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image_feature.py

@@ -55,9 +55,10 @@ DEVICE = torch.device('cpu')
 # model = AutoModel.from_pretrained("google/vit-base-patch16-224-in21k").to(DEVICE)
 # processor = AutoImageProcessor.from_pretrained("chanhua/autotrain-izefx-v3qh0")
 # model = AutoModel.from_pretrained("chanhua/autotrain-izefx-v3qh0").to(DEVICE)
-
-processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')
-model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
+processor = ViTImageProcessor.from_pretrained('google/vit-large-patch16-224-in21k')
+model = ViTModel.from_pretrained('google/vit-large-patch16-224-in21k')
+# processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224-in21k')
+# model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
 
 
 # tensor([0.6061], device='cuda:0', grad_fn=<SumBackward1>)
@@ -102,16 +103,6 @@ def infer4(url1, url2):
         # 无论是否发生异常，都会执行此代码块
         print("这是finally块")
 
-#  推理
-def infer3(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")
-    image = Image.open(url).convert('RGB')
-
-    inputs = processor(image, return_tensors="pt").to(DEVICE)
-    outputs = model(**inputs)
-    return outputs.pooler_output
-
 #  推理
 def infer2(url):
     # image_real = Image.open(requests.get(img_urls[0], stream=True).raw).convert("RGB")