def validate(epoch, calib_loader, val_loader, criterion, val_meters,
model_wrapper, ema, phase):
"""Calibrate and validate."""
assert phase in ['test', 'val']
model_eval_wrapper = mc.get_ema_model(ema, model_wrapper)
# bn_calibration
if FLAGS.get('bn_calibration', False):
if not FLAGS.use_distributed:
logging.warning(
'Only GPU0 is used when calibration when use DataParallel')
with torch.no_grad():
_ = run_one_epoch(epoch,
calib_loader,
model_eval_wrapper,
criterion,
None,
None,
None,
None,
val_meters,
max_iter=FLAGS.bn_calibration_steps,
phase='bn_calibration')
if FLAGS.use_distributed:
udist.allreduce_bn(model_eval_wrapper)
# val
with torch.no_grad():
results = run_one_epoch(epoch,
val_loader,
model_eval_wrapper,
criterion,
None,
None,
None,
None,
val_meters,
phase=phase)
summary_bn(model_eval_wrapper, phase)
return results, model_eval_wrapper
def validate(epoch,
calib_loader,
val_loader,
criterion,
val_meters,
model_wrapper,
ema,
phase,
segval=None,
val_set=None):
"""Calibrate and validate."""
assert phase in ['test', 'val']
model_eval_wrapper = mc.get_ema_model(ema, model_wrapper)
# bn_calibration
if FLAGS.prune_params['method'] is not None:
if FLAGS.get('bn_calibration', False):
if not FLAGS.use_distributed:
logging.warning(
'Only GPU0 is used when calibration when use DataParallel')
with torch.no_grad():
_ = run_one_epoch(epoch,
calib_loader,
model_eval_wrapper,
criterion,
None,
None,
None,
None,
val_meters,
max_iter=FLAGS.bn_calibration_steps,
phase='bn_calibration')
if FLAGS.use_distributed:
udist.allreduce_bn(model_eval_wrapper)
# val
with torch.no_grad():
if FLAGS.model_kwparams.task == 'segmentation':
if FLAGS.dataset == 'coco':
results = 0
if udist.is_master():
results = keypoint_val(val_set, val_loader,
model_eval_wrapper.module,
criterion)
else:
assert segval is not None
results = segval.run(
epoch, val_loader, model_eval_wrapper.module
if FLAGS.single_gpu_test else model_eval_wrapper, FLAGS)
else:
results = run_one_epoch(epoch,
val_loader,
model_eval_wrapper,
criterion,
None,
None,
None,
None,
val_meters,
phase=phase)
summary_bn(model_eval_wrapper, phase)
return results, model_eval_wrapper