def get_player(directory=None,
files_list=None,
viz=False,
task='play',
saveGif=False,
saveVideo=False,
agents=2,
reward_strategy=1):
# in atari paper, max_num_frames = 30000
env = MedicalPlayer(directory=directory,
screen_dims=IMAGE_SIZE,
viz=viz,
saveGif=saveGif,
saveVideo=saveVideo,
task=task,
files_list=files_list,
agents=agents,
max_num_frames=1500,
reward_strategy=reward_strategy)
if (task != 'train'):
# in training, env will be decorated by ExpReplay, and history
# is taken care of in expreplay buffer
# otherwise, FrameStack modifies self.step to save observations into a queue
env = FrameStack(env, FRAME_HISTORY, agents=agents)
return env
def get_player(directory=None,
files_list=None,
landmark_ids=None,
viz=False,
task="play",
file_type="brain",
saveGif=False,
saveVideo=False,
multiscale=True,
history_length=20,
agents=1,
logger=None):
env = MedicalPlayer(directory=directory,
screen_dims=IMAGE_SIZE,
viz=viz,
saveGif=saveGif,
saveVideo=saveVideo,
task=task,
files_list=files_list,
file_type=file_type,
landmark_ids=landmark_ids,
history_length=history_length,
multiscale=multiscale,
agents=agents,
logger=logger)
if task != "train":
# in training, env will be decorated by ExpReplay, and history
# is taken care of in expreplay buffer
# otherwise, FrameStack modifies self.step to save observations into a
# queue
env = FrameStack(env, FRAME_HISTORY, agents)
return env
def get_player(
directory=None,
files_list=None,
viz=False,
task="play",
saveGif=False,
saveVideo=False,
agents=2,
fiducials=None,
infDir="../inference",
):
# in atari paper, max_num_frames = 30000
env = MedicalPlayer(
directory=directory,
screen_dims=IMAGE_SIZE,
viz=viz,
saveGif=saveGif,
saveVideo=saveVideo,
task=task,
files_list=files_list,
agents=agents,
max_num_frames=1500,
fiducials=fiducials,
infDir=infDir,
)
if task != "train":
# in training, env will be decorated by ExpReplay, and history
# is taken care of in expreplay buffer
# otherwise, FrameStack modifies self.step to save observations into a queue
env = FrameStack(env, FRAME_HISTORY, agents=agents)
return env
if args.task == 'play':
error_message = """Wrong input files {} for {} task - should be 1 \'images.txt\' """.format(
len(args.files), args.task)
assert len(args.files) == 1
else:
error_message = """Wrong input files {} for {} task - should be 2 [\'images.txt\', \'landmarks.txt\'] """.format(
len(args.files), args.task)
assert len(args.files) == 2, (error_message)
args.agents = int(args.agents)
METHOD = args.algo
# load files into env to set num_actions, num_validation_files
init_player = MedicalPlayer(files_list=args.files,
screen_dims=IMAGE_SIZE,
task='train',
agents=args.agents,
reward_strategy=args.reward_strategy)
NUM_ACTIONS = init_player.action_space.n
num_files = init_player.files.num_files
##########################################################
#initialize states and Qvalues for the various agents
state_names = []
qvalue_names = []
for i in range(0, args.agents):
state_names.append('state_{}'.format(i))
qvalue_names.append('Qvalue_{}'.format(i))
############################################################
assert len(args.files) == 1, (error_message)
else:
error_message = f"""Wrong input files {len(args.files)} for
{args.task} task - should be 2 [\'images.txt\',
\'landmarks.txt\'] """
assert len(args.files) == 2, (error_message)
logger = Logger(args.logDir, args.write, args.save_freq)
# load files into env to set num_actions, num_validation_files
# TODO: is this necessary?
init_player = MedicalPlayer(
files_list=args.files,
file_type=args.file_type,
landmark_ids=args.landmarks,
screen_dims=IMAGE_SIZE,
# TODO: why is this always play?
task='play',
agents=agents,
logger=logger)
NUM_ACTIONS = init_player.action_space.n
if args.task != 'train':
# TODO: refactor DQN to not have to create both a q_network and
# target_network
dqn = DQN(agents,
frame_history=FRAME_HISTORY,
logger=logger,
type=args.model_name)
model = dqn.q_network
model.load_state_dict(torch.load(args.load, map_location=model.device))
)
assert len(args.files) == 1
else:
error_message = """Wrong input files {} for {} task - should be 2 [\'images.txt\', \'landmarks.txt\'] """.format(
len(args.files), args.task
)
assert len(args.files) == 2, error_message
# args.agents=int(args.agents)
METHOD = args.algo
# load files into env to set num_actions, num_validation_files
init_player = MedicalPlayer(
files_list=args.files,
screen_dims=IMAGE_SIZE,
task=args.task,
agents=args.agents,
fiducials=args.fiducials,
infDir=args.inferDir,
)
NUM_ACTIONS = init_player.action_space.n
num_files = init_player.files.num_files
##########################################################
# initialize states and Qvalues for the various agents
state_names = []
qvalue_names = []
for i in range(0, args.agents):
state_names.append("state_{}".format(i))
qvalue_names.append("Qvalue_{}".format(i))
############################################################
# check input files
if args.task == 'play':
error_message = """Wrong input files {} for {} task - should be 1 \'images.txt\' """.format(
len(args.files), args.task)
assert len(args.files) == 1, (error_message)
else:
error_message = """Wrong input files {} for {} task - should be 2 [\'images.txt\', \'landmarks.txt\'] """.format(
len(args.files), args.task)
assert len(args.files) == 2, (error_message)
METHOD = args.algo
# load files into env to set num_actions, num_validation_files
init_player = MedicalPlayer(
files_list=args.files,
screen_dims=IMAGE_SIZE,
# TODO: why is this always play?
task='play',
agents=args.agents,
reward_strategy=args.reward_strategy)
NUM_ACTIONS = init_player.action_space.n
num_files = init_player.files.num_files
if args.task != 'train':
assert args.load is not None
pred = OfflinePredictor(
PredictConfig(model=Model(),
session_init=get_model_loader(args.load),
input_names=['state'],
output_names=['Qvalue']))
# demo pretrained model one episode at a time
if args.task == 'play' or args.task == 'eval':
# check input files
if args.task == 'play':
error_message = """Wrong input files {} for {} task - should be 1 \'images.txt\' """.format(
len(args.files), args.task)
assert len(args.files) == 1
else: # this is 'train'
print("args.task:", args.task)
error_message = """Wrong input files {} for {} task - should be 2 [\'images.txt\', \'landmarks.txt\'] """.format(
len(args.files), args.task)
assert len(args.files) == 2, (error_message)
METHOD = args.algo
# load files into env to set num_actions, num_validation_files
init_player = MedicalPlayer(files_list=args.files,
screen_dims=IMAGE_SIZE,
task='play')
NUM_ACTIONS = init_player.action_space.n
num_files = init_player.files.num_files
if args.task != 'train':
assert args.load is not None
pred = OfflinePredictor(
PredictConfig(model=Model(),
session_init=get_model_loader(args.load),
input_names=['state'],
output_names=['Qvalue']))
# demo pretrained model one episode at a time
if args.task == 'play':
# check input files
if args.task == "play":
error_message = """Wrong input files {} for {} task - should be 1 \'images.txt\' """.format(
len(args.files), args.task)
assert len(args.files) == 1
else:
error_message = """Wrong input files {} for {} task - should be 2 [\'images.txt\', \'landmarks.txt\'] """.format(
len(args.files), args.task)
assert len(args.files) == 2, error_message
args.agents = int(args.agents)
METHOD = args.algo
# load files into env to set num_actions, num_validation_files
init_player = MedicalPlayer(files_list=args.files,
screen_dims=IMAGE_SIZE,
task="train",
agents=args.agents)
NUM_ACTIONS = init_player.action_space.n
num_files = init_player.files.num_files
##########################################################
# initialize states and Qvalues for the various agents
state_names = []
qvalue_names = []
for i in range(0, args.agents):
state_names.append("state_{}".format(i))
qvalue_names.append("Qvalue_{}".format(i))
############################################################
if args.task != "train":
help='save gif image of the game',
action='store_true',
default=False)
parser.add_argument('--saveVideo',
help='save video of the game',
action='store_true',
default=False)
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
METHOD = args.algo
# load files into env to set num_actions, num_validation_files
init_player = MedicalPlayer(directory=data_dir,
files_list=test_list,
screen_dims=IMAGE_SIZE)
NUM_ACTIONS = init_player.action_space.n
num_files = init_player.files.num_files
if args.task != 'train':
assert args.load is not None
pred = OfflinePredictor(
PredictConfig(model=Model(),
session_init=get_model_loader(args.load),
input_names=['state'],
output_names=['Qvalue']))
# demo pretrained model one episode at a time
if args.task == 'play':
play_n_episodes(
get_player(directory=data_dir,
