def start_train():
'''
训练
'''
use_amp = True
# 前向反传N次，再更新参数 目的：增大batch（理论batch= batch_size * N）
iter_size = 8
myNet = MyNet(use_amp).to("cuda:0")
myNet = torch.nn.DataParallel(myNet, device_ids=[0, 1]) # 数据并行
myNet.train()
# 训练开始前初始化 梯度缩放器
scaler = GradScaler() if use_amp else None
# 加载预训练权重
if resume_train:
scaler.load_state_dict(checkpoint['scaler']) # amp自动混合精度用到
optimizer.load_state_dict(checkpoint['optimizer'])
myNet.load_state_dict(checkpoint["model"])
for epoch in range(1, 100):
for batch_idx, (input, target) in enumerate(dataloader_train):
# 数据 转到每个并行模型的主卡上
input = input.to("cuda:0")
target = target.to("cuda:0")
# 自动混合精度训练
if use_amp:
# 自动广播 将支持半精度操作自动转为FP16
with autocast():
# 提取特征
feature = myNet(input)
losses = loss_function(target, feature)
loss = losses / iter_size
scaler.scale(loss).backward()
else:
feature = myNet(input, target)
losses = loss_function(target, feature)
loss = losses / iter_size
loss.backward()
# 梯度累积,再更新参数
if (batch_idx + 1) % iter_size == 0:
# 梯度更新
if use_amp:
scaler.step(optimizer)
scaler.update()
else:
optimizer.step()
# 梯度清零
optimizer.zero_grad()
# scaler 具有状态。恢复训练时需要加载
state = {
'net': myNet.state_dict(),
'optimizer': optimizer.state_dict(),
'scaler': scaler.state_dict()
}
torch.save(state, "filename.pth")
class Detector:
def __init__(self, net_path, board_size, n):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.net = MyNet().to(self.device)
self.net.load_state_dict(torch.load(net_path))
# self.board_size = args.board_size
# self.n = args.number
self.number_playout = args.n_playout
self.env = GameState(board_size, n)
self.net.eval()
self.mcts_player = Player(policy=self.policy,
number_playout=1000,
is_self_play=False,
print_detail=True)
def policy(self, env):
# 获取可用动作 15*15=225
action_avail = env.action_avail
# 获得当前状态
state = torch.from_numpy(env.get_state).unsqueeze(0).to(self.device)
# 放入神经网络得到预测的log动作概率以及当前状态的胜率
log_action_probs, value = self.net(state)
# 把 log 动作概率转换为动作概率并过滤不可用动作
act_probs = torch.exp(
log_action_probs).detach().cpu().numpy().flatten()
act_probs = zip(action_avail, act_probs[action_avail])
value = value.item()
# 返回动作概率，当前局面价值
return act_probs, value
def detect(self):
while True:
action = None
# 当玩家切换到人机以及游戏未结束时，人机使用MCTS算法得到最优动作
if self.env.current_player == 1 and not self.env.pause:
action = self.mcts_player.get_action(self.env.game)
self.env.step(action)
class Trainer:
def __init__(self, net_path, board_size=15, n=5):
# 游戏棋盘大小
self.board_size = board_size
# 连五子胜利
self.n = n
# 环境实例化
self.env = Game(board_size, n)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.number_playout = args.n_playout
# 记忆库大小
self.buffer_size = args.buffer_size
self.buffer = deque(maxlen=self.buffer_size)
self.batch_size = args.batch_size
# 自我对局1次后进行训练
self.n_games = args.n_games
# 自我对局后进行5次训练
self.epochs = args.epochs
# 打印保存模型间隔
self.check_freq = args.check_freq
# 总共游戏次数
self.game_num = args.game_num
self.net_path = net_path
self.net = MyNet().to(self.device)
self.MSELoss = nn.MSELoss()
self.optimizer = torch.optim.Adam(self.net.parameters(),
weight_decay=1e-4)
# 实例化蒙特卡洛玩家，参数：游戏策略，探索常数，模拟次数，是否自我对弈（测试时为False）
self.mcts_player = Player(policy=self.policy,
number_playout=self.number_playout,
is_self_play=True)
self.writer = SummaryWriter()
if os.path.exists(net_path):
self.net.load_state_dict(torch.load(net_path))
else:
self.net.apply(self.weight_init)
def weight_init(self, net):
if isinstance(net, nn.Linear) or isinstance(net, nn.Conv2d):
nn.init.normal_(net.weight, mean=0., std=0.1)
nn.init.constant_(net.bias, 0.)
def train(self):
for i in range(self.game_num):
# 环境先自我对弈获得棋局状态，动作概率以及玩家可以赢的概率值
for _ in range(self.n_games):
winner, data = self.env.self_play(self.mcts_player, temp=1.0)
# 打印每局对局信息
print(self.env, "\n", "------------------xx--------")
# 将获得的数据多样化存入样本池
self.extend_sample(data)
# 取样训练
batch = random.sample(self.buffer,
min(len(self.buffer), self.batch_size))
# 解包
state_batch, mcts_probs_batch, winner_value_batch = zip(*batch)
loss = 0.
for _ in range(self.epochs):
# 数据处理
state_batch = torch.tensor(state_batch).to(self.device)
mcts_probs_batch = torch.tensor(mcts_probs_batch).to(
self.device)
winner_value_batch = torch.tensor(winner_value_batch).to(
self.device)
# 通过神经网络输出动作概率，价值用于训练
log_act_probs, value = self.net(state_batch)
# 计算损失
# 价值损失：输出价值与该状态所在对局最终胜负的值（-1/0/1）（均方差）
# 策略损失：蒙特卡洛树模拟的概率值与神经网络模拟的概率值的相似度 (-log(pi) * p)(交叉熵)
value_loss = self.MSELoss(value,
winner_value_batch.view_as(value))
policy_loss = -torch.mean(
torch.sum(mcts_probs_batch * log_act_probs, dim=-1))
loss = value_loss + policy_loss
# 反向传播
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
print(f"epoch:{i},loss:{loss}")
self.writer.add_scalar("loss", loss, i)
self.net.add_histogram(self.writer, i)
if (i + 1) % self.check_freq == 0:
torch.save(self.net.state_dict(), self.net_path)
# 多样化数据样本
def extend_sample(self, data):
extend_data = []
for state, mcts_prob, winner_value in data:
extend_data.append((state, mcts_prob, winner_value))
# 分别旋转 90度/180度/270度，增加数据多样性
for i in range(1, 4):
# 同时旋转棋盘状态和概率值
state_ = np.rot90(state, i, (1, 2))
mcts_prob_ = np.rot90(
mcts_prob.reshape(self.env.height, self.env.width), i)
extend_data.append(
(state_, mcts_prob_.flatten(), winner_value))
# 翻转棋盘,将矩阵中的每一位玩家的状态进行翻转
state_ = np.array([np.fliplr(s) for s in state_])
mcts_prob_ = np.fliplr(mcts_prob_)
extend_data.append(
(state_, mcts_prob_.flatten(), winner_value))
# 将样本存入样本池
self.buffer.extend(extend_data)
# 用于player调用神经网络获得动作概率，当前局面价值
def policy(self, env):
# 获取可用动作 15*15=225
action_avail = env.action_avail
# 获得当前状态
state = torch.from_numpy(env.get_state).unsqueeze(0).to(self.device)
# 放入神经网络得到预测的log动作概率以及当前状态的胜率
log_action_probs, value = self.net(state)
# 把 log 动作概率转换为动作概率并过滤不可用动作
act_probs = torch.exp(
log_action_probs).detach().cpu().numpy().flatten()
act_probs = zip(action_avail, act_probs[action_avail])
value = value.item()
# 返回动作概率，当前局面价值
return act_probs, value
testset = torchvision.datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transform)
testloader = torch.utils.data.DataLoader(testset,
batch_size=10000,
shuffle=False,
num_workers=2)
# Define the classes in the dataset
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck')
# Reload the neural net
net = MyNet()
net.load_state_dict(torch.load("./" + net_name + ".pth"))
# Get accuracy of the trained net as a whole
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' %
(100 * correct / total))
args, _ = parser.parse_known_args()
device = 'cuda'
gpus = get_gpus(args.gpus)
if __name__ == '__main__':
model = MyNet()
model = torch.nn.DataParallel(model, device_ids=gpus)
batch_size = args.batch_size * len(gpus)
if os.path.exists(args.data_dir):
download = False
else:
download = True
val_loader = get_dataloader(args.data_dir,
batch_size,
num_workers=args.num_workers,
shuffle=False,
train=False,
download=download)
model.to(device)
criterion = torch.nn.CrossEntropyLoss().cuda()
if not os.path.exists(args.pretrained):
print('Pretrained model do not exist!')
model.load_state_dict(torch.load(args.pretrained))
acc1, acc5 = evaluate(val_loader, model, criterion)
print('acc1={},acc5={}'.format(acc1, acc5))
