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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import platform
import shutil
import time
import warnings
import torch
import annotator.uniformer.mmcv as mmcv
from .base_runner import BaseRunner
from .builder import RUNNERS
from .checkpoint import save_checkpoint
from .utils import get_host_info
@RUNNERS.register_module()
class EpochBasedRunner(BaseRunner):
"""Epoch-based Runner.
This runner train models epoch by epoch.
"""
def run_iter(self, data_batch, train_mode, **kwargs):
if self.batch_processor is not None:
outputs = self.batch_processor(
self.model, data_batch, train_mode=train_mode, **kwargs)
elif train_mode:
outputs = self.model.train_step(data_batch, self.optimizer,
**kwargs)
else:
outputs = self.model.val_step(data_batch, self.optimizer, **kwargs)
if not isinstance(outputs, dict):
raise TypeError('"batch_processor()" or "model.train_step()"'
'and "model.val_step()" must return a dict')
if 'log_vars' in outputs:
self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
self.outputs = outputs
def train(self, data_loader, **kwargs):
self.model.train()
self.mode = 'train'
self.data_loader = data_loader
self._max_iters = self._max_epochs * len(self.data_loader)
self.call_hook('before_train_epoch')
time.sleep(2) # Prevent possible deadlock during epoch transition
for i, data_batch in enumerate(self.data_loader):
self._inner_iter = i
self.call_hook('before_train_iter')
self.run_iter(data_batch, train_mode=True, **kwargs)
self.call_hook('after_train_iter')
self._iter += 1
self.call_hook('after_train_epoch')
self._epoch += 1
@torch.no_grad()
def val(self, data_loader, **kwargs):
self.model.eval()
self.mode = 'val'
self.data_loader = data_loader
self.call_hook('before_val_epoch')
time.sleep(2) # Prevent possible deadlock during epoch transition
for i, data_batch in enumerate(self.data_loader):
self._inner_iter = i
self.call_hook('before_val_iter')
self.run_iter(data_batch, train_mode=False)
self.call_hook('after_val_iter')
self.call_hook('after_val_epoch')
def run(self, data_loaders, workflow, max_epochs=None, **kwargs):
"""Start running.
Args:
data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
and validation.
workflow (list[tuple]): A list of (phase, epochs) to specify the
running order and epochs. E.g, [('train', 2), ('val', 1)] means
running 2 epochs for training and 1 epoch for validation,
iteratively.
"""
assert isinstance(data_loaders, list)
assert mmcv.is_list_of(workflow, tuple)
assert len(data_loaders) == len(workflow)
if max_epochs is not None:
warnings.warn(
'setting max_epochs in run is deprecated, '
'please set max_epochs in runner_config', DeprecationWarning)
self._max_epochs = max_epochs
assert self._max_epochs is not None, (
'max_epochs must be specified during instantiation')
for i, flow in enumerate(workflow):
mode, epochs = flow
if mode == 'train':
self._max_iters = self._max_epochs * len(data_loaders[i])
break
work_dir = self.work_dir if self.work_dir is not None else 'NONE'
self.logger.info('Start running, host: %s, work_dir: %s',
get_host_info(), work_dir)
self.logger.info('Hooks will be executed in the following order:\n%s',
self.get_hook_info())
self.logger.info('workflow: %s, max: %d epochs', workflow,
self._max_epochs)
self.call_hook('before_run')
while self.epoch < self._max_epochs:
for i, flow in enumerate(workflow):
mode, epochs = flow
if isinstance(mode, str): # self.train()
if not hasattr(self, mode):
raise ValueError(
f'runner has no method named "{mode}" to run an '
'epoch')
epoch_runner = getattr(self, mode)
else:
raise TypeError(
'mode in workflow must be a str, but got {}'.format(
type(mode)))
for _ in range(epochs):
if mode == 'train' and self.epoch >= self._max_epochs:
break
epoch_runner(data_loaders[i], **kwargs)
time.sleep(1) # wait for some hooks like loggers to finish
self.call_hook('after_run')
def save_checkpoint(self,
out_dir,
filename_tmpl='epoch_{}.pth',
save_optimizer=True,
meta=None,
create_symlink=True):
"""Save the checkpoint.
Args:
out_dir (str): The directory that checkpoints are saved.
filename_tmpl (str, optional): The checkpoint filename template,
which contains a placeholder for the epoch number.
Defaults to 'epoch_{}.pth'.
save_optimizer (bool, optional): Whether to save the optimizer to
the checkpoint. Defaults to True.
meta (dict, optional): The meta information to be saved in the
checkpoint. Defaults to None.
create_symlink (bool, optional): Whether to create a symlink
"latest.pth" to point to the latest checkpoint.
Defaults to True.
"""
if meta is None:
meta = {}
elif not isinstance(meta, dict):
raise TypeError(
f'meta should be a dict or None, but got {type(meta)}')
if self.meta is not None:
meta.update(self.meta)
# Note: meta.update(self.meta) should be done before
# meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise
# there will be problems with resumed checkpoints.
# More details in https://github.com/open-mmlab/mmcv/pull/1108
meta.update(epoch=self.epoch + 1, iter=self.iter)
filename = filename_tmpl.format(self.epoch + 1)
filepath = osp.join(out_dir, filename)
optimizer = self.optimizer if save_optimizer else None
save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta)
# in some environments, `os.symlink` is not supported, you may need to
# set `create_symlink` to False
if create_symlink:
dst_file = osp.join(out_dir, 'latest.pth')
if platform.system() != 'Windows':
mmcv.symlink(filename, dst_file)
else:
shutil.copy(filepath, dst_file)
@RUNNERS.register_module()
class Runner(EpochBasedRunner):
"""Deprecated name of EpochBasedRunner."""
def __init__(self, *args, **kwargs):
warnings.warn(
'Runner was deprecated, please use EpochBasedRunner instead')
super().__init__(*args, **kwargs)
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