def main():
""" Training
"""
opt = Options().parse()
data = load_data(opt)
model = load_model(opt, data)
model.train()
def main():
""" Training
"""
opt = Options().parse()
data = load_data(opt) # 所得到的data包括train_data和test_data，用data.train_data获取训练数据，data.valid_data获取测试数据。
model = load_model(opt, data)
model.train()
def main():
""" Training
"""
torch.autograd.set_detect_anomaly(True)
wandb.init(entity="wenxun", project="tutorial")
opt = Options().parse()
data = load_data(opt)
model = load_model(opt, data)
model.train()
def main():
""" Training
"""
opt = Options().parse()
opt.print_freq = opt.batchsize
seed(opt.manualseed)
print("Seed:", str(torch.seed()))
if opt.phase == "inference":
opt.batchsize=1
data = load_data(opt)
model = load_model(opt, data)
if opt.phase == "inference":
model.inference()
else:
if opt.path_to_weights:
model.test()
else:
train_start = time.time()
model.train()
train_time = time.time() - train_start
print (f'Train time: {train_time} secs')
def main():
""" Training
"""
opt = Options().parse()
data = load_data(opt)
load_then_export_model(opt, data)