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Rename 'labels' to 'instances' #9066

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Merged
glenn-jocher merged 4 commits into from
Aug 21, 2022
Merged

Rename 'labels' to 'instances' #9066

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fe70e2f
Rename labels to instances
glenn-jocher Aug 21, 2022
e0c9e27
Rename labels to instances
glenn-jocher Aug 21, 2022
368f825
align val
glenn-jocher Aug 21, 2022
da94ac8
Merge branch 'master' into rename/instances
glenn-jocher Aug 21, 2022
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4 changes: 2 additions & 2 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -271,7 +271,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
if RANK != -1:
train_loader.sampler.set_epoch(epoch)
pbar = enumerate(train_loader)
LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
if RANK in {-1, 0}:
pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
optimizer.zero_grad()
Expand Down Expand Up @@ -326,7 +326,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
if RANK in {-1, 0}:
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
pbar.set_description(('%10s' * 2 + '%10.4g' * 5) %
pbar.set_description(('%11s' * 2 + '%11.4g' * 5) %
(f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots)
if callbacks.stop_training:
Expand Down
4 changes: 2 additions & 2 deletions val.py
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Original file line number Diff line number Diff line change
Expand Up @@ -186,7 +186,7 @@ def run(
if isinstance(names, (list, tuple)): # old format
names = dict(enumerate(names))
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
dt, p, r, f1, mp, mr, map50, map = (Profile(), Profile(), Profile()), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class = [], [], [], []
Expand Down Expand Up @@ -270,7 +270,7 @@ def run(
nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class

# Print results
pf = '%20s' + '%11i' * 2 + '%11.3g' * 4 # print format
pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format
LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
if nt.sum() == 0:
LOGGER.warning(f'WARNING: no labels found in {task} set, can not compute metrics without labels ⚠️')
Expand Down