Commit
•
9fdb0fb
1
Parent(s):
8b6f582
AutoAnchor bug fix # 117
Browse files- train.py +3 -1
- utils/utils.py +8 -5
train.py
CHANGED
@@ -200,7 +200,8 @@ def train(hyp):
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tb_writer.add_histogram('classes', c, 0)
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# Check anchors
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-
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# Exponential moving average
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ema = torch_utils.ModelEMA(model)
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@@ -374,6 +375,7 @@ if __name__ == '__main__':
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parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
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parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
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parser.add_argument('--notest', action='store_true', help='only test final epoch')
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parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
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parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
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parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
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tb_writer.add_histogram('classes', c, 0)
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# Check anchors
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+
if not opt.noautoanchor:
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check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
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# Exponential moving average
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ema = torch_utils.ModelEMA(model)
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parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
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parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
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parser.add_argument('--notest', action='store_true', help='only test final epoch')
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parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
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parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
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parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
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parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
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utils/utils.py
CHANGED
@@ -56,7 +56,7 @@ def check_img_size(img_size, s=32):
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def check_anchors(dataset, model, thr=4.0, imgsz=640):
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# Check anchor fit to data, recompute if necessary
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print('\nAnalyzing anchors... ', end='')
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-
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shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
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wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])).float() # wh
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@@ -66,14 +66,17 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
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best = x.max(1)[0] # best_x
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return (best > 1. / thr).float().mean() # best possible recall
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bpr = metric(
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print('Best Possible Recall (BPR) = %.4f' % bpr, end='')
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if bpr < 0.99: # threshold to recompute
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print('. Attempting to generate improved anchors, please wait...' % bpr)
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-
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new_bpr = metric(new_anchors.reshape(-1, 2))
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-
if new_bpr > bpr:
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-
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print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')
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else:
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print('Original anchors better than new anchors. Proceeding with original anchors.')
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def check_anchors(dataset, model, thr=4.0, imgsz=640):
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# Check anchor fit to data, recompute if necessary
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print('\nAnalyzing anchors... ', end='')
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m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect()
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shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
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wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])).float() # wh
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best = x.max(1)[0] # best_x
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return (best > 1. / thr).float().mean() # best possible recall
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bpr = metric(m.anchor_grid.clone().cpu().view(-1, 2))
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print('Best Possible Recall (BPR) = %.4f' % bpr, end='')
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if bpr < 0.99: # threshold to recompute
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print('. Attempting to generate improved anchors, please wait...' % bpr)
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na = m.anchor_grid.numel() // 2 # number of anchors
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new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
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new_bpr = metric(new_anchors.reshape(-1, 2))
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if new_bpr > bpr: # replace anchors
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new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)
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m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid) # for inference
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m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss
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print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')
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else:
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print('Original anchors better than new anchors. Proceeding with original anchors.')
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