def save_checkpoint(self):
"""Saves a checkpoint of the network and other variables."""
net = self.actor.net.module if multigpu.is_multi_gpu(
self.actor.net) else self.actor.net
actor_type = type(self.actor).__name__
net_type = type(net).__name__
state = {
'epoch': self.epoch,
'actor_type': actor_type,
'net_type': net_type,
'net': net.state_dict(),
'net_info': getattr(net, 'info', None),
'constructor': getattr(net, 'constructor', None),
'optimizer': self.optimizer.state_dict(),
'stats': self.stats,
'settings': self.settings
}
directory = '{}/{}'.format(self._checkpoint_dir,
self.settings.project_path)
if not os.path.exists(directory):
os.makedirs(directory)
# First save as a tmp file
tmp_file_path = '{}/{}_ep{:04d}.tmp'.format(directory, net_type,
self.epoch)
torch.save(state, tmp_file_path)
file_path = '{}/{}_ep{:04d}.pth.tar'.format(directory, net_type,
self.epoch)
# Now rename to actual checkpoint. os.rename seems to be atomic if files are on same filesystem. Not 100% sure
os.rename(tmp_file_path, file_path)
def load_pretrained(self,
checkpoint=None,
fields=None,
ignore_fields=None,
load_constructor=False):
""" Loads a pre-trained network parameter from a checkpoint file. """
from ltr.admin import loading, multigpu
net = self.actor.net.module if multigpu.is_multi_gpu(
self.actor.net) else self.actor.net
net_type = type(net).__name__
# Load network
print("load from: {}".format(checkpoint))
checkpoint_dict = loading.torch_load_legacy(checkpoint)
assert net_type == checkpoint_dict[
'net_type'], 'Network is not of correct type.'
if fields is None:
fields = checkpoint_dict.keys()
# Load all fields
for key in fields:
if key == 'net':
net.load_state_dict(checkpoint_dict[key])
return True
def save_checkpoint(self):
"""Saves a checkpoint of the network and other variables."""
net = self.actor.net.module if multigpu.is_multi_gpu(
self.actor.net) else self.actor.net
actor_type = type(self.actor).__name__
net_type = type(net).__name__
state = {
'epoch': self.epoch,
'actor_type': actor_type,
'net_type': net_type,
'net': net.state_dict(),
'net_info': getattr(net, 'info', None),
'constructor': getattr(net, 'constructor', None),
'optimizer': self.optimizer.state_dict(),
'stats': self.stats,
'settings': self.settings
}
directory = '{}/{}'.format(self._checkpoint_dir,
self.settings.project_path)
if not os.path.exists(directory):
os.makedirs(directory)
file_path = '{}/{}_ep{:04d}.pth.tar'.format(directory, net_type,
self.epoch)
torch.save(state, file_path)
def load_weights(self, checkpoint = None):
"""Loads network weights"""
net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net
# Load network
checkpoint_dict = loading.torch_load_legacy(checkpoint)
net.load_state_dict(checkpoint_dict['net'], strict=False)
return True
def load_checkpoint(self,
checkpoint=None,
fields=None,
ignore_fields=None,
load_constructor=False):
"""Loads a network checkpoint file.
Can be called in three different ways:
load_checkpoint():
Loads the latest epoch from the workspace. Use this to continue training.
load_checkpoint(epoch_num):
Loads the network at the given epoch number (int).
load_checkpoint(path_to_checkpoint):
Loads the file from the given absolute path (str).
"""
net = self.actor.net.module if multigpu.is_multi_gpu(
self.actor.net) else self.actor.net
actor_type = type(self.actor).__name__
net_type = type(net).__name__
if checkpoint is None:
# Load most recent checkpoint
# checkpoint_list = sorted(glob.glob('{}/{}/{}_ep*.pth.tar'.format(self._checkpoint_dir,
# self.settings.project_path, net_type)))
checkpoint_list = sorted(
glob.glob('{}/{}/{}_ep*.pth.tar'.format(
self._checkpoint_dir, self.settings.project_path,
net_type)))
print('{}/{}/{}_ep*.pth.tar'.format(self._checkpoint_dir,
self.settings.project_path,
net_type))
if checkpoint_list:
checkpoint_path = checkpoint_list[-1]
else:
print('No matching checkpoint file found')
return
elif isinstance(checkpoint, int):
# Checkpoint is the epoch number
checkpoint_path = '{}/{}/{}_ep{:04d}.pth.tar'.format(
self._checkpoint_dir, self.settings.project_path, net_type,
checkpoint)
elif isinstance(checkpoint, str):
# checkpoint is the path
if os.path.isdir(checkpoint):
checkpoint_list = sorted(
glob.glob('{}/*_ep*.pth.tar'.format(checkpoint)))
if checkpoint_list:
checkpoint_path = checkpoint_list[-1]
else:
raise Exception('No checkpoint found')
else:
checkpoint_path = os.path.expanduser(checkpoint)
else:
raise TypeError
# Load network
checkpoint_dict = loading.torch_load_legacy(checkpoint_path)
# assert net_type == checkpoint_dict['net_type'], 'Network is not of correct type.'
if fields is None:
fields = checkpoint_dict.keys()
if ignore_fields is None:
ignore_fields = ['settings']
# Never load the scheduler. It exists in older checkpoints.
ignore_fields.extend([
'lr_scheduler', 'constructor', 'net_type', 'actor_type', 'net_info'
])
# Load all fields
for key in fields:
if key in ignore_fields:
continue
if key == 'net':
# net.load_state_dict(checkpoint_dict[key])
# Song for merge layer
model_dict = net.state_dict()
pretrained_dict = {
k: v
for k, v in checkpoint_dict[key].items() if k in model_dict
}
model_dict.update(pretrained_dict)
net.load_state_dict(model_dict)
elif key == 'optimizer':
try:
self.optimizer.load_state_dict(checkpoint_dict[key])
except:
continue
else:
setattr(self, key, checkpoint_dict[key])
# Set the net info
if load_constructor and 'constructor' in checkpoint_dict and checkpoint_dict[
'constructor'] is not None:
net.constructor = checkpoint_dict['constructor']
if 'net_info' in checkpoint_dict and checkpoint_dict[
'net_info'] is not None:
net.info = checkpoint_dict['net_info']
# Update the epoch in lr scheduler
if 'epoch' in fields:
self.lr_scheduler.last_epoch = self.epoch
return True
def load_checkpoint(self, checkpoint = None, fields = None, ignore_fields = None, load_constructor = False):
"""Loads a network checkpoint file.
Can be called in three different ways:
load_checkpoint():
Loads the latest epoch from the workspace. Use this to continue training.
load_checkpoint(epoch_num):
Loads the network at the given epoch number (int).
load_checkpoint(path_to_checkpoint):
Loads the file from the given absolute path (str).
"""
net = self.actor.net.module if multigpu.is_multi_gpu(self.actor.net) else self.actor.net
actor_type = type(self.actor).__name__
net_type = type(net).__name__
if checkpoint is None:
# Load most recent checkpoint
checkpoint_list = sorted(glob.glob('{}/{}/{}_ep*.pth.tar'.format(self._checkpoint_dir,
self.settings.project_path, net_type)))
print(checkpoint_list)
if checkpoint_list:
checkpoint_path = checkpoint_list[-1]
else:
print('No matching checkpoint file found')
return
elif isinstance(checkpoint, int):
# Checkpoint is the epoch number
checkpoint_path = '{}/{}/{}_ep{:04d}.pth.tar'.format(self._checkpoint_dir, self.settings.project_path,
net_type, checkpoint)
elif isinstance(checkpoint, str):
# checkpoint is the path
if os.path.isdir(checkpoint):
checkpoint_list = sorted(glob.glob('{}/*_ep*.pth.tar'.format(checkpoint)))
if checkpoint_list:
checkpoint_path = checkpoint_list[-1]
else:
raise Exception('No checkpoint found')
else:
checkpoint_path = os.path.expanduser(checkpoint)
else:
raise TypeError
# Load network
checkpoint_dict = loading.torch_load_legacy(checkpoint_path)
#print([net_type, checkpoint_dict['net_type']])['DiMPnet_rgbd', 'DiMPnet']
assert checkpoint_dict['net_type'] in net_type, 'Network is not of correct type.'
if fields is None:
fields = checkpoint_dict.keys()
if ignore_fields is None:
ignore_fields = ['settings']
# Never load the scheduler. It exists in older checkpoints.
ignore_fields.extend(['lr_scheduler', 'constructor', 'net_type', 'actor_type', 'net_info'])
# Load all fields
for key in fields:
if key in ignore_fields:
continue
if key == 'net':
#print(checkpoint_dict[key].keys())
net.load_state_dict(checkpoint_dict[key], strict=False)
elif key == 'optimizer':# and 'dimp50.pth' not in checkpoint_path:
# param_lens = (len(g['params']) for g in self.optimizer.param_groups)
# saved_lens = (len(g['params']) for g in checkpoint_dict[key]['param_groups'])
# # for p_len,s_len in zip(param_lens, saved_lens):
# # print([p_len, s_len])
# print(self.optimizer.param_groups[3]['params'][0].shape)
# if any(p_len != s_len for p_len, s_len in zip(param_lens, saved_lens)):
# raise ValueError("loaded state dict contains a parameter group "
# "that doesn't match the size of optimizer's group")
self.optimizer.load_state_dict(checkpoint_dict[key])
else:
setattr(self, key, checkpoint_dict[key])
# Set the net info
if load_constructor and 'constructor' in checkpoint_dict and checkpoint_dict['constructor'] is not None:
net.constructor = checkpoint_dict['constructor']
if 'net_info' in checkpoint_dict and checkpoint_dict['net_info'] is not None:
net.info = checkpoint_dict['net_info']
# Update the epoch in lr scheduler
if 'epoch' in fields:
self.lr_scheduler.last_epoch = self.epoch
return True
