本文整理汇总了Python中utils.training_stats.TrainingStats方法的典型用法代码示例。如果您正苦于以下问题：Python training_stats.TrainingStats方法的具体用法？Python training_stats.TrainingStats怎么用？Python training_stats.TrainingStats使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类utils.training_stats的用法示例。
在下文中一共展示了training_stats.TrainingStats方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: train_model
# 需要导入模块: from utils import training_stats [as 别名]
# 或者: from utils.training_stats import TrainingStats [as 别名]
def train_model():
"""Model training loop."""
logger = logging.getLogger(__name__)
model, weights_file, start_iter, checkpoints, output_dir = create_model()
if 'final' in checkpoints:
# The final model was found in the output directory, so nothing to do
return checkpoints
setup_model_for_training(model, weights_file, output_dir)
training_stats = TrainingStats(model)
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
for cur_iter in range(start_iter, cfg.SOLVER.MAX_ITER):
training_stats.IterTic()
lr = model.UpdateWorkspaceLr(cur_iter, lr_policy.get_lr_at_iter(cur_iter))
workspace.RunNet(model.net.Proto().name)
if cur_iter == start_iter:
nu.print_net(model)
training_stats.IterToc()
training_stats.UpdateIterStats()
training_stats.LogIterStats(cur_iter, lr)
if (cur_iter + 1) % CHECKPOINT_PERIOD == 0 and cur_iter > start_iter:
checkpoints[cur_iter] = os.path.join(
output_dir, 'model_iter{}.pkl'.format(cur_iter)
)
nu.save_model_to_weights_file(checkpoints[cur_iter], model)
if cur_iter == start_iter + training_stats.LOG_PERIOD:
# Reset the iteration timer to remove outliers from the first few
# SGD iterations
training_stats.ResetIterTimer()
if np.isnan(training_stats.iter_total_loss):
logger.critical('Loss is NaN, exiting...')
model.roi_data_loader.shutdown()
envu.exit_on_error()
# Save the final model
checkpoints['final'] = os.path.join(output_dir, 'model_final.pkl')
nu.save_model_to_weights_file(checkpoints['final'], model)
# Shutdown data loading threads
model.roi_data_loader.shutdown()
return checkpoints 示例2: train_model
# 需要导入模块: from utils import training_stats [as 别名]
# 或者: from utils.training_stats import TrainingStats [as 别名]
def train_model():
"""Model training loop."""
logger = logging.getLogger(__name__)
model, start_iter, checkpoints, output_dir = create_model()
if 'final' in checkpoints:
# The final model was found in the output directory, so nothing to do
return checkpoints
setup_model_for_training(model, output_dir)
training_stats = TrainingStats(model)
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
for cur_iter in range(start_iter, cfg.SOLVER.MAX_ITER):
training_stats.IterTic()
lr = model.UpdateWorkspaceLr(cur_iter, lr_policy.get_lr_at_iter(cur_iter))
workspace.RunNet(model.net.Proto().name)
if cur_iter == start_iter:
nu.print_net(model)
training_stats.IterToc()
training_stats.UpdateIterStats()
training_stats.LogIterStats(cur_iter, lr)
if (cur_iter + 1) % CHECKPOINT_PERIOD == 0 and cur_iter > start_iter:
checkpoints[cur_iter] = os.path.join(
output_dir, 'model_iter{}.pkl'.format(cur_iter)
)
nu.save_model_to_weights_file(checkpoints[cur_iter], model)
if cur_iter == start_iter + training_stats.LOG_PERIOD:
# Reset the iteration timer to remove outliers from the first few
# SGD iterations
training_stats.ResetIterTimer()
if np.isnan(training_stats.iter_total_loss):
logger.critical('Loss is NaN, exiting...')
model.roi_data_loader.shutdown()
envu.exit_on_error()
# Save the final model
checkpoints['final'] = os.path.join(output_dir, 'model_final.pkl')
nu.save_model_to_weights_file(checkpoints['final'], model)
# Shutdown data loading threads
model.roi_data_loader.shutdown()
return checkpoints