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code/C3/01_bge_visualized.py

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+import torch
+from visual_bge.visual_bge.modeling import Visualized_BGE
+
+model = Visualized_BGE(model_name_bge="BAAI/bge-base-en-v1.5",
+                      model_weight="../../models/bge/Visualized_base_en_v1.5.pth")
+model.eval()
+
+with torch.no_grad():
+    text_emb = model.encode(text="datawhale开源组织的logo")
+    img_emb_1 = model.encode(image="../../data/C3/imgs/datawhale01.png")
+    multi_emb_1 = model.encode(image="../../data/C3/imgs/datawhale01.png", text="datawhale开源组织的logo")
+    img_emb_2 = model.encode(image="../../data/C3/imgs/datawhale02.png")
+    multi_emb_2 = model.encode(image="../../data/C3/imgs/datawhale02.png", text="datawhale开源组织的logo")
+
+# 计算相似度
+sim_1 = img_emb_1 @ img_emb_2.T
+sim_2 = img_emb_1 @ multi_emb_1.T
+sim_3 = text_emb @ multi_emb_1.T
+sim_4 = multi_emb_1 @ multi_emb_2.T
+
+print("=== 相似度计算结果 ===")
+print(f"纯图像 vs 纯图像: {sim_1}")
+print(f"图文结合1 vs 纯图像: {sim_2}")
+print(f"图文结合1 vs 纯文本: {sim_3}")
+print(f"图文结合1 vs 图文结合2: {sim_4}")
+
+# 向量信息分析
+print("\n=== 嵌入向量信息 ===")
+print(f"多模态向量维度: {multi_emb_1.shape}")
+print(f"图像向量维度: {img_emb_1.shape}")
+print(f"多模态向量示例 (前10个元素): {multi_emb_1[0][:10]}")
+print(f"图像向量示例 (前10个元素):   {img_emb_1[0][:10]}")