def _init_model(self):
"""Load model desc from save path and parse to model."""
model = self.trainer.model
if self.trainer.config.is_detection_trainer:
model_desc = self.trainer.model_desc
else:
model_desc = self._get_model_desc()
if model_desc:
ModelConfig.model_desc = model_desc
pretrained_model_file = self._get_pretrained_model_file()
if not model:
if not model_desc:
raise Exception(
"Failed to Init model, can not get model description.")
model = ModelZoo.get_model(model_desc, pretrained_model_file)
if model:
if zeus.is_torch_backend():
import torch
if self.trainer.use_cuda:
model = model.cuda()
if General._parallel and General.devices_per_trainer > 1:
model = torch.nn.DataParallel(self.trainer.model)
if zeus.is_tf_backend():
if pretrained_model_file:
model_folder = os.path.dirname(pretrained_model_file)
FileOps.copy_folder(model_folder,
self.trainer.get_local_worker_path())
return model
def _get_model(args):
"""Get model."""
from zeus.model_zoo import ModelZoo
model = ModelZoo.get_model(args.model_desc, args.model)
if vega.is_torch_backend():
if args.device == "GPU":
model = model.cuda()
model.eval()
return model
def load_model(self):
"""Load model."""
if not self.model_desc and not self.weights_file:
saved_folder = self.get_local_worker_path(self.step_name,
self.worker_id)
self.weights_file = FileOps.join_path(
saved_folder, 'model_{}.pth'.format(self.worker_id))
self.model_desc = FileOps.join_path(
saved_folder, 'desc_{}.json'.format(self.worker_id))
if 'modules' not in self.model_desc:
self.model_desc = ModelConfig.model_desc
self.model = ModelZoo.get_model(self.model_desc, self.weights_file)
def load_model(self):
"""Load model."""
self.saved_folder = self.get_local_worker_path(self.step_name, self.worker_id)
if not self.model_desc:
self.model_desc = FileOps.join_path(self.saved_folder, 'desc_{}.json'.format(self.worker_id))
if not self.weights_file:
if zeus.is_torch_backend():
self.weights_file = FileOps.join_path(self.saved_folder, 'model_{}.pth'.format(self.worker_id))
elif zeus.is_ms_backend():
for file in os.listdir(self.saved_folder):
if file.startswith("CKP") and file.endswith(".ckpt"):
self.weights_file = FileOps.join_path(self.saved_folder, file)
if 'modules' not in self.model_desc:
self.model_desc = ModelConfig.model_desc
self.model = ModelZoo.get_model(self.model_desc, self.weights_file)
def _init_model(self):
"""Load model desc from save path and parse to model."""
model = self.trainer.model
if self.trainer.config.is_detection_trainer:
model_desc = self.trainer.model_desc or self._get_model_desc()
else:
model_desc = self._get_model_desc()
pretrained_model_file = self._get_pretrained_model_file()
if not model:
if not model_desc:
raise Exception(
"Failed to Init model, can not get model description.")
model = ModelZoo.get_model(model_desc, pretrained_model_file,
ModelConfig.head)
if model:
self.trainer.model_desc = model.desc
if zeus.is_torch_backend():
import torch
if self.trainer.use_cuda:
model = model.cuda()
if General._parallel and General.devices_per_trainer > 1:
model = torch.nn.DataParallel(model)
return model
def load_model(self):
"""Load model."""
self.saved_folder = self.get_local_worker_path(self.step_name,
self.worker_id)
if not self.model_desc:
model_config = Config(
FileOps.join_path(self.saved_folder,
'desc_{}.json'.format(self.worker_id)))
if "type" not in model_config and "modules" not in model_config:
model_config = ModelConfig.model_desc
self.model_desc = model_config
if not self.weights_file:
if zeus.is_torch_backend():
self.weights_file = FileOps.join_path(
self.saved_folder, 'model_{}.pth'.format(self.worker_id))
elif zeus.is_ms_backend():
for file in os.listdir(self.saved_folder):
if file.endswith(".ckpt"):
self.weights_file = FileOps.join_path(
self.saved_folder, file)
elif zeus.is_tf_backend():
self.weights_file = FileOps.join_path(
self.saved_folder, 'model_{}'.format(self.worker_id))
self.model = ModelZoo.get_model(self.model_desc, self.weights_file)
def _get_model(args):
from zeus.model_zoo import ModelZoo
model = ModelZoo.get_model(args.model_desc_file, args.model_weights_file)
model = model.cuda()
model.eval()
return model