Update metric_for_tp_fp_samples.py

metric_for_tp_fp_samples.py

@@ -195,10 +195,10 @@ class metric_tp_fp_Datasets(evaluate.Metric):
 		"""Returns the scores"""
 		# TODO: Compute the different scores of the metric
 		predictions = torch.from_numpy(np.array(predictions, dtype = 'float32'))
-		classes = []
-		for value in references:
-			if value[0] not in classes:
-				classes.append(value[0])
+		classes = [i for i in range[len(predictions)[0]]
+		#for value in references:
+		#	if value[0] not in classes:
+		#		classes.append(value[0])
 		results = {}
 		for prediction_strategy in prediction_strategies:
 			prediction_strategy_name = '-'.join(map(str, prediction_strategy))