Update metric_for_tp_fp_samples.py

metric_for_tp_fp_samples.py

@@ -106,7 +106,9 @@ class metric_tp_fp_Datasets(evaluate.Metric):
 		for prediction in argmax:
 			predicted_indexes = [prediction.item()]
 			predictions.append(predicted_indexes)	
-		return predictions
+        print("JAAAAAAAAAARL")		
+        print(predictions)
+        return predictions
 	def softmax_threshold(logits, threshold):
 		predictions = []
 		softmax = torch.softmax(logits, dim=-1)
@@ -132,8 +134,7 @@ class metric_tp_fp_Datasets(evaluate.Metric):
 		return predictions
 	def topk(self, logits, topk):
 		predictions = logits.topk(topk).indices.tolist()
-		print(predictions)
-		return predictions
+        return predictions
 
 	#Builds a report with the metrics####################################################
 	def metrics_report(self, true_positives = "", false_positives = ""):