Fixed a bug for Hyperparameters Evolution. (#344)

* Update hyp.scratch.custom.yaml

anchors parameter missing, evolution won't start without.

* Update train.py

Updated the hyperparameter evolution metadata variable to match the hyp.yaml files for the evolution to run successfully.

* Update hyp.scratch.p5.yaml

Added the anchors parameter, evolution don't start without it.

* Update hyp.scratch.p6.yaml

added the anchors parameter for the hyperparameter evolution.

* Update hyp.scratch.tiny.yaml

added the anchors parameter for the hyperparameters evolution

* Update hyp.scratch.custom.yaml

* Update hyp.scratch.tiny.yaml

* Update hyp.scratch.p5.yaml

* Update hyp.scratch.p6.yaml

* Update train.py

* Update train_aux.py

data/hyp.scratch.custom.yaml

@@ -12,6 +12,7 @@ obj: 0.7  # obj loss gain (scale with pixels)
 obj_pw: 1.0  # obj BCELoss positive_weight
 iou_t: 0.20  # IoU training threshold
 anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
 fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
 hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
 hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)

data/hyp.scratch.p5.yaml

@@ -12,6 +12,7 @@ obj: 0.7  # obj loss gain (scale with pixels)
 obj_pw: 1.0  # obj BCELoss positive_weight
 iou_t: 0.20  # IoU training threshold
 anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
 fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
 hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
 hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)

data/hyp.scratch.p6.yaml

@@ -12,6 +12,7 @@ obj: 0.7  # obj loss gain (scale with pixels)
 obj_pw: 1.0  # obj BCELoss positive_weight
 iou_t: 0.20  # IoU training threshold
 anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
 fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
 hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
 hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)

data/hyp.scratch.tiny.yaml

@@ -12,6 +12,7 @@ obj: 1.0  # obj loss gain (scale with pixels)
 obj_pw: 1.0  # obj BCELoss positive_weight
 iou_t: 0.20  # IoU training threshold
 anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
 fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
 hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
 hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)

train.py

@@ -638,7 +638,14 @@ if __name__ == '__main__':
                 'flipud': (1, 0.0, 1.0),  # image flip up-down (probability)
                 'fliplr': (0, 0.0, 1.0),  # image flip left-right (probability)
                 'mosaic': (1, 0.0, 1.0),  # image mixup (probability)
-                'mixup': (1, 0.0, 1.0)}  # image mixup (probability)
+                'mixup': (1, 0.0, 1.0),   # image mixup (probability)
+                'copy_paste': (1, 0.0, 1.0),  # segment copy-paste (probability)
+                'paste_in': (1, 0.0, 1.0)}    # segment copy-paste (probability)
+        
+        with open(opt.hyp, errors='ignore') as f:
+            hyp = yaml.safe_load(f)  # load hyps dict
+            if 'anchors' not in hyp:  # anchors commented in hyp.yaml
+                hyp['anchors'] = 3
                 
         assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
         opt.notest, opt.nosave = True, True  # only test/save final epoch

train_aux.py

@@ -640,6 +640,11 @@ if __name__ == '__main__':
                 'mosaic': (1, 0.0, 1.0),  # image mixup (probability)
                 'mixup': (1, 0.0, 1.0)}  # image mixup (probability)
         
+        with open(opt.hyp, errors='ignore') as f:
+            hyp = yaml.safe_load(f)  # load hyps dict
+            if 'anchors' not in hyp:  # anchors commented in hyp.yaml
+                hyp['anchors'] = 3
+                
         assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
         opt.notest, opt.nosave = True, True  # only test/save final epoch
         # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices