def _load_training_state(self):
training_state = load_training_state()
if training_state:
self.agent.scaler = training_state['scaler']
self.last_step = training_state['last_step']
self.ep_reward_queue = training_state['ep_reward_queue']
print('training state loaded.')
def _load_training_state(self):
training_state = load_training_state()
if training_state:
self.agent.replay_memory.load_memory(
training_state['replay_memory'])
self.last_step = training_state['last_step']
self.ep_reward_queue = training_state['ep_reward_queue']
print('training state loaded.')
def finetuneparam(pre_imgnet, nllr, use_pre_ours=0, epocs=3):
torch.manual_seed(123)
param_val = 'p%dlr%d' % (pre_imgnet, nllr)
if (use_pre_ours == 0):
model = Frame2dResNet50(use_pretrain=pre_imgnet).to(device)
optimizer = optim.Adam(model.parameters(), lr=10**(-nllr))
torch.manual_seed(123)
else:
model = Frame2dResNet50().to(device)
optimizer = optim.Adam(model.parameters(), lr=10**(-nllr))
load_training_state(
os.path.join(savedPath,
'2dResNet-' + param_val + '-%d.pth' % (use_pre_ours)),
model, optimizer)
torch.manual_seed(123)
log_file = open('log-train/log-p%dlr%d.txt' % (pre_imgnet, nllr), 'a')
train_save(epocs,
model,
optimizer,
param_val,
print_to=log_file,
epoc_start=use_pre_ours)
log_file.close()
def _load_training_state(self):
training_state = load_training_state()
if training_state:
self.agent.scaler = training_state['scaler']
print('training state loaded.')