def main_loop(self):
evaluator = Evaluator(stats=False)
# Set game to online model.
env, args = load_env(os.environ,
evaluator=evaluator,
overrides=dict(
num_games=1,
batchsize=1,
num_games_per_thread=1,
greedy=True,
T=1,
additional_labels="aug_code,move_idx"))
GC = env["game"].initialize()
model = env["model_loaders"][0].load_model(GC.params)
mi = ModelInterface()
mi.add_model("model", model)
mi.add_model("actor",
model,
copy=True,
cuda=args.gpu is not None,
gpu_id=args.gpu)
mi["model"].eval()
mi["actor"].eval()
self.evaluator = evaluator
self.last_move_idx = None
def human_actor(batch):
print("In human_actor")
return self.prompt("DF> ", batch)
def actor(batch):
return self.actor(batch)
def train(batch):
self.prompt("DF Train> ", batch)
evaluator.setup(sampler=env["sampler"], mi=mi)
GC.reg_callback_if_exists("actor", actor)
GC.reg_callback_if_exists("human_actor", human_actor)
GC.reg_callback_if_exists("train", train)
GC.Start()
evaluator.episode_start(0)
while True:
GC.Run()
if self.exit: break
GC.Stop()
def main_loop(self):
evaluator = Evaluator(stats=False)
# Set game to online model.
env, args = load_env(os.environ, evaluator=evaluator, overrides=dict(num_games=1, batchsize=1, num_games_per_thread=1, greedy=True, T=1, additional_labels="aug_code,move_idx"))
GC = env["game"].initialize()
model = env["model_loaders"][0].load_model(GC.params)
mi = ModelInterface()
mi.add_model("model", model)
mi.add_model("actor", model, copy=True, cuda=args.gpu is not None, gpu_id=args.gpu)
mi["model"].eval()
mi["actor"].eval()
self.evaluator = evaluator
self.last_move_idx = None
def human_actor(batch):
print("In human_actor")
return self.prompt("DF> ", batch)
def actor(batch):
return self.actor(batch)
def train(batch):
self.prompt("DF Train> ", batch)
evaluator.setup(sampler=env["sampler"], mi=mi)
GC.reg_callback_if_exists("actor", actor)
GC.reg_callback_if_exists("human_actor", human_actor)
GC.reg_callback_if_exists("train", train)
GC.Start()
evaluator.episode_start(0)
while True:
GC.Run()
if self.exit: break
GC.Stop()
model = env["model_loaders"][0].load_model(GC.params)
env["mi"].add_model("actor",
model,
cuda=not args.gpu is None,
gpu_id=args.gpu)
env["mi"]["actor"].eval()
def actor(batch):
reply = evaluator.actor(batch)
'''
s = batch["s"][0][0]
seq = batch["seq"][0][0]
for i in range(s.size(0)):
print("[seq=%d][c=%d]: %s" % (seq, i, str(s[i])))
print("[seq=%d]: %s" % (seq, str(reply["pi"][0])))
print("[seq=%d]: %s" % (seq, str(reply["a"][0])))
'''
eval_iters.stats.feed_batch(batch)
return reply
evaluator.setup(sampler=env["sampler"], mi=env["mi"])
GC.reg_callback("actor", actor)
GC.Start()
evaluator.episode_start(0)
for n in eval_iters.iters():
GC.Run()
GC.Stop()
evaluator.setup(mi=mi)
total_batchsize = 0
total_sel_batchsize = 0
def actor(batch):
global total_batchsize, total_sel_batchsize
reply = evaluator.actor(batch)
total_sel_batchsize += batch.batchsize
total_batchsize += batch.max_batchsize
if total_sel_batchsize >= 5000:
print("Batch usage: %d/%d (%.2f%%)" %
(total_sel_batchsize, total_batchsize, 100.0 * total_sel_batchsize / total_batchsize))
total_sel_batchsize = 0
total_batchsize = 0
# import pdb
# pdb.set_trace()
return reply
GC.reg_callback_if_exists("actor", actor)
GC.Start()
evaluator.episode_start(0)
while True:
GC.Run()
GC.Stop()