| """Sinh các figure cho coursework report (PDF LaTeX) từ các file JSON kết quả. |
| |
| Không load model / không cần GPU — chỉ đọc JSON và vẽ matplotlib (nhẹ, không lo tràn RAM). |
| |
| Output: report/figures/*.png |
| """ |
| import json |
| import os |
|
|
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
| REPORT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "report") |
| FIG_DIR = os.path.join(REPORT_DIR, "figures") |
| os.makedirs(FIG_DIR, exist_ok=True) |
|
|
| plt.rcParams.update({ |
| "font.size": 10, |
| "axes.labelsize": 10, |
| "axes.titlesize": 11, |
| "legend.fontsize": 9, |
| "xtick.labelsize": 9, |
| "ytick.labelsize": 9, |
| "figure.dpi": 200, |
| "savefig.dpi": 200, |
| "savefig.bbox": "tight", |
| }) |
|
|
| VIT_COLOR = "#1f77b4" |
| CNN_COLOR = "#d62728" |
| GRAY = "#7f7f7f" |
|
|
|
|
| def load(path): |
| with open(path) as f: |
| return json.load(f) |
|
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| |
| |
| def fig_finetune_loss(): |
| d = load(os.path.join(REPORT_DIR, "..", "outputs", "results", "finetune_insight_report.json")) |
| hist = d["history"] |
| epochs = [h["epoch"] for h in hist] |
| train_loss = [h["train_loss"] for h in hist] |
| val_acc = [h["accuracy"] * 100 for h in hist] |
| val_f1 = [h["f1"] * 100 for h in hist] |
|
|
| fig, ax1 = plt.subplots(figsize=(6.2, 3.8)) |
| ax1.plot(epochs, train_loss, "o-", color=VIT_COLOR, label="Train loss") |
| ax1.set_xlabel("Epoch") |
| ax1.set_ylabel("Train loss", color=VIT_COLOR) |
| ax1.tick_params(axis="y", labelcolor=VIT_COLOR) |
| ax1.set_xticks(epochs) |
|
|
| ax2 = ax1.twinx() |
| ax2.plot(epochs, val_acc, "s--", color=CNN_COLOR, label="Val accuracy") |
| ax2.plot(epochs, val_f1, "^--", color=GRAY, label="Val F1") |
| ax2.set_ylabel("Validation (%)", color=CNN_COLOR) |
| ax2.tick_params(axis="y", labelcolor=CNN_COLOR) |
| ax2.set_ylim(85, 100) |
|
|
| lines = ax1.get_lines() + ax2.get_lines() |
| ax1.legend(lines, [l.get_label() for l in lines], loc="center right", frameon=False) |
| ax1.set_title("Fine-tuning DINOv3 ViT-S/16 (5 epochs)") |
| fig.tight_layout() |
| out = os.path.join(FIG_DIR, "finetune_loss.png") |
| fig.savefig(out) |
| plt.close(fig) |
| print("Wrote", out) |
|
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| |
| |
| def fig_linprobe(): |
| d = load(os.path.join(REPORT_DIR, "..", "outputs", "results", "comparison_report_v2.json")) |
| vit = d["models"]["vit"]["metrics"] |
| cnn = d["models"]["cnn"]["metrics"] |
| metrics = ["accuracy", "precision", "recall", "f1", "roc_auc"] |
| labels = ["Accuracy", "Precision", "Recall", "F1", "ROC-AUC"] |
| vit_vals = [vit[m] * 100 for m in metrics] |
| cnn_vals = [cnn[m] * 100 for m in metrics] |
|
|
| x = np.arange(len(metrics)) |
| w = 0.36 |
| fig, ax = plt.subplots(figsize=(6.2, 3.6)) |
| b1 = ax.bar(x - w / 2, vit_vals, w, label="ViT-S/16 (Plus)", color=VIT_COLOR) |
| b2 = ax.bar(x + w / 2, cnn_vals, w, label="ConvNeXt-Tiny", color=CNN_COLOR) |
| ax.set_xticks(x) |
| ax.set_xticklabels(labels) |
| ax.set_ylabel("Score (%)") |
| ax.set_ylim(0, 100) |
| ax.legend(frameon=False, loc="lower right") |
| for bars in (b1, b2): |
| for b in bars: |
| ax.annotate(f"{b.get_height():.1f}", (b.get_x() + b.get_width() / 2, b.get_height()), |
| ha="center", va="bottom", fontsize=7.5) |
| ax.set_title("Linear probe on DeepFakeFace (identity-disjoint test)") |
| fig.tight_layout() |
| out = os.path.join(FIG_DIR, "linprobe_comparison.png") |
| fig.savefig(out) |
| plt.close(fig) |
| print("Wrote", out) |
|
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| |
| |
| def fig_timit(): |
| d = load(os.path.join(REPORT_DIR, "..", "outputs", "benchmark", "deepfaketimit_report.json")) |
| vit = d["vit"] |
| cnn = d["cnn"] |
| groups = ["Overall", "HQ (128px)", "LQ (64px)"] |
| vit_vals = [vit["detection_rate"] * 100, vit["hq_detection_rate"] * 100, vit["lq_detection_rate"] * 100] |
| cnn_vals = [cnn["detection_rate"] * 100, cnn["hq_detection_rate"] * 100, cnn["lq_detection_rate"] * 100] |
|
|
| x = np.arange(len(groups)) |
| w = 0.36 |
| fig, ax = plt.subplots(figsize=(5.6, 3.4)) |
| b1 = ax.bar(x - w / 2, vit_vals, w, label="ViT-S/16 (Plus)", color=VIT_COLOR) |
| b2 = ax.bar(x + w / 2, cnn_vals, w, label="ConvNeXt-Tiny", color=CNN_COLOR) |
| ax.set_xticks(x) |
| ax.set_xticklabels(groups) |
| ax.set_ylabel("Detection rate (%)") |
| ax.set_ylim(0, 105) |
| ax.legend(frameon=False, loc="lower left") |
| for bars in (b1, b2): |
| for b in bars: |
| ax.annotate(f"{b.get_height():.1f}", (b.get_x() + b.get_width() / 2, b.get_height()), |
| ha="center", va="bottom", fontsize=8) |
| ax.set_title("DeepfakeTIMIT (all frames fake)") |
| fig.tight_layout() |
| out = os.path.join(FIG_DIR, "timit_detection.png") |
| fig.savefig(out) |
| plt.close(fig) |
| print("Wrote", out) |
|
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| |
| |
| def fig_timit_hist(): |
| d = load(os.path.join(REPORT_DIR, "..", "outputs", "benchmark", "deepfaketimit_report.json")) |
| vit = np.array(d["vit"]["prob_fake"]) |
| cnn = np.array(d["cnn"]["prob_fake"]) |
| bins = np.linspace(0, 1, 21) |
| fig, axes = plt.subplots(1, 2, figsize=(6.4, 2.9), sharey=True) |
| axes[0].hist(vit, bins=bins, color=VIT_COLOR, alpha=0.85) |
| axes[0].set_title("ViT-S/16 (Plus)") |
| axes[0].set_xlabel("Predicted p(fake)") |
| axes[1].hist(cnn, bins=bins, color=CNN_COLOR, alpha=0.85) |
| axes[1].set_title("ConvNeXt-Tiny") |
| axes[1].set_xlabel("Predicted p(fake)") |
| axes[0].set_ylabel("Number of frames") |
| for ax in axes: |
| ax.axvline(0.5, color="k", ls="--", lw=0.8) |
| fig.suptitle("Confidence distribution on DeepfakeTIMIT (640 fake frames)", fontsize=10) |
| fig.tight_layout() |
| out = os.path.join(FIG_DIR, "timit_prob_hist.png") |
| fig.savefig(out) |
| plt.close(fig) |
| print("Wrote", out) |
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| |
| def fig_finetune_cm(): |
| d = load(os.path.join(REPORT_DIR, "..", "outputs", "results", "finetune_insight_report.json")) |
| cm = np.array(d["test"]["confusion_matrix"]) |
| fig, ax = plt.subplots(figsize=(3.4, 3.0)) |
| im = ax.imshow(cm, cmap="Blues") |
| ax.set_xticks([0, 1]); ax.set_xticklabels(["Pred Real", "Pred Fake"]) |
| ax.set_yticks([0, 1]); ax.set_yticklabels(["Real", "Fake"]) |
| for i in range(2): |
| for j in range(2): |
| ax.text(j, i, f"{cm[i, j]}", ha="center", va="center", |
| color="white" if cm[i, j] > cm.max() / 2 else "black", fontsize=12) |
| ax.set_title("Fine-tuned ViT test set (n=11920)") |
| fig.tight_layout() |
| out = os.path.join(FIG_DIR, "finetune_cm.png") |
| fig.savefig(out) |
| plt.close(fig) |
| print("Wrote", out) |
|
|
|
|
| if __name__ == "__main__": |
| fig_finetune_loss() |
| fig_linprobe() |
| fig_timit() |
| fig_timit_hist() |
| fig_finetune_cm() |
| print("Done.") |
|
|
