update

handler.py

@@ -1,11 +1,15 @@
 import os
+import sys
 import json
 import torch
-import numpy as np
-from transformers import BertTokenizer
 from ts.torch_handler.base_handler import BaseHandler
-from model import ImprovedBERTClass
-from sklearn.preprocessing import OneHotEncoder
+from transformers import BertTokenizer, BertForSequenceClassification
+
+# Add the model directory to the Python path
+model_dir = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(model_dir)
+
+from model import ImprovedBERTClass  # Ensure this import matches your model file name
 
 class UICardMappingHandler(BaseHandler):
     def __init__(self):
@@ -16,96 +20,43 @@ class UICardMappingHandler(BaseHandler):
         self.manifest = context.manifest
         properties = context.system_properties
         model_dir = properties.get("model_dir")
-        
-        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        
-        # Load config
-        with open(os.path.join(model_dir, 'config.json'), 'r') as f:
-            self.config = json.load(f)
-        
-        # Initialize encoder and labels
-        self.labels = ['Videos', 'Unit Conversion', 'Translation', 'Shopping Product Comparison', 'Restaurants', 'Product', 'Information', 'Images', 'Gift', 'General Comparison', 'Flights', 'Answer', 'Aircraft Seat Map']
-        labels_np = np.array(self.labels).reshape(-1, 1)
-        self.encoder = OneHotEncoder(sparse_output=False)
-        self.encoder.fit(labels_np)
-        
-        # Load model
+        self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
+
+        self.tokenizer = BertTokenizer.from_pretrained(model_dir)
         self.model = ImprovedBERTClass()
         self.model.load_state_dict(torch.load(os.path.join(model_dir, 'model.pth'), map_location=self.device))
         self.model.to(self.device)
         self.model.eval()
-        
-        # Load tokenizer
-        self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-        
+
         self.initialized = True
 
     def preprocess(self, data):
-        text = data[0].get("body").get("text", "")
-        k = data[0].get("body").get("k", 3)
-        
-        inputs = self.tokenizer.encode_plus(
-            text,
-            add_special_tokens=True,
-            max_length=64,
-            padding='max_length',
-            return_tensors='pt',
-            truncation=True
-        )
-        
-        return {
-            "ids": inputs['input_ids'].to(self.device, dtype=torch.long),
-            "mask": inputs['attention_mask'].to(self.device, dtype=torch.long),
-            "token_type_ids": inputs['token_type_ids'].to(self.device, dtype=torch.long),
-            "k": k
-        }
+        text = data[0].get("data")
+        if text is None:
+            text = data[0].get("body")
+        inputs = self.tokenizer(text, return_tensors="pt", max_length=64, padding='max_length', truncation=True)
+        return inputs.to(self.device)
 
-    def inference(self, data):
+    def inference(self, inputs):
         with torch.no_grad():
-            outputs = self.model(data["ids"], data["mask"], data["token_type_ids"])
-        probabilities = torch.sigmoid(outputs)
-        return probabilities.cpu().detach().numpy().flatten(), data["k"]
+            outputs = self.model(**inputs)
+        return torch.sigmoid(outputs.logits)
 
     def postprocess(self, inference_output):
-        probabilities, k = inference_output
+        probabilities = inference_output.cpu().numpy().flatten()
+        labels = ['Videos', 'Unit Conversion', 'Translation', 'Shopping Product Comparison', 'Restaurants', 'Product', 'Information', 'Images', 'Gift', 'General Comparison', 'Flights', 'Answer', 'Aircraft Seat Map']
         
-        # Get top k predictions
-        top_k_indices = np.argsort(probabilities)[-k:][::-1]
+        top_k = 3  # You can adjust this value
+        top_k_indices = probabilities.argsort()[-top_k:][::-1]
         top_k_probs = probabilities[top_k_indices]
         
-        # Create one-hot encodings for top k indices
-        top_k_one_hot = np.zeros((k, len(probabilities)))
-        for i, idx in enumerate(top_k_indices):
-            top_k_one_hot[i, idx] = 1
+        top_k_predictions = [{"card": labels[i], "probability": float(p)} for i, p in zip(top_k_indices, top_k_probs)]
         
-        # Decode the top k predictions
-        top_k_cards = [self.decode_vector(one_hot.reshape(1, -1)) for one_hot in top_k_one_hot]
+        most_likely_card = "Answer" if sum(probabilities > 0.5) == 0 else labels[probabilities.argmax()]
         
-        # Create a list of tuples (card, probability) for top k predictions
-        top_k_predictions = list(zip(top_k_cards, top_k_probs.tolist()))
-        
-        # Determine the most likely card
-        predicted_labels = (probabilities > 0.5).astype(int)
-        if sum(predicted_labels) == 0:
-            most_likely_card = "Answer"
-        else:
-            most_likely_card = self.decode_vector(predicted_labels.reshape(1, -1))
-        
-        # Prepare the response
         result = {
             "most_likely_card": most_likely_card,
             "top_k_predictions": top_k_predictions
         }
         
         return [result]
-
-    def decode_vector(self, vector):
-        original_label = self.encoder.inverse_transform(vector)
-        return original_label[0][0]  # Returns the label as a string
-
-    def handle(self, data, context):
-        self.context = context
-        data = self.preprocess(data)
-        data = self.inference(data)
-        data = self.postprocess(data)
-        return data

inference.py

@@ -3,8 +3,49 @@ import json
 import torch
 import numpy as np
 from transformers import BertTokenizer
-from model import ImprovedBERTClass  # Ensure this import matches your model file name
 from sklearn.preprocessing import OneHotEncoder
+import transformers
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class AttentionPool(nn.Module):
+    def __init__(self, hidden_size):
+        super().__init__()
+        self.attention = nn.Linear(hidden_size, 1)
+    
+    def forward(self, last_hidden_state):
+        attention_scores = self.attention(last_hidden_state).squeeze(-1)
+        attention_weights = F.softmax(attention_scores, dim=1)
+        pooled_output = torch.bmm(attention_weights.unsqueeze(1), last_hidden_state).squeeze(1)
+        return pooled_output
+
+class MultiSampleDropout(nn.Module):
+    def __init__(self, dropout=0.5, num_samples=5):
+        super().__init__()
+        self.dropout = nn.Dropout(dropout)
+        self.num_samples = num_samples
+    
+    def forward(self, x):
+        return torch.mean(torch.stack([self.dropout(x) for _ in range(self.num_samples)]), dim=0)
+
+
+class ImprovedBERTClass(nn.Module):
+    def __init__(self, num_classes=13):
+        super().__init__()
+        self.bert = transformers.BertModel.from_pretrained('bert-base-uncased')
+        self.attention_pool = AttentionPool(768)
+        self.dropout = MultiSampleDropout()
+        self.norm = nn.LayerNorm(768)
+        self.classifier = nn.Linear(768, num_classes)
+        
+    def forward(self, input_ids, attention_mask, token_type_ids):
+        bert_output = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
+        pooled_output = self.attention_pool(bert_output.last_hidden_state)
+        pooled_output = self.dropout(pooled_output)
+        pooled_output = self.norm(pooled_output)
+        logits = self.classifier(pooled_output)
+        return logits
 
 def handler(data, context):
     """Handle incoming requests to the SageMaker endpoint."""