| import os
|
| import torch
|
| import numpy as np
|
| import matplotlib.pyplot as plt
|
|
|
|
|
| script_dir = os.path.dirname(os.path.abspath(__file__))
|
| file_path = os.path.join(script_dir, "token_7.pt")
|
| try:
|
| data = torch.load(file_path, map_location=torch.device('cpu'))[0]
|
| except FileNotFoundError:
|
| raise FileNotFoundError(f"File {file_path} not found. Please check the path.")
|
| data = data[:, :4000]
|
|
|
|
|
| if data.dim() == 1:
|
| probs = data.numpy()
|
| elif data.dim() == 2:
|
| probs = data.mean(dim=0).numpy()
|
| else:
|
| raise ValueError(f"Unexpected tensor shape: {data.shape}")
|
|
|
| if abs(probs.sum() - 1.0) > 1e-5:
|
| probs = probs / probs.sum()
|
|
|
| labels = np.arange(len(probs))
|
|
|
|
|
| plt.rcParams['font.weight'] = 'bold'
|
| plt.figure(figsize=(10, 6))
|
| plt.bar(labels, probs, color='skyblue', edgecolor='black')
|
| plt.xlabel('Text Token Index', fontsize=16, fontweight='bold')
|
| plt.ylabel('Probability', fontsize=16, fontweight='bold')
|
| plt.ylim(0, 0.1)
|
| plt.grid(True, axis='y', linestyle='--', alpha=0.7)
|
| ax = plt.gca()
|
| for label in ax.get_xticklabels() + ax.get_yticklabels():
|
| label.set_fontweight('bold')
|
| plt.tight_layout()
|
| save_path = os.path.join(script_dir, 'MLLM_P.png')
|
| plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
| print(f"已保存: {save_path}")
|
| plt.show()
|
|
|
