def dl_init():
opt = Config()
try:
if opt.MODEL == 'MiracleWeightWideNet':
net = miracle_weight_wide_net.MiracleWeightWideNet(opt)
elif opt.MODEL == 'MiracleWideNet':
net = miracle_wide_net.MiracleWideNet(opt)
elif opt.MODEL == 'MiracleNet':
net = miracle_net.MiracleNet(opt)
elif opt.MODEL == 'MiracleLineConvNet':
net = miracle_lineconv_net.MiracleLineConvNet(opt)
except KeyError('Your model is not found.'):
exit(0)
else:
print("==> Model initialized successfully.")
net_save_prefix = opt.NET_SAVE_PATH + opt.MODEL + '_' + opt.PROCESS_ID + '/'
temp_model_name = net_save_prefix + "best_model.dat"
if os.path.exists(temp_model_name):
net, *_ = net.load(temp_model_name)
print("Load existing model: %s" % temp_model_name)
if opt.USE_CUDA:
net.cuda()
print("==> Using CUDA.")
else:
raise FileNotFoundError()
return opt, net
def dl_init():
opt = Config()
if opt.MODEL == 'MiracleWeightWideNet':
net = miracle_weight_wide_net.MiracleWeightWideNet(opt)
elif opt.MODEL == 'MiracleWideNet':
net = miracle_wide_net.MiracleWideNet(opt)
elif opt.MODEL == 'MiracleNet':
net = miracle_net.MiracleNet(opt)
elif opt.MODEL == 'MiracleLineConvNet':
net = miracle_lineconv_net.MiracleLineConvNet(opt)
NET_SAVE_PREFIX = opt.NET_SAVE_PATH + opt.MODEL + '_' + opt.PROCESS_ID + '/'
temp_model_name = NET_SAVE_PREFIX + "best_model.dat"
if os.path.exists(temp_model_name):
net, *_ = net.load(temp_model_name)
print("Load existing model: %s" % temp_model_name)
else:
FileNotFoundError()
return opt, net
def main():
# Initializing configs
allDataset = None
all_loader = None
# opt = Config()
folder_init(opt)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load data
train_pairs, test_pairs = load_data(opt, './TempData/')
trainDataset = POISSON(train_pairs, opt)
train_loader = DataLoader(dataset=trainDataset,
batch_size=opt.BATCH_SIZE,
shuffle=True,
num_workers=opt.NUM_WORKERS,
drop_last=False)
testDataset = POISSON(test_pairs, opt)
test_loader = DataLoader(dataset=testDataset,
batch_size=opt.TEST_BATCH_SIZE,
shuffle=False,
num_workers=opt.NUM_WORKERS,
drop_last=False)
if opt.TRAIN_ALL or opt.TEST_ALL:
train_pairs.extend(test_pairs)
all_pairs = train_pairs
allDataset = POISSON(all_pairs, opt)
all_loader = DataLoader(dataset=allDataset,
batch_size=opt.TEST_BATCH_SIZE,
shuffle=True,
num_workers=opt.NUM_WORKERS,
drop_last=False)
opt.NUM_TEST = len(testDataset)
print("==> All datasets are generated successfully.")
# Initialize model chosen
try:
if opt.MODEL == 'MiracleWeightWideNet':
net = miracle_weight_wide_net.MiracleWeightWideNet(opt)
elif opt.MODEL == 'MiracleWideNet':
net = miracle_wide_net.MiracleWideNet(opt)
elif opt.MODEL == 'MiracleNet':
net = miracle_net.MiracleNet(opt)
elif opt.MODEL == 'MiracleLineConvNet':
net = miracle_lineconv_net.MiracleLineConvNet(opt)
except KeyError('==> Your model is not found.'):
exit(0)
else:
print("==> Model initialized successfully.")
# Instantiation of tensorboard and add net graph to it
writer = SummaryWriter(opt.SUMMARY_PATH)
dummy_input = Variable(torch.rand(opt.BATCH_SIZE, 2, 9, 41))
writer.add_graph(net, dummy_input)
# Start training or testing
if opt.TEST_ALL:
results = []
net, *_ = load_model(net, device, "best_model.dat")
results = test_all(opt, all_loader, net, results, device)
out_file = './source/val_results/' + opt.MODEL + '_' + opt.PROCESS_ID + '_results.pkl'
pickle.dump(results, open(out_file, 'wb+'))
else:
pre_epoch = 0
best_loss = 100
if opt.LOAD_SAVED_MOD:
try:
net, pre_epoch, best_loss = load_model(net, device,
"temp_model.dat")
except FileNotFoundError:
net = model_to_device(net, device)
else:
net = model_to_device(net, device)
if opt.TRAIN_ALL:
opt.NUM_TRAIN = len(allDataset)
_ = training(opt, writer, all_loader, test_loader, net, pre_epoch,
device, best_loss)
else:
opt.NUM_TRAIN = len(trainDataset)
_ = training(opt, writer, train_loader, test_loader, net,
pre_epoch, device, best_loss)