import torch, json, os, sys import numpy as np from collections import defaultdict import warnings warnings.filterwarnings('ignore') os.chdir('/weka/scratch/melhila1/karan/EMMA2_text_conditioning_contextual') BASE = 'experiments_final' MODELS = [ ('combined_v1', 'combined_v1'), ('no_TSDL', 'no_TSDL_old_mixtures'), ] TEST_SETS = [ ('In-Domain (old)', 'eval_outputs_test_3k'), ('OOD Backgrounds', 'eval_outputs_OOD_backgrounds'), ('OOD Both', 'eval_outputs_OOD_both'), ('OOD Distractors', 'eval_outputs_OOD_distractors'), ] # Per-sample metrics ALL_METRICS = [ 'scale_invariant_signal_noise_ratio', 'signal_noise_ratio', 'si_snr', 'td_loss', 'delta_ILD', 'delta_ITD', 'delta_ITD_gcc', 'spatial_clap_score', 'msclap_score', ] METRIC_NAMES = { 'scale_invariant_signal_noise_ratio': 'SI-SNRi(dB)', 'signal_noise_ratio': 'SNRi(dB)', 'si_snr': 'SI-SNR(dB)', 'td_loss': 'TD Loss', 'delta_ILD': 'd_ILD', 'delta_ITD': 'd_ITD(xcorr)', 'delta_ITD_gcc': 'd_ITD(gcc)', 'spatial_clap_score': 'CLAP(spat)', 'msclap_score': 'CLAP(ms)', } LOWER_BETTER = {'td_loss', 'delta_ILD', 'delta_ITD', 'delta_ITD_gcc'} COL_W = 20 def load_results(pth_path): batches = torch.load(pth_path, map_location='cpu', weights_only=False) all_metrics = defaultdict(list) all_cmd_types = [] all_dist_counts = [] available_metrics = set(batches[0].keys()) - {'metadata'} for batch in batches: n = len(batch['signal_noise_ratio']) for m in ALL_METRICS: if m in batch: all_metrics[m].extend(batch[m]) for i in range(n): meta = batch['metadata'][i] cmd = meta.get('command_variant', {}).get('command_type', 'unknown') dist_count = meta.get('distractor_count', 0) all_cmd_types.append(cmd) all_dist_counts.append(dist_count) return {m: np.array(v) for m, v in all_metrics.items()}, all_cmd_types, all_dist_counts, available_metrics def print_table(title, count, model_data, model_names, mask_fn): print(f"\n{'='*170}", flush=True) print(f"{title} ({count} samples)", flush=True) print('='*170, flush=True) # Determine which metrics are available across loaded models active_metrics = [m for m in ALL_METRICS if any(m in model_data[nm][0] for nm in model_names)] header = f"{'Model':<16}" for m in active_metrics: header += f"{METRIC_NAMES[m]:>{COL_W}}" print(header, flush=True) print("-"*len(header), flush=True) # Find best value per metric best = {} for m in active_metrics: vals = [] for nm in model_names: mets = model_data[nm][0] if m not in mets: continue cl, dl = model_data[nm][1], model_data[nm][2] mask = mask_fn(cl, dl) if mask.sum() == 0: continue vals.append(float(np.mean(mets[m][mask]))) if not vals: continue best[m] = min(vals) if m in LOWER_BETTER else max(vals) for nm in model_names: mets, cl, dl = model_data[nm] mask = mask_fn(cl, dl) row = f"{nm:<16}" for m in active_metrics: if m not in mets: row += f"{'N/A':>{COL_W}}" else: vals = mets[m][mask] if len(vals) == 0: row += f"{'N/A':>{COL_W}}" continue mean_val = float(np.mean(vals)) std_val = float(np.std(vals)) is_best = m in best and abs(mean_val - best[m]) < 1e-6 marker = "*" if is_best else " " cell = f"{mean_val:.2f}\u00b1{std_val:.2f}" row += f"{cell:>{COL_W-1}}{marker}" print(row, flush=True) # ── Main ────────────────────────────────────────────────────────────────────── for test_label, eval_dir_name in TEST_SETS: # Collect models that have results for this test set model_data = {} model_names = [] for display_name, folder_name in MODELS: pth = os.path.join(BASE, folder_name, eval_dir_name, 'results.eval.pth') if not os.path.exists(pth): print(f"[SKIP] {display_name} / {eval_dir_name}: results not found", flush=True) continue metrics, cmd_types, dist_counts, avail = load_results(pth) model_data[display_name] = (metrics, cmd_types, dist_counts) model_names.append(display_name) print(f"Loaded {display_name} / {eval_dir_name}: {len(cmd_types)} samples, " f"metrics: {sorted(avail)}", flush=True) if len(model_names) == 0: print(f"\n[SKIP] No results available for test set: {test_label}\n", flush=True) continue n_samples = len(model_data[model_names[0]][1]) # TABLE 1: Global print_table(f"{test_label} — GLOBAL PERFORMANCE", n_samples, model_data, model_names, lambda c, d: np.ones(len(c), dtype=bool)) # TABLE 2: By command type all_cmd = model_data[model_names[0]][1] cmd_types_set = sorted(set(all_cmd)) for i, cmd in enumerate(cmd_types_set): n_cmd = sum(1 for c in all_cmd if c == cmd) print_table(f"{test_label} — COMMAND TYPE = '{cmd}'", n_cmd, model_data, model_names, lambda c, d, _cmd=cmd: np.array([x == _cmd for x in c])) # TABLE 3: By distractor count all_dist = model_data[model_names[0]][2] dist_set = sorted(set(all_dist)) for i, dc in enumerate(dist_set): n_dc = sum(1 for d in all_dist if d == dc) print_table(f"{test_label} — DISTRACTOR COUNT = {dc}", n_dc, model_data, model_names, lambda c, d, _dc=dc: np.array([x == _dc for x in d])) print(f"\n{'='*170}", flush=True) print("LEGEND: * = best model for that metric", flush=True) print(" SI-SNRi(dB) = SI-SNR improvement over input mixture", flush=True) print(" SNRi(dB) = SNR improvement over input mixture", flush=True) print(" SI-SNR(dB) = Absolute SI-SNR of output", flush=True) print(" Higher=better: SI-SNRi(dB), SNRi(dB), SI-SNR(dB), CLAP(spat), CLAP(ms)", flush=True) print(" Lower=better: TD Loss, d_ILD, d_ITD(xcorr), d_ITD(gcc)", flush=True) print(" N/A = metric not available for that model", flush=True) print('='*170, flush=True)