iglob file-search improvements

detect.py

@@ -183,6 +183,9 @@ if __name__ == '__main__':
     parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
     parser.add_argument('--augment', action='store_true', help='augmented inference')
     opt = parser.parse_args()
+    opt.cfg = list(glob.iglob('./**/' + opt.cfg, recursive=True))[0]  # find file
+    opt.names = list(glob.iglob('./**/' + opt.names, recursive=True))[0]  # find file
+    opt.weights = list(glob.iglob('./**/' + opt.weights, recursive=True))[0]  # find file
     print(opt)
 
     with torch.no_grad():

test.py

@@ -242,6 +242,9 @@ if __name__ == '__main__':
     parser.add_argument('--augment', action='store_true', help='augmented inference')
     opt = parser.parse_args()
     opt.save_json = opt.save_json or any([x in opt.data for x in ['coco.data', 'coco2014.data', 'coco2017.data']])
+    opt.weights = list(glob.iglob('./**/' + opt.weights, recursive=True))[0]  # find file
+    opt.cfg = list(glob.iglob('./**/' + opt.cfg, recursive=True))[0]  # find file
+    opt.data = list(glob.iglob('./**/' + opt.data, recursive=True))[0]  # find file
     print(opt)
 
     # task = 'test', 'study', 'benchmark'

train.py

@@ -251,6 +251,7 @@ def train(hyp):
                     if 'momentum' in x:
                         x['momentum'] = np.interp(ni, [0, n_burn], [0.9, hyp['momentum']])
 
+
             # Multi-Scale
             if opt.multi_scale:
                 if ni / accumulate % 1 == 0:  #  adjust img_size (67% - 150%) every 1 batch
@@ -396,6 +397,8 @@ if __name__ == '__main__':
     opt = parser.parse_args()
     opt.weights = last if opt.resume else opt.weights
     check_git_status()
+    opt.cfg = list(glob.iglob('./**/' + opt.cfg, recursive=True))[0]  # find file
+    opt.data = list(glob.iglob('./**/' + opt.data, recursive=True))[0]  # find file
     print(opt)
     opt.img_size.extend([opt.img_size[-1]] * (3 - len(opt.img_size)))  # extend to 3 sizes (min, max, test)
     device = torch_utils.select_device(opt.device, apex=mixed_precision, batch_size=opt.batch_size)