def load_model(config):
model = CChessModel(config)
if config.internet.distributed or config.opts.new or not load_best_model_weight(
model):
model.build()
save_as_best_model(model)
return model
def load_model(config, weight_path, digest, config_file=None):
model = CChessModel(config)
use_history = False
if not config_file:
config_path = config.resource.model_best_config_path
use_history = False
else:
config_path = os.path.join(config.resource.model_dir, config_file)
logger.debug(f"config_path = {config_path}, digest = {digest}")
if (not load_model_weight(model, config_path, weight_path)) or model.digest != digest:
logger.info(f"开始下载权重 {digest[0:8]}")
url = config.internet.download_base_url + digest + '.h5'
download_file(url, weight_path)
try:
if not load_model_weight(model, config_path, weight_path):
logger.info(f"待评测权重还未上传，请稍后再试")
sys.exit()
except ValueError as e:
logger.error(f"权重架构不匹配，自动重新加载 {e}")
return load_model(config, weight_path, digest, 'model_192x10_config.json')
except Exception as e:
logger.error(f"加载权重发生错误：{e}，10s后自动重试下载")
os.remove(weight_path)
sleep(10)
return load_model(config, weight_path, digest)
logger.info(f"加载权重 {digest[0:8]} 成功")
return model, use_history
def load_model(self):
sess = set_session_config(per_process_gpu_memory_fraction=1,
allow_growth=True,
device_list=self.config.opts.device_list)
self.model = CChessModel(self.config)
if self.config.opts.new or not load_best_model_weight(self.model):
self.model.build()
self.model.sess = sess
def plot_model():
from keras.utils import plot_model
from cchess_alphazero.agent.model import CChessModel
from cchess_alphazero.config import Config
from cchess_alphazero.lib.model_helper import save_as_best_model
config = Config('distribute')
model = CChessModel(config)
model.build()
save_as_best_model(model)
plot_model(model.model,
to_file='model.png',
show_shapes=True,
show_layer_names=True)
def load_model(config, model_file=None):
use_history = False
model = CChessModel(config)
if not model_file:
config_path = config.resource.model_best_path
use_history = False
else:
config_path = os.path.join(config.resource.model_dir, model_file)
try:
if not load_model_weight(model, config_path):
# model.build()
save_as_best_model(model)
use_history = True
except Exception as e:
logger.info(f"Exception {e}, 重新加载权重")
return load_model(config, model_file='model_192x10_config.json')
return model, use_history
def load_model(self, model_file=None):
use_history = False
model = CChessModel(self.config)
# weight_path = self.config.resource.model_best_weight_path
if not model_file:
model_path = self.config.resource.model_best_path
use_history = False
else:
model_path = os.path.join(self.config.resource.model_dir,
model_file)
try:
if not load_model_weight(model, model_path):
save_as_best_model(model)
use_history = True
except Exception as e:
logger.info(f"Exception {e}, 重新加载权重")
return self.load_model(model_file='model_192x10_config.json')
return model, use_history
def load_model(self, config_file=None):
use_history = True
self.model = CChessModel(self.config)
weight_path = self.config.resource.model_best_weight_path
if not config_file:
config_path = config.resource.model_best_path
use_history = False
else:
config_path = os.path.join(config.resource.model_dir, config_file)
try:
if not load_model_weight(self.model, config_path, weight_path):
self.model.build()
use_history = True
except Exception as e:
logger.info(f"Exception {e}, 重新加载权重")
return self.load_model(config_file='model_128_l1_config.json')
logger.info(f"use_history = {use_history}")
return use_history
def load_model(self):
self.model = CChessModel(self.config)
if self.config.opts.new or not load_best_model_weight(self.model):
self.model.build()
def load_model(self):
model = CChessModel(self.config)
if self.config.opts.new or not load_sl_best_model_weight(model):
model.build()
save_as_sl_best_model(model)
return model
def load_model(config, config_path, weight_path, name=None):
model = CChessModel(config)
if not load_model_weight(model, config_path, weight_path, name):
return None
return model
