| """Event file -> loss_curves.png (diff/repa/lr + fid). watcher가 매 사이클 갱신.""" |
| import glob, os |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from tensorboard.backend.event_processing.event_accumulator import EventAccumulator |
|
|
| BASE = os.path.join(os.path.dirname(os.path.abspath(__file__)), |
| "output/tokenizer/models_l_spatial_phase2") |
| LOGDIR = os.path.join(BASE, "logs/semanticist") |
| OUT = os.path.join(BASE, "loss_curves.png") |
|
|
|
|
| def load(): |
| ea = EventAccumulator(sorted(glob.glob(os.path.join(LOGDIR, "events*")))[-1]) |
| ea.Reload() |
| d = {} |
| for t in ea.Tags()["scalars"]: |
| s = ea.Scalars(t) |
| d[t] = ([x.step for x in s], [x.value for x in s]) |
| return d |
|
|
|
|
| def main(): |
| d = load() |
| fig, ax = plt.subplots(2, 2, figsize=(13, 8)) |
| if "diff_loss" in d: |
| ax[0, 0].plot(*d["diff_loss"], lw=0.7, color="C0") |
| ax[0, 0].set_title(f"diff_loss (last {d['diff_loss'][1][-1]:.4f})") |
| ax[0, 0].set_xlabel("step"); ax[0, 0].grid(alpha=.3) |
| if "repa_loss" in d: |
| ax[0, 1].plot(*d["repa_loss"], lw=0.7, color="C3") |
| ax[0, 1].set_title(f"repa_loss (last {d['repa_loss'][1][-1]:.4f})") |
| ax[0, 1].set_xlabel("step"); ax[0, 1].grid(alpha=.3) |
| if "lr" in d: |
| ax[1, 0].plot(*d["lr"], lw=0.9, color="C2") |
| ax[1, 0].set_title("lr (warmup)") |
| ax[1, 0].set_xlabel("step"); ax[1, 0].grid(alpha=.3) |
| if "fid" in d and len(d["fid"][0]) > 0: |
| ax[1, 1].plot(*d["fid"], "o-", color="C1", label="fid") |
| ax[1, 1].set_title(f"eval fid (last {d['fid'][1][-1]:.2f} @{d['fid'][0][-1]})") |
| ax[1, 1].set_xlabel("step"); ax[1, 1].grid(alpha=.3) |
| last_step = d.get("diff_loss", ([0], [0]))[0][-1] |
| fig.suptitle(f"SpatialDiffuseSlot-L Phase2 — step {last_step}", fontsize=13) |
| fig.tight_layout() |
| fig.savefig(OUT, dpi=110) |
| print("wrote", OUT, "@step", last_step) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
