def main():
experiment_name = "lani-asynchronous-training"
experiment = "./results/" + experiment_name
print("EXPERIMENT NAME: ", experiment_name)
# Create the experiment folder
if not os.path.exists(experiment):
os.makedirs(experiment)
# Define log settings
log_path = experiment + '/train_baseline.log'
multiprocess_logging_manager = MultiprocessingLoggerManager(
file_path=log_path, logging_level=logging.INFO)
master_logger = multiprocess_logging_manager.get_logger("Master")
master_logger.log(
"----------------------------------------------------------------")
master_logger.log(
" STARING NEW EXPERIMENT ")
master_logger.log(
"----------------------------------------------------------------")
with open("data/nav_drone/config_localmoves_6000.json") as f:
config = json.load(f)
with open("data/shared/contextual_bandit_constants.json") as f:
constants = json.load(f)
print(json.dumps(config, indent=2))
setup_validator = NavDroneSetupValidator()
setup_validator.validate(config, constants)
# log core experiment details
master_logger.log("CONFIG DETAILS")
for k, v in sorted(config.items()):
master_logger.log(" %s --- %r" % (k, v))
master_logger.log("CONSTANTS DETAILS")
for k, v in sorted(constants.items()):
master_logger.log(" %s --- %r" % (k, v))
master_logger.log("START SCRIPT CONTENTS")
with open(__file__) as f:
for line in f.readlines():
master_logger.log(">>> " + line.strip())
master_logger.log("END SCRIPT CONTENTS")
action_space = ActionSpace(config["action_names"], config["stop_action"])
meta_data_util = MetaDataUtil()
# Number of processes
num_processes = 6
try:
# Create the model
master_logger.log("CREATING MODEL")
model_type = IncrementalModelOracleGoldProb
shared_model = model_type(config, constants)
# Initialize the model using random weights or from a file
shared_model.init_weights()
# shared_model.load_saved_model(
# "./results/model-folder-name/contextual_bandit_5_epoch_19")
# Make the shared model use share memory
shared_model.share_memory()
master_logger.log("MODEL CREATED")
print("Created Model...")
# Read the dataset
all_train_data = DatasetParser.parse(
"data/nav_drone/train_annotations_6000.json", config)
num_train = (len(all_train_data) * 19) // 20
while all_train_data[num_train].get_scene_name().split("_")[1] \
== all_train_data[num_train - 1].get_scene_name().split("_")[1]:
num_train += 1
train_split = all_train_data[:num_train]
tune_split = all_train_data[num_train:]
master_logger.log("Created train dataset of size %d " %
len(train_split))
master_logger.log("Created tuning dataset of size %d " %
len(tune_split))
processes = []
# The simulator file is used to launch the client
simulator_file = "./simulators/NavDroneLinuxBuild.x86_64"
# Split the train data between processes
train_split_process_chunks = []
chunk_size = int(len(train_split) / num_processes)
pad = 0
for i in range(0, num_processes):
chunk = train_split[pad:pad + chunk_size]
pad += chunk_size
train_split_process_chunks.append(chunk)
# Start the training thread(s)
ports = find_k_ports(num_processes)
for i, port in enumerate(ports):
train_chunk = train_split_process_chunks[i]
tmp_config = {k: v for k, v in config.items()}
tmp_config["port"] = port
if i == num_processes - 1:
# Master client which does testing. Don't want each client to do testing.
tmp_tune_split = tune_split
else:
tmp_tune_split = []
print("Client " + str(i) + " getting a validation set of size ",
len(tmp_tune_split))
server = NavDroneServerPy3(tmp_config,
action_space,
multi_client=True)
client_logger = multiprocess_logging_manager.get_logger(i)
p = mp.Process(target=AsynchronousContextualBandit.do_train,
args=(simulator_file, shared_model, tmp_config,
action_space, meta_data_util, constants,
train_chunk, tmp_tune_split, experiment,
experiment_name, i, server, client_logger,
model_type))
p.daemon = False
p.start()
processes.append(p)
for p in processes:
p.join()
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)
logging.info("START SCRIPT CONTENTS")
with open(__file__) as f:
for line in f.readlines():
logging.info(">>> " + line.strip())
logging.info("END SCRIPT CONTENTS")
action_space = ActionSpace(config["action_names"], config["stop_action"])
meta_data_util = MetaDataUtil()
# Find a free port
ports = find_k_ports(1)
config["port"] = ports[0]
# Create the server
logging.log(logging.DEBUG, "STARTING SERVER")
server = NavDroneServerPy3(config, action_space)
logging.log(logging.DEBUG, "STARTED SERVER")
try:
# Create the model
logging.log(logging.DEBUG, "CREATING MODEL")
model = IncrementalModelOracleGoldProb(config, constants, use_image=False)
model.load_saved_model(
"./results/oracle_gold_prob_cb_6000/contextual_bandit_5_epoch_17")
logging.log(logging.DEBUG, "MODEL CREATED")
# Create the agent
logging.log(logging.DEBUG, "STARTING AGENT")
agent = Agent(server=server,
def main():
experiment_name = "a3c_ga_chaplot_baseline_6000paragraphs"
experiment = "./results/" + experiment_name
print("EXPERIMENT NAME: ", experiment_name)
# Create the experiment folder
if not os.path.exists(experiment):
os.makedirs(experiment)
# Define log settings
log_path = experiment + '/train_baseline.log'
multiprocess_logging_manager = MultiprocessingLoggerManager(
file_path=log_path, logging_level=logging.INFO)
master_logger = multiprocess_logging_manager.get_logger("Master")
master_logger.log("----------------------------------------------------------------")
master_logger.log(" STARING NEW EXPERIMENT ")
master_logger.log("----------------------------------------------------------------")
parser = argparse.ArgumentParser(description='Gated-Attention for Grounding')
# Environment arguments
parser.add_argument('-l', '--max-episode-length', type=int, default=50,
help='maximum length of an episode (default: 40)')
parser.add_argument('-d', '--difficulty', type=str, default="hard",
help="""Difficulty of the environment,
"easy", "medium" or "hard" (default: hard)""")
parser.add_argument('--living-reward', type=float, default=0,
help="""Default reward at each time step (default: 0,
change to -0.005 to encourage shorter paths)""")
parser.add_argument('--frame-width', type=int, default=300,
help='Frame width (default: 300)')
parser.add_argument('--frame-height', type=int, default=168,
help='Frame height (default: 168)')
parser.add_argument('-v', '--visualize', type=int, default=0,
help="""Visualize the envrionment (default: 0,
use 0 for faster training)""")
parser.add_argument('--sleep', type=float, default=0,
help="""Sleep between frames for better
visualization (default: 0)""")
parser.add_argument('--scenario-path', type=str, default="maps/room.wad",
help="""Doom scenario file to load
(default: maps/room.wad)""")
parser.add_argument('--interactive', type=int, default=0,
help="""Interactive mode enables human to play
(default: 0)""")
parser.add_argument('--all-instr-file', type=str,
default="data/instructions_all.json",
help="""All instructions file
(default: data/instructions_all.json)""")
parser.add_argument('--train-instr-file', type=str,
default="data/instructions_train.json",
help="""Train instructions file
(default: data/instructions_train.json)""")
parser.add_argument('--test-instr-file', type=str,
default="data/instructions_test.json",
help="""Test instructions file
(default: data/instructions_test.json)""")
parser.add_argument('--object-size-file', type=str,
default="data/object_sizes.txt",
help='Object size file (default: data/object_sizes.txt)')
# A3C arguments
parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.001)')
parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
help='discount factor for rewards (default: 0.99)')
parser.add_argument('--tau', type=float, default=1.00, metavar='T',
help='parameter for GAE (default: 1.00)')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('-n', '--num-processes', type=int, default=6, metavar='N',
help='how many training processes to use (default: 6)')
parser.add_argument('--num-steps', type=int, default=20, metavar='NS',
help='number of forward steps in A3C (default: 20)')
parser.add_argument('--load', type=str, default="0",
help='model path to load, 0 to not reload (default: 0)')
parser.add_argument('-e', '--evaluate', type=int, default=0,
help="""0:Train, 1:Evaluate MultiTask Generalization
2:Evaluate Zero-shot Generalization (default: 0)""")
parser.add_argument('--dump-location', type=str, default="./saved/",
help='path to dump models and log (default: ./saved/)')
args = parser.parse_args()
print(args)
with open("data/nav_drone/config_localmoves_6000.json") as f:
config = json.load(f)
with open("data/shared/contextual_bandit_constants.json") as f:
constants = json.load(f)
print(json.dumps(config,indent=2))
setup_validator = NavDroneSetupValidator()
setup_validator.validate(config, constants)
args.input_size = config['vocab_size'] + 2
# log core experiment details
master_logger.log("CONFIG DETAILS")
for k, v in sorted(config.items()):
master_logger.log(" %s --- %r" % (k, v))
master_logger.log("CONSTANTS DETAILS")
for k, v in sorted(constants.items()):
master_logger.log(" %s --- %r" % (k, v))
master_logger.log("START SCRIPT CONTENTS")
with open(__file__) as f:
for line in f.readlines():
master_logger.log(">>> " + line.strip())
master_logger.log("END SCRIPT CONTENTS")
action_space = ActionSpace(config["action_names"], config["stop_action"])
meta_data_util = MetaDataUtil()
try:
# create tensorboard
tensorboard = None # Tensorboard(experiment_name)
# Create the model
master_logger.log("CREATING MODEL")
model_type = a3c_lstm_ga_default
shared_model = model_type(args, config=config)
# make the shared model use share memory
shared_model.share_memory()
lstm_size = 256
if isinstance(shared_model, a3c_lstm_ga_concat_gavector):
lstm_size *= 3
contextual_bandit = False
model = ChaplotBaseline(args, shared_model, config, constants,
tensorboard, use_contextual_bandit=contextual_bandit, lstm_size=lstm_size)
# model.load_image_text_model("./results/realdata_goal_prediction_supervised_trajectories"
# "/chaplot_model_client_5_epoch_36")
master_logger.log("MODEL CREATED")
print("Created Model...")
# Read the dataset
all_train_data = DatasetParser.parse("data/nav_drone/train_annotations_6000.json", config)
num_train = (len(all_train_data) * 19) // 20
while all_train_data[num_train].get_scene_name().split("_")[1] \
== all_train_data[num_train - 1].get_scene_name().split("_")[1]:
num_train += 1
train_split = all_train_data[:num_train]
tune_split = all_train_data[num_train:]
master_logger.log("Created train dataset of size %d " % len(train_split))
master_logger.log("Created tuning dataset of size %d " % len(tune_split))
processes = []
# Split the train data between processes
train_split_process_chunks = []
chunk_size = int(len(train_split)/args.num_processes)
pad = 0
for i in range(0, args.num_processes):
chunk = train_split[pad: pad + chunk_size]
pad += chunk_size
train_split_process_chunks.append(chunk)
# Start the training thread(s)
ports = find_k_ports(args.num_processes)
for i, port in enumerate(ports):
train_chunk = train_split_process_chunks[i]
tmp_config = {k: v for k, v in config.items()}
tmp_config["port"] = port
if i == args.num_processes - 1:
# Master client which does testing. Don't want each client to do testing.
tmp_tune_split = tune_split
else:
tmp_tune_split = []
print ("Client " + str(i) + " getting a validation set of size ", len(tmp_tune_split))
server = NavDroneServerPy3(tmp_config, action_space, multi_client=True)
client_logger = multiprocess_logging_manager.get_logger(i)
p = mp.Process(target=ChaplotBaseline.do_train, args=(model, shared_model, tmp_config,
action_space, meta_data_util, args,
constants, train_chunk, tmp_tune_split,
experiment, experiment_name, i, server,
client_logger, model_type,
contextual_bandit))
p.daemon = False
p.start()
processes.append(p)
for p in processes:
p.join()
except Exception:
exc_info = sys.exc_info()
traceback.print_exception(*exc_info)