"""Generate paper-quality figures comparing the user's fine-tuned model against two baselines (Qwen3-Omni vanilla, MiniCPM-o-4.5) on the Shift (sync) and VGGSoundSync benchmarks. All figures use DejaVu Serif, no titles, 300 dpi PDF + PNG, paper-friendly fonttype=42. """ import json from pathlib import Path from typing import Dict, List import matplotlib.pyplot as plt import numpy as np # --------------------------------------------------------------------------- # Style # --------------------------------------------------------------------------- plt.rcParams.update({ "font.family": "DejaVu Serif", "font.serif": ["DejaVu Serif"], "mathtext.fontset": "dejavuserif", "font.size": 11, "axes.titlesize": 12, "axes.titleweight": "bold", "axes.labelsize": 11, "xtick.labelsize": 10.5, "ytick.labelsize": 10.5, "legend.fontsize": 10, "pdf.fonttype": 42, "ps.fonttype": 42, "savefig.dpi": 300, "savefig.bbox": "tight", "axes.spines.top": False, "axes.spines.right": False, }) # --------------------------------------------------------------------------- # Data sources # --------------------------------------------------------------------------- ROOT = Path("/home/ubuntu/eval_results/paper_shift_backup") OUT = Path("/home/ubuntu/figs/my_model_vs_baselines") OUT.mkdir(parents=True, exist_ok=True) MODELS = [ # (display, sync_dir, vggsync_dir, color) ("Qwen3-Omni", ROOT/"sync/sync_qwen3omni_vanilla", ROOT/"vggsoundsync/vggsync_freetext_vanilla_qwen3omni_3k", "#5b8def"), ("MiniCPM-o", ROOT/"minicpmo_sync", ROOT/"minicpmo_vggsync", "#f4a04a"), ("Ours", ROOT/"sync/sync_sft_dpo_mdpo_fin_avmcqa_longform", ROOT/"vggsoundsync/vggsync_freetext_sft_dpo_mdpo_fin_avmcqa_longform_3k", "#2fa363"), ] def load_metrics(p: Path) -> dict: return json.loads((p / "metrics.json").read_text()) def load_jsonl(p: Path): f = p / "eval_results.jsonl" if not f.exists(): return [] return [json.loads(l) for l in f.open() if l.strip()] # =========================================================================== # Figure 1 — Headline three-metric bar chart (2 panels: Shift + VGGSoundSync) # =========================================================================== def fig_headline(): metric_keys = [ ("three_class_accuracy", "3-class Acc."), ("sync_desync_accuracy", "Sync/Desync Acc."), ("direction_accuracy_on_desync","Direction Acc."), ] panels = [ ("Shift", [load_metrics(m[1]) for m in MODELS]), ("VGGSoundSync", [load_metrics(m[2]) for m in MODELS]), ] fig, axes = plt.subplots(1, 2, figsize=(11.0, 4.2), gridspec_kw={"wspace": 0.22}) n_models = len(MODELS) n_metric = len(metric_keys) bar_w = 0.24 for ax, (title, mets) in zip(axes, panels): x = np.arange(n_metric) for i, (m, met) in enumerate(zip(MODELS, mets)): offsets = (i - (n_models - 1) / 2) * bar_w heights = [met.get(k, 0.0) for k, _ in metric_keys] bars = ax.bar(x + offsets, heights, bar_w, color=m[3], edgecolor="white", linewidth=0.6, label=m[0], zorder=3) for b, v in zip(bars, heights): ax.text(b.get_x() + b.get_width() / 2, v + 0.012, f"{v*100:.1f}", ha="center", va="bottom", fontsize=9, color="#1a1a1a") ax.set_xticks(x) ax.set_xticklabels([lbl for _, lbl in metric_keys]) ax.set_ylim(0, 1.05) ax.set_yticks(np.arange(0, 1.01, 0.2)) ax.set_yticklabels([f"{int(v*100)}" for v in np.arange(0, 1.01, 0.2)]) ax.set_ylabel("Accuracy (%)" if ax is axes[0] else "") ax.set_title(title, pad=6) ax.grid(axis="y", color="#e0e0e0", lw=0.6, zorder=0) ax.set_axisbelow(True) handles, labels = axes[0].get_legend_handles_labels() fig.legend(handles, labels, loc="upper center", bbox_to_anchor=(0.5, 1.04), ncol=n_models, frameon=False, fontsize=11) fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.savefig(OUT/"fig1_headline.pdf"); fig.savefig(OUT/"fig1_headline.png") plt.close(fig) # =========================================================================== # Figure 2 — Per-direction accuracy on Shift (synced / delay / early) # =========================================================================== def fig_per_direction(): cats = [("synced_accuracy", "Synced (Orig.)"), ("delay_accuracy", "Delay"), ("early_accuracy", "Early")] fig, ax = plt.subplots(figsize=(7.4, 4.0)) n_models = len(MODELS) bar_w = 0.25 x = np.arange(len(cats)) for i, (name, sync_dir, _, color) in enumerate(MODELS): m = load_metrics(sync_dir) pc = m["per_category"] heights = [pc[k] for k, _ in cats] offsets = (i - (n_models - 1) / 2) * bar_w bars = ax.bar(x + offsets, heights, bar_w, color=color, edgecolor="white", linewidth=0.6, label=name, zorder=3) for b, v in zip(bars, heights): ax.text(b.get_x() + b.get_width()/2, v + 0.012, f"{v*100:.1f}", ha="center", va="bottom", fontsize=9, color="#1a1a1a") ax.set_xticks(x); ax.set_xticklabels([lbl for _, lbl in cats]) ax.set_ylim(0, 1.10) ax.set_yticks(np.arange(0, 1.01, 0.2)) ax.set_yticklabels([f"{int(v*100)}" for v in np.arange(0, 1.01, 0.2)]) ax.set_ylabel("Accuracy (%)") ax.grid(axis="y", color="#e0e0e0", lw=0.6, zorder=0); ax.set_axisbelow(True) ax.legend(loc="upper right", frameon=False) fig.tight_layout() fig.savefig(OUT/"fig2_per_direction.pdf"); fig.savefig(OUT/"fig2_per_direction.png") plt.close(fig) # =========================================================================== # Figure 3 — Per-difficulty line chart on VGGSoundSync # =========================================================================== def fig_per_difficulty(): diff_order = ["synced", "very_easy", "easy", "medium", "hard"] diff_labels = ["Orig.\n(synced)", "Very Easy\n(2.0–2.5s)", "Easy\n(1.5–2.0s)", "Medium\n(1.0–1.5s)", "Hard\n(0.5–1.0s)"] fig, ax = plt.subplots(figsize=(8.2, 4.4)) x = np.arange(len(diff_order)) for name, _, vggsync_dir, color in MODELS: m = load_metrics(vggsync_dir) pd_ = m["per_difficulty"] ys = [pd_[d]["accuracy"] for d in diff_order] ax.plot(x, ys, marker="o", linewidth=2.2, markersize=8.5, color=color, label=name, markeredgecolor="white", markeredgewidth=1.2, zorder=3) for xi, v in zip(x, ys): ax.text(xi, v + 0.025, f"{v*100:.0f}", ha="center", va="bottom", fontsize=9, color=color) ax.set_xticks(x); ax.set_xticklabels(diff_labels) ax.set_ylim(0, 1.0) ax.set_yticks(np.arange(0, 1.01, 0.2)) ax.set_yticklabels([f"{int(v*100)}" for v in np.arange(0, 1.01, 0.2)]) ax.set_ylabel("Accuracy (%)") ax.set_xlabel("VGGSoundSync difficulty bucket (offset magnitude shown)") ax.grid(axis="y", color="#e0e0e0", lw=0.6, zorder=0); ax.set_axisbelow(True) ax.legend(loc="upper right", frameon=False) fig.tight_layout() fig.savefig(OUT/"fig3_per_difficulty.pdf"); fig.savefig(OUT/"fig3_per_difficulty.png") plt.close(fig) # =========================================================================== # Figure 4 — Offset CDF on Shift and VGGSoundSync (two panels) # =========================================================================== def _abs_offset_errors(rows): """Extract |pred_offset - gt_offset| only on samples where the model actually claimed a desync (i.e. produced a non-zero offset).""" out = [] for r in rows: gt_off = r.get("gt_offset_sec") if gt_off is None: continue # gt_synced=True samples have no offset to evaluate if r.get("gt_synced") is True: continue pred_off = r.get("pred_offset_sec") if pred_off is None: continue # only include evaluations where the model also said it was offset if r.get("pred_synced") is True: continue out.append(abs(float(pred_off) - float(gt_off))) return np.array(out, dtype=float) def fig_offset_cdf(): fig, axes = plt.subplots(1, 2, figsize=(11.0, 4.2), gridspec_kw={"wspace": 0.22}) panels = [ ("Shift", [m[1] for m in MODELS]), ("VGGSoundSync", [m[2] for m in MODELS]), ] thresholds = np.linspace(0, 3.0, 121) for ax, (title, dirs) in zip(axes, panels): for (name, _, _, color), d in zip(MODELS, dirs): rows = load_jsonl(d) errs = _abs_offset_errors(rows) if len(errs) == 0: continue # CDF over ALL desync samples (denominator = total desync gt count) n_desync = sum(1 for r in rows if r.get("gt_synced") is False or r.get("gt_direction") in ("delay", "early")) ys = [(errs <= t).sum() / max(n_desync, 1) for t in thresholds] ax.plot(thresholds, ys, color=color, linewidth=2.4, label=name, zorder=3) # mark the value at 0.5s (a key threshold) with a dot t_mark = 0.5 y_mark = (errs <= t_mark).sum() / max(n_desync, 1) ax.scatter([t_mark], [y_mark], s=70, color=color, edgecolor="white", linewidth=1.2, zorder=4) ax.text(t_mark + 0.05, y_mark + 0.018, f"{y_mark*100:.0f}%", color=color, fontsize=9, weight="bold") ax.set_xlim(0, 3.0) ax.set_ylim(0, 1.0) ax.set_yticks(np.arange(0, 1.01, 0.2)) ax.set_yticklabels([f"{int(v*100)}" for v in np.arange(0, 1.01, 0.2)]) ax.set_xlabel("Offset error tolerance |Δ| (sec)") ax.set_ylabel("Fraction of desync samples within tolerance (%)" if ax is axes[0] else "") ax.set_title(title, pad=6) ax.axvline(0.5, color="#bbb", linestyle=":", linewidth=0.8, zorder=1) ax.grid(axis="both", color="#e8e8e8", lw=0.5, zorder=0) ax.set_axisbelow(True) handles, labels = axes[0].get_legend_handles_labels() fig.legend(handles, labels, loc="upper center", bbox_to_anchor=(0.5, 1.04), ncol=len(MODELS), frameon=False, fontsize=11) fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.savefig(OUT/"fig4_offset_cdf.pdf"); fig.savefig(OUT/"fig4_offset_cdf.png") plt.close(fig) # =========================================================================== # Figure 5 — Per-class scatter on VGGSoundSync (vanilla vs ours) # =========================================================================== def fig_per_class_scatter(): vanilla = load_metrics(MODELS[0][2])["per_class"] ours = load_metrics(MODELS[2][2])["per_class"] minicpm = load_metrics(MODELS[1][2])["per_class"] classes = sorted(set(vanilla) & set(ours) & set(minicpm)) fig, axes = plt.subplots(1, 2, figsize=(11.0, 5.2), gridspec_kw={"wspace": 0.25}) panels = [ (axes[0], "vs Qwen3-Omni", vanilla, MODELS[0][3]), (axes[1], "vs MiniCPM-o", minicpm, MODELS[1][3]), ] for ax, sub_title, baseline_d, base_color in panels: bx = np.array([baseline_d[c]["accuracy"] for c in classes]) oy = np.array([ours[c]["accuracy"] for c in classes]) sizes = np.array([vanilla[c]["count"] for c in classes]) # Use lighter "base_color" mix for the dots; gradient by sample count ax.scatter(bx, oy, s=10 + sizes * 1.5, color="#2fa363", alpha=0.65, edgecolor="white", linewidth=0.5, zorder=3) ax.plot([0, 1], [0, 1], ls="--", color="#888", lw=1.0, zorder=2) # Highlight a few extreme points with class names deltas = oy - bx top_imp = np.argsort(-deltas)[:5] bot_imp = np.argsort(deltas)[:3] for idx in list(top_imp) + list(bot_imp): ax.annotate(classes[idx], (bx[idx], oy[idx]), xytext=(4, 4), textcoords="offset points", fontsize=7.5, color="#444", style="italic") # Stats annotation win = (deltas > 0).sum() ax.text(0.02, 0.97, f"Ours wins on {win}/{len(classes)} classes\n" f"Mean Δ = +{deltas.mean()*100:.1f} pts", transform=ax.transAxes, va="top", ha="left", fontsize=10, color="#1a1a1a", bbox=dict(facecolor="#fefef0", edgecolor="#888", boxstyle="round,pad=0.35")) ax.set_xlim(0, 1.0); ax.set_ylim(0, 1.0) ax.set_xticks(np.arange(0, 1.01, 0.2)) ax.set_yticks(np.arange(0, 1.01, 0.2)) ax.set_xticklabels([f"{int(v*100)}" for v in np.arange(0, 1.01, 0.2)]) ax.set_yticklabels([f"{int(v*100)}" for v in np.arange(0, 1.01, 0.2)]) ax.set_xlabel(f"Baseline accuracy {sub_title.split()[1]} (%)") ax.set_ylabel("Ours accuracy (%)" if ax is axes[0] else "") ax.set_title(sub_title, pad=6) ax.grid(color="#eee", lw=0.5, zorder=0) ax.set_axisbelow(True) fig.tight_layout() fig.savefig(OUT/"fig5_per_class_scatter.pdf") fig.savefig(OUT/"fig5_per_class_scatter.png") plt.close(fig) # =========================================================================== # Figure 6 — Within-tolerance bar grid (offset attribution success) # =========================================================================== def fig_within_thresholds(): """Show the % of desync samples for which the predicted offset is within a fixed tolerance, computed against the FULL desync sample count (not just samples where the model attempted an offset).""" fig, axes = plt.subplots(1, 2, figsize=(11.0, 4.2), gridspec_kw={"wspace": 0.22}) sync_thresh = ["offset_within_0.5s", "offset_within_1.0s"] sync_labels = ["≤ 0.5 s", "≤ 1.0 s"] vggsync_thresh = ["offset_within_0.2s", "offset_within_0.5s"] vggsync_labels = ["≤ 0.2 s", "≤ 0.5 s"] panels = [ ("Shift", sync_thresh, sync_labels, 1), ("VGGSoundSync", vggsync_thresh, vggsync_labels, 2), ] bar_w = 0.25 for ax, (title, ks, labs, dir_idx) in zip(axes, panels): x = np.arange(len(ks)) # denominator: total desync samples (= total - synced_count) for i, (name, sd, vd, color) in enumerate(MODELS): m = load_metrics(sd if dir_idx == 1 else vd) if dir_idx == 1: n_synced = m["per_category"]["synced_count"] else: n_synced = m["per_difficulty"]["synced"]["count"] n_desync = m["total_samples"] - n_synced heights = [m.get(k, 0) / max(n_desync, 1) for k in ks] offsets = (i - (len(MODELS) - 1) / 2) * bar_w bars = ax.bar(x + offsets, heights, bar_w, color=color, edgecolor="white", linewidth=0.6, label=name, zorder=3) for b, v in zip(bars, heights): ax.text(b.get_x() + b.get_width()/2, v + 0.012, f"{v*100:.1f}", ha="center", va="bottom", fontsize=9, color="#1a1a1a") ax.set_xticks(x); ax.set_xticklabels(labs) ax.set_xlabel("Offset tolerance") ax.set_ylim(0, max(0.4, ax.get_ylim()[1]) + 0.05) ax.set_ylabel("Desync samples within tolerance (%)" if ax is axes[0] else "") ax.set_title(title, pad=6) ax.grid(axis="y", color="#e0e0e0", lw=0.6, zorder=0) ax.set_axisbelow(True) # convert ticks to % yticks = ax.get_yticks() ax.set_yticklabels([f"{int(v*100)}" for v in yticks]) handles, labels = axes[0].get_legend_handles_labels() fig.legend(handles, labels, loc="upper center", bbox_to_anchor=(0.5, 1.04), ncol=len(MODELS), frameon=False, fontsize=11) fig.tight_layout(rect=(0, 0, 1, 0.95)) fig.savefig(OUT/"fig6_within_thresholds.pdf") fig.savefig(OUT/"fig6_within_thresholds.png") plt.close(fig) # =========================================================================== # Run all # =========================================================================== if __name__ == "__main__": fig_headline() fig_per_direction() fig_per_difficulty() fig_offset_cdf() fig_per_class_scatter() fig_within_thresholds() print("All figures saved to:", OUT) for p in sorted(OUT.glob("*.png")): print(" ", p.name)