def run(self):
m = Manager()
global_ep, res_dict = m.dict(), m.dict()
global_ep['g_ep'] = 0
global_ep['g_sum_step'] = 0
res_dict['res_q'] = []
rew_queue = m.Queue()
agent_input_dict_list = m.list([self.input_dict for _ in range(1)])
agent_return_dict_list = m.list([m.dict() for _ in range(1)])
a3c_workers = [
A3CWorker(global_ep, res_dict, rew_queue, agent_input_dict_list[i],
agent_return_dict_list[i], i) for i in range(1)
]
[w.start() for w in a3c_workers]
[w.join() for w in a3c_workers]
res = res_dict['res_q']
print('game_result:', res)
for agent_return_dict in agent_return_dict_list:
print(agent_return_dict)
win_rate, tie_rate, lose_rate, step_game = self.calculate_statistics(
agent_return_dict_list)
print(win_rate, tie_rate, lose_rate, step_game)
self.return_dict[int(self.nth_pbt_process)] = [win_rate, step_game]
def hdf5_to_dict(hdf5_file_path):
data = {}
manager = Manager()
md_data = manager.dict()
with h5py.File(hdf5_file_path, 'r') as read_file:
for scene in tqdm(read_file.keys()):
# data[scene] = {}
data[scene] = manager.dict()
for pos in read_file[scene].keys():
data[scene][pos] = read_file[scene][pos][()]
md_data.update(data)
return md_data
def colorize(request, context):
try:
manager = Manager()
return_dict = manager.dict()
p = Process(target=mp_colorize, args=(request.img_input,
request.render_factor,
return_dict))
p.start()
p.join()
response = return_dict.get("response", None)
if not response or "error" in response:
error_msg = response.get("error", None) if response else None
log.error(error_msg)
context.set_details(error_msg)
context.set_code(grpc.StatusCode.INTERNAL)
return Output()
log.debug("colorize({})={}".format(request.img_input[:50], response["img_colorized"][:50]))
return Output(img_colorized=response["img_colorized"])
except Exception as e:
traceback.print_exc()
log.error(e)
return Output()
def run(args):
# setup
N, D = args.N, args.D
n_examples = args.n_train_examples
n_threads = args.n_threads
n_examples_per_thread = n_examples // n_threads
# create policy network
policy_network = alphatsp.util.get_policy_network(args.policy_network)
# generate examples
print("Generating examples and training...")
manager = Manager()
train_queue = manager.Queue()
shared_dict = manager.dict()
shared_dict["success"] = False
producers = []
for _ in range(n_threads):
producers.append(
Process(target=generate_examples,
args=(n_examples_per_thread, train_queue, args)))
for p in producers:
p.start()
c = Process(target=train,
args=(policy_network, train_queue, shared_dict, args))
c.start()
for p in producers:
p.join()
train_queue.put(None)
c.join()
status = shared_dict["success"]
if not status:
print("Experiment failed.")
return -1
count_tie = 0
count_lose = 0
count_sum_game = 0
count_sum_step = 0
# calculate the count number of games wins or loses, and the count number of steps,
# and return the average number of them
for agent_return_dict in agent_return_dict_list:
for idx in range(len(agent_return_dict['game_result'])):
count_sum_game += 1
count_sum_step += agent_return_dict['game_steps'][idx]
if agent_return_dict['game_result'][idx] == "Win":
count_win += 1
if agent_return_dict['game_result'][idx] == "Tie":
count_tie += 1
if agent_return_dict['game_result'][idx] == "Lose":
count_lose += 1
assert count_sum_game != 0, 'No Game Counted!!!'
print("Count Sum games : ", count_sum_game)
return count_win / count_sum_game, count_tie / count_sum_game, count_lose / count_sum_game, count_sum_step / count_sum_game
if __name__ == '__main__':
mp.set_start_method("spawn")
manager = Manager()
return_dict = manager.dict()
input_dict = manager.dict()
uniq_name = "" #sys.argv[1]
instance = TestFunc(uniq_name, 0, input_dict, return_dict)
instance.run()
def train(experiment: int, batch: int, resume: bool):
cfg = OthelloConfig(experiment, batch)
manager = Manager()
buffer = manager.list()
replay_buffer = ReplayBuffer(buffer)
shared_state_dicts = manager.dict()
message_queue = Queue()
log_queue = Queue(
) # a single log is dictionary and "gs", "type" keys are must
writer = SummaryWriter(cfg.dir_log)
if resume:
print("Loading replay buffer to resume training...")
with open(cfg.dir_replay_buffer, "rb") as f:
buff_list = pickle.load(f)
replay_buffer.save_training_data(buff_list)
del buff_list
print("Replay buffer loaded.")
training_worker = TrainingWorker("Training Worker", message_queue,
log_queue, shared_state_dicts,
replay_buffer, cfg.device_name_tw, cfg,
resume)
evaluation_worker = EvaluationWorker("Evaluation Worker", message_queue,
log_queue, shared_state_dicts,
cfg.device_name_ew, cfg, resume)
self_play_workers = []
for i in range(cfg.num_self_play_workers):
self_play_workers.append(
SelfPlayWorker("Self-Play Worker-" + str(i), message_queue,
log_queue, shared_state_dicts, replay_buffer,
cfg.device_names_sp[i], cfg))
print("Starting training...")
training_worker.start()
evaluation_worker.start()
for worker in self_play_workers:
worker.start()
print("Training started.")
try:
while training_worker.is_alive():
if log_queue.empty():
time.sleep(1.0)
continue
log = log_queue.get()
for k, v in log.items():
if k in ["gs", "type"]:
continue
if log["type"] == "scalar":
writer.add_scalar(k, v, log["gs"])
else:
print("Unknown log type found:", log["type"])
del log
except KeyboardInterrupt:
print("KeyboardInterrupt, stopping training...")
finally:
for i in range(cfg.num_self_play_workers * 5):
message_queue.put(cfg.message_interrupt)
training_worker.join()
evaluation_worker.join()
for worker in self_play_workers:
worker.join()
print("Saving replay buffer...")
buff_list = list(buffer)
with open(cfg.dir_replay_buffer, "wb") as f:
pickle.dump(buff_list, f)
del buff_list
print("Replay buffer saved.")
def main():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
args = flag_parser.parse_arguments()
create_shared_model = model_class(args.model)
init_agent = agent_class(args.agent_type)
args.episode_type = "TestValEpisode"
args.test_or_val = "val"
tb_log_dir = args.log_dir + "/" + '{}_{}_{}'.format(
args.title, args.test_or_val,
time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime(time.time())))
log_writer = SummaryWriter(log_dir=tb_log_dir)
print('Start Loading!')
optimal_action_path = './data/AI2thor_Combine_Dataset/Optimal_Path_Combine.json'
with open(optimal_action_path, 'r') as read_file:
optimal_action_dict = json.load(read_file)
manager = Manager()
optimal_action = manager.dict()
optimal_action.update(optimal_action_dict)
glove_file_path = './data/AI2thor_Combine_Dataset/det_feature_512_eval.hdf5'
glove_file = hdf5_to_dict(glove_file_path)
print('Loading Success!')
# Get all valid saved_models for the given title and sort by train_ep.
checkpoints = [(f, f.split("_")) for f in os.listdir(args.save_model_dir)]
checkpoints = [(f, int(s[-3])) for (f, s) in checkpoints
if len(s) >= 4 and f.startswith(args.title)]
checkpoints.sort(key=lambda x: x[1])
best_model_on_val = None
best_performance_on_val = 0.0
for (f, train_ep) in tqdm(checkpoints, desc="Checkpoints."):
model = os.path.join(args.save_model_dir, f)
args.load_model = model
# run eval on model
args.test_or_val = "val"
main_eval(args, create_shared_model, init_agent, glove_file,
optimal_action)
# check if best on val.
with open(args.results_json, "r") as f:
results = json.load(f)
if results["success"] > best_performance_on_val:
best_model_on_val = model
best_performance_on_val = results["success"]
log_writer.add_scalar("val/success", results["success"], train_ep)
log_writer.add_scalar("val/spl", results["spl"], train_ep)
# run on test.
args.test_or_val = "test"
main_eval(args, create_shared_model, init_agent, glove_file,
optimal_action)
with open(args.results_json, "r") as f:
results = json.load(f)
log_writer.add_scalar("test/success", results["success"], train_ep)
log_writer.add_scalar("test/spl", results["spl"], train_ep)
args.record_route = True
args.test_or_val = "test"
args.load_model = best_model_on_val
main_eval(args, create_shared_model, init_agent, glove_file,
optimal_action)
with open(args.results_json, "r") as f:
results = json.load(f)
print(
tabulate(
[
["SPL >= 1:", results["GreaterThan/1/spl"]],
["Success >= 1:", results["GreaterThan/1/success"]],
["SPL >= 5:", results["GreaterThan/5/spl"]],
["Success >= 5:", results["GreaterThan/5/success"]],
],
headers=["Metric", "Result"],
tablefmt="orgtbl",
))
print("Best model:", args.load_model)
def main():
setproctitle.setproctitle("Train/Test Manager")
args = flag_parser.parse_arguments()
if args.model == "BaseModel" or args.model == "GCN":
args.learned_loss = False
args.num_steps = 50
target = nonadaptivea3c_val if args.eval else nonadaptivea3c_train
else:
args.learned_loss = True
args.num_steps = 6
target = savn_val if args.eval else savn_train
create_shared_model = model_class(args.model)
init_agent = agent_class(args.agent_type)
optimizer_type = optimizer_class(args.optimizer)
if args.eval:
main_eval(args, create_shared_model, init_agent)
return
start_time = time.time()
local_start_time_str = time.strftime("%Y-%m-%d_%H:%M:%S",
time.localtime(start_time))
np.random.seed(args.seed)
torch.manual_seed(args.seed)
random.seed(args.seed)
if args.log_dir is not None:
tb_log_dir = args.log_dir + "/" + args.title + "-" + local_start_time_str
log_writer = SummaryWriter(log_dir=tb_log_dir)
else:
log_writer = SummaryWriter(comment=args.title)
if args.gpu_ids == -1:
args.gpu_ids = [-1]
else:
torch.cuda.manual_seed(args.seed)
mp.set_start_method("spawn")
shared_model = create_shared_model(args)
train_total_ep = 0
n_frames = 0
if shared_model is not None:
shared_model.share_memory()
optimizer = optimizer_type(
filter(lambda p: p.requires_grad, shared_model.parameters()), args)
optimizer.share_memory()
print(shared_model)
else:
assert (args.agent_type == "RandomNavigationAgent"
), "The model is None but agent is not random agent"
optimizer = None
processes = []
print('Start Loading!')
optimal_action_path = './data/AI2thor_Combine_Dataset/Optimal_Path_Combine.json'
with open(optimal_action_path, 'r') as read_file:
optimal_action_dict = json.load(read_file)
manager = Manager()
optimal_action = manager.dict()
optimal_action.update(optimal_action_dict)
glove_file_path = './data/AI2thor_Combine_Dataset/det_feature_512_train.hdf5'
glove_file = hdf5_to_dict(glove_file_path)
# det_gt_path = './data/AI2thor_Combine_Dataset/Instance_Detection_Combine.pkl'
# with open(det_gt_path, 'rb') as read_file:
# det_gt = pickle.load(read_file)
print('Loading Success!')
end_flag = mp.Value(ctypes.c_bool, False)
train_res_queue = mp.Queue()
for rank in range(0, args.workers):
p = mp.Process(
target=target,
args=(
rank,
args,
create_shared_model,
shared_model,
init_agent,
optimizer,
train_res_queue,
end_flag,
glove_file,
optimal_action,
# det_gt,
),
)
p.start()
processes.append(p)
time.sleep(0.1)
print("Train agents created.")
train_thin = args.train_thin
train_scalars = ScalarMeanTracker()
# start_ep_time = time.time()
try:
while train_total_ep < args.max_ep:
train_result = train_res_queue.get()
train_scalars.add_scalars(train_result)
train_total_ep += 1
n_frames += train_result["ep_length"]
# if train_total_ep % 10 == 0:
# print(n_frames / train_total_ep)
# print((time.time() - start_ep_time) / train_total_ep)
if (train_total_ep % train_thin) == 0:
log_writer.add_scalar("n_frames", n_frames, train_total_ep)
tracked_means = train_scalars.pop_and_reset()
for k in tracked_means:
log_writer.add_scalar(k + "/train", tracked_means[k],
train_total_ep)
if (train_total_ep % args.ep_save_freq) == 0:
print(n_frames)
if not os.path.exists(args.save_model_dir):
os.makedirs(args.save_model_dir)
state_to_save = shared_model.state_dict()
save_path = os.path.join(
args.save_model_dir,
"{0}_{1}_{2}_{3}.dat".format(args.title, n_frames,
train_total_ep,
local_start_time_str),
)
torch.save(state_to_save, save_path)
finally:
log_writer.close()
end_flag.value = True
for p in processes:
time.sleep(0.1)
p.join()
def recognize_scene(self, request, context):
"""Wraps the scene recognition model to make sure inputs and outputs match the service requirements."""
# Store the names of the images to delete them afterwards
created_images = []
# Python command call arguments. Key = argument name, value = tuple(type, required?, default_value)
arguments = {
"input_image": ("image", True, None),
"predict": ("string", True, self.prediction_list)
}
# Treat inputs
try:
image_path, predict, file_index_str = self.treat_inputs(
request, arguments, created_images)
except HTTPError as e:
error_message = "Error downloading the input image \n" + str(e)
log.error(error_message)
self.result.data = error_message
context.set_details(error_message)
context.set_code(grpc.StatusCode.INTERNAL)
return self.result
except Exception as e:
log.error(e)
self.result.data = e
context.set_details(str(e))
context.set_code(grpc.StatusCode.INTERNAL)
return self.result
# Get cam (color activation mappings) file path
input_filename = os.path.split(created_images[0])[1]
log.debug("Input file name: {}".format(input_filename))
output_image_path = self.output_dir + '/' + input_filename
log.debug("Output image path (cam_path): {}".format(output_image_path))
created_images.append(output_image_path)
manager = Manager()
return_dict = manager.dict()
p = Process(target=mp_recognize,
args=(image_path, predict, output_image_path, return_dict))
p.start()
p.join()
response = return_dict.get("response", None)
if not response or "error" in response:
error_msg = response.get("error", None) if response else None
log.error(error_msg)
context.set_details(error_msg)
context.set_code(grpc.StatusCode.INTERNAL)
return SceneRecognitionResult()
# Prepare gRPC output message
self.result = SceneRecognitionResult()
log.debug("Got result.")
self.result.data = json.dumps(response)
log.debug("Output generated. Service successfully completed.")
for image in created_images:
service.serviceUtils.clear_file(image)
return self.result
# Get save directory
save_dir = os.path.join(os.getcwd(), args.save_dir, exp_name)
if not os.path.exists(save_dir):
os.makedirs(os.path.join(save_dir, 'configs'))
print('Save directory:\t {}'.format(save_dir))
# Get device ID
if torch.cuda.is_available():
device = torch.cuda.device_count()
else:
device = 'cpu'
print('Device:\t {}'.format(device))
#init master
master = manager.dict()
master['start_time'] = strftime("%Y-%m-%dT%H-%M-%S", gmtime())
master['experiment_name'] = exp_name
master['device'] = device
master['summaries'] = {}
# Run a single experiment
if args.index >= 0:
if args.index < len(config_files):
config_files = [config_files[args.index]]
else:
print(
"WARNING: Index out of range, will run all experiments in folder."
)
if device == 'cpu':
