@@ -93,7 +93,7 @@ def test(data, | |||
confusion_matrix = ConfusionMatrix(nc=nc) | |||
names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)} | |||
coco91class = coco80_to_coco91_class() | |||
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') | |||
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') | |||
p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. | |||
loss = torch.zeros(3, device=device) | |||
jdict, stats, ap, ap_class, wandb_images = [], [], [], [], [] | |||
@@ -223,7 +223,7 @@ def test(data, | |||
nt = torch.zeros(1) | |||
# Print results | |||
pf = '%20s' + '%12.3g' * 6 # print format | |||
pf = '%20s' + '%12i' * 2 + '%12.3g' * 4 # print format | |||
print(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) | |||
# Print results per class |
@@ -264,7 +264,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None): | |||
if rank != -1: | |||
dataloader.sampler.set_epoch(epoch) | |||
pbar = enumerate(dataloader) | |||
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size')) | |||
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size')) | |||
if rank in [-1, 0]: | |||
pbar = tqdm(pbar, total=nb) # progress bar | |||
optimizer.zero_grad() |
@@ -120,7 +120,7 @@ def profile(x, ops, n=100, device=None): | |||
s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' | |||
s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' | |||
p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters | |||
print(f'{p:12.4g}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') | |||
print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') | |||
def is_parallel(model): |