Download eval_code/plot_tiled_decode_results.py from phuckstnk63/abcxyz: direct link, hf CLI and curl.
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https://huggingface.co/datasets/phuckstnk63/abcxyz/resolve/main/eval_code/plot_tiled_decode_results.py
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curl -L -o plot_tiled_decode_results.py https://huggingface.co/datasets/phuckstnk63/abcxyz/resolve/main/eval_code/plot_tiled_decode_results.py
2.31 kB
| """ | |
| Plot comparison of optim_tiled_decode vs optim_tiled_decode_cached_stats. | |
| Usage: | |
| python eval_code/plot_tiled_decode_results.py \ | |
| --csv eval_code/tiled_decode_results/results.csv \ | |
| --output eval_code/tiled_decode_results/comparison_plot.png | |
| """ | |
| import argparse | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import matplotlib.ticker as ticker | |
| import numpy as np | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--csv", type=str, default="eval_code/tiled_decode_results/results.csv") | |
| parser.add_argument("--output", type=str, default="eval_code/tiled_decode_results/comparison_plot.png") | |
| args = parser.parse_args() | |
| df = pd.read_csv(args.csv) | |
| ovf = df["overlap_factor"].values | |
| x = np.arange(len(ovf)) | |
| x_labels = [str(v) for v in ovf] | |
| color_std = "#4C72B0" | |
| color_cs = "#DD8452" | |
| fig, ax = plt.subplots(figsize=(5, 4)) | |
| ax.plot(ovf, df["std_overlap_mse"], "o-", color=color_std, linewidth=2, | |
| markersize=7, label="Standard VAE Decode") | |
| ax.plot(ovf, df["cs_overlap_mse"], "s-", color=color_cs, linewidth=2, | |
| markersize=7, label="Sprase VAE Decode (ours)") | |
| ax.set_xlabel("Overlap Factor", fontsize=12) | |
| ax.set_ylabel("MSE (log scale)", fontsize=12) | |
| ax.set_yscale("log") | |
| ax.set_yticks([5e-4, 1e-3, 8e-3]) | |
| ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda v, _: f"{v:.0e}")) | |
| ax.yaxis.set_minor_locator(ticker.NullLocator()) | |
| ax.legend(fontsize=10) | |
| ax.grid(True, alpha=0.3) | |
| ax.tick_params(labelsize=10) | |
| fig.tight_layout() | |
| fig.savefig(args.output, dpi=300, bbox_inches="tight") | |
| print(f"Plot saved to {args.output}") | |
| # ββ Print reduction ratio βββββββββββββββββββββββββββββββββββββββββββββ | |
| print("\nOverlap MSE reduction (cached_stats vs standard):") | |
| for _, row in df.iterrows(): | |
| ratio = row["cs_overlap_mse"] / row["std_overlap_mse"] | |
| reduction = (1 - ratio) * 100 | |
| print(f" overlap_factor={row['overlap_factor']:.5f} " | |
| f"std={row['std_overlap_mse']:.6f} " | |
| f"cs={row['cs_overlap_mse']:.6f} " | |
| f"reduction={reduction:.1f}%") | |
| if __name__ == "__main__": | |
| main() | |