update sequence classfication outpus

modelscope/pipelines/nlp/nli_pipeline.py

@@ -32,24 +32,25 @@ class NLIPipeline(Pipeline):
         """
         assert isinstance(model, str) or isinstance(model, SbertForNLI), \
             'model must be a single str or SbertForNLI'
-        sc_model = model if isinstance(
+        model = model if isinstance(
             model, SbertForNLI) else Model.from_pretrained(model)
         if preprocessor is None:
             preprocessor = NLIPreprocessor(
-                sc_model.model_dir,
+                model.model_dir,
                 first_sequence=first_sequence,
                 second_sequence=second_sequence)
-        sc_model.eval()
-        super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
-        assert len(sc_model.id2label) > 0
+        model.eval()
+        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
+        assert len(model.id2label) > 0
 
     def forward(self, inputs: Dict[str, Any],
                 **forward_params) -> Dict[str, Any]:
         with torch.no_grad():
             return super().forward(inputs, **forward_params)
 
-    def postprocess(self, inputs: Dict[str, Any],
-                    **postprocess_params) -> Dict[str, str]:
+    def postprocess(self,
+                    inputs: Dict[str, Any],
+                    topk: int = 5) -> Dict[str, str]:
         """process the prediction results
 
         Args:
@@ -59,30 +60,13 @@ class NLIPipeline(Pipeline):
             Dict[str, str]: the prediction results
         """
 
-        probs = inputs['probabilities']
-        logits = inputs['logits']
-        predictions = np.argsort(-probs, axis=-1)
-        preds = predictions[0]
-        b = 0
-        new_result = list()
-        for pred in preds:
-            new_result.append({
-                'pred': self.model.id2label[pred],
-                'prob': float(probs[b][pred]),
-                'logit': float(logits[b][pred])
-            })
-        new_results = list()
-        new_results.append({
-            'id':
-            inputs['id'][b] if 'id' in inputs else str(uuid.uuid4()),
-            'output':
-            new_result,
-            'predictions':
-            new_result[0]['pred'],
-            'probabilities':
-            ','.join([str(t) for t in inputs['probabilities'][b]]),
-            'logits':
-            ','.join([str(t) for t in inputs['logits'][b]])
-        })
+        probs = inputs['probabilities'][0]
+        num_classes = probs.shape[0]
+        topk = min(topk, num_classes)
+        top_indices = np.argpartition(probs, -topk)[-topk:]
+        cls_ids = top_indices[np.argsort(probs[top_indices])]
+        probs = probs[cls_ids].tolist()
 
-        return new_results[0]
+        cls_names = [self.model.id2label[cid] for cid in cls_ids]
+
+        return {'scores': probs, 'labels': cls_names}

modelscope/pipelines/nlp/sentiment_classification_pipeline.py

@@ -36,25 +36,26 @@ class SentimentClassificationPipeline(Pipeline):
         """
         assert isinstance(model, str) or isinstance(model, SbertForSentimentClassification), \
             'model must be a single str or SbertForSentimentClassification'
-        sc_model = model if isinstance(
+        model = model if isinstance(
             model,
             SbertForSentimentClassification) else Model.from_pretrained(model)
         if preprocessor is None:
             preprocessor = SentimentClassificationPreprocessor(
-                sc_model.model_dir,
+                model.model_dir,
                 first_sequence=first_sequence,
                 second_sequence=second_sequence)
-        sc_model.eval()
-        super().__init__(model=sc_model, preprocessor=preprocessor, **kwargs)
-        assert len(sc_model.id2label) > 0
+        model.eval()
+        super().__init__(model=model, preprocessor=preprocessor, **kwargs)
+        assert len(model.id2label) > 0
 
     def forward(self, inputs: Dict[str, Any],
                 **forward_params) -> Dict[str, Any]:
         with torch.no_grad():
             return super().forward(inputs, **forward_params)
 
-    def postprocess(self, inputs: Dict[str, Any],
-                    **postprocess_params) -> Dict[str, str]:
+    def postprocess(self,
+                    inputs: Dict[str, Any],
+                    topk: int = 5) -> Dict[str, str]:
         """process the prediction results
 
         Args:
@@ -64,30 +65,13 @@ class SentimentClassificationPipeline(Pipeline):
             Dict[str, str]: the prediction results
         """
 
-        probs = inputs['probabilities']
-        logits = inputs['logits']
-        predictions = np.argsort(-probs, axis=-1)
-        preds = predictions[0]
-        b = 0
-        new_result = list()
-        for pred in preds:
-            new_result.append({
-                'pred': self.model.id2label[pred],
-                'prob': float(probs[b][pred]),
-                'logit': float(logits[b][pred])
-            })
-        new_results = list()
-        new_results.append({
-            'id':
-            inputs['id'][b] if 'id' in inputs else str(uuid.uuid4()),
-            'output':
-            new_result,
-            'predictions':
-            new_result[0]['pred'],
-            'probabilities':
-            ','.join([str(t) for t in inputs['probabilities'][b]]),
-            'logits':
-            ','.join([str(t) for t in inputs['logits'][b]])
-        })
+        probs = inputs['probabilities'][0]
+        num_classes = probs.shape[0]
+        topk = min(topk, num_classes)
+        top_indices = np.argpartition(probs, -topk)[-topk:]
+        cls_ids = top_indices[np.argsort(probs[top_indices])]
+        probs = probs[cls_ids].tolist()
 
-        return new_results[0]
+        cls_names = [self.model.id2label[cid] for cid in cls_ids]
+
+        return {'scores': probs, 'labels': cls_names}

modelscope/pipelines/outputs.py

@@ -101,6 +101,20 @@ TASK_OUTPUTS = {
     #   }
     Tasks.sentence_similarity: ['scores', 'labels'],
 
+    # sentiment classification result for single sample
+    #   {
+    #       "labels": ["happy", "sad", "calm", "angry"],
+    #       "scores": [0.9, 0.1, 0.05, 0.05]
+    #   }
+    Tasks.sentiment_classification: ['scores', 'labels'],
+
+    # nli result for single sample
+    #   {
+    #       "labels": ["happy", "sad", "calm", "angry"],
+    #       "scores": [0.9, 0.1, 0.05, 0.05]
+    #   }
+    Tasks.nli: ['scores', 'labels'],
+
     # ============ audio tasks ===================
 
     # audio processed for single file in PCM format