Generalized regression criterion renaming (#1120)

data/hyp.finetune.yaml

@@ -15,7 +15,7 @@ weight_decay: 0.00036
 warmup_epochs: 2.0
 warmup_momentum: 0.5
 warmup_bias_lr: 0.05
-giou: 0.0296
+box: 0.0296
 cls: 0.243
 cls_pw: 0.631
 obj: 0.301

data/hyp.scratch.yaml

@@ -10,7 +10,7 @@ weight_decay: 0.0005  # optimizer weight decay 5e-4
 warmup_epochs: 3.0  # warmup epochs (fractions ok)
 warmup_momentum: 0.8  # warmup initial momentum
 warmup_bias_lr: 0.1  # warmup initial bias lr
-giou: 0.05  # box loss gain
+box: 0.05  # box loss gain
 cls: 0.5  # cls loss gain
 cls_pw: 1.0  # cls BCELoss positive_weight
 obj: 1.0  # obj loss gain (scale with pixels)

sotabench.py

@@ -113,7 +113,7 @@ def test(data,
 
             # Compute loss
             if training:  # if model has loss hyperparameters
-                loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # GIoU, obj, cls
+                loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # box, obj, cls
 
             # Run NMS
             t = time_synchronized()

test.py

@@ -106,7 +106,7 @@ def test(data,
 
             # Compute loss
             if training:  # if model has loss hyperparameters
-                loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # GIoU, obj, cls
+                loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # box, obj, cls
 
             # Run NMS
             t = time_synchronized()

train.py

@@ -195,7 +195,7 @@ def train(hyp, opt, device, tb_writer=None):
     hyp['cls'] *= nc / 80.  # scale coco-tuned hyp['cls'] to current dataset
     model.nc = nc  # attach number of classes to model
     model.hyp = hyp  # attach hyperparameters to model
-    model.gr = 1.0  # giou loss ratio (obj_loss = 1.0 or giou)
+    model.gr = 1.0  # iou loss ratio (obj_loss = 1.0 or iou)
     model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
     model.names = names
 
@@ -204,7 +204,7 @@ def train(hyp, opt, device, tb_writer=None):
     nw = max(round(hyp['warmup_epochs'] * nb), 1e3)  # number of warmup iterations, max(3 epochs, 1k iterations)
     # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
     maps = np.zeros(nc)  # mAP per class
-    results = (0, 0, 0, 0, 0, 0, 0)  # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
+    results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
     scheduler.last_epoch = start_epoch - 1  # do not move
     scaler = amp.GradScaler(enabled=cuda)
     logger.info('Image sizes %g train, %g test\nUsing %g dataloader workers\nLogging results to %s\n'
@@ -234,7 +234,7 @@ def train(hyp, opt, device, tb_writer=None):
         if rank != -1:
             dataloader.sampler.set_epoch(epoch)
         pbar = enumerate(dataloader)
-        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
+        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size'))
         if rank in [-1, 0]:
             pbar = tqdm(pbar, total=nb)  # progress bar
         optimizer.zero_grad()
@@ -245,7 +245,7 @@ def train(hyp, opt, device, tb_writer=None):
             # Warmup
             if ni <= nw:
                 xi = [0, nw]  # x interp
-                # model.gr = np.interp(ni, xi, [0.0, 1.0])  # giou loss ratio (obj_loss = 1.0 or giou)
+                # model.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)
                 accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
                 for j, x in enumerate(optimizer.param_groups):
                     # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
@@ -319,21 +319,21 @@ def train(hyp, opt, device, tb_writer=None):
 
             # Write
             with open(results_file, 'a') as f:
-                f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
+                f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
             if len(opt.name) and opt.bucket:
                 os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
 
             # Tensorboard
             if tb_writer:
-                tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
+                tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
                         'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
-                        'val/giou_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
+                        'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
                         'x/lr0', 'x/lr1', 'x/lr2']  # params
                 for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
                     tb_writer.add_scalar(tag, x, epoch)
 
             # Update best mAP
-            fi = fitness(np.array(results).reshape(1, -1))  # fitness_i = weighted combination of [P, R, mAP, F1]
+            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
             if fi > best_fitness:
                 best_fitness = fi
 
@@ -463,7 +463,7 @@ if __name__ == '__main__':
                 'warmup_epochs': (1, 0.0, 5.0),  # warmup epochs (fractions ok)
                 'warmup_momentum': (1, 0.0, 0.95),  # warmup initial momentum
                 'warmup_bias_lr': (1, 0.0, 0.2),  # warmup initial bias lr
-                'giou': (1, 0.02, 0.2),  # GIoU loss gain
+                'box': (1, 0.02, 0.2),  # box loss gain
                 'cls': (1, 0.2, 4.0),  # cls loss gain
                 'cls_pw': (1, 0.5, 2.0),  # cls BCELoss positive_weight
                 'obj': (1, 0.2, 4.0),  # obj loss gain (scale with pixels)

utils/general.py

@@ -509,11 +509,11 @@ def compute_loss(p, targets, model):  # predictions, targets, model
             pxy = ps[:, :2].sigmoid() * 2. - 0.5
             pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
             pbox = torch.cat((pxy, pwh), 1).to(device)  # predicted box
-            giou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # giou(prediction, target)
+            iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
-            lbox += (1.0 - giou).mean()  # giou loss
+            lbox += (1.0 - iou).mean()  # iou loss
 
             # Objectness
-            tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().clamp(0).type(tobj.dtype)  # giou ratio
+            tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
 
             # Classification
             if model.nc > 1:  # cls loss (only if multiple classes)
@@ -528,7 +528,7 @@ def compute_loss(p, targets, model):  # predictions, targets, model
         lobj += BCEobj(pi[..., 4], tobj) * balance[i]  # obj loss
 
     s = 3 / np  # output count scaling
-    lbox *= h['giou'] * s
+    lbox *= h['box'] * s
     lobj *= h['obj'] * s * (1.4 if np == 4 else 1.)
     lcls *= h['cls'] * s
     bs = tobj.shape[0]  # batch size
@@ -1234,7 +1234,7 @@ def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.general im
 def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_results_overlay()
     # Plot training 'results*.txt', overlaying train and val losses
     s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
-    t = ['GIoU', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
+    t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
     for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
         results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
         n = results.shape[1]  # number of rows
@@ -1254,13 +1254,13 @@ def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_
         fig.savefig(f.replace('.txt', '.png'), dpi=200)
 
 
-def plot_results(start=0, stop=0, bucket='', id=(), labels=(),
+def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
-                 save_dir=''):  # from utils.general import *; plot_results()
+    # from utils.general import *; plot_results()
     # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
     fig, ax = plt.subplots(2, 5, figsize=(12, 6))
     ax = ax.ravel()
-    s = ['GIoU', 'Objectness', 'Classification', 'Precision', 'Recall',
+    s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
-         'val GIoU', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
+         'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
     if bucket:
         # os.system('rm -rf storage.googleapis.com')
         # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]