def reload_network(self):
"""
Reload entire model or only decoder (atlasnet) depending on the options
:return:
"""
if self.opt.reload_model_path != "":
yellow_print(f"Network weights loaded from {self.opt.reload_model_path}!")
# print(self.network.state_dict().keys())
# print(torch.load(self.opt.reload_model_path).keys())
#self.network.module.load_state_dict(torch.load(self.opt.reload_model_path, map_location='cuda:0'))
self.network.load_state_dict(torch.load(self.opt.reload_model_path, map_location='cuda:0')) #kekw
elif self.opt.reload_decoder_path != "":
opt = deepcopy(self.opt)
opt.SVR = False
network = EncoderDecoder(opt)
network = nn.DataParallel(network, device_ids=opt.multi_gpu)
#network.module.load_state_dict(torch.load(opt.reload_decoder_path, map_location='cuda:0'))
network.load_state_dict(torch.load(opt.reload_decoder_path, map_location='cuda:0')) #kekw
self.network.module.decoder = network.module.decoder
yellow_print(f"Network Decoder weights loaded from {self.opt.reload_decoder_path}!")
else:
yellow_print("No network weights to reload!")
class TrainerModel(object):
def __init__(self):
"""
This class creates the architectures and implements all trainer functions related to architecture.
Author : Thibault Groueix 01.11.2019
"""
super(TrainerModel, self).__init__()
def build_network(self):
"""
Create network architecture. Refer to auxiliary.model
:return:
"""
if torch.cuda.is_available():
self.opt.device = torch.device(f"cuda:{self.opt.multi_gpu[0]}")
else:
# Run on CPU
self.opt.device = torch.device(f"cpu")
self.network = EncoderDecoder(self.opt)
self.network = nn.DataParallel(self.network, device_ids=self.opt.multi_gpu)
self.reload_network()
def reload_network(self):
"""
Reload entire model or only decoder (atlasnet) depending on the options
:return:
"""
if self.opt.reload_model_path != "":
yellow_print(f"Network weights loaded from {self.opt.reload_model_path}!")
# print(self.network.state_dict().keys())
# print(torch.load(self.opt.reload_model_path).keys())
#self.network.module.load_state_dict(torch.load(self.opt.reload_model_path, map_location='cuda:0'))
self.network.load_state_dict(torch.load(self.opt.reload_model_path, map_location='cuda:0')) #kekw
elif self.opt.reload_decoder_path != "":
opt = deepcopy(self.opt)
opt.SVR = False
network = EncoderDecoder(opt)
network = nn.DataParallel(network, device_ids=opt.multi_gpu)
#network.module.load_state_dict(torch.load(opt.reload_decoder_path, map_location='cuda:0'))
network.load_state_dict(torch.load(opt.reload_decoder_path, map_location='cuda:0')) #kekw
self.network.module.decoder = network.module.decoder
yellow_print(f"Network Decoder weights loaded from {self.opt.reload_decoder_path}!")
else:
yellow_print("No network weights to reload!")
def build_optimizer(self):
"""
Create optimizer
"""
if self.opt.train_only_encoder:
# To train a resnet image encoder with a pre-trained atlasnet decoder.
yellow_print("only train the Encoder")
self.optimizer = optim.Adam(self.network.module.encoder.parameters(), lr=self.opt.lrate)
else:
self.optimizer = optim.Adam(self.network.module.parameters(), lr=self.opt.lrate)
if self.opt.reload_optimizer_path != "":
try:
self.optimizer.load_state_dict(torch.load(self.opt.reload_optimizer_path, map_location='cuda:0'))
# yellow_print(f"Reloaded optimizer {self.opt.reload_optimizer_path}")
except:
yellow_print(f"Failed to reload optimizer {self.opt.reload_optimizer_path}")
# Set policy for warm-up if you use multiple GPUs
self.next_learning_rates = []
if len(self.opt.multi_gpu) > 1:
self.next_learning_rates = np.linspace(self.opt.lrate, self.opt.lrate * len(self.opt.multi_gpu),
5).tolist()
self.next_learning_rates.reverse()