def get_config(config):
config = TransformerConfig.from_json_file(config)
config.compute_type = mstype.float16
config.dtype = mstype.float32
return config
from corpora.ami import AMI
from corpora.midas import MIDAS
from corpora.daily_dialog import DailyDialog
from taggers.transformer_tagger import TransformerTagger
from trainers.transformer_trainer import TransformerTrainer
from config import TransformerConfig
import torch.optim as optim
from corpora.corpus import Utterance
if __name__ == "__main__":
config = TransformerConfig(
taxonomy=Taxonomy.ISO,
device="cpu",
optimizer=optim.Adam,
lr=2e-5,
n_epochs=1,
batch_size=256,
max_seq_len=128,
out_folder="models/transformer_example/",
)
trainer = TransformerTrainer(
config,
corpora_list=[
# (Maptask, str(Path("data/Maptask").resolve())),
# (AMI, str(Path("data/AMI/corpus").resolve())),
(Switchboard, str(Path("data/Switchboard").resolve())),
(DailyDialog, str(Path("data/DailyDialog").resolve())),
],
)
t = trainer.train()
if os.path.isfile(词数词典路径) and os.path.isfile(数_词表路径):
词_数表, 数_词表 = 读出引索(词数词典路径, 数_词表路径)
with open(词数词典路径, encoding='utf8') as f:
词数词典 = json.load(f)
with open(操作查询路径, encoding='utf8') as f:
操作查询词典 = json.load(f)
方向表 = ['上移', '下移', '左移', '右移', '左上移', '左下移', '右上移', '右下移']
设备 = MyMNTDevice(_DEVICE_ID)
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
mod = torchvision.models.resnet101(
pretrained=True).eval().cuda(device).requires_grad_(False)
resnet101 = myResnet(mod)
config = TransformerConfig()
model = get_model(config, 130, 模型名称)
model = model.cuda(device).requires_grad_(False)
while True:
if AI打开:
图片路径 = 训练数据保存目录 + '/{}/'.format(str(int(time.time())))
os.mkdir(图片路径)
记录文件 = open(图片路径 + '_操作数据.json', 'w+')
图片张量 = torch.Tensor(0)
操作张量 = torch.Tensor(0)
def from_folder(folder: str) -> "TransformerTagger":
with open(f"{folder}/config.json") as f:
config = json.load(f)
return TransformerTagger(TransformerConfig.from_dict(config))
def get_config(config_file):
tfm_config = TransformerConfig.from_json_file(config_file)
tfm_config.compute_type = mstype.float16
tfm_config.dtype = mstype.float32
return tfm_config
def main():
global AI打开
global 操作列
加三技能 = '6'
加二技能 = '5'
加一技能 = '4'
购买 = 'f1'
词数词典路径 = "./json/词_数表.json"
数_词表路径 = "./json/数_词表.json"
操作查询路径 = "./json/名称_操作.json"
操作词典 = {"图片号": "0", "移动操作": "无移动", "动作操作": "无动作"}
th = threading.Thread(target=start_listen, )
th.start() # 启动线程
if os.path.isfile(词数词典路径) and os.path.isfile(数_词表路径):
词_数表, 数_词表 = 读出引索(词数词典路径, 数_词表路径)
with open(词数词典路径, encoding='utf8') as f:
词数词典 = json.load(f)
with open(操作查询路径, encoding='utf8') as f:
操作查询词典 = json.load(f)
方向表 = ['上移', '下移', '左移', '右移', '左上移', '左下移', '右上移', '右下移']
设备 = MyMNTDevice(_DEVICE_ID)
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
# mod = torchvision.models.resnet101(pretrained=True).eval().cuda(device).requires_grad_(False)
# mod = torchvision.models.resnet101(pretrained=True).eval().cpu().requires_grad_(False)
# mod = torchvision.models.resnet50(pretrained=True).eval().cpu().requires_grad_(False)
# mod = torchvision.models.resnet34(pretrained=True).eval().cpu().requires_grad_(False)
mod = torchvision.models.resnet18(
pretrained=True).eval().cpu().requires_grad_(False)
resnet101 = myResnet(mod)
config = TransformerConfig()
model = get_model(config, 130, 模型名称)
# model = model.cuda(device).requires_grad_(False)
model = model.cpu().requires_grad_(False)
抽样np = 0
if AI打开:
图片张量 = torch.Tensor(0)
操作张量 = torch.Tensor(0)
# 伪词序列 = torch.from_numpy(np.ones((1, 60)).astype(np.int64)).cuda(device).unsqueeze(0)
伪词序列 = torch.from_numpy(np.ones(
(1, 60)).astype(np.int64)).cpu().unsqueeze(0)
操作序列 = np.ones((1, ))
操作序列[0] = 128
计数 = 0
time_start = time.time()
旧指令 = '移动停'
for i in range(1000000):
# logger.info("++++001+++++")
if AI打开 == False:
break
try:
imgA = 取图(窗口名称)
except:
AI打开 = False
print('取图失败')
break
# logger.info("+++++002++++")
计时开始 = time.time()
# preprocess
图片张量, 操作序列 = preprocess(图片张量, imgA, resnet101, 操作序列, 抽样np)
pre_process = time.time()
logger.info("pre_process : {} ms ".format(pre_process - 计时开始))
# transform model
# 操作张量 = torch.from_numpy(操作序列.astype(np.int64)).cuda(device)
操作张量 = torch.from_numpy(操作序列.astype(np.int64)).cpu()
src_mask, trg_mask = create_masks(操作张量.unsqueeze(0),
操作张量.unsqueeze(0), device)
输出_实际_A = model(图片张量.unsqueeze(0), 操作张量.unsqueeze(0), trg_mask)
# logger.info("+++++003++++")
LI = 操作张量.contiguous().view(-1)
# LA=输出_实际_A.view(-1, 输出_实际_A.size(-1))
if 计数 % 20 == 0 and 计数 != 0:
print("jineng + zhuangbei ")
设备.发送(购买)
设备.发送(加三技能)
设备.发送(加二技能)
设备.发送(加一技能)
设备.发送('移动停')
logger.warning("{} {}".format(旧指令, '周期'))
# print(旧指令, '周期')
# time.sleep(0.02)
设备.发送(旧指令)
# logger.info("++++004+++++")
if 计数 % 1 == 0:
time_end = time.time()
输出_实际_A = F.softmax(输出_实际_A, dim=-1)
输出_实际_A = 输出_实际_A[:, -1, :]
抽样 = torch.multinomial(输出_实际_A, num_samples=1)
抽样np = 抽样.cpu().numpy()
指令 = 数_词表[str(抽样np[0, -1])]
指令集 = 指令.split('_')
# 操作词典 = {"图片号": "0", "移动操作": "无移动", "动作操作": "无动作"}
操作词典['图片号'] = str(i)
方向结果 = 处理方向()
# logger.info("++++005+++++")
logger.info("方向结果:{} 操作列:{} 攻击态:{}".format(
方向结果, len(操作列), 攻击态))
# deal with output
操作列, output_suc = output(方向结果, 操作列, 操作词典, 指令集, 旧指令, 设备, imgA,
i)
if output_suc == 0:
AI打开 = False
break
# logging
# logger.info("++++008+++++")
用时1 = 0.22 - (time.time() - 计时开始)
if 用时1 > 0:
logger.info("++++sleep+++++")
time.sleep(用时1)
logger.info("+++++++++")
用时 = time_end - time_start
print("用时{} 第{}张 延时{}".format(用时, i, 用时1), 'A键按下', A键按下,
'W键按下', W键按下, 'S键按下', S键按下, 'D键按下', D键按下, '旧指令', 旧指令,
'AI打开', AI打开, '操作列', 操作列)
计数 = 计数 + 1
# logger.info("++++009+++++")
记录文件.close()
time.sleep(1)
print('AI打开', AI打开)