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image_feature.py

@@ -49,8 +49,8 @@ DEVICE = torch.device('cpu')
 
 
 # 第二种方式推理图片相似度
-processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
-model = AutoModel.from_pretrained("google/vit-base-patch16-224").to(DEVICE)
+# processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
+# model = AutoModel.from_pretrained("google/vit-base-patch16-224").to(DEVICE)
 
 
 # tensor([0.6061], device='cuda:0', grad_fn=<SumBackward1>)
@@ -83,35 +83,37 @@ def infer3(url1, url2):
 
     except Exception as e:
         print(f"发生了一个错误: {e}")
+
+        return 0.0
     finally:
         # 无论是否发生异常，都会执行此代码块
         print("这是finally块")
 
 
 #  推理
-def infer2(url):
-    # image_real = Image.open(requests.get(img_urls[0], stream=True).raw).convert("RGB")
-    image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
-    inputs = processor(image, return_tensors="pt").to(DEVICE)
-    outputs = model(**inputs)
-    return outputs.pooler_output
+# def infer2(url):
+#     # image_real = Image.open(requests.get(img_urls[0], stream=True).raw).convert("RGB")
+#     image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+#     inputs = processor(image, return_tensors="pt").to(DEVICE)
+#     outputs = model(**inputs)
+#     return outputs.pooler_output
 
 
 #  推理
-def infer1(image1, image2):
-    try:
-        embed_real = infer2(image1)
-        embed_gen = infer2(image2)
-        similarity_score = cosine_similarity(embed_real, embed_gen, dim=1)
-        print(similarity_score)
-        # 如果你想在CPU上操作这个值，你需要先将tensor移动到CPU
-        t_cpu = similarity_score.cpu()
-
-        # 然后提取这个值
-        return t_cpu.item()
-
-    except Exception as e:
-        print(f"发生了一个错误: {e}")
-    finally:
-        # 无论是否发生异常，都会执行此代码块
-        print("这是finally块")
+# def infer1(image1, image2):
+#     try:
+#         embed_real = infer2(image1)
+#         embed_gen = infer2(image2)
+#         similarity_score = cosine_similarity(embed_real, embed_gen, dim=1)
+#         print(similarity_score)
+#         # 如果你想在CPU上操作这个值，你需要先将tensor移动到CPU
+#         t_cpu = similarity_score.cpu()
+#
+#         # 然后提取这个值
+#         return t_cpu.item()
+#
+#     except Exception as e:
+#         print(f"发生了一个错误: {e}")
+#     finally:
+#         # 无论是否发生异常，都会执行此代码块
+#         print("这是finally块")