def load_pretrained(self, model_folder, iteration=None):
"""
Loads network and optimizer parameters from a previous training run.
Note: It is up to the user to ensure that the model parameters match.
e.g. width, height, descriptor dimension etc.
:param model_folder: location of the folder containing the param files 001000.pth. Can be absolute or relative path. If relative then it is relative to pdc/trained_models/
:type model_folder:
:param iteration: which index to use, e.g. 3500, if None it loads the latest one
:type iteration:
:return: iteration
:rtype:
"""
if not os.path.isdir(model_folder):
pdc_path = utils.getPdcPath()
model_folder = os.path.join(pdc_path, "trained_models",
model_folder)
# find idx.pth and idx.pth.opt files
if iteration is None:
files = os.listdir(model_folder)
model_param_file = sorted(fnmatch.filter(files, '*.pth'))[-1]
iteration = int(model_param_file.split(".")[0])
optim_param_file = sorted(fnmatch.filter(files, '*.pth.opt'))[-1]
else:
prefix = utils.getPaddedString(iteration, width=6)
model_param_file = prefix + ".pth"
optim_param_file = prefix + ".pth.opt"
print("model_param_file", model_param_file)
model_param_file = os.path.join(model_folder, model_param_file)
optim_param_file = os.path.join(model_folder, optim_param_file)
self._dcn = self.build_network()
self._dcn.load_state_dict(torch.load(model_param_file))
self._dcn.cuda()
self._dcn.train()
self._optimizer = self._construct_optimizer(self._dcn.parameters())
self._optimizer.load_state_dict(torch.load(optim_param_file))
return iteration
def load_pretrained(self, model_folder, iteration=None):
"""
Loads network and optimizer parameters from a previous training run.
Note: It is up to the user to ensure that the model parameters match.
e.g. width, height, descriptor dimension etc.
:param model_folder: location of the folder containing the param files 001000.pth. Can be absolute or relative path. If relative then it is relative to pdc/trained_models/
:type model_folder:
:param iteration: which index to use, e.g. 3500, if None it loads the latest one
:type iteration:
:return: iteration
:rtype:
"""
if not os.path.isdir(model_folder):
pdc_path = utils.getPdcPath()
model_folder = os.path.join(pdc_path, "trained_models", model_folder)
# find idx.pth and idx.pth.opt files
if iteration is None:
files = os.listdir(model_folder)
model_param_file = sorted(fnmatch.filter(files, '*.pth'))[-1]
iteration = int(model_param_file.split(".")[0])
optim_param_file = sorted(fnmatch.filter(files, '*.pth.opt'))[-1]
else:
prefix = utils.getPaddedString(iteration, width=6)
model_param_file = prefix + ".pth"
optim_param_file = prefix + ".pth.opt"
print "model_param_file", model_param_file
model_param_file = os.path.join(model_folder, model_param_file)
optim_param_file = os.path.join(model_folder, optim_param_file)
self._dcn = self.build_network()
self._dcn.load_state_dict(torch.load(model_param_file))
self._dcn.cuda()
self._dcn.train()
self._optimizer = self._construct_optimizer(self._dcn.parameters())
self._optimizer.load_state_dict(torch.load(optim_param_file))
return iteration
