opt.img_weights bug fix (#885)

train.py

@@ -216,18 +216,15 @@ def train(hyp, opt, device, tb_writer=None):
         model.train()
 
         # Update image weights (optional)
-        if dataset.image_weights:
+        if opt.img_weights:
             # Generate indices
             if rank in [-1, 0]:
-                w = model.class_weights.cpu().numpy() * (1 - maps) ** 2  # class weights
-                image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
-                dataset.indices = random.choices(range(dataset.n), weights=image_weights,
-                                                 k=dataset.n)  # rand weighted idx
+                cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2  # class weights
+                iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
+                dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
             # Broadcast if DDP
             if rank != -1:
-                indices = torch.zeros([dataset.n], dtype=torch.int)
-                if rank == 0:
-                    indices[:] = torch.tensor(dataset.indices, dtype=torch.int)
+                indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
                 dist.broadcast(indices, 0)
                 if rank != 0:
                     dataset.indices = indices.cpu().numpy()
@@ -388,7 +385,8 @@ if __name__ == '__main__':
     parser.add_argument('--hyp', type=str, default='', help='hyperparameters path, i.e. data/hyp.scratch.yaml')
     parser.add_argument('--epochs', type=int, default=300)
     parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='train,test sizes')
+    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
+    parser.add_argument('--img-weights', action='store_true', help='use weighted image selection for training')
     parser.add_argument('--rect', action='store_true', help='rectangular training')
     parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
     parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
@@ -471,7 +469,7 @@ if __name__ == '__main__':
                 'obj_pw': (1, 0.5, 2.0),  # obj BCELoss positive_weight
                 'iou_t': (0, 0.1, 0.7),  # IoU training threshold
                 'anchor_t': (1, 2.0, 8.0),  # anchor-multiple threshold
-                 # 'anchors': (1, 2.0, 10.0),  # anchors per output grid (0 to ignore)
+                # 'anchors': (1, 2.0, 10.0),  # anchors per output grid (0 to ignore)
                 'fl_gamma': (0, 0.0, 2.0),  # focal loss gamma (efficientDet default gamma=1.5)
                 'hsv_h': (1, 0.0, 0.1),  # image HSV-Hue augmentation (fraction)
                 'hsv_s': (1, 0.0, 0.9),  # image HSV-Saturation augmentation (fraction)