mirrors
/
yolov5
mirror of https://github.com/ultralytics/yolov5.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Validate `best.pt` on train end (#4889) 

* Validate best.pt on train end

* 0.7 iou for COCO only

* pass callbacks

* active model.float() if not half

* print Validating best.pt...

* add newline
pull/4931/head
Glenn Jocher 2021-09-26 11:54:53 -07:00 committed by GitHub
parent e687873436
commit d856c48298
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 14 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

27
train.py
View File

@ -356,9 +356,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
single_cls=single_cls,
dataloader=val_loader,
save_dir=save_dir,
save_json=is_coco and final_epoch,
verbose=nc < 50 and final_epoch,
plots=plots and final_epoch,
plots=False,
callbacks=callbacks,
compute_loss=compute_loss)
@ -404,23 +402,24 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
# end training -----------------------------------------------------------------------------------------------------
if RANK in [-1, 0]:
LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
if not evolve:
if is_coco: # COCO dataset
for m in [last, best] if best.exists() else [last]: # speed, mAP tests
for f in last, best:
if f.exists():
strip_optimizer(f) # strip optimizers
if f is best:
LOGGER.info(f'\nValidating {f}...')
results, _, _ = val.run(data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
imgsz=imgsz,
model=attempt_load(m, device).half(),
iou_thres=0.7, # NMS IoU threshold for best pycocotools results
model=attempt_load(f, device).half(),
iou_thres=0.7 if is_coco else 0.6, # best pycocotools results at 0.7
single_cls=single_cls,
dataloader=val_loader,
save_dir=save_dir,
save_json=True,
plots=False)
# Strip optimizers
for f in last, best:
if f.exists():
strip_optimizer(f) # strip optimizers
save_json=is_coco,
verbose=True,
plots=True,
callbacks=callbacks) # val best model with plots
callbacks.run('on_train_end', last, best, plots, epoch)
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")

3
val.py
View File

@ -133,8 +133,7 @@ def run(data,
# Half
half &= device.type != 'cpu' # half precision only supported on CUDA
if half:
model.half()
model.half() if half else model.float()
# Configure
model.eval()