TEST_DIF_DATA_PATH,
'rb',
) as f:
test_dif_data = pickle.load(f)
if torch.cuda.is_available():
cuda_flag = True
CUDA_VISIBLE_DEVICES = '0'
print('Current GPU index: ' + CUDA_VISIBLE_DEVICES)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = CUDA_VISIBLE_DEVICES
else:
cuda_flag = False
maze_core = MazeCore(hidden_dim=100)
npi = NPI(maze_core, CONFIG)
npi.load_networks(load_path)
if cuda_flag:
npi = npi.cuda()
if cuda_flag:
npi = npi.cuda()
print_net(npi)
mode = 'test_same'
cur_same_results = run_epoch(npi, mode, test_same_data, writer)
same_def_loss, same_total_loss, same_ter_accs, same_pro_accs = cur_same_results
'rb',
) as f:
test_dif_data = pickle.load(f)
CUDA_VISIBLE_DEVICES = '1'
print('Current GPU index: ' + CUDA_VISIBLE_DEVICES)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = CUDA_VISIBLE_DEVICES
if torch.cuda.is_available():
cuda_flag = True
else:
cuda_flag = False
maze_core = MazeCore()
npi = NPI(maze_core, CONFIG)
if cuda_flag:
npi = npi.cuda()
print_net(npi)
parameters = filter(lambda p: p.requires_grad, npi.parameters())
optimizer = torch.optim.SGD(parameters,
lr=0.0001,
momentum=0.9,
nesterov=True,
weight_decay=5e-4)
# optimizer = torch.optim.Adam(parameters, lr=0.0001, betas=(0.5, 0.999))
lr_schedulers = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, T_max=max_num_epochs)
TEST_DIF_DATA_PATH = 'Data/test_10_50.pik'
with open(TEST_DIF_DATA_PATH, 'rb', ) as f:
test_dif_data = pickle.load(f)
CUDA_VISIBLE_DEVICES = '3'
print('Current GPU index: ' + CUDA_VISIBLE_DEVICES)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = CUDA_VISIBLE_DEVICES
if torch.cuda.is_available():
cuda_flag = True
else:
cuda_flag = False
maze_core = MazeCore(hidden_dim=100)
npi = NPI(maze_core, CONFIG)
if cuda_flag:
npi = npi.cuda()
print_net(npi)
parameters = filter(lambda p: p.requires_grad,
npi.parameters())
# optimizer = torch.optim.SGD(parameters,
# lr=0.0001,
# momentum=0.9,
# nesterov=True,
# weight_decay=5e-4)
optimizer = torch.optim.Adam(parameters, lr=0.0001, betas=(0.5, 0.999))
lr_schedulers = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer,
T_max=max_num_epochs)