AutoAnchor bug fix # 117

train.py

@@ -200,6 +200,7 @@ def train(hyp):
         tb_writer.add_histogram('classes', c, 0)
 
     # Check anchors
+    if not opt.noautoanchor:
         check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
 
     # Exponential moving average
@@ -374,6 +375,7 @@ if __name__ == '__main__':
     parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
     parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
     parser.add_argument('--notest', action='store_true', help='only test final epoch')
+    parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
     parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
     parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
     parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')

utils/utils.py

@@ -56,7 +56,7 @@ def check_img_size(img_size, s=32):
 def check_anchors(dataset, model, thr=4.0, imgsz=640):
     # Check anchor fit to data, recompute if necessary
     print('\nAnalyzing anchors... ', end='')
-    anchors = model.module.model[-1].anchor_grid if hasattr(model, 'module') else model.model[-1].anchor_grid
+    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
     shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
     wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])).float()  # wh
 
@@ -66,14 +66,17 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
         best = x.max(1)[0]  # best_x
         return (best > 1. / thr).float().mean()  #  best possible recall
 
-    bpr = metric(anchors.clone().cpu().view(-1, 2))
+    bpr = metric(m.anchor_grid.clone().cpu().view(-1, 2))
     print('Best Possible Recall (BPR) = %.4f' % bpr, end='')
     if bpr < 0.99:  # threshold to recompute
         print('. Attempting to generate improved anchors, please wait...' % bpr)
-        new_anchors = kmean_anchors(dataset, n=anchors.numel() // 2, img_size=imgsz, thr=thr, gen=1000, verbose=False)
+        na = m.anchor_grid.numel() // 2  # number of anchors
+        new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
         new_bpr = metric(new_anchors.reshape(-1, 2))
-        if new_bpr > bpr:
+        if new_bpr > bpr:  # replace anchors
-            anchors[:] = torch.tensor(new_anchors).view_as(anchors).type_as(anchors)
+            new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)
+            m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid)  # for inference
+            m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
             print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')
         else:
             print('Original anchors better than new anchors. Proceeding with original anchors.')