updates

cfg/coco.data

@@ -1,6 +1,6 @@
 classes=80
-train=/Users/glennjocher/Downloads/DATA/coco/trainvalno5k.txt
-valid=/Users/glennjocher/Downloads/DATA/coco/5k.txt
+train=../coco/trainvalno5k.txt
+valid=../coco/5k.txt
 names=data/coco.names
 backup=backup/
 eval=coco

train.py

@@ -44,10 +44,7 @@ def main(opt):
     # Configure run
     data_config = parse_data_config(opt.data_config_path)
     num_classes = int(data_config['classes'])
-    if platform == 'darwin':  # MacOS (local)
-        train_path = data_config['train']
-    else:  # linux (cloud, i.e. gcp)
-        train_path = '../coco/trainvalno5k.part'
+    train_path = '../coco/trainvalno5k.txt'
 
     # Initialize model
     model = Darknet(opt.cfg, opt.img_size)

utils/datasets.py

@@ -66,12 +66,7 @@ class load_images_and_labels():  # for training
         with open(path, 'r') as file:
             self.img_files = file.readlines()
 
-        if platform == 'darwin':  # MacOS (local)
-            self.img_files = [path.replace('\n', '').replace('/images', '/Users/glennjocher/Downloads/data/coco/images')
-                              for path in self.img_files]
-        else:  # linux (gcp cloud)
-            self.img_files = [path.replace('\n', '').replace('/images', '../coco/images') for path in self.img_files]
-
+        self.img_files = [path.replace('\n', '') for path in self.img_files]
         self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt') for path in
                             self.img_files]
 
@@ -287,7 +282,7 @@ def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scal
         return imw
 
 
-def convert_tif2bmp(p='/Users/glennjocher/Downloads/DATA/xview/val_images_bmp'):
+def convert_tif2bmp(p='../xview/val_images_bmp'):
     import glob
     import cv2
     files = sorted(glob.glob('%s/*.tif' % p))

utils/utils.py

@@ -424,7 +424,7 @@ def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
     torch.save(a, filename.replace('.pt', '_lite.pt'))
 
 
-def coco_class_count(path='/Users/glennjocher/downloads/DATA/coco/labels/train2014/'):
+def coco_class_count(path='../coco/labels/train2014/'):
     import glob
 
     nC = 80  # number classes