diff --git a/test.py b/test.py
index ee2b0ab..292c4c4 100644
--- a/test.py
+++ b/test.py
@@ -255,7 +255,7 @@ if __name__ == '__main__':
     opt = parser.parse_args()
     opt.img_size = check_img_size(opt.img_size)
     opt.save_json = opt.save_json or opt.data.endswith('coco.yaml')
-    opt.data = glob.glob('./**/' + opt.data, recursive=True)[0]  # find file
+    opt.data = check_file(opt.data)  # check file
     print(opt)
 
     # task = 'val', 'test', 'study'
diff --git a/train.py b/train.py
index a3112ee..b615b47 100644
--- a/train.py
+++ b/train.py
@@ -384,8 +384,8 @@ if __name__ == '__main__':
     parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
     opt = parser.parse_args()
     opt.weights = last if opt.resume else opt.weights
-    opt.cfg = glob.glob('./**/' + opt.cfg, recursive=True)[0]  # find file
-    opt.data = glob.glob('./**/' + opt.data, recursive=True)[0]  # find file
+    opt.cfg = check_file(opt.cfg)  # check file
+    opt.data = check_file(opt.data)  # check file
     print(opt)
     opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)
     device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size)
diff --git a/utils/activations.py b/utils/activations.py
index 0d23a92..cf226fe 100644
--- a/utils/activations.py
+++ b/utils/activations.py
@@ -1,4 +1,5 @@
 import torch
+import torch.nn as nn
 import torch.nn.functional as F
 import torch.nn as nn
 
diff --git a/utils/utils.py b/utils/utils.py
index 860dfe5..bce8a10 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -64,6 +64,16 @@ def check_best_possible_recall(dataset, anchors, thr):
                       'Compute new anchors with utils.utils.kmeans_anchors() and update model before training.' % bpr
 
 
+def check_file(file):
+    # Searches for file if not found locally
+    if os.path.isfile(file):
+        return file
+    else:
+        files = glob.glob('./**/' + file, recursive=True)  # find file
+        assert len(files), 'File Not Found: %s' % file  # assert file was found
+        return files[0]  # return first file if multiple found
+
+
 def make_divisible(x, divisor):
     # Returns x evenly divisble by divisor
     return math.ceil(x / divisor) * divisor