): + return np.zeros_like(x, dtype=np.uint8) + mn = float(np.nanmin(x)) + mx = float(np.nanmax(x)) + if mx <= mn + 1e-6: + return np.clip(x, 0, 255).astype(np.uint8) + x = (x - mn) / (mx - mn) + return (x * 255.0).clip(0, 255).astype(np.uint8) + + +def _save_augment_grid_from_dataset(ds: tf.data.Dataset, out_path: Path) -> bool: + batch = None + for x, _ in ds.take(1): + batch = x.numpy() + if batch is None or batch.shape[0] == 0: + return False + + n = min(16, int(batch.shape[0])) + h = int(batch.shape[1]) + w = int(batch.shape[2]) + grid = Image.new("L", (4 * w, 4 * h), color=0) + for i in range(n): + r = i // 4 + c = i % 4 + tile = _tile_to_uint8(batch[i]) + grid.paste(Image.fromarray(tile, mode="L"), (c * w, r * h)) + out_path.parent.mkdir(parents=True, exist_ok=True) + grid.save(out_path) + return True + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--frame-bank-root", type=Path, default=Path("evals/frame_bank_merged")) + parser.add_argument( + "--split-json", + type=Path, + default=Path("evals/splits/ivus_split_merged_600.json"), + help="Existing split JSON to reuse.", + ) + parser.add_argument("--train-fraction", type=float, default=0.7) + parser.add_argument("--val-fraction", type=float, default=0.15) + parser.add_argument("--test-fraction", type=float, default=0.15) + parser.add_argument("--seed", type=int, default=7) + parser.add_argument("--epochs", type=int, default=50) + parser.add_argument("--batch-size", type=int, default=8) + parser.add_argument("--learning-rate", type=float, default=1e-3, help="Base LR (custom model or transfer warmup).") + parser.add_argument( + "--model-type", + type=str, + default="efficientnetb0", + choices=["custom", "efficientnetb0"], + help="Classifier architecture.", + ) + parser.add_argument("--freeze-epochs", type=int, default=5, help="Frozen-backbone warmup epochs (transfer only).") + parser.add_argument("--fine-tune-learning-rate", type=float, default=1e-5, help="LR for transfer fine-tune stage.") + parser.add_argument("--augment", dest="augment", action="store_true", help="Enable train-time augmentation.") + parser.add_argument("--no-augment", dest="augment", action="store_false", help="Disable train-time augmentation.") + parser.add_argument( + "--class-weight-mode", + type=str, + default="balanced", + choices=["none", "balanced"], + help="Class-weight strategy for imbalanced labels.", + ) + parser.add_argument( + "--early-stop-monitor", + type=str, + default="val_auc", + choices=["val_auc", "val_loss", "val_accuracy", "loss", "auc"], + help="Metric used for early stopping and best-model selection.", + ) + parser.add_argument("--early-stop-patience", type=int, default=10, help="Early stopping patience (epochs).") + parser.add_argument("--early-stop-min-delta", type=float, default=1e-4, help="Minimum improvement for early stopping.") + parser.add_argument("--tb-logdir", type=Path, default=Path("output/tensorboard"), help="TensorBoard root logdir.") + parser.add_argument("--tb-run-name", type=str, default=None, help="Optional TensorBoard run name.") + parser.add_argument("--output-dir", type=Path, default=Path("models/standalone/bifurcation")) + parser.add_argument("--save-split-json", type=Path, default=None, help="Optional split output path.") + parser.set_defaults(augment=True) + args = parser.parse_args() + + tf.random.set_seed(args.seed) + np.random.seed(args.seed) + + annotations = load_bifurcation_annotations(args.frame_bank_root) + if not annotations: + raise RuntimeError(f"No annotations under: {args.frame_bank_root}") + + if args.split_json is not None: + split_ids = load_split_ids(args.split_json) + train_ids = split_ids["train"] + val_ids = split_ids["val"] + test_ids = split_ids["test"] + else: + split = stratified_frame_split( + annotations=annotations, + train_fraction=args.train_fraction, + val_fraction=args.val_fraction, + test_fraction=args.test_fraction, + seed=args.seed, + ) + train_ids = set(annotations[i].sample_id for i in split["train"]) + val_ids = set(annotations[i].sample_id for i in split["val"]) + test_ids = set(annotations[i].sample_id for i in split["test"]) + if args.save_split_json is not None: + save_split_json(args.save_split_json, annotations, split, seed=args.seed) + + train_anns = [ann for ann in annotations if ann.sample_id in train_ids] + val_anns = [ann for ann in annotations if ann.sample_id in val_ids] + test_anns = [ann for ann in annotations if ann.sample_id in test_ids] + if not train_anns: + raise RuntimeError("Train split is empty for classifier.") + + summary = split_summary( + annotations, + { + "train": [i for i, ann in enumerate(annotations) if ann.sample_id in train_ids], + "val": [i for i, ann in enumerate(annotations) if ann.sample_id in val_ids], + "test": [i for i, ann in enumerate(annotations) if ann.sample_id in test_ids], + }, + ) + print(f"Split summary: {summary}") + + train_x, train_y = _sample_arrays(train_anns) + val_x, val_y = _sample_arrays(val_anns) if val_anns else (np.zeros((0,) + train_x.shape[1:], dtype=train_x.dtype), np.zeros((0,), dtype=np.float32)) + test_x, test_y = _sample_arrays(test_anns) if test_anns else (np.zeros((0,) + train_x.shape[1:], dtype=train_x.dtype), np.zeros((0,), dtype=np.float32)) + + center_images = args.model_type == "custom" + train_ds = _build_dataset(train_x, train_y, args.batch_size, shuffle=True, seed=args.seed, center_images=center_images) + if args.augment: + train_ds = train_ds.enumerate().map( + lambda i, data: _augment_cls_batch(data[0], data[1], seed=args.seed + tf.cast(i, tf.int32)), + num_parallel_calls=tf.data.AUTOTUNE, + ).prefetch(tf.data.AUTOTUNE) + val_ds = ( + _build_dataset(val_x, val_y, args.batch_size, shuffle=False, seed=args.seed, center_images=center_images) + if len(val_x) + else None + ) + test_ds = ( + _build_dataset(test_x, test_y, args.batch_size, shuffle=False, seed=args.seed, center_images=center_images) + if len(test_x) + else None + ) + + backbone = None + if args.model_type == "custom": + model = _make_custom_model((train_x.shape[1], train_x.shape[2], 3)) + else: + model, backbone = _make_transfer_model((train_x.shape[1], train_x.shape[2], 3)) + backbone.trainable = False + + def _compile(current_model: tf.keras.Model, lr: float) -> None: + current_model.compile( + optimizer=tf.keras.optimizers.Adam(learning_rate=lr), + loss=tf.keras.losses.BinaryCrossentropy(), + metrics=[ + tf.keras.metrics.BinaryAccuracy(name="accuracy"), + tf.keras.metrics.Precision(name="precision"), + tf.keras.metrics.Recall(name="recall"), + tf.keras.metrics.AUC(name="auc"), + ], + ) + + _compile(model, args.learning_rate) + + class_weight = None + if args.class_weight_mode == "balanced": + neg = float(np.sum(train_y == 0)) + pos = float(np.sum(train_y == 1)) + total = max(neg + pos, 1.0) + # sklearn-style balanced weighting. + w_neg = total / max(2.0 * neg, 1.0) + w_pos = total / max(2.0 * pos, 1.0) + class_weight = {0: w_neg, 1: w_pos} + print(f"Using class weights: {class_weight} (neg={int(neg)}, pos={int(pos)})") + + args.output_dir.mkdir(parents=True, exist_ok=True) + best_model_path = args.output_dir / "best_bifurcation_classifier.keras" + final_model_path = args.output_dir / "bifurcation_classifier.keras" + steps_per_epoch = int(np.ceil(max(1, len(train_x)) / float(args.batch_size))) + monitor_name = args.early_stop_monitor + if val_ds is None and monitor_name.startswith("val_"): + fallback = "loss" if monitor_name == "val_loss" else "auc" + print(f"Validation split is empty; switching monitor from {monitor_name} to {fallback}.") + monitor_name = fallback + monitor_mode = "min" if "loss" in monitor_name else "max" + tb_run_name = args.tb_run_name or datetime.datetime.now().strftime("bif_classifier_%Y%m%d_%H%M%S") + tb_run_dir = args.tb_logdir / tb_run_name + tb_run_dir.mkdir(parents=True, exist_ok=True) + tb_writer = tf.summary.create_file_writer(str(tb_run_dir)) + print(f"TensorBoard logdir: {tb_run_dir}") + if args.augment: + aug_preview_path = tb_run_dir / "augment_preview_4x4.png" + if _save_augment_grid_from_dataset(train_ds, aug_preview_path): + print(f"Saved augmentation preview: {aug_preview_path}") + + all_histories: list[dict[str, list[float]]] = [] + epochs_done = 0 + + checkpoint_cb = tf.keras.callbacks.ModelCheckpoint( + filepath=str(best_model_path), + monitor=monitor_name, + mode=monitor_mode, + save_best_only=True, + save_weights_only=False, + verbose=1, + ) + + if args.model_type == "efficientnetb0": + warmup_epochs = max(0, min(args.freeze_epochs, args.epochs)) + fine_tune_epochs = max(0, args.epochs - warmup_epochs) + + if warmup_epochs > 0: + print(f"Stage 1/2: transfer warmup with frozen backbone for {warmup_epochs} epochs") + warmup_callbacks = [ + _TqdmEpochCallback(steps_per_epoch=steps_per_epoch, total_epochs=warmup_epochs), + checkpoint_cb, + ] + warmup_history = model.fit( + train_ds, + validation_data=val_ds, + epochs=warmup_epochs, + verbose=0, + callbacks=warmup_callbacks, + class_weight=class_weight, + ) + all_histories.append(warmup_history.history) + epochs_done += len(warmup_history.history.get("loss", [])) + + if fine_tune_epochs > 0: + print(f"Stage 2/2: transfer fine-tune with unfrozen backbone for up to {fine_tune_epochs} epochs") + backbone.trainable = True + # Keep BatchNorm frozen for stability on small datasets. + for layer in backbone.layers: + if isinstance(layer, tf.keras.layers.BatchNormalization): + layer.trainable = False + _compile(model, args.fine_tune_learning_rate) + finetune_callbacks = [ + _TqdmEpochCallback(steps_per_epoch=steps_per_epoch, total_epochs=fine_tune_epochs), + checkpoint_cb, + tf.keras.callbacks.EarlyStopping( + monitor=monitor_name, + mode=monitor_mode, + patience=args.early_stop_patience, + min_delta=args.early_stop_min_delta, + restore_best_weights=True, + verbose=1, + ), + ] + finetune_history = model.fit( + train_ds, + validation_data=val_ds, + epochs=fine_tune_epochs, + verbose=0, + callbacks=finetune_callbacks, + class_weight=class_weight, + ) + all_histories.append(finetune_history.history) + epochs_done += len(finetune_history.history.get("loss", [])) + else: + callbacks = [ + _TqdmEpochCallback(steps_per_epoch=steps_per_epoch, total_epochs=args.epochs), + checkpoint_cb, + tf.keras.callbacks.EarlyStopping( + monitor=monitor_name, + mode=monitor_mode, + patience=args.early_stop_patience, + min_delta=args.early_stop_min_delta, + restore_best_weights=True, + verbose=1, + ), + ] + history = model.fit( + train_ds, + validation_data=val_ds, + epochs=args.epochs, + verbose=0, + callbacks=callbacks, + class_weight=class_weight, + ) + all_histories.append(history.history) + epochs_done += len(history.history.get("loss", [])) + + history_dict = _merge_histories(all_histories) + + with tb_writer.as_default(): + for epoch in range(len(history_dict.get("loss", []))): + for key in ("loss", "accuracy", "precision", "recall", "auc", "val_loss", "val_accuracy", "val_precision", "val_recall", "val_auc"): + values = history_dict.get(key) + if values is None or epoch >= len(values): + continue + tf.summary.scalar(f"fit/{key}", float(values[epoch]), step=epoch + 1) + monitor_values = history_dict.get(monitor_name) + if monitor_values is not None and epoch < len(monitor_values): + tf.summary.scalar("early_stop/monitor_value", float(monitor_values[epoch]), step=epoch + 1) + + if best_model_path.exists(): + model = tf.keras.models.load_model(best_model_path) + + val_metrics = _eval_metrics(model, val_ds) if val_ds is not None else {} + test_metrics = _eval_metrics(model, test_ds) if test_ds is not None else {} + with tb_writer.as_default(): + for key, value in val_metrics.items(): + tf.summary.scalar(f"val_final/{key}", float(value), step=len(history_dict.get("loss", []))) + for key, value in test_metrics.items(): + tf.summary.scalar(f"test/{key}", float(value), step=len(history_dict.get("loss", []))) + tb_writer.flush() + tb_writer.close() + model.save(final_model_path) + + summary_path = args.output_dir / "training_summary.json" + payload = { + "split_summary": summary, + "num_train": int(len(train_x)), + "num_val": int(len(val_x)), + "num_test": int(len(test_x)), + "epochs_requested": int(args.epochs), + "epochs_trained": int(epochs_done), + "model_type": args.model_type, + "freeze_epochs": int(args.freeze_epochs), + "learning_rate": float(args.learning_rate), + "fine_tune_learning_rate": float(args.fine_tune_learning_rate), + "class_weight_mode": args.class_weight_mode, + "class_weight": class_weight, + "early_stop_monitor_requested": args.early_stop_monitor, + "early_stop_monitor_used": monitor_name, + "early_stop_patience": int(args.early_stop_patience), + "early_stop_min_delta": float(args.early_stop_min_delta), + "best_model_path": str(best_model_path), + "final_model_path": str(final_model_path), + "tensorboard_run_dir": str(tb_run_dir), + "val_metrics": val_metrics, + "test_metrics": test_metrics, + "history": history_dict, + } + with summary_path.open("w", encoding="utf-8") as fp: + json.dump(payload, fp, indent=2) + + print(f"Saved best classifier: {best_model_path}") + print(f"Saved final classifier: {final_model_path}") + print(f"Saved summary: {summary_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/lumen/finetune_lumen_from_saved_model.py b/scripts/finetune/lumen/finetune_lumen_from_saved_model.py new file mode 100644 index 0000000000000000000000000000000000000000..0aec47adc5da8b6e395e3f23131aecc5d0eb50a0 --- /dev/null +++ b/scripts/finetune/lumen/finetune_lumen_from_saved_model.py @@ -0,0 +1,768 @@ +#!/usr/bin/env python3 +"""Fine-tune an existing TF SavedModel using lumen-only frame-bank annotations.""" + +from __future__ import annotations + +import argparse +import json +import math +import re +import shutil +import datetime +from pathlib import Path + +import numpy as np +import tensorflow as tf +from PIL import Image + +from deepivus.config import resolve_lumen_model_dir +from scripts.finetune.shared.common import ( + build_images_and_masks, + load_lumen_annotations, + load_split_ids, + split_summary, + stratified_frame_split, +) + +try: + from tqdm.auto import tqdm +except Exception: + tqdm = None + +IMG_MEAN = tf.constant([60.3486], dtype=tf.float32) + + +def _extract_logits(model_output: object) -> tf.Tensor: + if isinstance(model_output, dict): + if not model_output: + raise RuntimeError("Model returned an empty dict output.") + return next(iter(model_output.values())) + if isinstance(model_output, (tuple, list)): + if not model_output: + raise RuntimeError("Model returned an empty sequence output.") + return model_output[0] + if tf.is_tensor(model_output): + return model_output + raise RuntimeError(f"Unsupported model output type: {type(model_output)!r}") + + +def _prepare_batch(images: tf.Tensor) -> tf.Tensor: + x_batch = tf.cast(images, dtype=tf.float32) - IMG_MEAN + if len(x_batch.shape) < 4: + x_batch = tf.expand_dims(x_batch, axis=3) + if x_batch.shape[-1] != 3: + x_batch = tf.tile(x_batch, [1, 1, 1, 3]) + return x_batch + + +def _build_dataset(images: np.ndarray, masks: np.ndarray, batch_size: int, shuffle: bool, seed: int) -> tf.data.Dataset: + ds = tf.data.Dataset.from_tensor_slices((images, masks)) + if shuffle: + ds = ds.shuffle(buffer_size=len(images), seed=seed, reshuffle_each_iteration=True) + ds = ds.batch(batch_size) + + def _map_fn(batch_images: tf.Tensor, batch_masks: tf.Tensor) -> tuple[tf.Tensor, tf.Tensor]: + x = tf.cast(batch_images, tf.float32) + x = _prepare_batch(x) + y = tf.cast(batch_masks, tf.float32) + return x, y + + ds = ds.map(_map_fn, num_parallel_calls=tf.data.AUTOTUNE).prefetch(tf.data.AUTOTUNE) + return ds + + +def _augment_seg_batch(images: tf.Tensor, masks: tf.Tensor, seed: int) -> tuple[tf.Tensor, tf.Tensor]: + """Apply identical geometric augments to image/mask and photometric augments to image.""" + stateless_seed = tf.convert_to_tensor([seed, 0], dtype=tf.int32) + + flip_lr = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5 + images = tf.cond(flip_lr, lambda: tf.image.flip_left_right(images), lambda: images) + masks = tf.cond(flip_lr, lambda: tf.image.flip_left_right(masks[..., tf.newaxis]), lambda: masks[..., tf.newaxis]) + + stateless_seed = tf.convert_to_tensor([seed, 1], dtype=tf.int32) + flip_ud = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5 + images = tf.cond(flip_ud, lambda: tf.image.flip_up_down(images), lambda: images) + masks = tf.cond(flip_ud, lambda: tf.image.flip_up_down(masks), lambda: masks) + + stateless_seed = tf.convert_to_tensor([seed, 2], dtype=tf.int32) + k = tf.random.stateless_uniform([], seed=stateless_seed, minval=0, maxval=4, dtype=tf.int32) + images = tf.image.rot90(images, k=k) + masks = tf.image.rot90(masks, k=k) + + # Shear + perspective warp (same transform for image and mask). + shx = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 30], dtype=tf.int32), minval=-0.08, maxval=0.08) + shy = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 31], dtype=tf.int32), minval=-0.08, maxval=0.08) + p0 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 32], dtype=tf.int32), minval=-8e-4, maxval=8e-4) + p1 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 33], dtype=tf.int32), minval=-8e-4, maxval=8e-4) + base_t = tf.stack( + [ + tf.constant(1.0, tf.float32), + shx, + tf.constant(0.0, tf.float32), + shy, + tf.constant(1.0, tf.float32), + tf.constant(0.0, tf.float32), + p0, + p1, + ] + ) + transforms = tf.tile(base_t[tf.newaxis, :], [tf.shape(images)[0], 1]) + out_shape = tf.shape(images)[1:3] + images = tf.raw_ops.ImageProjectiveTransformV3( + images=images, + transforms=transforms, + output_shape=out_shape, + interpolation="BILINEAR", + fill_mode="REFLECT", + fill_value=0.0, + ) + masks = tf.raw_ops.ImageProjectiveTransformV3( + images=masks, + transforms=transforms, + output_shape=out_shape, + interpolation="NEAREST", + fill_mode="REFLECT", + fill_value=0.0, + ) + + stateless_seed = tf.convert_to_tensor([seed, 3], dtype=tf.int32) + images = tf.image.stateless_random_brightness(images, max_delta=10.0, seed=stateless_seed) + stateless_seed = tf.convert_to_tensor([seed, 4], dtype=tf.int32) + images = tf.image.stateless_random_contrast(images, lower=0.9, upper=1.1, seed=stateless_seed) + images = tf.clip_by_value(images, -255.0, 255.0) + + return images, tf.squeeze(masks, axis=-1) + + +def _select_trainable_vars( + all_trainable: list[tf.Variable], + include_regex: str, + exclude_regex: str, + max_trainable: int, +) -> list[tf.Variable]: + include = re.compile(include_regex) if include_regex else None + exclude = re.compile(exclude_regex) if exclude_regex else None + + selected: list[tf.Variable] = [] + for var in all_trainable: + name = var.name + if include is not None and not include.search(name): + continue + if exclude is not None and exclude.search(name): + continue + selected.append(var) + if max_trainable > 0 and len(selected) >= max_trainable: + break + return selected + + +def _binary_logit_from_multiclass(logits: tf.Tensor, lumen_class: int) -> tf.Tensor: + num_classes = int(logits.shape[-1]) + if lumen_class < 0 or lumen_class >= num_classes: + raise ValueError(f"Invalid lumen_class={lumen_class} for num_classes={num_classes}") + positive = logits[..., lumen_class] + negatives = tf.concat([logits[..., :lumen_class], logits[..., lumen_class + 1 :]], axis=-1) + negative_logsumexp = tf.reduce_logsumexp(negatives, axis=-1) + return positive - negative_logsumexp + + +def _compute_lumen_loss( + logits: tf.Tensor, + targets: tf.Tensor, + lumen_class: int, + pos_weight: float, + dice_weight: float, +) -> tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: + h = tf.shape(targets)[1] + w = tf.shape(targets)[2] + logits = tf.image.resize(logits, (h, w)) + binary_logit = _binary_logit_from_multiclass(logits, lumen_class=lumen_class) + + bce = tf.nn.weighted_cross_entropy_with_logits( + labels=targets, + logits=binary_logit, + pos_weight=tf.constant(pos_weight, dtype=tf.float32), + ) + bce = tf.reduce_mean(bce) + + probs = tf.math.sigmoid(binary_logit) + if dice_weight > 0.0: + inter = tf.reduce_sum(probs * targets, axis=[1, 2]) + denom = tf.reduce_sum(probs + targets, axis=[1, 2]) + dice = (2.0 * inter + 1e-6) / (denom + 1e-6) + dice_loss = 1.0 - tf.reduce_mean(dice) + else: + dice_loss = tf.constant(0.0, dtype=tf.float32) + + total = bce + tf.constant(dice_weight, dtype=tf.float32) * dice_loss + return total, bce, dice_loss, probs + + +def _evaluate( + model: tf.types.experimental.TrackableResource, + ds: tf.data.Dataset, + lumen_class: int, + pos_weight: float, + dice_weight: float, +) -> dict[str, float]: + losses = [] + bces = [] + dlosses = [] + all_inter = 0.0 + all_union = 0.0 + all_pred = 0.0 + all_gt = 0.0 + + for batch_x, batch_y in ds: + logits = _extract_logits(model(batch_x, training=False)) + total, bce, dice_loss, probs = _compute_lumen_loss( + logits=logits, + targets=batch_y, + lumen_class=lumen_class, + pos_weight=pos_weight, + dice_weight=dice_weight, + ) + pred = probs >= 0.5 + gt = batch_y >= 0.5 + inter = tf.reduce_sum(tf.cast(pred & gt, tf.float32)).numpy().item() + union = tf.reduce_sum(tf.cast(pred | gt, tf.float32)).numpy().item() + pred_sum = tf.reduce_sum(tf.cast(pred, tf.float32)).numpy().item() + gt_sum = tf.reduce_sum(tf.cast(gt, tf.float32)).numpy().item() + + losses.append(float(total.numpy())) + bces.append(float(bce.numpy())) + dlosses.append(float(dice_loss.numpy())) + all_inter += float(inter) + all_union += float(union) + all_pred += float(pred_sum) + all_gt += float(gt_sum) + + iou = all_inter / max(all_union, 1.0) + dice = (2.0 * all_inter) / max(all_pred + all_gt, 1.0) + return { + "loss": float(np.mean(losses)) if losses else float("nan"), + "bce": float(np.mean(bces)) if bces else float("nan"), + "dice_loss": float(np.mean(dlosses)) if dlosses else float("nan"), + "iou": float(iou), + "dice": float(dice), + } + + +def _num_batches(num_samples: int, batch_size: int) -> int: + if num_samples <= 0: + return 0 + return int(math.ceil(float(num_samples) / float(batch_size))) + + +def _auto_detect_lumen_class( + model: tf.types.experimental.TrackableResource, + images: np.ndarray, + masks: np.ndarray, + max_samples: int, + batch_size: int, + seed: int, +) -> int: + total = len(images) + if max_samples > 0 and total > max_samples: + rng = np.random.default_rng(seed) + picks = rng.choice(total, size=max_samples, replace=False) + picks = np.sort(picks) + images = images[picks] + masks = masks[picks] + + class_iou_sum: np.ndarray | None = None + sample_count = 0 + num_batches = int(math.ceil(len(images) / float(batch_size))) + for bidx in range(num_batches): + chunk_images = images[bidx * batch_size : (bidx + 1) * batch_size] + chunk_masks = masks[bidx * batch_size : (bidx + 1) * batch_size] + batch = _prepare_batch(tf.convert_to_tensor(chunk_images, dtype=tf.float32)) + logits = _extract_logits(model(batch, training=False)) + logits = tf.image.resize(logits, (chunk_images.shape[1], chunk_images.shape[2])) + pred = tf.argmax(logits, axis=-1).numpy() + num_classes = int(logits.shape[-1]) + if class_iou_sum is None: + class_iou_sum = np.zeros((num_classes,), dtype=np.float64) + + for i in range(pred.shape[0]): + gt = chunk_masks[i] > 0.5 + if not np.any(gt): + continue + sample_count += 1 + for cls in range(num_classes): + pm = pred[i] == cls + inter = np.logical_and(pm, gt).sum() + union = np.logical_or(pm, gt).sum() + class_iou_sum[cls] += float(inter) / max(float(union), 1.0) + + if class_iou_sum is None or sample_count == 0: + raise RuntimeError("Automatic lumen class detection failed: no valid samples.") + mean_iou = class_iou_sum / float(sample_count) + return int(np.argmax(mean_iou)) + + +def _tile_to_uint8(tile: np.ndarray) -> np.ndarray: + x = np.asarray(tile, dtype=np.float32) + if x.ndim == 3 and x.shape[-1] > 1: + x = x[..., 0] + if not np.isfinite(x).any(): + return np.zeros_like(x, dtype=np.uint8) + mn = float(np.nanmin(x)) + mx = float(np.nanmax(x)) + if mx <= mn + 1e-6: + return np.clip(x, 0, 255).astype(np.uint8) + x = (x - mn) / (mx - mn) + return (x * 255.0).clip(0, 255).astype(np.uint8) + + +def _save_augment_grid_from_dataset(ds: tf.data.Dataset, out_path: Path) -> bool: + batch = None + for x, _ in ds.take(1): + batch = x.numpy() + if batch is None or batch.shape[0] == 0: + return False + + n = min(16, int(batch.shape[0])) + h = int(batch.shape[1]) + w = int(batch.shape[2]) + grid = Image.new("L", (4 * w, 4 * h), color=0) + for i in range(n): + r = i // 4 + c = i % 4 + tile = _tile_to_uint8(batch[i]) + grid.paste(Image.fromarray(tile, mode="L"), (c * w, r * h)) + out_path.parent.mkdir(parents=True, exist_ok=True) + grid.save(out_path) + return True + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--model-dir", + type=Path, + default=resolve_lumen_model_dir(), + help="Path to base TF SavedModel.", + ) + parser.add_argument( + "--output-model-dir", + type=Path, + required=True, + help="Directory to write the fine-tuned SavedModel.", + ) + parser.add_argument( + "--frame-bank-root", + type=Path, + default=Path("evals/frame_bank_merged"), + help="Path containing eval frame-bank JSONL files.", + ) + parser.add_argument( + "--lumen-class", + type=int, + default=-1, + help="Lumen class index. Use -1 to auto-detect from annotations.", + ) + parser.add_argument("--epochs", type=int, default=50, help="Max number of fine-tuning epochs.") + parser.add_argument("--batch-size", type=int, default=4, help="Mini-batch size.") + parser.add_argument("--learning-rate", type=float, default=1e-5, help="Adam learning rate.") + parser.add_argument("--dice-weight", type=float, default=0.3, help="Weight for dice loss term.") + parser.add_argument("--augment", action="store_true", help="Enable train-time augmentation.") + parser.add_argument( + "--tb-logdir", + type=Path, + default=Path("output/tensorboard"), + help="TensorBoard root log directory.", + ) + parser.add_argument( + "--tb-run-name", + type=str, + default=None, + help="Optional TensorBoard run name. Defaults to timestamped name.", + ) + parser.add_argument( + "--early-stop-monitor", + type=str, + default="val_loss", + choices=["val_loss", "val_dice", "val_iou", "train_loss"], + help="Metric to monitor for early stopping.", + ) + parser.add_argument("--early-stop-patience", type=int, default=8, help="Epochs with no improvement before stop.") + parser.add_argument( + "--early-stop-min-delta", + type=float, + default=1e-4, + help="Minimum improvement to reset early-stopping patience.", + ) + parser.add_argument("--train-fraction", type=float, default=0.7, help="Train split fraction.") + parser.add_argument("--val-fraction", type=float, default=0.15, help="Validation split fraction.") + parser.add_argument("--test-fraction", type=float, default=0.15, help="Test split fraction.") + parser.add_argument( + "--split-json", + type=Path, + default=Path("evals/splits/ivus_split_merged_600.json"), + help="Split JSON used for train/val/test partitioning.", + ) + parser.add_argument("--diameter", type=int, default=67, help="Center-circle preprocessing diameter.") + parser.add_argument("--seed", type=int, default=7, help="Random seed.") + parser.add_argument("--clip-norm", type=float, default=1.0, help="Global gradient clip norm (0=off).") + parser.add_argument("--include-var-regex", type=str, default=".*", help="Regex to include trainable vars.") + parser.add_argument("--exclude-var-regex", type=str, default="", help="Regex to exclude trainable vars.") + parser.add_argument("--max-trainable", type=int, default=0, help="Cap number of trainable vars (0=all).") + parser.add_argument( + "--auto-class-max-samples", + type=int, + default=120, + help="Max samples used when --lumen-class=-1 auto-detects class.", + ) + parser.add_argument( + "--overwrite-output", + action="store_true", + help="Remove existing output model directory before saving.", + ) + args = parser.parse_args() + + tf.random.set_seed(args.seed) + np.random.seed(args.seed) + + annotations = load_lumen_annotations(args.frame_bank_root) + if not annotations: + raise RuntimeError(f"No lumen annotations found under: {args.frame_bank_root}") + print(f"Loaded annotations: {len(annotations)}") + + images, masks, kept_annotations = build_images_and_masks(annotations, diameter=args.diameter) + print(f"Materialized samples: {len(kept_annotations)}") + + model = tf.saved_model.load(str(args.model_dir)) + all_trainable = list(getattr(model, "trainable_variables", [])) + if not all_trainable: + raise RuntimeError("Loaded model has no trainable variables.") + + trainable_vars = _select_trainable_vars( + all_trainable=all_trainable, + include_regex=args.include_var_regex, + exclude_regex=args.exclude_var_regex, + max_trainable=args.max_trainable, + ) + if not trainable_vars: + raise RuntimeError("No trainable variables matched include/exclude filters.") + + lumen_class = args.lumen_class + if lumen_class < 0: + lumen_class = _auto_detect_lumen_class( + model=model, + images=images, + masks=masks, + max_samples=args.auto_class_max_samples, + batch_size=args.batch_size, + seed=args.seed, + ) + print(f"Auto-detected lumen class: {lumen_class}") + + if args.split_json is not None: + split_ids = load_split_ids(args.split_json) + train_idx = np.asarray( + [i for i, ann in enumerate(kept_annotations) if ann.sample_id in split_ids["train"]], + dtype=np.int64, + ) + val_idx = np.asarray( + [i for i, ann in enumerate(kept_annotations) if ann.sample_id in split_ids["val"]], + dtype=np.int64, + ) + test_idx = np.asarray( + [i for i, ann in enumerate(kept_annotations) if ann.sample_id in split_ids["test"]], + dtype=np.int64, + ) + else: + split = stratified_frame_split( + annotations=kept_annotations, + train_fraction=args.train_fraction, + val_fraction=args.val_fraction, + test_fraction=args.test_fraction, + seed=args.seed, + ) + train_idx = np.asarray(split["train"], dtype=np.int64) + val_idx = np.asarray(split["val"], dtype=np.int64) + test_idx = np.asarray(split["test"], dtype=np.int64) + + if len(train_idx) == 0: + raise RuntimeError("Train split is empty.") + + train_images = images[train_idx] + train_masks = masks[train_idx] + val_images = images[val_idx] if len(val_idx) > 0 else np.zeros((0,) + images.shape[1:], dtype=images.dtype) + val_masks = masks[val_idx] if len(val_idx) > 0 else np.zeros((0,) + masks.shape[1:], dtype=masks.dtype) + test_images = images[test_idx] if len(test_idx) > 0 else np.zeros((0,) + images.shape[1:], dtype=images.dtype) + test_masks = masks[test_idx] if len(test_idx) > 0 else np.zeros((0,) + masks.shape[1:], dtype=masks.dtype) + + split_stats = split_summary( + kept_annotations, + {"train": train_idx.tolist(), "val": val_idx.tolist(), "test": test_idx.tolist()}, + ) + print( + "Train/Val/Test samples: " + f"{len(train_images)}/{len(val_images)}/{len(test_images)}" + ) + print(f"Split summary: {split_stats}") + print(f"Total trainable vars in model: {len(all_trainable)}") + print(f"Selected trainable vars: {len(trainable_vars)}") + + positive = float(np.sum(train_masks > 0.5)) + total_pixels = float(np.prod(train_masks.shape)) + negative = max(total_pixels - positive, 1.0) + pos_weight = float(np.clip(negative / max(positive, 1.0), 1.0, 40.0)) + print(f"Pos weight (class imbalance): {pos_weight:.4f}") + + train_ds = _build_dataset(train_images, train_masks, args.batch_size, shuffle=True, seed=args.seed) + if args.augment: + train_ds = train_ds.enumerate().map( + lambda i, data: _augment_seg_batch(data[0], data[1], seed=args.seed + tf.cast(i, tf.int32)), + num_parallel_calls=tf.data.AUTOTUNE, + ).prefetch(tf.data.AUTOTUNE) + val_ds = _build_dataset(val_images, val_masks, args.batch_size, shuffle=False, seed=args.seed) if len(val_images) else None + test_ds = _build_dataset(test_images, test_masks, args.batch_size, shuffle=False, seed=args.seed) if len(test_images) else None + + optimizer = tf.keras.optimizers.Adam(learning_rate=args.learning_rate) + history: list[dict[str, float]] = [] + best_val_iou = -1.0 + steps_per_epoch = _num_batches(len(train_images), args.batch_size) + early_stop_wait = 0 + best_monitor_value: float | None = None + run_name = args.tb_run_name or datetime.datetime.now().strftime("lumen_finetune_%Y%m%d_%H%M%S") + tb_run_dir = args.tb_logdir / run_name + tb_run_dir.mkdir(parents=True, exist_ok=True) + tb_writer = tf.summary.create_file_writer(str(tb_run_dir)) + print(f"TensorBoard logdir: {tb_run_dir}") + if args.augment: + aug_preview_path = tb_run_dir / "augment_preview_4x4.png" + if _save_augment_grid_from_dataset(train_ds, aug_preview_path): + print(f"Saved augmentation preview: {aug_preview_path}") + + for epoch in range(1, args.epochs + 1): + print(f"Starting epoch {epoch}/{args.epochs} ({steps_per_epoch} steps)") + epoch_losses = [] + epoch_bce = [] + epoch_dice_loss = [] + + train_iter = enumerate(train_ds, start=1) + if tqdm is not None: + pbar = tqdm(total=steps_per_epoch, desc=f"Epoch {epoch}/{args.epochs}", leave=False) + else: + pbar = None + + for step, (batch_x, batch_y) in train_iter: + with tf.GradientTape() as tape: + logits = _extract_logits(model(batch_x, training=True)) + total, bce, dice_loss, _ = _compute_lumen_loss( + logits=logits, + targets=batch_y, + lumen_class=lumen_class, + pos_weight=pos_weight, + dice_weight=args.dice_weight, + ) + + grads = tape.gradient(total, trainable_vars) + grad_var_pairs = [(g, v) for g, v in zip(grads, trainable_vars) if g is not None] + if not grad_var_pairs: + raise RuntimeError("All gradients are None; cannot continue training.") + + if args.clip_norm > 0.0: + grad_tensors = [g for g, _ in grad_var_pairs] + clipped, _ = tf.clip_by_global_norm(grad_tensors, clip_norm=args.clip_norm) + grad_var_pairs = list(zip(clipped, [v for _, v in grad_var_pairs])) + + optimizer.apply_gradients(grad_var_pairs) + epoch_losses.append(float(total.numpy())) + epoch_bce.append(float(bce.numpy())) + epoch_dice_loss.append(float(dice_loss.numpy())) + + if pbar is not None: + pbar.update(1) + pbar.set_postfix( + loss=f"{epoch_losses[-1]:.4f}", + bce=f"{epoch_bce[-1]:.4f}", + dice=f"{epoch_dice_loss[-1]:.4f}", + ) + elif step % 10 == 0: + print( + f"Epoch {epoch}/{args.epochs} Step {step}/{steps_per_epoch}: " + f"loss={epoch_losses[-1]:.4f} bce={epoch_bce[-1]:.4f} dice_loss={epoch_dice_loss[-1]:.4f}" + ) + + if pbar is not None: + pbar.close() + + train_metrics = { + "loss": float(np.mean(epoch_losses)), + "bce": float(np.mean(epoch_bce)), + "dice_loss": float(np.mean(epoch_dice_loss)), + } + val_metrics = ( + _evaluate( + model=model, + ds=val_ds, + lumen_class=lumen_class, + pos_weight=pos_weight, + dice_weight=args.dice_weight, + ) + if val_ds is not None + else {"loss": float("nan"), "bce": float("nan"), "dice_loss": float("nan"), "iou": float("nan"), "dice": float("nan")} + ) + + best_val_iou = max(best_val_iou, val_metrics["iou"] if np.isfinite(val_metrics["iou"]) else -1.0) + print( + f"Epoch {epoch}/{args.epochs} " + f"train_loss={train_metrics['loss']:.4f} " + f"val_loss={val_metrics['loss']:.4f} " + f"val_iou={val_metrics['iou']:.4f} " + f"val_dice={val_metrics['dice']:.4f}" + ) + + history.append( + { + "epoch": float(epoch), + "train_loss": train_metrics["loss"], + "train_bce": train_metrics["bce"], + "train_dice_loss": train_metrics["dice_loss"], + "val_loss": val_metrics["loss"], + "val_bce": val_metrics["bce"], + "val_dice_loss": val_metrics["dice_loss"], + "val_iou": val_metrics["iou"], + "val_dice": val_metrics["dice"], + } + ) + with tb_writer.as_default(): + tf.summary.scalar("train/loss", train_metrics["loss"], step=epoch) + tf.summary.scalar("train/bce", train_metrics["bce"], step=epoch) + tf.summary.scalar("train/dice_loss", train_metrics["dice_loss"], step=epoch) + tf.summary.scalar("val/loss", val_metrics["loss"], step=epoch) + tf.summary.scalar("val/bce", val_metrics["bce"], step=epoch) + tf.summary.scalar("val/dice_loss", val_metrics["dice_loss"], step=epoch) + tf.summary.scalar("val/iou", val_metrics["iou"], step=epoch) + tf.summary.scalar("val/dice", val_metrics["dice"], step=epoch) + + if args.early_stop_monitor == "train_loss": + monitor_value = train_metrics["loss"] + improved = ( + best_monitor_value is None + or monitor_value < (best_monitor_value - args.early_stop_min_delta) + ) + elif args.early_stop_monitor == "val_loss": + monitor_value = val_metrics["loss"] + improved = ( + np.isfinite(monitor_value) + and ( + best_monitor_value is None + or monitor_value < (best_monitor_value - args.early_stop_min_delta) + ) + ) + elif args.early_stop_monitor == "val_dice": + monitor_value = val_metrics["dice"] + improved = ( + np.isfinite(monitor_value) + and ( + best_monitor_value is None + or monitor_value > (best_monitor_value + args.early_stop_min_delta) + ) + ) + else: # val_iou + monitor_value = val_metrics["iou"] + improved = ( + np.isfinite(monitor_value) + and ( + best_monitor_value is None + or monitor_value > (best_monitor_value + args.early_stop_min_delta) + ) + ) + + if improved: + best_monitor_value = float(monitor_value) + early_stop_wait = 0 + print( + f"Early-stop monitor improved: {args.early_stop_monitor}={float(monitor_value):.6f}" + ) + with tb_writer.as_default(): + tf.summary.scalar("early_stop/monitor_value", float(monitor_value), step=epoch) + tf.summary.scalar("early_stop/wait", float(early_stop_wait), step=epoch) + else: + early_stop_wait += 1 + shown = float(monitor_value) if np.isfinite(monitor_value) else float("nan") + print( + f"Early-stop monitor stalled: {args.early_stop_monitor}={shown:.6f} " + f"(wait {early_stop_wait}/{args.early_stop_patience})" + ) + with tb_writer.as_default(): + tf.summary.scalar("early_stop/monitor_value", shown, step=epoch) + tf.summary.scalar("early_stop/wait", float(early_stop_wait), step=epoch) + if early_stop_wait >= args.early_stop_patience: + print( + f"Early stopping triggered at epoch {epoch} " + f"on {args.early_stop_monitor} (patience={args.early_stop_patience})." + ) + break + tb_writer.flush() + + if args.output_model_dir.exists(): + if not args.overwrite_output: + raise FileExistsError( + f"Output directory exists: {args.output_model_dir}. " + "Use --overwrite-output to replace it." + ) + shutil.rmtree(args.output_model_dir) + args.output_model_dir.mkdir(parents=True, exist_ok=True) + + tf.saved_model.save(model, str(args.output_model_dir)) + test_metrics = ( + _evaluate( + model=model, + ds=test_ds, + lumen_class=lumen_class, + pos_weight=pos_weight, + dice_weight=args.dice_weight, + ) + if test_ds is not None + else {"loss": float("nan"), "bce": float("nan"), "dice_loss": float("nan"), "iou": float("nan"), "dice": float("nan")} + ) + with tb_writer.as_default(): + tf.summary.scalar("test/loss", test_metrics["loss"], step=len(history)) + tf.summary.scalar("test/bce", test_metrics["bce"], step=len(history)) + tf.summary.scalar("test/dice_loss", test_metrics["dice_loss"], step=len(history)) + tf.summary.scalar("test/iou", test_metrics["iou"], step=len(history)) + tf.summary.scalar("test/dice", test_metrics["dice"], step=len(history)) + tb_writer.flush() + tb_writer.close() + summary_path = args.output_model_dir / "finetune_summary.json" + summary = { + "base_model_dir": str(args.model_dir), + "output_model_dir": str(args.output_model_dir), + "frame_bank_root": str(args.frame_bank_root), + "num_annotations": int(len(annotations)), + "num_samples": int(len(kept_annotations)), + "num_train_samples": int(len(train_images)), + "num_val_samples": int(len(val_images)), + "num_test_samples": int(len(test_images)), + "split_summary": split_stats, + "lumen_class": int(lumen_class), + "selected_trainable_variables": [v.name for v in trainable_vars], + "learning_rate": float(args.learning_rate), + "epochs": int(args.epochs), + "batch_size": int(args.batch_size), + "pos_weight": float(pos_weight), + "dice_weight": float(args.dice_weight), + "best_val_iou": float(best_val_iou), + "early_stop_monitor": args.early_stop_monitor, + "early_stop_patience": int(args.early_stop_patience), + "early_stop_min_delta": float(args.early_stop_min_delta), + "best_monitor_value": None if best_monitor_value is None else float(best_monitor_value), + "tensorboard_run_dir": str(tb_run_dir), + "final_test_metrics": test_metrics, + "history": history, + } + with summary_path.open("w", encoding="utf-8") as fp: + json.dump(summary, fp, indent=2) + + print(f"Saved fine-tuned model: {args.output_model_dir}") + print(f"Saved training summary: {summary_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/lumen/identify_lumen_class.py b/scripts/finetune/lumen/identify_lumen_class.py new file mode 100644 index 0000000000000000000000000000000000000000..818b77cb17dd55e088788e6f1b01adc74b237f10 --- /dev/null +++ b/scripts/finetune/lumen/identify_lumen_class.py @@ -0,0 +1,200 @@ +#!/usr/bin/env python3 +"""Identify which output class in the SavedModel corresponds to lumen.""" + +from __future__ import annotations + +import argparse +import json +import math +from pathlib import Path + +import numpy as np +import tensorflow as tf + +from deepivus.config import resolve_lumen_model_dir +from scripts.finetune.shared.common import ( + group_by_dicom, + load_lumen_annotations, + load_preprocessed_stack, + polygon_to_mask, +) + +IMG_MEAN = tf.constant([60.3486], dtype=tf.float32) + + +def _extract_logits(model_output: object) -> tf.Tensor: + if isinstance(model_output, dict): + if not model_output: + raise RuntimeError("Model returned an empty dict output.") + return next(iter(model_output.values())) + if isinstance(model_output, (tuple, list)): + if not model_output: + raise RuntimeError("Model returned an empty sequence output.") + return model_output[0] + if tf.is_tensor(model_output): + return model_output + raise RuntimeError(f"Unsupported model output type: {type(model_output)!r}") + + +def _prepare_batch(images: np.ndarray) -> tf.Tensor: + batch = tf.convert_to_tensor(images) + batch = tf.cast(batch, dtype=tf.float32) - IMG_MEAN + if len(batch.shape) < 4: + batch = tf.expand_dims(batch, axis=3) + if batch.shape[-1] != 3: + batch = tf.tile(batch, [1, 1, 1, 3]) + return batch + + +def _sample_annotations(annotations: list, max_samples: int | None, seed: int) -> list: + if max_samples is None or max_samples <= 0 or len(annotations) <= max_samples: + return annotations + rng = np.random.default_rng(seed) + picks = rng.choice(len(annotations), size=max_samples, replace=False) + return [annotations[int(i)] for i in sorted(picks.tolist())] + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--model-dir", + type=Path, + default=resolve_lumen_model_dir(), + help="Path to TF SavedModel directory.", + ) + parser.add_argument( + "--frame-bank-root", + type=Path, + default=Path("evals/frame_bank_merged"), + help="Path containing eval frame-bank JSONL files.", + ) + parser.add_argument("--batch-size", type=int, default=8, help="Inference batch size over annotated frames.") + parser.add_argument("--diameter", type=int, default=67, help="Center-circle preprocessing diameter.") + parser.add_argument("--max-samples", type=int, default=0, help="Optional cap on # annotated frames (0 = all).") + parser.add_argument("--seed", type=int, default=7, help="Random seed used with --max-samples.") + parser.add_argument("--save-json", type=Path, default=None, help="Optional path to save full metrics JSON.") + args = parser.parse_args() + + annotations = load_lumen_annotations(args.frame_bank_root) + if not annotations: + raise RuntimeError(f"No lumen annotations found under: {args.frame_bank_root}") + annotations = _sample_annotations(annotations, args.max_samples, args.seed) + print(f"Loaded annotations: {len(annotations)}") + + model = tf.saved_model.load(str(args.model_dir)) + grouped = group_by_dicom(annotations) + + num_classes: int | None = None + sample_count = 0 + sum_iou: np.ndarray | None = None + global_intersection: np.ndarray | None = None + global_union: np.ndarray | None = None + inside_prob_sum: np.ndarray | None = None + outside_prob_sum: np.ndarray | None = None + inside_argmax_sum: np.ndarray | None = None + + for dicom_path, ann_list in grouped.items(): + stack = load_preprocessed_stack(dicom_path, diameter=args.diameter) + ann_list = sorted(ann_list, key=lambda ann: ann.frame_idx) + + num_batches = int(math.ceil(len(ann_list) / float(args.batch_size))) + for bidx in range(num_batches): + chunk = ann_list[bidx * args.batch_size : (bidx + 1) * args.batch_size] + frame_indices = [ann.frame_idx for ann in chunk] + image_batch = stack[frame_indices] + h, w = int(image_batch.shape[1]), int(image_batch.shape[2]) + + x_batch = _prepare_batch(image_batch) + logits = _extract_logits(model(x_batch, training=False)) + logits = tf.image.resize(logits, (h, w)) + probs = tf.nn.softmax(logits, axis=-1).numpy() + pred = np.argmax(probs, axis=-1) + + if num_classes is None: + num_classes = int(probs.shape[-1]) + sum_iou = np.zeros((num_classes,), dtype=np.float64) + global_intersection = np.zeros((num_classes,), dtype=np.float64) + global_union = np.zeros((num_classes,), dtype=np.float64) + inside_prob_sum = np.zeros((num_classes,), dtype=np.float64) + outside_prob_sum = np.zeros((num_classes,), dtype=np.float64) + inside_argmax_sum = np.zeros((num_classes,), dtype=np.float64) + + for local_idx, ann in enumerate(chunk): + gt_mask = polygon_to_mask(ann.lumen_x, ann.lumen_y, (h, w)) + if not np.any(gt_mask): + continue + + sample_count += 1 + for class_id in range(num_classes): + pred_mask = pred[local_idx] == class_id + inter = float(np.logical_and(pred_mask, gt_mask).sum()) + union = float(np.logical_or(pred_mask, gt_mask).sum()) + iou = inter / max(union, 1.0) + + sum_iou[class_id] += iou + global_intersection[class_id] += inter + global_union[class_id] += union + + inside_probs = probs[local_idx, :, :, class_id][gt_mask] + outside_probs = probs[local_idx, :, :, class_id][~gt_mask] + inside_prob_sum[class_id] += float(inside_probs.mean()) if inside_probs.size else 0.0 + outside_prob_sum[class_id] += float(outside_probs.mean()) if outside_probs.size else 0.0 + + inside_argmax_sum[class_id] += float((pred[local_idx][gt_mask] == class_id).mean()) + + if sample_count == 0 or num_classes is None: + raise RuntimeError("No valid annotated samples were processed.") + + mean_iou = sum_iou / float(sample_count) + global_iou = global_intersection / np.maximum(global_union, 1.0) + mean_inside_prob = inside_prob_sum / float(sample_count) + mean_outside_prob = outside_prob_sum / float(sample_count) + mean_inside_argmax = inside_argmax_sum / float(sample_count) + + class_rows = [] + for class_id in range(num_classes): + class_rows.append( + { + "class_id": class_id, + "mean_iou_vs_lumen": float(mean_iou[class_id]), + "global_iou_vs_lumen": float(global_iou[class_id]), + "mean_prob_inside_lumen": float(mean_inside_prob[class_id]), + "mean_prob_outside_lumen": float(mean_outside_prob[class_id]), + "mean_argmax_fraction_inside_lumen": float(mean_inside_argmax[class_id]), + } + ) + + ranked = sorted(class_rows, key=lambda row: row["mean_iou_vs_lumen"], reverse=True) + best = ranked[0] + + print("") + print(f"Processed samples: {sample_count}") + print("Class ranking by mean IoU with annotated lumen:") + for row in ranked: + print( + f" class={row['class_id']} " + f"mean_iou={row['mean_iou_vs_lumen']:.4f} " + f"global_iou={row['global_iou_vs_lumen']:.4f} " + f"inside_prob={row['mean_prob_inside_lumen']:.4f} " + f"inside_argmax={row['mean_argmax_fraction_inside_lumen']:.4f}" + ) + + print("") + print(f"Recommended lumen class: {best['class_id']}") + + if args.save_json is not None: + args.save_json.parent.mkdir(parents=True, exist_ok=True) + payload = { + "model_dir": str(args.model_dir), + "frame_bank_root": str(args.frame_bank_root), + "sample_count": int(sample_count), + "recommended_lumen_class": int(best["class_id"]), + "ranked_classes": ranked, + } + with args.save_json.open("w", encoding="utf-8") as fp: + json.dump(payload, fp, indent=2) + print(f"Saved metrics JSON: {args.save_json}") + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/lumen/run_roboflow_grid_inference.py b/scripts/finetune/lumen/run_roboflow_grid_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..6fccc18de15844c38ec61ba91810b698b627dd27 --- /dev/null +++ b/scripts/finetune/lumen/run_roboflow_grid_inference.py @@ -0,0 +1,214 @@ +#!/usr/bin/env python3 +"""Run lumen + bifurcation inference on Roboflow JPGs and export a 4x4 overlay grid.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np +from PIL import Image, ImageDraw + +from deepivus.config import resolve_bifurcation_threshold +from deepivus.inference import predict, predict_bifurcation +from deepivus.io.exports import get_contours +from scripts.finetune.shared.common import polygon_to_mask + + +def _load_images(images_dir: Path) -> tuple[list[Path], np.ndarray]: + paths = sorted(images_dir.glob("*.jpg")) + if len(paths) != 16: + raise RuntimeError(f"Expected exactly 16 JPGs in {images_dir}, found {len(paths)}") + + arr = [] + for path in paths: + img = Image.open(path).convert("L") + arr.append(np.asarray(img, dtype=np.uint8)) + + shapes = {x.shape for x in arr} + if len(shapes) != 1: + raise RuntimeError(f"All images must have the same size for a fixed grid. Found shapes: {sorted(shapes)}") + return paths, np.stack(arr, axis=0) + + +def _draw_closed_contour(draw: ImageDraw.ImageDraw, x: list[float], y: list[float], color: tuple[int, int, int]) -> None: + if x is None or y is None: + return + if len(x) == 0 or len(y) == 0 or len(x) != len(y): + return + pts = [(float(x[i]), float(y[i])) for i in range(len(x))] + if len(pts) <= 1: + return + pts.append(pts[0]) + draw.line(pts, fill=color, width=2) + + +def _overlay_frame( + gray: np.ndarray, + pred_lumen_x: list[float], + pred_lumen_y: list[float], + gt_lumen_x: list[float] | None = None, + gt_lumen_y: list[float] | None = None, +) -> Image.Image: + frame = Image.fromarray(gray, mode="L").convert("RGB") + draw = ImageDraw.Draw(frame) + # Prediction: green, Ground-truth annotation: cyan + _draw_closed_contour(draw, pred_lumen_x, pred_lumen_y, color=(0, 255, 0)) + if gt_lumen_x is not None and gt_lumen_y is not None: + _draw_closed_contour(draw, gt_lumen_x, gt_lumen_y, color=(0, 220, 255)) + return frame + + +def _load_lumen_annotations(annotations_jsonl: Path) -> dict[str, tuple[np.ndarray, np.ndarray]]: + if not annotations_jsonl.exists(): + return {} + out: dict[str, tuple[np.ndarray, np.ndarray]] = {} + with annotations_jsonl.open("r", encoding="utf-8") as fp: + for raw in fp: + line = raw.strip() + if not line: + continue + rec = json.loads(line) + if rec.get("record_type") != "frame": + continue + lumen = rec.get("lumen", {}) + xs = np.asarray(lumen.get("x", []), dtype=np.float32) + ys = np.asarray(lumen.get("y", []), dtype=np.float32) + if xs.size < 3 or ys.size < 3 or xs.size != ys.size: + continue + fname = rec.get("filename") + if not fname: + img_path = rec.get("image_path") + fname = Path(img_path).name if img_path else None + if fname: + out[str(fname)] = (xs, ys) + return out + + +def _dice_iou(pred_mask: np.ndarray, gt_mask: np.ndarray) -> tuple[float, float]: + pred = pred_mask.astype(bool) + gt = gt_mask.astype(bool) + inter = float(np.logical_and(pred, gt).sum()) + union = float(np.logical_or(pred, gt).sum()) + pred_sum = float(pred.sum()) + gt_sum = float(gt.sum()) + iou = inter / max(union, 1.0) + dice = (2.0 * inter) / max(pred_sum + gt_sum, 1.0) + return dice, iou + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--images-dir", type=Path, default=Path("data/roboflow")) + parser.add_argument("--output-dir", type=Path, default=Path("output/roboflow_inference")) + parser.add_argument( + "--annotations-jsonl", + type=Path, + default=Path("evals/roboflow_bifurcation_annotations.jsonl"), + help="Optional JSONL with lumen polygons for Dice/IoU metrics.", + ) + parser.add_argument("--threshold", type=float, default=None, help="Optional bifurcation threshold override.") + args = parser.parse_args() + + image_paths, images = _load_images(args.images_dir) + threshold = args.threshold if args.threshold is not None else resolve_bifurcation_threshold(default=0.5) + + masks, _, class_conf = predict(images, return_confidence=True, return_class_confidence=True) + lumen_labels = np.where(masks == 1, 1, 0).astype(np.uint8) + _, _, lumen, _ = get_contours(lumen_labels, [0.5, 1.5], images.shape[1:3]) + + bif_probs, bif_labels = predict_bifurcation(images, threshold=threshold) + gt_lumen = _load_lumen_annotations(args.annotations_jsonl) + + overlays = [] + rows = [] + dice_values = [] + iou_values = [] + for i, img_path in enumerate(image_paths): + gt_x = None + gt_y = None + if img_path.name in gt_lumen: + gt_x, gt_y = gt_lumen[img_path.name] + rgb = _overlay_frame(images[i], lumen[0][i], lumen[1][i], gt_x, gt_y) + overlays.append(rgb) + pred_lumen_mask = lumen_labels[i] == 1 + + dice = None + iou = None + if img_path.name in gt_lumen: + gt_x, gt_y = gt_lumen[img_path.name] + gt_mask = polygon_to_mask(gt_x, gt_y, image_shape=images[i].shape) + d, j = _dice_iou(pred_lumen_mask, gt_mask) + dice = float(d) + iou = float(j) + dice_values.append(dice) + iou_values.append(iou) + + rows.append( + { + "index": i, + "filename": img_path.name, + "image_path": str(img_path.resolve()), + "lumen_confidence": None if np.isnan(class_conf["lumen"][i]) else float(class_conf["lumen"][i]), + "bifurcation_probability": float(bif_probs[i]), + "bifurcation": bool(bif_labels[i]), + "threshold": float(threshold), + "dice": dice, + "iou": iou, + } + ) + + tile_h, tile_w = int(images.shape[1]), int(images.shape[2]) + label_h = 34 + grid = Image.new("RGB", (4 * tile_w, 4 * (tile_h + label_h)), color=(0, 0, 0)) + grid_draw = ImageDraw.Draw(grid) + + for i in range(16): + row_idx = i // 4 + col_idx = i % 4 + ox = col_idx * tile_w + oy = row_idx * (tile_h + label_h) + grid.paste(overlays[i], (ox, oy)) + tag = "Branch" if rows[i]["bifurcation"] else "Non-branch" + lumen_conf = rows[i]["lumen_confidence"] + lumen_conf_txt = "nan" if lumen_conf is None else f"{float(lumen_conf):.2f}" + label = ( + f"{tag} p={rows[i]['bifurcation_probability']:.2f} | " + f"lumen_conf={lumen_conf_txt}" + ) + grid_draw.rectangle([(ox, oy + tile_h), (ox + tile_w, oy + tile_h + label_h)], fill=(0, 0, 0)) + grid_draw.text((ox + 4, oy + tile_h + 8), label, fill=(255, 255, 255)) + + args.output_dir.mkdir(parents=True, exist_ok=True) + grid_path = args.output_dir / "roboflow_4x4_lumen_bifurcation_grid.png" + grid.save(grid_path) + + report_path = args.output_dir / "roboflow_inference_report.json" + with report_path.open("w", encoding="utf-8") as fp: + json.dump( + { + "images_dir": str(args.images_dir.resolve()), + "num_images": len(rows), + "threshold": float(threshold), + "annotations_jsonl": str(args.annotations_jsonl.resolve()), + "num_images_with_lumen_gt": int(len(dice_values)), + "mean_dice": None if not dice_values else float(np.mean(dice_values)), + "mean_iou": None if not iou_values else float(np.mean(iou_values)), + "grid_png": str(grid_path), + "predictions": rows, + }, + fp, + indent=2, + ) + + print(f"Saved grid: {grid_path}") + print(f"Saved report: {report_path}") + if dice_values: + print(f"Mean Dice={float(np.mean(dice_values)):.4f} Mean IoU={float(np.mean(iou_values)):.4f}") + else: + print("No valid lumen polygons found in annotation JSONL; Dice/IoU skipped.") + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/lumen/run_single_dicom_inference.py b/scripts/finetune/lumen/run_single_dicom_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..7f79196df832920e2bf53bb91391c69ff81f2ba2 --- /dev/null +++ b/scripts/finetune/lumen/run_single_dicom_inference.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python3 +"""Run inference on one DICOM using the fine-tuned lumen model. + +Defaults are intentionally simple: +- model: models/standalone/lumen +- dicom: FILE00005.dcm (or FILE0005.dcm fallback) under data/* + +Outputs: +- output/single_inference/_contours.jsonl +- output/single_inference/_overlay.mp4 +- output/single_inference/_summary.json +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path + +import numpy as np +import tensorflow as tf + +REPO_ROOT = Path(__file__).resolve().parents[2] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from deepivus.config import resolve_bifurcation_threshold, resolve_lumen_model_dir +from deepivus.inference import predict_bifurcation +from deepivus.io.dicom import read_dicom +from deepivus.io.exports import get_contours, write_jsonl +from deepivus.processing.preprocessing import apply_center_black_circle +from deepivus.visualization.video import write_overlay_video, write_overlay_video_with_bifurcation_flags + +IMG_MEAN = tf.constant([60.3486], dtype=tf.float32) +DEFAULT_MODEL_DIR = resolve_lumen_model_dir() +DEFAULT_OUTPUT_DIR = REPO_ROOT / "output" / "single_inference" +DEFAULT_DICOM_CANDIDATES = [ + REPO_ROOT / "data" / "paul" / "FILE00005.dcm", + REPO_ROOT / "data" / "paul" / "FILE0005.dcm", + REPO_ROOT / "data" / "bifurcation" / "FILE00005.dcm", + REPO_ROOT / "data" / "bifurcation" / "FILE0005.dcm", +] + + +def _resolve_default_dicom() -> Path: + for path in DEFAULT_DICOM_CANDIDATES: + if path.exists(): + return path + raise FileNotFoundError( + "Could not find default DICOM. Checked: " + + ", ".join(str(p) for p in DEFAULT_DICOM_CANDIDATES) + ) + + +def _extract_logits(model_output: object) -> tf.Tensor: + if isinstance(model_output, dict): + if not model_output: + raise RuntimeError("Model returned empty dict output.") + return next(iter(model_output.values())) + if isinstance(model_output, (tuple, list)): + if not model_output: + raise RuntimeError("Model returned empty sequence output.") + return model_output[0] + if tf.is_tensor(model_output): + return model_output + raise RuntimeError(f"Unsupported model output type: {type(model_output)!r}") + + +def _prepare_batch(images: np.ndarray) -> tf.Tensor: + x = tf.convert_to_tensor(images) + x = tf.cast(x, tf.float32) - IMG_MEAN + if len(x.shape) < 4: + x = tf.expand_dims(x, axis=3) + if x.shape[-1] != 3: + x = tf.tile(x, [1, 1, 1, 3]) + return x + + +def _predict_labels_and_lumen_conf( + model: tf.types.experimental.TrackableResource, + images: np.ndarray, + lumen_class: int = 1, + batch_size: int = 16, +) -> tuple[np.ndarray, np.ndarray]: + preds = [] + lumen_conf = [] + n = images.shape[0] + for start in range(0, n, batch_size): + end = min(start + batch_size, n) + batch = _prepare_batch(images[start:end]) + logits = _extract_logits(model(batch, training=False)) + logits = tf.image.resize(logits, (images.shape[1], images.shape[2])) + probs = tf.nn.softmax(logits, axis=-1) + pred = tf.argmax(probs, axis=-1, output_type=tf.int32).numpy() + preds.append(pred) + + pred_lumen = tf.equal(tf.convert_to_tensor(pred), lumen_class) + lumen_probs = probs[..., lumen_class] + lumen_sum = tf.reduce_sum( + tf.where(pred_lumen, lumen_probs, tf.zeros_like(lumen_probs)), axis=[1, 2] + ) + lumen_count = tf.reduce_sum(tf.cast(pred_lumen, tf.float32), axis=[1, 2]) + frame_conf = tf.where( + lumen_count > 0, + lumen_sum / lumen_count, + tf.constant(np.nan, dtype=tf.float32), + ).numpy() + lumen_conf.append(frame_conf) + + return np.concatenate(preds, axis=0), np.concatenate(lumen_conf, axis=0) + + +def _empty_contours(num_frames: int) -> list[list[list[float]]]: + return [[[] for _ in range(num_frames)], [[] for _ in range(num_frames)]] + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument( + "--dicom-path", + type=Path, + default=None, + help="Optional DICOM path. If omitted, auto-picks FILE00005/FILE0005 from data/*.", + ) + args = parser.parse_args() + + dicom_path = args.dicom_path if args.dicom_path is not None else _resolve_default_dicom() + if not dicom_path.exists(): + raise FileNotFoundError(f"DICOM not found: {dicom_path}") + if not DEFAULT_MODEL_DIR.exists(): + raise FileNotFoundError(f"Fine-tuned model directory not found: {DEFAULT_MODEL_DIR}") + + ds, images = read_dicom(str(dicom_path)) + images = apply_center_black_circle(images, diameter=67) + + model = tf.saved_model.load(str(DEFAULT_MODEL_DIR)) + pred_labels, lumen_conf = _predict_labels_and_lumen_conf(model=model, images=images, lumen_class=1) + + # Lumen-only: build contours from predicted lumen mask and keep plaque empty. + lumen_only_labels = np.where(pred_labels == 1, 1, 0).astype(np.uint8) + _, _, lumen, _ = get_contours(lumen_only_labels, [0.5, 1.5], images.shape[1:3]) + plaque = _empty_contours(num_frames=images.shape[0]) + + DEFAULT_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + stem = dicom_path.stem + output_prefix = str(DEFAULT_OUTPUT_DIR / stem) + contours_json = write_jsonl( + output_prefix=output_prefix, + lumen=lumen, + plaque=plaque, + lumen_conf=lumen_conf, + plaque_conf=None, + ) + fps = float(getattr(ds, "CineRate", 30.0)) if getattr(ds, "CineRate", None) else 30.0 + overlay_video = f"{output_prefix}_overlay.mp4" + write_overlay_video(images, lumen, plaque, overlay_video, fps=fps) + bif_threshold = resolve_bifurcation_threshold(default=0.5) + bif_probs, bif_labels = predict_bifurcation(images, threshold=bif_threshold) + bif_overlay_video = f"{output_prefix}_overlay_bifurcation.mp4" + write_overlay_video_with_bifurcation_flags( + images=images, + lumen=lumen, + plaque=plaque, + video_path=bif_overlay_video, + fps=fps, + bifurcation_probabilities=bif_probs, + bifurcation_labels=bif_labels, + threshold=bif_threshold, + ) + + summary = { + "dicom_path": str(dicom_path), + "model_dir": str(DEFAULT_MODEL_DIR), + "num_frames": int(images.shape[0]), + "image_shape": [int(images.shape[1]), int(images.shape[2])], + "lumen_class": 1, + "contours_jsonl": contours_json, + "overlay_video": overlay_video, + "overlay_bifurcation_video": bif_overlay_video, + "bifurcation_threshold": float(bif_threshold), + "fps": float(fps), + "mean_lumen_confidence": None + if not np.any(np.isfinite(lumen_conf)) + else float(np.nanmean(lumen_conf)), + } + summary_path = DEFAULT_OUTPUT_DIR / f"{stem}_summary.json" + with summary_path.open("w", encoding="utf-8") as fp: + json.dump(summary, fp, indent=2) + + print(f"DICOM: {dicom_path}") + print(f"Model: {DEFAULT_MODEL_DIR}") + print(f"Contours JSONL: {contours_json}") + print(f"Overlay video: {overlay_video}") + print(f"Summary JSON: {summary_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/lumen/run_test_inference.py b/scripts/finetune/lumen/run_test_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..15d16d66aca2a229e08ba4a1bdebf58ce69a416e --- /dev/null +++ b/scripts/finetune/lumen/run_test_inference.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python3 +"""Run lumen inference on the test split and report segmentation metrics.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np +import tensorflow as tf + +from scripts.finetune.shared.common import build_images_and_masks, load_lumen_annotations, load_split_ids + +IMG_MEAN = tf.constant([60.3486], dtype=tf.float32) + + +def _prepare_batch(images: np.ndarray) -> tf.Tensor: + x = tf.convert_to_tensor(images) + x = tf.cast(x, tf.float32) - IMG_MEAN + if len(x.shape) < 4: + x = tf.expand_dims(x, axis=3) + if x.shape[-1] != 3: + x = tf.tile(x, [1, 1, 1, 3]) + return x + + +def _extract_logits(model_output: object) -> tf.Tensor: + if isinstance(model_output, dict): + if not model_output: + raise RuntimeError("Model returned an empty dict output.") + return next(iter(model_output.values())) + if isinstance(model_output, (tuple, list)): + if not model_output: + raise RuntimeError("Model returned an empty sequence output.") + return model_output[0] + if tf.is_tensor(model_output): + return model_output + raise RuntimeError(f"Unsupported model output type: {type(model_output)!r}") + + +def _evaluate_model( + model_dir: Path, + images: np.ndarray, + masks: np.ndarray, + lumen_class: int, + batch_size: int, +) -> dict[str, float]: + model = tf.saved_model.load(str(model_dir)) + intersection = 0.0 + union = 0.0 + pred_sum = 0.0 + gt_sum = 0.0 + + n = images.shape[0] + for start in range(0, n, batch_size): + end = min(start + batch_size, n) + batch_images = images[start:end] + batch_gt = masks[start:end] > 0.5 + + x_batch = _prepare_batch(batch_images) + logits = _extract_logits(model(x_batch, training=False)) + logits = tf.image.resize(logits, (batch_gt.shape[1], batch_gt.shape[2])) + pred = tf.argmax(logits, axis=-1).numpy() == int(lumen_class) + + intersection += float(np.logical_and(pred, batch_gt).sum()) + union += float(np.logical_or(pred, batch_gt).sum()) + pred_sum += float(pred.sum()) + gt_sum += float(batch_gt.sum()) + + iou = intersection / max(union, 1.0) + dice = (2.0 * intersection) / max(pred_sum + gt_sum, 1.0) + return { + "iou": float(iou), + "dice": float(dice), + "intersection": float(intersection), + "union": float(union), + "pred_pixels": float(pred_sum), + "gt_pixels": float(gt_sum), + } + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--frame-bank-root", type=Path, default=Path("evals/frame_bank_merged")) + parser.add_argument( + "--split-json", + type=Path, + default=Path("evals/splits/ivus_split_merged_600.json"), + help="Split JSON with test IDs.", + ) + parser.add_argument("--model-dir", type=Path, required=True, help="Primary model (e.g. fine-tuned model).") + parser.add_argument("--compare-model-dir", type=Path, default=None, help="Optional second model (e.g. base model).") + parser.add_argument("--lumen-class", type=int, default=1, help="Class index for lumen.") + parser.add_argument("--diameter", type=int, default=67, help="Center-circle preprocessing diameter.") + parser.add_argument("--batch-size", type=int, default=8, help="Inference batch size.") + parser.add_argument( + "--output-json", + type=Path, + default=Path("output/test_inference_metrics.json"), + help="Output JSON path.", + ) + args = parser.parse_args() + + annotations = load_lumen_annotations(args.frame_bank_root) + if not annotations: + raise RuntimeError(f"No lumen annotations found under: {args.frame_bank_root}") + images, masks, kept_annotations = build_images_and_masks(annotations, diameter=args.diameter) + + split_ids = load_split_ids(args.split_json) + test_set = split_ids["test"] + test_idx = [i for i, ann in enumerate(kept_annotations) if ann.sample_id in test_set] + if not test_idx: + raise RuntimeError(f"No test samples found from split file: {args.split_json}") + + test_images = images[test_idx] + test_masks = masks[test_idx] + test_bif = sum(1 for i in test_idx if kept_annotations[i].bifurcation) + + primary = _evaluate_model( + model_dir=args.model_dir, + images=test_images, + masks=test_masks, + lumen_class=args.lumen_class, + batch_size=args.batch_size, + ) + + payload: dict[str, object] = { + "test_samples": int(len(test_idx)), + "test_bifurcation_true": int(test_bif), + "test_bifurcation_false": int(len(test_idx) - test_bif), + "lumen_class": int(args.lumen_class), + "model_dir": str(args.model_dir), + "model_metrics": primary, + } + + if args.compare_model_dir is not None: + compare = _evaluate_model( + model_dir=args.compare_model_dir, + images=test_images, + masks=test_masks, + lumen_class=args.lumen_class, + batch_size=args.batch_size, + ) + payload["compare_model_dir"] = str(args.compare_model_dir) + payload["compare_model_metrics"] = compare + payload["delta_vs_compare"] = { + "iou": float(primary["iou"] - compare["iou"]), + "dice": float(primary["dice"] - compare["dice"]), + } + + args.output_json.parent.mkdir(parents=True, exist_ok=True) + with args.output_json.open("w", encoding="utf-8") as fp: + json.dump(payload, fp, indent=2) + + print(f"Test samples: {len(test_idx)}") + print( + f"Model IoU={primary['iou']:.4f} Dice={primary['dice']:.4f} " + f"(saved: {args.output_json})" + ) + if args.compare_model_dir is not None: + delta = payload["delta_vs_compare"] + print( + "Delta vs compare model: " + f"IoU={delta['iou']:+.4f} Dice={delta['dice']:+.4f}" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/multitask/run_multitask_test_inference.py b/scripts/finetune/multitask/run_multitask_test_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..2e9f5e7617b72c9b8f4a340e499689a995c93608 --- /dev/null +++ b/scripts/finetune/multitask/run_multitask_test_inference.py @@ -0,0 +1,312 @@ +#!/usr/bin/env python3 +"""Run test-set inference for multitask model (lumen segmentation + bifurcation classification).""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np +import tensorflow as tf + +from deepivus.config import resolve_bifurcation_threshold_path +from scripts.finetune.shared.common import ( + load_bifurcation_annotations, + load_lumen_annotations, + load_preprocessed_stack, + load_split_ids, + polygon_to_mask, +) + +IMG_MEAN = tf.constant([60.3486], dtype=tf.float32) + + +def _extract_logits(model_output: object) -> tf.Tensor: + if isinstance(model_output, dict): + if not model_output: + raise RuntimeError("Model returned an empty dict output.") + return next(iter(model_output.values())) + if isinstance(model_output, (tuple, list)): + if not model_output: + raise RuntimeError("Model returned an empty sequence output.") + return model_output[0] + if tf.is_tensor(model_output): + return model_output + raise RuntimeError(f"Unsupported model output type: {type(model_output)!r}") + + +def _prepare_batch(images: np.ndarray) -> tf.Tensor: + x = tf.convert_to_tensor(images, dtype=tf.float32) + x = x - IMG_MEAN + x = tf.expand_dims(x, axis=-1) + x = tf.tile(x, [1, 1, 1, 3]) + return x + + +def _binary_logit_from_multiclass(logits: tf.Tensor, lumen_class: int) -> tf.Tensor: + num_classes = int(logits.shape[-1]) + if lumen_class < 0 or lumen_class >= num_classes: + raise ValueError(f"Invalid lumen_class={lumen_class} for num_classes={num_classes}") + positive = logits[..., lumen_class] + negatives = tf.concat([logits[..., :lumen_class], logits[..., lumen_class + 1 :]], axis=-1) + negative_logsumexp = tf.reduce_logsumexp(negatives, axis=-1) + return positive - negative_logsumexp + + +def _make_cls_head(num_seg_classes: int) -> tf.keras.Model: + inp = tf.keras.Input(shape=(None, None, num_seg_classes), name="seg_logits") + x = tf.keras.layers.GlobalAveragePooling2D()(inp) + x = tf.keras.layers.Dense(96, activation="relu")(x) + x = tf.keras.layers.Dropout(0.3)(x) + out = tf.keras.layers.Dense(1, activation="sigmoid", name="bifurcation_prob")(x) + return tf.keras.Model(inp, out, name="bifurcation_head") + + +def _build_arrays(annotations_bif, lumen_map: dict[str, object], diameter: int): + grouped: dict[Path, list] = {} + for ann in annotations_bif: + grouped.setdefault(ann.dicom_path, []).append(ann) + + images = [] + masks = [] + bif_labels = [] + has_mask = [] + sample_ids = [] + + for dicom_path, ann_list in grouped.items(): + stack = load_preprocessed_stack(dicom_path, diameter=diameter) + h, w = int(stack.shape[1]), int(stack.shape[2]) + for ann in ann_list: + fidx = ann.frame_idx + if fidx < 0 or fidx >= stack.shape[0]: + continue + sid = ann.sample_id + images.append(stack[fidx]) + bif_labels.append(1.0 if ann.bifurcation else 0.0) + sample_ids.append(sid) + + lann = lumen_map.get(sid) + if lann is None: + masks.append(np.zeros((h, w), dtype=np.float32)) + has_mask.append(0.0) + else: + m = polygon_to_mask(lann.lumen_x, lann.lumen_y, (h, w)).astype(np.float32) + masks.append(m) + has_mask.append(1.0 if np.any(m > 0.5) else 0.0) + + if not images: + raise RuntimeError("No valid samples produced from frame bank.") + + return ( + np.stack(images, axis=0), + np.stack(masks, axis=0), + np.asarray(bif_labels, dtype=np.float32), + np.asarray(has_mask, dtype=np.float32), + sample_ids, + ) + + +def _predict_probs( + base_model, + cls_head, + images: np.ndarray, + lumen_class: int, + batch_size: int, +) -> tuple[np.ndarray, np.ndarray]: + seg_probs = [] + cls_probs = [] + for start in range(0, len(images), batch_size): + end = min(start + batch_size, len(images)) + x = _prepare_batch(images[start:end]) + logits = _extract_logits(base_model(x, training=False)) + logits = tf.image.resize(logits, (images.shape[1], images.shape[2])) + + bin_logit = _binary_logit_from_multiclass(logits, lumen_class=lumen_class) + seg = tf.math.sigmoid(bin_logit).numpy() + cls = tf.reshape(cls_head(logits, training=False), [-1]).numpy() + + seg_probs.append(seg) + cls_probs.append(cls) + + return np.concatenate(seg_probs, axis=0), np.concatenate(cls_probs, axis=0) + + +def _seg_metrics(seg_probs: np.ndarray, masks: np.ndarray, has_mask: np.ndarray, threshold: float = 0.5) -> dict[str, float]: + pred = seg_probs >= threshold + gt = masks >= 0.5 + valid = has_mask > 0.5 + + inter = 0.0 + union = 0.0 + pred_sum = 0.0 + gt_sum = 0.0 + count = 0 + for i in range(pred.shape[0]): + if not valid[i]: + continue + pi = pred[i] + gi = gt[i] + inter += float(np.logical_and(pi, gi).sum()) + union += float(np.logical_or(pi, gi).sum()) + pred_sum += float(pi.sum()) + gt_sum += float(gi.sum()) + count += 1 + + return { + "seg_count": int(count), + "seg_iou": float(inter / max(union, 1.0)), + "seg_dice": float((2.0 * inter) / max(pred_sum + gt_sum, 1.0)), + } + + +def _cls_metrics(y_true: np.ndarray, y_prob: np.ndarray, threshold: float) -> dict[str, float]: + y_true_i = y_true.astype(np.int32) + y_pred = (y_prob >= threshold).astype(np.int32) + + tp = int(np.sum((y_pred == 1) & (y_true_i == 1))) + fp = int(np.sum((y_pred == 1) & (y_true_i == 0))) + fn = int(np.sum((y_pred == 0) & (y_true_i == 1))) + tn = int(np.sum((y_pred == 0) & (y_true_i == 0))) + + acc = float((tp + tn) / max(tp + tn + fp + fn, 1)) + prec = float(tp / max(tp + fp, 1)) + rec = float(tp / max(tp + fn, 1)) + f1 = float((2.0 * prec * rec) / max(prec + rec, 1e-12)) + + if y_true_i.size > 1 and len(np.unique(y_true_i)) > 1: + auc_metric = tf.keras.metrics.AUC(curve="ROC") + auc_metric.update_state(y_true, y_prob) + auc = float(auc_metric.result().numpy()) + else: + auc = float("nan") + + return { + "threshold": float(threshold), + "cls_accuracy": acc, + "cls_precision": prec, + "cls_recall": rec, + "cls_f1": f1, + "cls_auc": auc, + "tp": tp, + "fp": fp, + "fn": fn, + "tn": tn, + } + + +def _select_threshold(y_true_val: np.ndarray, y_prob_val: np.ndarray, metric: str) -> tuple[float, dict[str, float]]: + candidates = np.linspace(0.05, 0.95, 91) + best = None + for th in candidates: + row = _cls_metrics(y_true_val, y_prob_val, threshold=float(th)) + score = row[metric] + if best is None or score > best[metric]: + best = row + return float(best["threshold"]), best + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--frame-bank-root", type=Path, default=Path("evals/frame_bank_merged")) + parser.add_argument("--split-json", type=Path, default=Path("evals/splits/ivus_split_merged_600.json")) + parser.add_argument("--base-model-dir", type=Path, default=Path("models/multitask/lumen_multitask_base")) + parser.add_argument("--cls-head-path", type=Path, default=Path("models/multitask/bifurcation_head.keras")) + parser.add_argument("--lumen-class", type=int, default=1) + parser.add_argument("--diameter", type=int, default=67) + parser.add_argument("--batch-size", type=int, default=16) + parser.add_argument("--threshold", type=float, default=None, help="Fixed bif threshold; if omitted, selected on val.") + parser.add_argument("--select-metric", type=str, default="cls_f1", choices=["cls_f1", "cls_accuracy", "cls_precision", "cls_recall", "cls_auc"]) + parser.add_argument("--output-json", type=Path, default=Path("output/multitask_test_inference.json")) + parser.add_argument( + "--save-threshold", + action=argparse.BooleanOptionalAction, + default=True, + help="Persist selected threshold to /threshold.json for runtime inference.", + ) + args = parser.parse_args() + + bif_anns = load_bifurcation_annotations(args.frame_bank_root) + lumen_anns = load_lumen_annotations(args.frame_bank_root) + if not bif_anns: + raise RuntimeError(f"No bifurcation annotations under: {args.frame_bank_root}") + + split_ids = load_split_ids(args.split_json) + train_ids = split_ids["train"] + val_ids = split_ids["val"] + test_ids = split_ids["test"] + keep_ids = train_ids | val_ids | test_ids + + bif_anns = [a for a in bif_anns if a.sample_id in keep_ids] + lumen_map = {a.sample_id: a for a in lumen_anns} + images, masks, bif_labels, has_mask, sample_ids = _build_arrays(bif_anns, lumen_map=lumen_map, diameter=args.diameter) + + idx_val = np.asarray([i for i, sid in enumerate(sample_ids) if sid in val_ids], dtype=np.int64) + idx_test = np.asarray([i for i, sid in enumerate(sample_ids) if sid in test_ids], dtype=np.int64) + if len(idx_test) == 0: + raise RuntimeError("No test samples found in multitask arrays.") + + base_model = tf.saved_model.load(str(args.base_model_dir)) + cls_head = tf.keras.models.load_model(args.cls_head_path) + + seg_val, cls_val = (None, None) + if args.threshold is None: + if len(idx_val) == 0: + raise RuntimeError("Threshold selection requested but val split is empty.") + _, cls_val = _predict_probs(base_model, cls_head, images[idx_val], lumen_class=args.lumen_class, batch_size=args.batch_size) + selected_threshold, val_best = _select_threshold(bif_labels[idx_val], cls_val, metric=args.select_metric) + threshold_info = { + "method": "validation_sweep", + "metric": args.select_metric, + "selected_threshold": float(selected_threshold), + "val_best": val_best, + } + else: + selected_threshold = float(args.threshold) + threshold_info = {"method": "fixed", "selected_threshold": float(selected_threshold)} + + seg_test, cls_test = _predict_probs(base_model, cls_head, images[idx_test], lumen_class=args.lumen_class, batch_size=args.batch_size) + seg_metrics = _seg_metrics(seg_test, masks[idx_test], has_mask[idx_test], threshold=0.5) + cls_metrics = _cls_metrics(bif_labels[idx_test], cls_test, threshold=selected_threshold) + + payload = { + "base_model_dir": str(args.base_model_dir), + "cls_head_path": str(args.cls_head_path), + "split_json": str(args.split_json), + "num_test_samples": int(len(idx_test)), + "num_test_with_lumen": int(np.sum(has_mask[idx_test] > 0.5)), + "threshold_info": threshold_info, + "segmentation_metrics": seg_metrics, + "bifurcation_metrics": cls_metrics, + } + + args.output_json.parent.mkdir(parents=True, exist_ok=True) + with args.output_json.open("w", encoding="utf-8") as fp: + json.dump(payload, fp, indent=2) + + if args.save_threshold: + threshold_path = resolve_bifurcation_threshold_path(model_path=args.cls_head_path) + threshold_path.parent.mkdir(parents=True, exist_ok=True) + threshold_payload = { + "selected_threshold": float(selected_threshold), + "selection": threshold_info, + "source_split_json": str(args.split_json), + "source_model_path": str(args.cls_head_path), + "source_base_model_dir": str(args.base_model_dir), + } + with threshold_path.open("w", encoding="utf-8") as fp: + json.dump(threshold_payload, fp, indent=2) + + print(f"Test samples: {len(idx_test)} (with lumen gt: {int(np.sum(has_mask[idx_test] > 0.5))})") + print( + f"Seg Dice={seg_metrics['seg_dice']:.4f} IoU={seg_metrics['seg_iou']:.4f} | " + f"Cls Acc={cls_metrics['cls_accuracy']:.4f} AUC={cls_metrics['cls_auc']:.4f} F1={cls_metrics['cls_f1']:.4f} " + f"(th={selected_threshold:.3f})" + ) + print(f"Saved: {args.output_json}") + if args.save_threshold: + print(f"Saved threshold: {threshold_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/multitask/train_multitask_deepivus.py b/scripts/finetune/multitask/train_multitask_deepivus.py new file mode 100644 index 0000000000000000000000000000000000000000..987b9fc3f60ada302c179f41d0330e2edc6bbdd3 --- /dev/null +++ b/scripts/finetune/multitask/train_multitask_deepivus.py @@ -0,0 +1,664 @@ +#!/usr/bin/env python3 +"""Multitask fine-tuning: DeepIVUS lumen segmentation + bifurcation classification. + +Uses: +- all bifurcation-labeled samples for classification +- only lumen-polygon samples for segmentation loss (masked per-sample) + +Data are loaded on-the-fly from frame bank JSONL + source DICOMs. +""" + +from __future__ import annotations + +import argparse +import datetime +import json +import math +import re +from pathlib import Path + +import numpy as np +import tensorflow as tf +from PIL import Image + +from deepivus.config import resolve_lumen_model_dir +from scripts.finetune.shared.common import ( + load_bifurcation_annotations, + load_lumen_annotations, + load_preprocessed_stack, + load_split_ids, + polygon_to_mask, +) + +try: + from tqdm.auto import tqdm +except Exception: + tqdm = None + +IMG_MEAN = tf.constant([60.3486], dtype=tf.float32) + + +def _extract_logits(model_output: object) -> tf.Tensor: + if isinstance(model_output, dict): + if not model_output: + raise RuntimeError("Model returned an empty dict output.") + return next(iter(model_output.values())) + if isinstance(model_output, (tuple, list)): + if not model_output: + raise RuntimeError("Model returned an empty sequence output.") + return model_output[0] + if tf.is_tensor(model_output): + return model_output + raise RuntimeError(f"Unsupported model output type: {type(model_output)!r}") + + +def _prepare_batch(images: tf.Tensor) -> tf.Tensor: + x = tf.cast(images, tf.float32) - IMG_MEAN + if len(x.shape) < 4: + x = tf.expand_dims(x, axis=3) + if x.shape[-1] != 3: + x = tf.tile(x, [1, 1, 1, 3]) + return x + + +def _select_trainable_vars( + all_trainable: list[tf.Variable], + include_regex: str, + exclude_regex: str, + max_trainable: int, +) -> list[tf.Variable]: + include = re.compile(include_regex) if include_regex else None + exclude = re.compile(exclude_regex) if exclude_regex else None + selected: list[tf.Variable] = [] + for var in all_trainable: + name = var.name + if include is not None and not include.search(name): + continue + if exclude is not None and exclude.search(name): + continue + selected.append(var) + if max_trainable > 0 and len(selected) >= max_trainable: + break + return selected + + +def _binary_logit_from_multiclass(logits: tf.Tensor, lumen_class: int) -> tf.Tensor: + num_classes = int(logits.shape[-1]) + if lumen_class < 0 or lumen_class >= num_classes: + raise ValueError(f"Invalid lumen_class={lumen_class} for num_classes={num_classes}") + positive = logits[..., lumen_class] + negatives = tf.concat([logits[..., :lumen_class], logits[..., lumen_class + 1 :]], axis=-1) + negative_logsumexp = tf.reduce_logsumexp(negatives, axis=-1) + return positive - negative_logsumexp + + +def _augment_multitask_batch( + images: tf.Tensor, + masks: tf.Tensor, + bif_labels: tf.Tensor, + has_mask: tf.Tensor, + seed: int, +) -> tuple[tf.Tensor, tf.Tensor, tf.Tensor, tf.Tensor]: + stateless_seed = tf.convert_to_tensor([seed, 0], dtype=tf.int32) + flip_lr = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5 + images = tf.cond(flip_lr, lambda: tf.image.flip_left_right(images), lambda: images) + masks = tf.cond(flip_lr, lambda: tf.image.flip_left_right(masks[..., tf.newaxis]), lambda: masks[..., tf.newaxis]) + + stateless_seed = tf.convert_to_tensor([seed, 1], dtype=tf.int32) + flip_ud = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5 + images = tf.cond(flip_ud, lambda: tf.image.flip_up_down(images), lambda: images) + masks = tf.cond(flip_ud, lambda: tf.image.flip_up_down(masks), lambda: masks) + + stateless_seed = tf.convert_to_tensor([seed, 2], dtype=tf.int32) + k = tf.random.stateless_uniform([], seed=stateless_seed, minval=0, maxval=4, dtype=tf.int32) + images = tf.image.rot90(images, k=k) + masks = tf.image.rot90(masks, k=k) + + shx = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 30], dtype=tf.int32), minval=-0.08, maxval=0.08) + shy = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 31], dtype=tf.int32), minval=-0.08, maxval=0.08) + p0 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 32], dtype=tf.int32), minval=-8e-4, maxval=8e-4) + p1 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 33], dtype=tf.int32), minval=-8e-4, maxval=8e-4) + base_t = tf.stack([ + tf.constant(1.0, tf.float32), shx, tf.constant(0.0, tf.float32), + shy, tf.constant(1.0, tf.float32), tf.constant(0.0, tf.float32), + p0, p1, + ]) + transforms = tf.tile(base_t[tf.newaxis, :], [tf.shape(images)[0], 1]) + out_shape = tf.shape(images)[1:3] + images = tf.raw_ops.ImageProjectiveTransformV3( + images=images, + transforms=transforms, + output_shape=out_shape, + interpolation="BILINEAR", + fill_mode="REFLECT", + fill_value=0.0, + ) + masks = tf.raw_ops.ImageProjectiveTransformV3( + images=masks, + transforms=transforms, + output_shape=out_shape, + interpolation="NEAREST", + fill_mode="REFLECT", + fill_value=0.0, + ) + + stateless_seed = tf.convert_to_tensor([seed, 3], dtype=tf.int32) + images = tf.image.stateless_random_brightness(images, max_delta=10.0, seed=stateless_seed) + stateless_seed = tf.convert_to_tensor([seed, 4], dtype=tf.int32) + images = tf.image.stateless_random_contrast(images, lower=0.9, upper=1.1, seed=stateless_seed) + images = tf.clip_by_value(images, -255.0, 255.0) + + return images, tf.squeeze(masks, axis=-1), bif_labels, has_mask + + +def _build_dataset( + images: np.ndarray, + masks: np.ndarray, + bif_labels: np.ndarray, + has_mask: np.ndarray, + batch_size: int, + shuffle: bool, + seed: int, + augment: bool, +) -> tf.data.Dataset: + ds = tf.data.Dataset.from_tensor_slices((images, masks, bif_labels, has_mask)) + if shuffle: + ds = ds.shuffle(buffer_size=len(images), seed=seed, reshuffle_each_iteration=True) + ds = ds.batch(batch_size) + + def _map_fn(batch_images, batch_masks, batch_bif, batch_has): + x = _prepare_batch(batch_images) + y_mask = tf.cast(batch_masks, tf.float32) + y_bif = tf.cast(batch_bif, tf.float32) + y_has = tf.cast(batch_has, tf.float32) + return x, y_mask, y_bif, y_has + + ds = ds.map(_map_fn, num_parallel_calls=tf.data.AUTOTUNE) + if augment: + ds = ds.enumerate().map( + lambda i, data: _augment_multitask_batch(data[0], data[1], data[2], data[3], seed=seed + tf.cast(i, tf.int32)), + num_parallel_calls=tf.data.AUTOTUNE, + ) + return ds.prefetch(tf.data.AUTOTUNE) + + +def _tile_to_uint8(tile: np.ndarray) -> np.ndarray: + x = np.asarray(tile, dtype=np.float32) + if x.ndim == 3 and x.shape[-1] > 1: + x = x[..., 0] + if not np.isfinite(x).any(): + return np.zeros_like(x, dtype=np.uint8) + mn = float(np.nanmin(x)) + mx = float(np.nanmax(x)) + if mx <= mn + 1e-6: + return np.clip(x, 0, 255).astype(np.uint8) + x = (x - mn) / (mx - mn) + return (x * 255.0).clip(0, 255).astype(np.uint8) + + +def _save_augment_grid_from_dataset(ds: tf.data.Dataset, out_path: Path) -> bool: + batch = None + for x, _, _, _ in ds.take(1): + batch = x.numpy() + if batch is None or batch.shape[0] == 0: + return False + n = min(16, int(batch.shape[0])) + h = int(batch.shape[1]) + w = int(batch.shape[2]) + grid = Image.new("L", (4 * w, 4 * h), color=0) + for i in range(n): + r = i // 4 + c = i % 4 + grid.paste(Image.fromarray(_tile_to_uint8(batch[i]), mode="L"), (c * w, r * h)) + out_path.parent.mkdir(parents=True, exist_ok=True) + grid.save(out_path) + return True + + +def _build_arrays(annotations_bif, lumen_map: dict[str, object], diameter: int): + grouped: dict[Path, list] = {} + for ann in annotations_bif: + grouped.setdefault(ann.dicom_path, []).append(ann) + + images = [] + masks = [] + bif_labels = [] + has_mask = [] + sample_ids = [] + + for dicom_path, ann_list in grouped.items(): + stack = load_preprocessed_stack(dicom_path, diameter=diameter) + h, w = int(stack.shape[1]), int(stack.shape[2]) + for ann in ann_list: + fidx = ann.frame_idx + if fidx < 0 or fidx >= stack.shape[0]: + continue + sid = ann.sample_id + images.append(stack[fidx]) + bif_labels.append(1.0 if ann.bifurcation else 0.0) + sample_ids.append(sid) + + lann = lumen_map.get(sid) + if lann is None: + masks.append(np.zeros((h, w), dtype=np.float32)) + has_mask.append(0.0) + else: + m = polygon_to_mask(lann.lumen_x, lann.lumen_y, (h, w)).astype(np.float32) + masks.append(m) + has_mask.append(1.0 if np.any(m > 0.5) else 0.0) + + if not images: + raise RuntimeError("No valid samples produced from frame bank.") + + return ( + np.stack(images, axis=0), + np.stack(masks, axis=0), + np.asarray(bif_labels, dtype=np.float32), + np.asarray(has_mask, dtype=np.float32), + sample_ids, + ) + + +def _make_cls_head(num_seg_classes: int) -> tf.keras.Model: + inp = tf.keras.Input(shape=(None, None, num_seg_classes), name="seg_logits") + x = tf.keras.layers.GlobalAveragePooling2D()(inp) + x = tf.keras.layers.Dense(96, activation="relu")(x) + x = tf.keras.layers.Dropout(0.3)(x) + out = tf.keras.layers.Dense(1, activation="sigmoid", name="bifurcation_prob")(x) + return tf.keras.Model(inp, out, name="bifurcation_head") + + +def _compute_metrics( + base_model, + cls_head, + ds: tf.data.Dataset, + lumen_class: int, + pos_weight: float, + seg_weight: float, + cls_weight: float, +) -> dict[str, float]: + seg_losses = [] + cls_losses = [] + total_losses = [] + seg_inter = 0.0 + seg_union = 0.0 + seg_pred_sum = 0.0 + seg_gt_sum = 0.0 + seg_count = 0.0 + + y_true = [] + y_prob = [] + + for x, y_mask, y_bif, y_has in ds: + logits = _extract_logits(base_model(x, training=False)) + resized_logits = tf.image.resize(logits, (tf.shape(y_mask)[1], tf.shape(y_mask)[2])) + + bin_logit = _binary_logit_from_multiclass(resized_logits, lumen_class=lumen_class) + bce_map = tf.nn.weighted_cross_entropy_with_logits(labels=y_mask, logits=bin_logit, pos_weight=pos_weight) + bce_per_sample = tf.reduce_mean(bce_map, axis=[1, 2]) + + probs_mask = tf.math.sigmoid(bin_logit) + inter = tf.reduce_sum(probs_mask * y_mask, axis=[1, 2]) + denom = tf.reduce_sum(probs_mask + y_mask, axis=[1, 2]) + dice_loss_per_sample = 1.0 - (2.0 * inter + 1e-6) / (denom + 1e-6) + seg_per_sample = bce_per_sample + 0.3 * dice_loss_per_sample + + valid = y_has > 0.5 + valid_count = tf.reduce_sum(tf.cast(valid, tf.float32)) + seg_loss = tf.where( + valid_count > 0, + tf.reduce_sum(tf.where(valid, seg_per_sample, tf.zeros_like(seg_per_sample))) / valid_count, + tf.constant(0.0, dtype=tf.float32), + ) + + bif_prob = cls_head(resized_logits, training=False) + bif_prob = tf.reshape(bif_prob, [-1]) + cls_loss = tf.reduce_mean(tf.keras.losses.binary_crossentropy(y_bif, bif_prob)) + + total = seg_weight * seg_loss + cls_weight * cls_loss + + seg_losses.append(float(seg_loss.numpy())) + cls_losses.append(float(cls_loss.numpy())) + total_losses.append(float(total.numpy())) + + y_true.append(y_bif.numpy().reshape(-1)) + y_prob.append(bif_prob.numpy().reshape(-1)) + + pred_mask = probs_mask >= 0.5 + gt_mask = y_mask >= 0.5 + valid_np = valid.numpy().astype(bool) + pred_np = pred_mask.numpy() + gt_np = gt_mask.numpy() + for i in range(pred_np.shape[0]): + if not valid_np[i]: + continue + pi = pred_np[i] + gi = gt_np[i] + seg_inter += float(np.logical_and(pi, gi).sum()) + seg_union += float(np.logical_or(pi, gi).sum()) + seg_pred_sum += float(pi.sum()) + seg_gt_sum += float(gi.sum()) + seg_count += 1.0 + + y_true_arr = np.concatenate(y_true) if y_true else np.zeros((0,), dtype=np.float32) + y_prob_arr = np.concatenate(y_prob) if y_prob else np.zeros((0,), dtype=np.float32) + y_pred_arr = (y_prob_arr >= 0.5).astype(np.int32) + y_true_int = y_true_arr.astype(np.int32) + tp = int(np.sum((y_pred_arr == 1) & (y_true_int == 1))) + fp = int(np.sum((y_pred_arr == 1) & (y_true_int == 0))) + fn = int(np.sum((y_pred_arr == 0) & (y_true_int == 1))) + tn = int(np.sum((y_pred_arr == 0) & (y_true_int == 0))) + cls_acc = float((tp + tn) / max(tp + tn + fp + fn, 1)) + cls_prec = float(tp / max(tp + fp, 1)) + cls_rec = float(tp / max(tp + fn, 1)) + cls_f1 = float((2.0 * cls_prec * cls_rec) / max(cls_prec + cls_rec, 1e-12)) + + if y_true_arr.size > 1 and len(np.unique(y_true_int)) > 1: + auc_metric = tf.keras.metrics.AUC(curve="ROC") + auc_metric.update_state(y_true_arr, y_prob_arr) + cls_auc = float(auc_metric.result().numpy()) + else: + cls_auc = float("nan") + + seg_iou = seg_inter / max(seg_union, 1.0) + seg_dice = (2.0 * seg_inter) / max(seg_pred_sum + seg_gt_sum, 1.0) + + return { + "total_loss": float(np.mean(total_losses)) if total_losses else float("nan"), + "seg_loss": float(np.mean(seg_losses)) if seg_losses else float("nan"), + "cls_loss": float(np.mean(cls_losses)) if cls_losses else float("nan"), + "seg_iou": float(seg_iou), + "seg_dice": float(seg_dice), + "seg_count": float(seg_count), + "cls_accuracy": cls_acc, + "cls_precision": cls_prec, + "cls_recall": cls_rec, + "cls_f1": cls_f1, + "cls_auc": cls_auc, + } + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--model-dir", type=Path, default=resolve_lumen_model_dir()) + parser.add_argument("--frame-bank-root", type=Path, default=Path("evals/frame_bank_merged")) + parser.add_argument("--split-json", type=Path, default=Path("evals/splits/ivus_split_merged_600.json")) + parser.add_argument("--output-dir", type=Path, default=Path("models/multitask")) + parser.add_argument("--lumen-class", type=int, default=1) + parser.add_argument("--epochs", type=int, default=50) + parser.add_argument("--batch-size", type=int, default=4) + parser.add_argument("--learning-rate", type=float, default=1e-5) + parser.add_argument("--seg-loss-weight", type=float, default=1.0) + parser.add_argument("--cls-loss-weight", type=float, default=1.0) + parser.add_argument("--augment", action="store_true") + parser.add_argument("--seed", type=int, default=7) + parser.add_argument("--diameter", type=int, default=67) + parser.add_argument("--clip-norm", type=float, default=1.0) + parser.add_argument("--include-var-regex", type=str, default=".*") + parser.add_argument("--exclude-var-regex", type=str, default="") + parser.add_argument("--max-trainable", type=int, default=0) + parser.add_argument("--early-stop-patience", type=int, default=10) + parser.add_argument("--early-stop-min-delta", type=float, default=1e-4) + parser.add_argument("--tb-logdir", type=Path, default=Path("output/tensorboard")) + parser.add_argument("--tb-run-name", type=str, default=None) + args = parser.parse_args() + + tf.random.set_seed(args.seed) + np.random.seed(args.seed) + + bif_anns = load_bifurcation_annotations(args.frame_bank_root) + lumen_anns = load_lumen_annotations(args.frame_bank_root) + if not bif_anns: + raise RuntimeError(f"No bifurcation annotations under: {args.frame_bank_root}") + + split_ids = load_split_ids(args.split_json) + train_ids = split_ids["train"] + val_ids = split_ids["val"] + test_ids = split_ids["test"] + + bif_anns = [a for a in bif_anns if a.sample_id in (train_ids | val_ids | test_ids)] + lumen_map = {a.sample_id: a for a in lumen_anns} + + images, masks, bif_labels, has_mask, sample_ids = _build_arrays( + bif_anns, + lumen_map=lumen_map, + diameter=args.diameter, + ) + + idx_train = np.asarray([i for i, sid in enumerate(sample_ids) if sid in train_ids], dtype=np.int64) + idx_val = np.asarray([i for i, sid in enumerate(sample_ids) if sid in val_ids], dtype=np.int64) + idx_test = np.asarray([i for i, sid in enumerate(sample_ids) if sid in test_ids], dtype=np.int64) + + if len(idx_train) == 0: + raise RuntimeError("Train split is empty.") + + tr_x, tr_m, tr_b, tr_h = images[idx_train], masks[idx_train], bif_labels[idx_train], has_mask[idx_train] + va_x, va_m, va_b, va_h = images[idx_val], masks[idx_val], bif_labels[idx_val], has_mask[idx_val] + te_x, te_m, te_b, te_h = images[idx_test], masks[idx_test], bif_labels[idx_test], has_mask[idx_test] + + print( + f"Samples train/val/test: {len(tr_x)}/{len(va_x)}/{len(te_x)} | " + f"lumen-supervised train/val/test: {int(np.sum(tr_h))}/{int(np.sum(va_h))}/{int(np.sum(te_h))}" + ) + + train_ds = _build_dataset(tr_x, tr_m, tr_b, tr_h, args.batch_size, shuffle=True, seed=args.seed, augment=args.augment) + val_ds = _build_dataset(va_x, va_m, va_b, va_h, args.batch_size, shuffle=False, seed=args.seed, augment=False) if len(va_x) else None + test_ds = _build_dataset(te_x, te_m, te_b, te_h, args.batch_size, shuffle=False, seed=args.seed, augment=False) if len(te_x) else None + + base_model = tf.saved_model.load(str(args.model_dir)) + all_trainable = list(getattr(base_model, "trainable_variables", [])) + if not all_trainable: + raise RuntimeError("Loaded base model has no trainable variables.") + base_vars = _select_trainable_vars( + all_trainable, + include_regex=args.include_var_regex, + exclude_regex=args.exclude_var_regex, + max_trainable=args.max_trainable, + ) + if not base_vars: + raise RuntimeError("No base-model trainable variables selected.") + + # Build class head on top of segmentation logits. + probe = _extract_logits(base_model(_prepare_batch(tf.convert_to_tensor(tr_x[:1], dtype=tf.float32)), training=False)) + num_seg_classes = int(probe.shape[-1]) + cls_head = _make_cls_head(num_seg_classes=num_seg_classes) + + pos_pixels = float(np.sum(tr_m > 0.5)) + total_pixels = float(np.prod(tr_m.shape)) + neg_pixels = max(total_pixels - pos_pixels, 1.0) + pos_weight = float(np.clip(neg_pixels / max(pos_pixels, 1.0), 1.0, 40.0)) + + optimizer = tf.keras.optimizers.Adam(learning_rate=args.learning_rate) + + args.output_dir.mkdir(parents=True, exist_ok=True) + run_name = args.tb_run_name or datetime.datetime.now().strftime("multitask_%Y%m%d_%H%M%S") + tb_run_dir = args.tb_logdir / run_name + tb_run_dir.mkdir(parents=True, exist_ok=True) + tb_writer = tf.summary.create_file_writer(str(tb_run_dir)) + print(f"TensorBoard logdir: {tb_run_dir}") + + if args.augment: + aug_preview_path = tb_run_dir / "augment_preview_4x4.png" + if _save_augment_grid_from_dataset(train_ds, aug_preview_path): + print(f"Saved augmentation preview: {aug_preview_path}") + + ckpt_dir = args.output_dir / "checkpoints" + ckpt_dir.mkdir(parents=True, exist_ok=True) + ckpt = tf.train.Checkpoint(base=base_model, cls_head=cls_head, optimizer=optimizer) + manager = tf.train.CheckpointManager(ckpt, directory=str(ckpt_dir), max_to_keep=1) + + steps_per_epoch = int(math.ceil(len(tr_x) / float(args.batch_size))) + best_val = None + wait = 0 + history = [] + + for epoch in range(1, args.epochs + 1): + if tqdm is not None: + pbar = tqdm(total=steps_per_epoch, desc=f"Epoch {epoch}/{args.epochs}", leave=False) + else: + pbar = None + + epoch_total = [] + epoch_seg = [] + epoch_cls = [] + + for step, (x, y_mask, y_bif, y_has) in enumerate(train_ds, start=1): + with tf.GradientTape() as tape: + logits = _extract_logits(base_model(x, training=True)) + resized_logits = tf.image.resize(logits, (tf.shape(y_mask)[1], tf.shape(y_mask)[2])) + + bin_logit = _binary_logit_from_multiclass(resized_logits, lumen_class=args.lumen_class) + bce_map = tf.nn.weighted_cross_entropy_with_logits(labels=y_mask, logits=bin_logit, pos_weight=pos_weight) + bce_per_sample = tf.reduce_mean(bce_map, axis=[1, 2]) + probs_mask = tf.math.sigmoid(bin_logit) + inter = tf.reduce_sum(probs_mask * y_mask, axis=[1, 2]) + denom = tf.reduce_sum(probs_mask + y_mask, axis=[1, 2]) + dice_loss_per_sample = 1.0 - (2.0 * inter + 1e-6) / (denom + 1e-6) + seg_per_sample = bce_per_sample + 0.3 * dice_loss_per_sample + + valid = y_has > 0.5 + valid_count = tf.reduce_sum(tf.cast(valid, tf.float32)) + seg_loss = tf.where( + valid_count > 0, + tf.reduce_sum(tf.where(valid, seg_per_sample, tf.zeros_like(seg_per_sample))) / valid_count, + tf.constant(0.0, dtype=tf.float32), + ) + + bif_prob = cls_head(resized_logits, training=True) + bif_prob = tf.reshape(bif_prob, [-1]) + cls_loss = tf.reduce_mean(tf.keras.losses.binary_crossentropy(y_bif, bif_prob)) + + total_loss = args.seg_loss_weight * seg_loss + args.cls_loss_weight * cls_loss + + train_vars = base_vars + cls_head.trainable_variables + grads = tape.gradient(total_loss, train_vars) + grad_var_pairs = [(g, v) for g, v in zip(grads, train_vars) if g is not None] + if args.clip_norm > 0 and grad_var_pairs: + grad_tensors = [g for g, _ in grad_var_pairs] + clipped, _ = tf.clip_by_global_norm(grad_tensors, clip_norm=args.clip_norm) + grad_var_pairs = list(zip(clipped, [v for _, v in grad_var_pairs])) + optimizer.apply_gradients(grad_var_pairs) + + epoch_total.append(float(total_loss.numpy())) + epoch_seg.append(float(seg_loss.numpy())) + epoch_cls.append(float(cls_loss.numpy())) + + if pbar is not None: + pbar.update(1) + pbar.set_postfix(loss=f"{epoch_total[-1]:.4f}", seg=f"{epoch_seg[-1]:.4f}", cls=f"{epoch_cls[-1]:.4f}") + elif step % 10 == 0: + print(f"Step {step}/{steps_per_epoch} loss={epoch_total[-1]:.4f} seg={epoch_seg[-1]:.4f} cls={epoch_cls[-1]:.4f}") + + if pbar is not None: + pbar.close() + + train_metrics = { + "total_loss": float(np.mean(epoch_total)) if epoch_total else float("nan"), + "seg_loss": float(np.mean(epoch_seg)) if epoch_seg else float("nan"), + "cls_loss": float(np.mean(epoch_cls)) if epoch_cls else float("nan"), + } + val_metrics = _compute_metrics( + base_model, + cls_head, + val_ds, + lumen_class=args.lumen_class, + pos_weight=pos_weight, + seg_weight=args.seg_loss_weight, + cls_weight=args.cls_loss_weight, + ) if val_ds is not None else {"total_loss": float("nan")} + + print( + f"Epoch {epoch}/{args.epochs} train_total={train_metrics['total_loss']:.4f} " + f"val_total={val_metrics.get('total_loss', float('nan')):.4f} " + f"val_seg_dice={val_metrics.get('seg_dice', float('nan')):.4f} " + f"val_cls_auc={val_metrics.get('cls_auc', float('nan')):.4f}" + ) + + history_row = {"epoch": epoch, **train_metrics} + history_row.update({f"val_{k}": v for k, v in val_metrics.items()}) + history.append(history_row) + + with tb_writer.as_default(): + for k, v in train_metrics.items(): + tf.summary.scalar(f"train/{k}", v, step=epoch) + for k, v in val_metrics.items(): + tf.summary.scalar(f"val/{k}", v, step=epoch) + + monitor = val_metrics.get("total_loss", float("nan")) + improved = np.isfinite(monitor) and (best_val is None or monitor < (best_val - args.early_stop_min_delta)) + if improved: + best_val = float(monitor) + wait = 0 + manager.save(checkpoint_number=epoch) + print(f"Saved best checkpoint at epoch {epoch} (val_total_loss={best_val:.6f})") + else: + wait += 1 + print(f"No val improvement: wait {wait}/{args.early_stop_patience}") + if wait >= args.early_stop_patience: + print("Early stopping triggered.") + break + + tb_writer.flush() + + if manager.latest_checkpoint: + ckpt.restore(manager.latest_checkpoint).expect_partial() + print(f"Restored best checkpoint: {manager.latest_checkpoint}") + + base_out = args.output_dir / "lumen_multitask_base" + cls_out = args.output_dir / "bifurcation_head.keras" + tf.saved_model.save(base_model, str(base_out)) + cls_head.save(str(cls_out)) + + test_metrics = _compute_metrics( + base_model, + cls_head, + test_ds, + lumen_class=args.lumen_class, + pos_weight=pos_weight, + seg_weight=args.seg_loss_weight, + cls_weight=args.cls_loss_weight, + ) if test_ds is not None else {} + + with tb_writer.as_default(): + for k, v in test_metrics.items(): + tf.summary.scalar(f"test/{k}", v, step=len(history)) + tb_writer.flush() + tb_writer.close() + + summary = { + "model_dir": str(args.model_dir), + "output_dir": str(args.output_dir), + "split_json": str(args.split_json), + "num_train": int(len(tr_x)), + "num_val": int(len(va_x)), + "num_test": int(len(te_x)), + "num_train_with_lumen": int(np.sum(tr_h > 0.5)), + "num_val_with_lumen": int(np.sum(va_h > 0.5)), + "num_test_with_lumen": int(np.sum(te_h > 0.5)), + "selected_trainable_variables": [v.name for v in base_vars], + "lumen_class": int(args.lumen_class), + "pos_weight": float(pos_weight), + "seg_loss_weight": float(args.seg_loss_weight), + "cls_loss_weight": float(args.cls_loss_weight), + "learning_rate": float(args.learning_rate), + "epochs": int(args.epochs), + "early_stop_patience": int(args.early_stop_patience), + "early_stop_min_delta": float(args.early_stop_min_delta), + "best_val_total_loss": None if best_val is None else float(best_val), + "tensorboard_run_dir": str(tb_run_dir), + "saved_base_model": str(base_out), + "saved_cls_head": str(cls_out), + "history": history, + "test_metrics": test_metrics, + } + summary_path = args.output_dir / "multitask_summary.json" + with summary_path.open("w", encoding="utf-8") as fp: + json.dump(summary, fp, indent=2) + + print(f"Saved multitask base model: {base_out}") + print(f"Saved multitask classifier head: {cls_out}") + print(f"Saved summary: {summary_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/shared/common.py b/scripts/finetune/shared/common.py new file mode 100644 index 0000000000000000000000000000000000000000..8afc2a1ba328429153a29f878d927fa866270674 --- /dev/null +++ b/scripts/finetune/shared/common.py @@ -0,0 +1,307 @@ +#!/usr/bin/env python3 +"""Shared utilities for lumen class discovery and fine-tuning scripts.""" + +from __future__ import annotations + +import json +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable + +import numpy as np +from matplotlib.path import Path as MplPath + +# common.py lives at scripts/finetune/shared/common.py, so repo root is parents[3]. +REPO_ROOT = Path(__file__).resolve().parents[3] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from deepivus.io.dicom import read_dicom +from deepivus.processing.preprocessing import apply_center_black_circle + + +@dataclass(frozen=True) +class LumenAnnotation: + """Single manually-annotated lumen polygon for one frame.""" + + bank_path: Path + group: str + dicom_path: Path + frame_idx: int + bifurcation: bool + lumen_x: np.ndarray + lumen_y: np.ndarray + + @property + def sample_id(self) -> str: + rel_bank = self.bank_path.resolve().relative_to(REPO_ROOT) + return f"{rel_bank.as_posix()}::{self.frame_idx}" + + +@dataclass(frozen=True) +class BifurcationAnnotation: + """Single frame-level bifurcation annotation.""" + + bank_path: Path + group: str + dicom_path: Path + frame_idx: int + bifurcation: bool + + @property + def sample_id(self) -> str: + rel_bank = self.bank_path.resolve().relative_to(REPO_ROOT) + return f"{rel_bank.as_posix()}::{self.frame_idx}" + + +def _read_jsonl(path: Path) -> list[dict]: + rows: list[dict] = [] + with path.open("r", encoding="utf-8") as fp: + for raw in fp: + line = raw.strip() + if not line: + continue + rows.append(json.loads(line)) + return rows + + +def _resolve_dicom_path(bank_path: Path, meta: dict) -> Path | None: + group = str(meta.get("group", bank_path.parent.name)) + dicom_raw = meta.get("dicom_path") + if dicom_raw: + dicom_path = Path(str(dicom_raw)) + if not dicom_path.is_absolute(): + dicom_path = (REPO_ROOT / dicom_path).resolve() + if dicom_path.exists(): + return dicom_path + + fallback = REPO_ROOT / "data" / group / f"{bank_path.stem}.dcm" + if fallback.exists(): + return fallback.resolve() + return None + + +def load_lumen_annotations(frame_bank_root: Path) -> list[LumenAnnotation]: + """Load all frame-bank lumen annotations with valid polygons.""" + annotations: list[LumenAnnotation] = [] + bank_files = sorted(frame_bank_root.glob("*/*.jsonl")) + for bank_path in bank_files: + rows = _read_jsonl(bank_path) + if not rows: + continue + meta = rows[0] + if meta.get("record_type") != "meta": + continue + + group = str(meta.get("group", bank_path.parent.name)) + dicom_path = _resolve_dicom_path(bank_path, meta) + if dicom_path is None: + continue + + for rec in rows: + if rec.get("record_type") != "frame": + continue + lumen = rec.get("lumen", {}) + xs = np.asarray(lumen.get("x", []), dtype=np.float32) + ys = np.asarray(lumen.get("y", []), dtype=np.float32) + if xs.size < 3 or ys.size < 3 or xs.size != ys.size: + continue + if not np.all(np.isfinite(xs)) or not np.all(np.isfinite(ys)): + continue + annotations.append( + LumenAnnotation( + bank_path=bank_path, + group=group, + dicom_path=dicom_path, + frame_idx=int(rec["frame"]), + bifurcation=bool(rec.get("bifurcation", False)), + lumen_x=xs, + lumen_y=ys, + ) + ) + return annotations + + +def load_bifurcation_annotations(frame_bank_root: Path) -> list[BifurcationAnnotation]: + """Load all frame-bank bifurcation labels, independent of lumen polygon presence.""" + annotations: list[BifurcationAnnotation] = [] + bank_files = sorted(frame_bank_root.glob("*/*.jsonl")) + for bank_path in bank_files: + rows = _read_jsonl(bank_path) + if not rows: + continue + meta = rows[0] + if meta.get("record_type") != "meta": + continue + group = str(meta.get("group", bank_path.parent.name)) + dicom_path = _resolve_dicom_path(bank_path, meta) + if dicom_path is None: + continue + for rec in rows: + if rec.get("record_type") != "frame": + continue + bif = rec.get("bifurcation") + if bif is None: + continue + annotations.append( + BifurcationAnnotation( + bank_path=bank_path, + group=group, + dicom_path=dicom_path, + frame_idx=int(rec["frame"]), + bifurcation=bool(bif), + ) + ) + return annotations + + +def polygon_to_mask(x_coords: np.ndarray, y_coords: np.ndarray, image_shape: tuple[int, int]) -> np.ndarray: + """Rasterize a polygon in image coordinates to a binary mask.""" + if x_coords.size < 3 or y_coords.size < 3: + return np.zeros(image_shape, dtype=bool) + + vertices = np.column_stack((x_coords.astype(np.float32), y_coords.astype(np.float32))) + polygon = MplPath(vertices, closed=True) + + h, w = image_shape + yy, xx = np.mgrid[0:h, 0:w] + points = np.column_stack((xx.ravel(), yy.ravel())) + mask = polygon.contains_points(points, radius=0.5).reshape((h, w)) + return mask + + +def group_by_dicom(annotations: Iterable[LumenAnnotation]) -> dict[Path, list[LumenAnnotation]]: + grouped: dict[Path, list[LumenAnnotation]] = {} + for ann in annotations: + grouped.setdefault(ann.dicom_path, []).append(ann) + return grouped + + +def load_preprocessed_stack(dicom_path: Path, diameter: int) -> np.ndarray: + """Load one DICOM stack and apply the same preprocessing as pipeline inference.""" + _, images = read_dicom(str(dicom_path)) + return apply_center_black_circle(images, diameter=diameter) + + +def build_images_and_masks( + annotations: list[LumenAnnotation], + diameter: int, +) -> tuple[np.ndarray, np.ndarray, list[LumenAnnotation]]: + """Materialize image and mask arrays for a list of annotations.""" + images_out: list[np.ndarray] = [] + masks_out: list[np.ndarray] = [] + kept: list[LumenAnnotation] = [] + + grouped = group_by_dicom(annotations) + for dicom_path, ann_list in grouped.items(): + stack = load_preprocessed_stack(dicom_path, diameter=diameter) + h, w = int(stack.shape[1]), int(stack.shape[2]) + for ann in ann_list: + if ann.frame_idx < 0 or ann.frame_idx >= int(stack.shape[0]): + continue + mask = polygon_to_mask(ann.lumen_x, ann.lumen_y, (h, w)) + if not np.any(mask): + continue + images_out.append(stack[ann.frame_idx]) + masks_out.append(mask.astype(np.float32)) + kept.append(ann) + + if not images_out: + raise RuntimeError("No usable annotations found after loading DICOM frames and rasterizing masks.") + + return np.stack(images_out, axis=0), np.stack(masks_out, axis=0), kept + + +def stratified_frame_split( + annotations: list[LumenAnnotation], + train_fraction: float, + val_fraction: float, + test_fraction: float, + seed: int, +) -> dict[str, list[int]]: + """Frame-level stratified split by bifurcation label.""" + total = train_fraction + val_fraction + test_fraction + if total <= 0: + raise ValueError("Split fractions must sum to a positive value.") + train_fraction /= total + val_fraction /= total + test_fraction /= total + + labels = np.asarray([1 if ann.bifurcation else 0 for ann in annotations], dtype=np.int32) + indices = np.arange(len(annotations), dtype=np.int64) + rng = np.random.default_rng(seed) + + train_ids: list[int] = [] + val_ids: list[int] = [] + test_ids: list[int] = [] + + for label in (0, 1): + cls_idx = indices[labels == label] + rng.shuffle(cls_idx) + n = len(cls_idx) + if n == 0: + continue + n_train = int(round(n * train_fraction)) + n_val = int(round(n * val_fraction)) + n_test = n - n_train - n_val + + # Keep at least one sample per split when class has enough samples. + if n >= 3: + n_train = max(1, n_train) + n_val = max(1, n_val) + n_test = max(1, n_test) + overflow = n_train + n_val + n_test - n + while overflow > 0: + if n_train >= n_val and n_train >= n_test and n_train > 1: + n_train -= 1 + elif n_val >= n_test and n_val > 1: + n_val -= 1 + elif n_test > 1: + n_test -= 1 + overflow -= 1 + + train_ids.extend(cls_idx[:n_train].tolist()) + val_ids.extend(cls_idx[n_train : n_train + n_val].tolist()) + test_ids.extend(cls_idx[n_train + n_val : n_train + n_val + n_test].tolist()) + + rng.shuffle(train_ids) + rng.shuffle(val_ids) + rng.shuffle(test_ids) + return {"train": train_ids, "val": val_ids, "test": test_ids} + + +def split_summary(annotations: list[LumenAnnotation], split: dict[str, list[int]]) -> dict[str, dict[str, int]]: + out: dict[str, dict[str, int]] = {} + for part, ids in split.items(): + bif = sum(1 for i in ids if annotations[i].bifurcation) + non_bif = len(ids) - bif + out[part] = { + "count": len(ids), + "bifurcation_true": bif, + "bifurcation_false": non_bif, + } + return out + + +def save_split_json(path: Path, annotations: list[LumenAnnotation], split: dict[str, list[int]], seed: int) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + payload = { + "seed": int(seed), + "summary": split_summary(annotations, split), + "splits": { + part: [annotations[i].sample_id for i in ids] for part, ids in split.items() + }, + } + with path.open("w", encoding="utf-8") as fp: + json.dump(payload, fp, indent=2) + + +def load_split_ids(path: Path) -> dict[str, set[str]]: + with path.open("r", encoding="utf-8") as fp: + payload = json.load(fp) + splits = payload.get("splits", {}) + out = {} + for part in ("train", "val", "test"): + out[part] = set(str(v) for v in splits.get(part, [])) + return out diff --git a/scripts/finetune/shared/preview_augmentations.py b/scripts/finetune/shared/preview_augmentations.py new file mode 100644 index 0000000000000000000000000000000000000000..8446a356fdfb66ab7c036487cbe2fe01c0168110 --- /dev/null +++ b/scripts/finetune/shared/preview_augmentations.py @@ -0,0 +1,205 @@ +#!/usr/bin/env python3 +"""Generate a 4x4 augmentation preview grid without training. + +Examples: + python -u scripts/finetune/shared/preview_augmentations.py \ + --image-path data/roboflow/frame_01_0001_001_png.rf.19fb74b147f4e2ea7aeeeba9a8f9bb60.jpg + + python -u scripts/finetune/shared/preview_augmentations.py \ + --mode bifurcation --image-path data/roboflow/frame_01_0001_001_png.rf.19fb74b147f4e2ea7aeeeba9a8f9bb60.jpg +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +import numpy as np +import tensorflow as tf +from PIL import Image + +IMG_MEAN = tf.constant([60.3486], dtype=tf.float32) + + +def _load_gray(image_path: Path) -> np.ndarray: + img = Image.open(image_path).convert("L") + return np.asarray(img, dtype=np.uint8) + + +def _prepare_lumen_input(gray: np.ndarray) -> tf.Tensor: + x = tf.convert_to_tensor(gray[None, ...], dtype=tf.float32) + x = x - IMG_MEAN + x = tf.expand_dims(x, axis=-1) + x = tf.tile(x, [1, 1, 1, 3]) + return x + + +def _prepare_bif_input(gray: np.ndarray, center_images: bool = False) -> tf.Tensor: + x = tf.convert_to_tensor(gray[None, ...], dtype=tf.float32) + if center_images: + x = x - IMG_MEAN + x = tf.expand_dims(x, axis=-1) + x = tf.tile(x, [1, 1, 1, 3]) + return x + + +def _augment_seg_batch(images: tf.Tensor, seed: int) -> tf.Tensor: + stateless_seed = tf.convert_to_tensor([seed, 0], dtype=tf.int32) + flip_lr = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5 + images = tf.cond(flip_lr, lambda: tf.image.flip_left_right(images), lambda: images) + + stateless_seed = tf.convert_to_tensor([seed, 1], dtype=tf.int32) + flip_ud = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5 + images = tf.cond(flip_ud, lambda: tf.image.flip_up_down(images), lambda: images) + + stateless_seed = tf.convert_to_tensor([seed, 2], dtype=tf.int32) + k = tf.random.stateless_uniform([], seed=stateless_seed, minval=0, maxval=4, dtype=tf.int32) + images = tf.image.rot90(images, k=k) + + shx = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 30], dtype=tf.int32), minval=-0.08, maxval=0.08) + shy = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 31], dtype=tf.int32), minval=-0.08, maxval=0.08) + p0 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 32], dtype=tf.int32), minval=-8e-4, maxval=8e-4) + p1 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 33], dtype=tf.int32), minval=-8e-4, maxval=8e-4) + base_t = tf.stack( + [ + tf.constant(1.0, tf.float32), + shx, + tf.constant(0.0, tf.float32), + shy, + tf.constant(1.0, tf.float32), + tf.constant(0.0, tf.float32), + p0, + p1, + ] + ) + transforms = tf.tile(base_t[tf.newaxis, :], [tf.shape(images)[0], 1]) + images = tf.raw_ops.ImageProjectiveTransformV3( + images=images, + transforms=transforms, + output_shape=tf.shape(images)[1:3], + interpolation="BILINEAR", + fill_mode="REFLECT", + fill_value=0.0, + ) + + stateless_seed = tf.convert_to_tensor([seed, 3], dtype=tf.int32) + images = tf.image.stateless_random_brightness(images, max_delta=10.0, seed=stateless_seed) + stateless_seed = tf.convert_to_tensor([seed, 4], dtype=tf.int32) + images = tf.image.stateless_random_contrast(images, lower=0.9, upper=1.1, seed=stateless_seed) + images = tf.clip_by_value(images, -255.0, 255.0) + return images + + +def _augment_cls_batch(images: tf.Tensor, seed: int) -> tf.Tensor: + stateless_seed = tf.convert_to_tensor([seed, 0], dtype=tf.int32) + flip_lr = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5 + images = tf.cond(flip_lr, lambda: tf.image.flip_left_right(images), lambda: images) + + stateless_seed = tf.convert_to_tensor([seed, 1], dtype=tf.int32) + flip_ud = tf.random.stateless_uniform([], seed=stateless_seed) > 0.5 + images = tf.cond(flip_ud, lambda: tf.image.flip_up_down(images), lambda: images) + + stateless_seed = tf.convert_to_tensor([seed, 2], dtype=tf.int32) + k = tf.random.stateless_uniform([], seed=stateless_seed, minval=0, maxval=4, dtype=tf.int32) + images = tf.image.rot90(images, k=k) + + shx = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 30], dtype=tf.int32), minval=-0.08, maxval=0.08) + shy = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 31], dtype=tf.int32), minval=-0.08, maxval=0.08) + p0 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 32], dtype=tf.int32), minval=-8e-4, maxval=8e-4) + p1 = tf.random.stateless_uniform([], seed=tf.convert_to_tensor([seed, 33], dtype=tf.int32), minval=-8e-4, maxval=8e-4) + base_t = tf.stack( + [ + tf.constant(1.0, tf.float32), + shx, + tf.constant(0.0, tf.float32), + shy, + tf.constant(1.0, tf.float32), + tf.constant(0.0, tf.float32), + p0, + p1, + ] + ) + transforms = tf.tile(base_t[tf.newaxis, :], [tf.shape(images)[0], 1]) + images = tf.raw_ops.ImageProjectiveTransformV3( + images=images, + transforms=transforms, + output_shape=tf.shape(images)[1:3], + interpolation="BILINEAR", + fill_mode="REFLECT", + fill_value=0.0, + ) + + stateless_seed = tf.convert_to_tensor([seed, 3], dtype=tf.int32) + images = tf.image.stateless_random_brightness(images, max_delta=10.0, seed=stateless_seed) + stateless_seed = tf.convert_to_tensor([seed, 4], dtype=tf.int32) + images = tf.image.stateless_random_contrast(images, lower=0.9, upper=1.1, seed=stateless_seed) + images = tf.clip_by_value(images, -255.0, 255.0) + return images + + +def _tile_to_uint8(tile: np.ndarray) -> np.ndarray: + x = np.asarray(tile, dtype=np.float32) + if x.ndim == 3 and x.shape[-1] > 1: + x = x[..., 0] + if not np.isfinite(x).any(): + return np.zeros_like(x, dtype=np.uint8) + mn = float(np.nanmin(x)) + mx = float(np.nanmax(x)) + if mx <= mn + 1e-6: + return np.clip(x, 0, 255).astype(np.uint8) + x = (x - mn) / (mx - mn) + return (x * 255.0).clip(0, 255).astype(np.uint8) + + +def _build_grid(tiles: list[np.ndarray], h: int, w: int) -> Image.Image: + grid = Image.new("L", (4 * w, 4 * h), color=0) + for i, tile in enumerate(tiles[:16]): + r = i // 4 + c = i % 4 + grid.paste(Image.fromarray(_tile_to_uint8(tile), mode="L"), (c * w, r * h)) + return grid + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--image-path", type=Path, required=True) + parser.add_argument("--mode", type=str, default="lumen", choices=["lumen", "bifurcation"]) + parser.add_argument("--seed", type=int, default=7) + parser.add_argument( + "--output-path", + type=Path, + default=Path("output/augment_preview/augment_preview_4x4.png"), + ) + parser.add_argument( + "--center-images", + action="store_true", + help="For bifurcation custom-model style preview (subtract IMG_MEAN before aug).", + ) + args = parser.parse_args() + + if not args.image_path.exists(): + raise FileNotFoundError(f"Image not found: {args.image_path}") + + gray = _load_gray(args.image_path) + h, w = int(gray.shape[0]), int(gray.shape[1]) + + if args.mode == "lumen": + base = _prepare_lumen_input(gray) + aug_fn = _augment_seg_batch + else: + base = _prepare_bif_input(gray, center_images=args.center_images) + aug_fn = _augment_cls_batch + + tiles: list[np.ndarray] = [] + for i in range(16): + out = aug_fn(base, seed=args.seed + i).numpy()[0] + tiles.append(out) + + grid = _build_grid(tiles, h=h, w=w) + args.output_path.parent.mkdir(parents=True, exist_ok=True) + grid.save(args.output_path) + print(f"Saved augmentation grid: {args.output_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/finetune/shared/preview_augmentations_from_frame_bank.py b/scripts/finetune/shared/preview_augmentations_from_frame_bank.py new file mode 100644 index 0000000000000000000000000000000000000000..494de9a238f540293c6f20cfea63f868149ca6c3 --- /dev/null +++ b/scripts/finetune/shared/preview_augmentations_from_frame_bank.py @@ -0,0 +1,103 @@ +#!/usr/bin/env python3 +"""Generate a 4x4 augmentation grid from a frame-bank frame (no training required). + +Example: + python -u scripts/finetune/shared/preview_augmentations_from_frame_bank.py \ + --bank-jsonl evals/frame_bank_merged/bifurcation/FILE0001.jsonl \ + --frame 0 +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import numpy as np +from PIL import Image + +from deepivus.io.dicom import read_dicom +from deepivus.processing.preprocessing import apply_center_black_circle +from scripts.finetune.shared.preview_augmentations import _prepare_bif_input, _prepare_lumen_input, _augment_cls_batch, _augment_seg_batch, _build_grid + + +def _read_jsonl(path: Path) -> list[dict]: + rows = [] + with path.open("r", encoding="utf-8") as fp: + for raw in fp: + line = raw.strip() + if not line: + continue + rows.append(json.loads(line)) + return rows + + +def _resolve_dicom_path(bank_jsonl: Path) -> Path: + rows = _read_jsonl(bank_jsonl) + if not rows or rows[0].get("record_type") != "meta": + raise RuntimeError(f"Invalid frame-bank JSONL: {bank_jsonl}") + dicom_path = Path(rows[0].get("dicom_path", "")) + if not dicom_path.exists(): + raise FileNotFoundError(f"DICOM path not found in meta: {dicom_path}") + return dicom_path + + +def _extract_frame(dicom_path: Path, frame_idx: int, diameter: int) -> np.ndarray: + _, images = read_dicom(str(dicom_path)) + images = apply_center_black_circle(images, diameter=diameter) + if frame_idx < 0 or frame_idx >= int(images.shape[0]): + raise IndexError(f"Frame index out of bounds: {frame_idx} (num_frames={images.shape[0]})") + frame = np.clip(images[frame_idx], 0, 255).astype(np.uint8) + return frame + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--bank-jsonl", type=Path, required=True) + parser.add_argument("--frame", type=int, required=True) + parser.add_argument("--mode", type=str, default="lumen", choices=["lumen", "bifurcation"]) + parser.add_argument("--seed", type=int, default=7) + parser.add_argument("--diameter", type=int, default=67) + parser.add_argument("--center-images", action="store_true", help="Only for bifurcation mode.") + parser.add_argument( + "--save-source-image", + action="store_true", + help="Also save extracted source frame PNG beside output grid.", + ) + parser.add_argument( + "--output-path", + type=Path, + default=Path("output/augment_preview/frame_bank_aug4x4.png"), + ) + args = parser.parse_args() + + dicom_path = _resolve_dicom_path(args.bank_jsonl) + gray = _extract_frame(dicom_path, frame_idx=args.frame, diameter=args.diameter) + + if args.mode == "lumen": + base = _prepare_lumen_input(gray) + aug_fn = _augment_seg_batch + else: + base = _prepare_bif_input(gray, center_images=args.center_images) + aug_fn = _augment_cls_batch + + tiles: list[np.ndarray] = [] + for i in range(16): + out = aug_fn(base, seed=args.seed + i).numpy()[0] + tiles.append(out) + + h, w = int(gray.shape[0]), int(gray.shape[1]) + grid = _build_grid(tiles, h=h, w=w) + + args.output_path.parent.mkdir(parents=True, exist_ok=True) + grid.save(args.output_path) + print(f"Saved augmentation grid: {args.output_path}") + + if args.save_source_image: + src_path = args.output_path.with_name(args.output_path.stem + "_source.png") + Image.fromarray(gray, mode="L").save(src_path) + print(f"Saved source frame: {src_path}") + + +if __name__ == "__main__": + main() diff --git a/scripts/model_check.py b/scripts/model_check.py new file mode 100644 index 0000000000000000000000000000000000000000..0817fea7536670723e3481f0443f996f709ca3d8 --- /dev/null +++ b/scripts/model_check.py @@ -0,0 +1,7 @@ +import tensorflow as tf + +m = tf.saved_model.load("./model") +print("Has call:", callable(m)) +print("Num variables:", len(getattr(m, "variables", []))) +print("Num trainable:", len(getattr(m, "trainable_variables", []))) +print("Signatures:", list(getattr(m, "signatures", {}).keys()))