remove unsued import

bibert_multitask_classification.py

@@ -3,54 +3,51 @@ import numpy as np
 import torch
 
 def softmax(_outputs):
-  maxes = np.max(_outputs, axis=-1, keepdims=True)
-  shifted_exp = np.exp(_outputs - maxes)
-  return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
+    maxes = np.max(_outputs, axis=-1, keepdims=True)
+    shifted_exp = np.exp(_outputs - maxes)
+    return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
 
 class BiBert_MultiTaskPipeline(Pipeline):
 
 
-  def _sanitize_parameters(self, **kwargs):
-
-    preprocess_kwargs = {}
-    if "task_id" in kwargs:
-      preprocess_kwargs["task_id"] = kwargs["task_id"]
-    
-    forward_kwargs = {}
-    if "task_id" in kwargs:
-      forward_kwargs["task_id"] = kwargs["task_id"]
-
-    postprocess_kwargs = {}
-    if "top_k" in kwargs:
-      postprocess_kwargs["top_k"] = kwargs["top_k"]
-      postprocess_kwargs["_legacy"] = False
-    return preprocess_kwargs, forward_kwargs, postprocess_kwargs
-
-    
-
-  def preprocess(self, inputs, task_id):
-    return_tensors = self.framework
-    feature = self.tokenizer(inputs, padding = True, return_tensors=return_tensors).to(self.device)
-    task_ids = np.full(shape=1,fill_value=task_id, dtype=int)
-    feature["task_ids"] = torch.IntTensor(task_ids)
-    return feature
-  
-  def _forward(self, model_inputs, task_id):
-    return self.model(**model_inputs)
-
-  def postprocess(self, model_outputs, top_k=1, _legacy=True):
-    outputs = model_outputs["logits"][0]
-    outputs = outputs.numpy()
-    scores = softmax(outputs)
-
-    if top_k == 1 and _legacy:
-      return {"label": self.model.config.id2label[scores.argmax().item()], "score": scores.max().item()}
-
-    dict_scores = [
-        {"label": self.model.config.id2label[i], "score": score.item()} for i, score in enumerate(scores)
-    ]
-    if not _legacy:
-      dict_scores.sort(key=lambda x: x["score"], reverse=True)
-      if top_k is not None:
-          dict_scores = dict_scores[:top_k]
-    return dict_scores
+    def _sanitize_parameters(self, **kwargs):
+
+        preprocess_kwargs = {}
+        if "task_id" in kwargs:
+            preprocess_kwargs["task_id"] = kwargs["task_id"]
+        
+
+        postprocess_kwargs = {}
+        if "top_k" in kwargs:
+            postprocess_kwargs["top_k"] = kwargs["top_k"]
+            postprocess_kwargs["_legacy"] = False
+        return preprocess_kwargs, {}, postprocess_kwargs
+
+        
+
+    def preprocess(self, inputs, task_id):
+        return_tensors = self.framework
+        feature = self.tokenizer(inputs, padding = True, return_tensors=return_tensors).to(self.device)
+        task_ids = np.full(shape=1,fill_value=task_id, dtype=int)
+        feature["task_ids"] = torch.IntTensor(task_ids)
+        return feature
+
+    def _forward(self, model_inputs):
+        return self.model(**model_inputs)
+
+    def postprocess(self, model_outputs, top_k=1, _legacy=True):
+        outputs = model_outputs["logits"][0]
+        outputs = outputs.numpy()
+        scores = softmax(outputs)
+
+        if top_k == 1 and _legacy:
+            return {"label": self.model.config.id2label[scores.argmax().item()], "score": scores.max().item()}
+
+        dict_scores = [
+            {"label": self.model.config.id2label[i], "score": score.item()} for i, score in enumerate(scores)
+        ]
+        if not _legacy:
+            dict_scores.sort(key=lambda x: x["score"], reverse=True)
+            if top_k is not None:
+                dict_scores = dict_scores[:top_k]
+        return dict_scores