add variables to mixup

Code/main.py

@@ -67,7 +67,7 @@ def run(raw_args):
 	#train...
 	print("training..")
 	if args.train_loop=="swp":
-		evaluation_test, evaluation_train = train.train(model, args.architecture,args.random_seed, args.gradient_accumulation_steps, args.mix_up, train_dataset, test_dataset, args.epochs, args.learning_rate, args.batch_size, args.test_batch_size)
+		evaluation_test, evaluation_train = train.train(model, args.architecture,args.random_seed, args.gradient_accumulation_steps, args.mix_up, args.threshold, args.lambda_value, train_dataset, test_dataset, args.epochs, args.learning_rate, args.batch_size, args.test_batch_size)
 	elif args.train_loop=="salami":
 		evaluation_test = train.train_salami(model,args.random_seed, train_dataset, test_dataset, args.batch_size, args.test_batch_size, args.learning_rate, args.epochs)
 	else:
@@ -187,6 +187,20 @@ if __name__ == "__main__":
 		help="whether or not to apply mixup during training",
 		action="store_true")
 
+	parser.add_argument(
+		"-threshold",
+		"--threshold",
+		help="specifies the value for mixup threshold",
+		type=float,
+		default=0.05)
+
+	parser.add_argument(
+		"-lambda",
+		"--lambda_value",
+		help="speficies the lambda value for mixup",
+		type=float,
+		default=0.4)
+
 
 	#Test arguments
 	parser.add_argument(

Code/train.py

@@ -22,7 +22,7 @@ torch.cuda.empty_cache()
 
 
 
-def train(model, name, seed,gradient_accumulation_steps,mixup, train_dataset, test_dataset, num_epochs, learning_rate, batch_size, test_batch_size):
+def train(model, name, seed,gradient_accumulation_steps,mixup, threshold, lambda_value, train_dataset, test_dataset, num_epochs, learning_rate, batch_size, test_batch_size):
 	"""Write Train loop for model with certain train dataset"""
 	#set_seed(seed)
 	#if model_name[0] == "b":
@@ -85,7 +85,7 @@ def train(model, name, seed,gradient_accumulation_steps,mixup, train_dataset, te
 			#	#	print("outputs {0}: {1}".format(i, outputs[i].size()))
 			if mixup == True:
 				#print("length of outputs: ", len(outputs))
-				new_matrix_batch, new_labels_batch = mixup_function(outputs[2], labels)
+				new_matrix_batch, new_labels_batch = mixup_function(outputs[2], labels, lambda_value, threshold)
                 #for matrix in new_matrix_batch
 				new_matrix_batch.to("cuda")
 				new_labels_batch.to("cuda")
@@ -125,18 +125,18 @@ def train(model, name, seed,gradient_accumulation_steps,mixup, train_dataset, te
 
 
 	
-def mixup_function(batch_of_matrices, batch_of_labels):
+def mixup_function(batch_of_matrices, batch_of_labels, l, t):
 	runs = math.floor(batch_of_matrices.size()[0]/2)
 	counter=0
 	results=[]
 	result_labels=[]
 	for i in range(runs):
-		print("doing interpolation...")
+		print("doing interpolation with lambda: {0} and threshold: {1}...".format(l, t))
 		matrix1=batch_of_matrices[counter]
 		label1=batch_of_labels[counter]
 		matrix2=batch_of_matrices[counter+1]
 		label2=batch_of_labels[counter+1]
-		new_matrix, new_label=interpolate(matrix1, label1, matrix2, label2, 0.4, 0.05)
+		new_matrix, new_label=interpolate(matrix1, label1, matrix2, label2, l, t)
 		if new_matrix != None:
 			results.append(new_matrix)
 			result_labels.append(new_label)