updates

detect.py

@@ -8,7 +8,7 @@ from utils.utils import *
 
 
 def detect(cfg,
-           data_cfg,
+           data,
            weights,
            images='data/samples',  # input folder
            output='output',  # output folder
@@ -59,7 +59,7 @@ def detect(cfg,
         dataloader = LoadImages(images, img_size=img_size)
 
     # Get classes and colors
-    classes = load_classes(parse_data_cfg(data_cfg)['names'])
+    classes = load_classes(parse_data_cfg(data)['names'])
     colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))]
 
     for i, (path, img, im0, vid_cap) in enumerate(dataloader):
@@ -133,7 +133,7 @@ if __name__ == '__main__':
 
     with torch.no_grad():
         detect(opt.cfg,
-               opt.data_cfg,
+               opt.data,
                opt.weights,
                images=opt.images,
                img_size=opt.img_size,

test.py

@@ -9,7 +9,7 @@ from utils.utils import *
 
 
 def test(cfg,
-         data_cfg,
+         data,
          weights=None,
          batch_size=16,
          img_size=416,
@@ -37,10 +37,10 @@ def test(cfg,
         device = next(model.parameters()).device  # get model device
 
     # Configure run
-    data_cfg = parse_data_cfg(data_cfg)
-    nc = int(data_cfg['classes'])  # number of classes
-    test_path = data_cfg['valid']  # path to test images
-    names = load_classes(data_cfg['names'])  # class names
+    data = parse_data_cfg(data)
+    nc = int(data['classes'])  # number of classes
+    test_path = data['valid']  # path to test images
+    names = load_classes(data['names'])  # class names
 
     # Dataloader
     dataset = LoadImagesAndLabels(test_path, img_size, batch_size)
@@ -203,7 +203,7 @@ if __name__ == '__main__':
 
     with torch.no_grad():
         mAP = test(opt.cfg,
-                   opt.data_cfg,
+                   opt.data,
                    opt.weights,
                    opt.batch_size,
                    opt.img_size,

train.py

@@ -62,7 +62,7 @@ hyp = {'giou': 1.153,  # giou loss gain
 
 
 def train(cfg,
-          data_cfg,
+          data,
           img_size=416,
           epochs=100,  # 500200 batches at bs 16, 117263 images = 273 epochs
           batch_size=16,
@@ -81,7 +81,7 @@ def train(cfg,
         img_size = img_sz_max * 32  # initiate with maximum multi_scale size
 
     # Configure run
-    data_dict = parse_data_cfg(data_cfg)
+    data_dict = parse_data_cfg(data)
     train_path = data_dict['train']
     nc = int(data_dict['classes'])  # number of classes
 
@@ -270,7 +270,7 @@ def train(cfg,
         # Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
         if not (opt.notest or (opt.nosave and epoch < 10)) or epoch == epochs - 1:
             with torch.no_grad():
-                results, maps = test.test(cfg, data_cfg, batch_size=batch_size, img_size=opt.img_size, model=model,
+                results, maps = test.test(cfg, data, batch_size=batch_size, img_size=opt.img_size, model=model,
                                           conf_thres=0.1)
 
         # Write epoch results
@@ -361,7 +361,7 @@ if __name__ == '__main__':
 
     # Train
     results = train(opt.cfg,
-                    opt.data_cfg,
+                    opt.data,
                     img_size=opt.img_size,
                     epochs=opt.epochs,
                     batch_size=opt.batch_size,
@@ -393,7 +393,7 @@ if __name__ == '__main__':
 
             # Train mutation
             results = train(opt.cfg,
-                            opt.data_cfg,
+                            opt.data,
                             img_size=opt.img_size,
                             epochs=opt.epochs,
                             batch_size=opt.batch_size,