Final work on rewrite_v3

analysis/evaluate_models.py

@@ -3,34 +3,61 @@
 from __future__ import annotations
 
 import argparse
+import sys
 from pathlib import Path
 from typing import Any
 
 import pandas as pd
+import torch
 from tqdm.auto import tqdm
 
-from .analysis_modules import (
-    run_calibration,
-    run_longitudinal,
-    run_performance,
-    run_physician,
-)
-from .data_access import load_backend_evaluation, load_clinical_table
-from .dataset_summary import run_dataset_summary
-from .defaults import (
-    DEFAULT_BACKENDS,
-    DEFAULT_BAYESIAN_MC_PASSES,
-    DEFAULT_CALIBRATION_BINS,
-    DEFAULT_DECISION_THRESHOLD,
-    DEFAULT_POSITIVE_CLASS_INDEX,
-    noise_factor_grid,
-    threshold_grid,
-)
-from .holdout_evaluation import ensure_backend_netcdf
-from .longitudinal_audit import run_longitudinal_breakdown_audit
-from .noise_analysis import run_noise_analysis
-from .noise_correlation import run_noise_accuracy_uncertainty_analysis
-from .runtime import backend_dir, init_runtime_paths, load_config, write_json
+if __package__ in (None, ""):
+    sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+    from analysis.analysis_modules import (
+        run_calibration,
+        run_longitudinal,
+        run_performance,
+        run_physician,
+    )
+    from analysis.data_access import load_backend_evaluation, load_clinical_table
+    from analysis.dataset_summary import run_dataset_summary
+    from analysis.defaults import (
+        DEFAULT_BACKENDS,
+        DEFAULT_BAYESIAN_MC_PASSES,
+        DEFAULT_CALIBRATION_BINS,
+        DEFAULT_DECISION_THRESHOLD,
+        DEFAULT_POSITIVE_CLASS_INDEX,
+        noise_factor_grid,
+        threshold_grid,
+    )
+    from analysis.holdout_evaluation import ensure_backend_netcdf
+    from analysis.longitudinal_audit import run_longitudinal_breakdown_audit
+    from analysis.noise_analysis import run_noise_analysis
+    from analysis.noise_correlation import run_noise_accuracy_uncertainty_analysis
+    from analysis.runtime import backend_dir, init_runtime_paths, load_config, write_json
+else:
+    from .analysis_modules import (
+        run_calibration,
+        run_longitudinal,
+        run_performance,
+        run_physician,
+    )
+    from .data_access import load_backend_evaluation, load_clinical_table
+    from .dataset_summary import run_dataset_summary
+    from .defaults import (
+        DEFAULT_BACKENDS,
+        DEFAULT_BAYESIAN_MC_PASSES,
+        DEFAULT_CALIBRATION_BINS,
+        DEFAULT_DECISION_THRESHOLD,
+        DEFAULT_POSITIVE_CLASS_INDEX,
+        noise_factor_grid,
+        threshold_grid,
+    )
+    from .holdout_evaluation import ensure_backend_netcdf
+    from .longitudinal_audit import run_longitudinal_breakdown_audit
+    from .noise_analysis import run_noise_analysis
+    from .noise_correlation import run_noise_accuracy_uncertainty_analysis
+    from .runtime import backend_dir, init_runtime_paths, load_config, write_json
 
 
 def _plot_description(filename: str) -> str:
@@ -95,6 +122,29 @@ def _write_backend_plot_report(backend: str, out_dir: Path) -> Path:
     return report_path
 
 
+def _print_device_info(config: dict[str, Any]) -> None:
+    device = torch.device(str(config["training"]["device"]))
+    print(f"Analysis device: {device}")
+
+    if device.type == "cuda":
+        if not torch.cuda.is_available():
+            print("CUDA is not available in this environment.")
+            return
+
+        index = device.index if device.index is not None else torch.cuda.current_device()
+        props = torch.cuda.get_device_properties(index)
+        print(f"GPU index: {index}")
+        print(f"GPU name: {torch.cuda.get_device_name(index)}")
+        print(f"GPU capability: {props.major}.{props.minor}")
+        print(f"GPU total memory: {props.total_memory / (1024 ** 3):.2f} GiB")
+        print(f"CUDA runtime: {torch.version.cuda or 'unknown'}")
+        print(f"Visible CUDA devices: {torch.cuda.device_count()}")
+    elif device.type == "mps":
+        print("Apple Metal Performance Shaders backend selected.")
+    else:
+        print("CPU backend selected.")
+
+
 def _parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(
         description=(
@@ -328,6 +378,7 @@ def main() -> None:
     analysis_dir = Path(__file__).resolve().parent
     paths = init_runtime_paths(analysis_dir=analysis_dir, run_name=args.run_name)
     config = load_config(paths.root_dir)
+    _print_device_info(config)
     clinical_df = load_clinical_table(config=config, root_dir=paths.root_dir)
 
     manifest: dict[str, Any] = {