updates

utils/datasets.py

@@ -130,7 +130,7 @@ class LoadWebcam:  # for inference
 
 
 class LoadImagesAndLabels(Dataset):  # for training/testing
-    def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=False):
+    def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True):
         with open(path, 'r') as f:
             img_files = f.read().splitlines()
             self.img_files = list(filter(lambda x: len(x) > 0, img_files))
@@ -181,8 +181,8 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
             self.batch = bi  # batch index of image
 
         # Preload images
-        # if n < 200:  # preload all images into memory if possible
-        #    self.imgs = [cv2.imread(img_files[i]) for i in range(n)]
+        if n < 1001:  # preload all images into memory if possible
+            self.imgs = [cv2.imread(self.img_files[i]) for i in range(n)]
 
         # Preload labels (required for weighted CE training)
         self.labels = [np.zeros((0, 5))] * n
@@ -201,11 +201,14 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         img_path = self.img_files[index]
         label_path = self.label_files[index]
 
-        # if hasattr(self, 'imgs'):  # preloaded
-        #    img = self.imgs[index]  # BGR
-        img = cv2.imread(img_path)  # BGR
+        # Load image
+        if hasattr(self, 'imgs'):  # preloaded
+            img = self.imgs[index]
+        else:
+            img = cv2.imread(img_path)  # BGR
         assert img is not None, 'File Not Found ' + img_path
 
+        # Augment colorspace
         augment_hsv = True
         if self.augment and augment_hsv:
             # SV augmentation by 50%

utils/utils.py

@@ -265,7 +265,7 @@ def compute_loss(p, targets, model):  # predictions, targets, model
     # Compute losses
     h = model.hyp  # hyperparameters
     bs = p[0].shape[0]  # batch size
-    k = h['k'] * bs  # loss gain
+    k = bs  # loss gain
     for i, pi0 in enumerate(p):  # layer i predictions, i
         b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
         tconf = torch.zeros_like(pi0[..., 0])  # conf