--image_weights bug fix (#1524)

train.py

@@ -181,8 +181,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
 
     # Trainloader
     dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
-                                            hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect,
-                                            rank=rank, world_size=opt.world_size, workers=opt.workers)
+                                            hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
+                                            world_size=opt.world_size, workers=opt.workers,
+                                            image_weights=opt.image_weights)
     mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
     nb = len(dataloader)  # number of batches
     assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)

utils/datasets.py

@@ -55,7 +55,7 @@ def exif_size(img):
 
 
 def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
-                      rank=-1, world_size=1, workers=8):
+                      rank=-1, world_size=1, workers=8, image_weights=False):
     # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
     with torch_distributed_zero_first(rank):
         dataset = LoadImagesAndLabels(path, imgsz, batch_size,
@@ -66,7 +66,8 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
                                       single_cls=opt.single_cls,
                                       stride=int(stride),
                                       pad=pad,
-                                      rank=rank)
+                                      rank=rank,
+                                      image_weights=image_weights)
 
     batch_size = min(batch_size, len(dataset))
     nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers])  # number of workers
@@ -392,6 +393,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         nb = bi[-1] + 1  # number of batches
         self.batch = bi  # batch index of image
         self.n = n
+        self.indices = range(n)
 
         # Rectangular Training
         if self.rect:
@@ -485,8 +487,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
     #     return self
 
     def __getitem__(self, index):
-        if self.image_weights:
-            index = self.indices[index]
+        index = self.indices[index]  # linear, shuffled, or image_weights
 
         hyp = self.hyp
         mosaic = self.mosaic and random.random() < hyp['mosaic']
@@ -497,7 +498,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
 
             # MixUp https://arxiv.org/pdf/1710.09412.pdf
             if random.random() < hyp['mixup']:
-                img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
+                img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1))
                 r = np.random.beta(8.0, 8.0)  # mixup ratio, alpha=beta=8.0
                 img = (img * r + img2 * (1 - r)).astype(np.uint8)
                 labels = np.concatenate((labels, labels2), 0)
@@ -619,7 +620,7 @@ def load_mosaic(self, index):
     labels4 = []
     s = self.img_size
     yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
-    indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)]  # 3 additional image indices
+    indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(3)]  # 3 additional image indices
     for i, index in enumerate(indices):
         # Load image
         img, _, (h, w) = load_image(self, index)