Update README.md

README.md

@@ -4,34 +4,37 @@ license: mit
 
 This is the structure of the BLIPNet model. You can load the model with it, or you can create a bigger model for your task.
 
-        class BLIPNet(torch.nn.Module):
-            def __init__(self, ):
-                super().__init__()
-                #Generation Model
-                self.model = BlipForConditionalGeneration.from_pretrained(MODEL_NAME, cache_dir="model")
-                #Same with https://huggingface.co/uf-aice-lab/BLIP-Math
-                self.ebd_dim = ebd_dim= 443136
-
-                #Classification Model
-                fc_dim = 64  # You can choose a higher number for better performance, for example, 1024.
-                self.head = nn.Sequential(
-                    nn.Linear(self.ebd_dim, fc_dim),
-                    nn.ReLU(), 
-                )
-                self.score = nn.Linear(fc_dim, 5) #5 classes
-
+  class BLIPNet(torch.nn.Module):
+      def __init__(self, ):
+          super().__init__()
+          #Generation Model
+          self.model = BlipForConditionalGeneration.from_pretrained(MODEL_NAME, cache_dir="model")
+          #Same with https://huggingface.co/uf-aice-lab/BLIP-Math
+          self.ebd_dim = ebd_dim= 443136
+  
+          #Classification Model
+          fc_dim = 64  # You can choose a higher number for better performance, for example, 1024.
+          self.head = nn.Sequential(
+              nn.Linear(self.ebd_dim, fc_dim),
+              nn.ReLU(), 
+          )
+          self.score = nn.Linear(fc_dim, 5) #5 classes
+  
+  
+      def forward(self, pixel_values, input_ids):
+          outputs = self.model(input_ids=input_ids, pixel_values=pixel_values, labels=input_ids)
+          image_text_embeds = self.model.vision_model(pixel_values, return_dict=True).last_hidden_state
+          image_text_embeds = self.head(image_embeds.view(-1, self.ebd_dim))
+  
+          #A classification model is based on embeddings from a generative model to leverage BLIP's powerful image-text encoding capabilities.
+          logits = self.score(image_embeds)
+  
+          #generated text, probabilities of classification
+          return outputs, logits  
+          
+  model = BLIPNet()
+  model.load_state_dict(torch.load(best_model_wts_path) ,strict=False)
   
-            def forward(self, pixel_values, input_ids):
-                outputs = self.model(input_ids=input_ids, pixel_values=pixel_values, labels=input_ids)
-                image_text_embeds = self.model.vision_model(pixel_values, return_dict=True).last_hidden_state
-                image_text_embeds = self.head(image_embeds.view(-1, self.ebd_dim))
-
-                #A classification model is based on embeddings from a generative model to leverage BLIP's powerful image-text encoding capabilities.
-                logits = self.score(image_embeds)
-
-                #generated text, probabilities of classification
-                return outputs, logits  
-
 You need to input the sample in the same way as: 
 https://huggingface.co/uf-aice-lab/BLIP-Math
 Then you can get the text and score at the same time.