| """ |
| visualize_grounding.py |
| Visualize layer-wise mean divergence for grounding ablation experiments, |
| comparing COCO vs VGMED across models and ablation types. |
| |
| Usage: |
| python visualize_grounding.py |
| python visualize_grounding.py --ablation mask_region |
| python visualize_grounding.py --q_type attribute |
| python visualize_grounding.py --output_dir my_plots |
| """ |
| import argparse |
| import os |
|
|
| import matplotlib.pyplot as plt |
| import numpy as np |
| import torch |
|
|
| GROUNDING_ROOT = "grounding_results" |
|
|
| MODELS = { |
| "HuatuoGPT-V-7B": "HuatuoGPT-V-7B/HuatuoGPT-Vision-7B", |
| "LLaVA-7B": "LLaVA-7B/llava-1.5-7b-hf", |
| "LLaVA-13B": "LLaVA-13B/llava-1.5-13b-hf", |
| "LLaVA-Med-7B": "LLaVA-Med-7B/llava-med-v1.5-mistral-7b", |
| "MedGemma-4B-IT": "MedGemma-4B-IT/medgemma-4b-it", |
| "Qwen2.5VL-3B": "Qwen2.5VL-3B/Qwen2.5-VL-3B-Instruct", |
| "Qwen2.5VL-7B": "Qwen2.5VL-7B/Qwen2.5-VL-7B-Instruct", |
| "Qwen3VL-2B": "Qwen3VL-2B/Qwen3-VL-2B-Instruct", |
| "Qwen3VL-4B": "Qwen3VL-4B/Qwen3-VL-4B-Instruct", |
| "Qwen3VL-8B": "Qwen3VL-8B/Qwen3-VL-8B-Instruct", |
| } |
|
|
| ABLATIONS = { |
| "mask_region": "Mask Key Regions", |
| "mask_text": "Mask Key Words", |
| "diff_region": "Mask Key Regions Compared with Mask Random Regions", |
| "diff_text": "Mask Key Words Compared with Mask Random Words", |
| } |
|
|
| Q_TYPES = ["localization", "attribute"] |
| DATASETS = ["COCO", "VGMED"] |
|
|
| |
| |
| |
| COLORS = {"COCO": "#2196F3", "VGMED": "#F44336"} |
| DIFF_POS = "#F44336" |
| DIFF_NEG = "#2196F3" |
| FIG_WIDTH = 8.0 |
| ROW_HEIGHT = 5.0 |
| TITLE_FS = 22 |
| AXIS_FS = 18 |
| TICK_FS = 16 |
| LEGEND_FS = 16 |
| MARKER = "o" |
| MARKER_SIZE = 9 |
| LINE_WIDTH = 3.5 |
| GRID_ALPHA = 0.3 |
| DPI = 600 |
| plt.rcParams["font.weight"] = "bold" |
| plt.rcParams["axes.labelweight"] = "bold" |
| plt.rcParams["axes.titleweight"] = "bold" |
| plt.rcParams["axes.linewidth"] = 1.5 |
| |
|
|
|
|
| def load_pt(path): |
| data = torch.load(path, map_location="cpu", weights_only=False) |
| if isinstance(data, torch.Tensor): |
| return data.numpy() |
| arr = np.stack(data, axis=0) |
| return arr.mean(axis=0) |
|
|
|
|
| def plot_ablation(ablation_key, ablation_label, q_type, output_dir, grounding_root): |
| """One standalone PNG per model: top = divergence curves, bottom = VGMED-COCO diff bars.""" |
| for model_name, model_dir in MODELS.items(): |
| fig, (ax_top, ax_bot) = plt.subplots( |
| 2, 1, figsize=(FIG_WIDTH, ROW_HEIGHT * 2), |
| ) |
| fig.suptitle( |
| f"{model_name} — {ablation_label} ({q_type})", |
| fontsize=TITLE_FS, fontweight="bold", |
| ) |
|
|
| loaded = {} |
| for ds in DATASETS: |
| fname = f"{ds}_{q_type}_mean_divergence_per_layer.pt" |
| fpath = os.path.join(grounding_root, model_dir, ablation_key, fname) |
| if not os.path.exists(fpath): |
| print(f"[skip] {fpath}") |
| continue |
| loaded[ds] = load_pt(fpath) |
|
|
| |
| for ds, vals in loaded.items(): |
| layers = np.arange(len(vals)) |
| ax_top.plot(layers, vals, label=ds, color=COLORS[ds], |
| linewidth=LINE_WIDTH, marker=MARKER, markersize=MARKER_SIZE) |
| ax_top.set_ylabel("Mean Divergence", fontsize=AXIS_FS) |
| ax_top.tick_params(labelsize=TICK_FS) |
| ax_top.grid(True, alpha=GRID_ALPHA) |
| if loaded: |
| ax_top.legend(fontsize=LEGEND_FS) |
|
|
| |
| if "VGMED" in loaded and "COCO" in loaded: |
| vgmed = loaded["VGMED"] |
| coco = loaded["COCO"] |
| n = min(len(vgmed), len(coco)) |
| diff = vgmed[:n] - coco[:n] |
| layers = np.arange(n) |
| ax_bot.bar(layers, diff, |
| color=[DIFF_POS if d >= 0 else DIFF_NEG for d in diff], |
| alpha=0.7, width=0.8) |
| ax_bot.axhline(0, color="black", linewidth=0.8, linestyle="--") |
| ax_bot.set_xlabel("Layer", fontsize=AXIS_FS) |
| ax_bot.set_ylabel("VGMED − COCO", fontsize=AXIS_FS) |
| ax_bot.tick_params(labelsize=TICK_FS) |
| ax_bot.grid(True, alpha=GRID_ALPHA, axis="y") |
| else: |
| ax_bot.set_visible(False) |
|
|
| plt.tight_layout() |
| q_type_dir = os.path.join(output_dir, q_type, ablation_key) |
| os.makedirs(q_type_dir, exist_ok=True) |
| out_path = os.path.join(q_type_dir, f"{model_name}_{ablation_key}_{q_type}.png") |
| plt.savefig(out_path, dpi=DPI, bbox_inches="tight") |
| plt.close() |
| print(f"Saved: {out_path}") |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--ablation", choices=list(ABLATIONS.keys()), default=None, |
| help="Plot only one ablation type (default: all)") |
| parser.add_argument("--q_type", choices=Q_TYPES, default=None, |
| help="Plot only one q_type (default: all)") |
| parser.add_argument("--grounding_root", default=GROUNDING_ROOT) |
| parser.add_argument("--output_dir", default="grounding_plots") |
| args = parser.parse_args() |
|
|
| ablations = {args.ablation: ABLATIONS[args.ablation]} if args.ablation else ABLATIONS |
| q_types = [args.q_type] if args.q_type else Q_TYPES |
|
|
| for ablation_key, ablation_label in ablations.items(): |
| for q_type in q_types: |
| plot_ablation(ablation_key, ablation_label, q_type, |
| args.output_dir, args.grounding_root) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
