Upload metric_for_tp_fp_samples.py

metric_for_tp_fp_samples.py

@@ -98,7 +98,7 @@ class metric_tp_fp_Datasets(evaluate.Metric):
 			results = self.threshold(logits, prediction_strategy[1])
 		elif prediction_strategy[0] == "topk":
 			results = self.topk(logits, prediction_strategy[1])
-        return results
+		return results
 	#Prediction strategy functions______________________________________________
 	def argmax_max(self, logits):
 		predictions = []
@@ -132,8 +132,8 @@ class metric_tp_fp_Datasets(evaluate.Metric):
 		return predictions
 	def topk(self, logits, topk):
 		predictions = logits.topk(topk).indices.tolist()
-        print(predictions)
-        return predictions
+    		print(predictions)
+    		return predictions
 
 	#Builds a report with the metrics####################################################
 	def metrics_report(self, true_positives = "", false_positives = ""):