def from_model_folder(model_folder, load_stored_params=True, model_param_file=None,
iteration=None):
"""
Loads a DenseCorrespondenceNetwork from a model folder
:param model_folder: the path to the folder where the model is stored. This direction contains
files like
- 003500.pth
- training.yaml
:type model_folder:
:return: a DenseCorrespondenceNetwork objecc t
:rtype:
"""
model_folder = utils.convert_to_absolute_path(model_folder)
if model_param_file is None:
model_param_file, _, _ = utils.get_model_param_file_from_directory(model_folder, iteration=iteration)
model_param_file = utils.convert_to_absolute_path(model_param_file)
training_config_filename = os.path.join(model_folder, "training.yaml")
training_config = utils.getDictFromYamlFilename(training_config_filename)
config = training_config["dense_correspondence_network"]
config["path_to_network_params_folder"] = model_folder
fcn = resnet_dilated.Resnet34_8s(num_classes=config['descriptor_dimension'])
dcn = DenseCorrespondenceNetwork(fcn, config['descriptor_dimension'],
image_width=config['image_width'],
image_height=config['image_height'])
# load the stored params
if load_stored_params:
# old syntax
try:
dcn.load_state_dict(torch.load(model_param_file))
except:
logging.info("loading params with the new style failed, falling back to dcn.fcn.load_state_dict")
dcn.fcn.load_state_dict(torch.load(model_param_file))
# this is the new format
#
dcn.cuda()
dcn.train()
dcn.config = config
return dcn
def from_model_folder(model_folder,
load_stored_params=True,
model_param_file=None,
iteration=None):
"""
Loads a DenseCorrespondenceNetwork from a model folder
:param model_folder: the path to the folder where the model is stored. This direction contains
files like
- 003500.pth
- training.yaml
:type model_folder:
:return: a DenseCorrespondenceNetwork objecc t
:rtype:
"""
from_model_folder = False
model_folder = utils.convert_to_absolute_path(model_folder)
if model_param_file is None:
model_param_file, _, _ = utils.get_model_param_file_from_directory(
model_folder, iteration=iteration)
from_model_folder = True
model_param_file = utils.convert_to_absolute_path(model_param_file)
training_config_filename = os.path.join(model_folder, "training.yaml")
training_config = utils.getDictFromYamlFilename(
training_config_filename)
config = training_config["dense_correspondence_network"]
config["path_to_network_params_folder"] = model_folder
config["model_param_filename_tail"] = os.path.split(
model_param_file)[1]
dcn = DenseCorrespondenceNetwork.from_config(
config,
load_stored_params=load_stored_params,
model_param_file=model_param_file)
# whether or not network was constructed from model folder
dcn.constructed_from_model_folder = from_model_folder
dcn.model_folder = model_folder
return dcn