### 绘制ViT (Vision Transformer) 模型架构图 为了可视化 Vision Transformer 的结构，可以利用 Python 中的 `matplotlib` 和 `networkx` 库来创建一个简单的图表表示。然而更专业的绘图通常会采用专门用于绘制神经网络架构的工具如 Netron 或者使用 TensorFlow/Keras 提供的相关功能。 下面提供一段基于 Matplotlib 的简单实现方法： ```python import matplotlib.pyplot as plt from matplotlib.patches import Rectangle, ConnectionPatch def draw_vit_architecture(): fig, ax = plt.subplots(figsize=(8, 6)) # 定义颜色和字体大小 rect_color = 'lightblue' text_size = 12 # 输入层矩形框 input_rect = Rectangle((1, 4), width=1.5, height=1, facecolor='white', edgecolor='black') ax.add_patch(input_rect) ax.text(1.75, 4.5, "Input\n(Patch Embedding)", ha='center', va='center', fontsize=text_size) # 多头自注意力机制模块 msa_rect = Rectangle((3, 4), width=2, height=1, facecolor=rect_color, edgecolor='black') ax.add_patch(msa_rect) ax.text(4, 4.5, "Multi-head Self-\nAttention", ha='center', va='center', fontsize=text_size) # 前馈神经网络FFN ffn_rect = Rectangle((6, 4), width=2, height=1, facecolor=rect_color, edgecolor='black') ax.add_patch(ffn_rect) ax.text(7, 4.5, "Feed Forward Network", ha='center', va='center', fontsize=text_size) # Layer Normalization 层 ln_rect_1 = Rectangle((3, 3), width=2, height=0.5, facecolor=rect_color, edgecolor='black') ln_rect_2 = Rectangle((6, 3), width=2, height=0.5, facecolor=rect_color, edgecolor='black') ax.add_patch(ln_rect_1) ax.add_patch(ln_rect_2) ax.text(4, 3.25, "Layer Norm", ha='center', va='center', fontsize=text_size) ax.text(7, 3.25, "Layer Norm", ha='center', va='center', fontsize=text_size) # 连接线 conn_style = dict(color="k", arrowstyle="-|>") con_in_msa = ConnectionPatch(xyA=[2.5, 4], coordsA=ax.transData, xyB=[3, 4], coordsB=ax.transData, **conn_style) con_msa_ln = ConnectionPatch(xyA=[5, 4], coordsA=ax.transData, xyB=[3, 3.5], coordsB=ax.transData, **conn_style) con_ffn_out = ConnectionPatch(xyA=[8, 4], coordsA=ax.transData, xyB=[9, 4], coordsB=ax.transData, **conn_style) ax.add_artist(con_in_msa) ax.add_artist(con_msa_ln) ax.add_artist(con_ffn_out) # 输出层 output_rect = Rectangle((9, 4), width=1.5, height=1, facecolor='white', edgecolor='black') ax.add_patch(output_rect) ax.text(9.75, 4.5, "Output\n(Classification Head)", ha='center', va='center', fontsize=text_size) # 设置坐标轴不可见并调整布局 ax.set_xlim([0, 11]) ax.set_ylim([0, 6]) ax.axis('off') plt.show() draw_vit_architecture() ``` 这段代码通过定义不同的矩形区域代表 ViT 架构的不同部分，并用线条连接这些组件以展示数据流方向[^1]。