def __init__(self,
obs_space,
action_space,
model_dir,
device=None,
argmax=False,
num_envs=1,
use_memory=False,
use_text=False):
obs_space, self.preprocess_obss = utils.get_obss_preprocessor(
obs_space)
self.acmodel = ACModel(obs_space,
action_space,
use_memory=use_memory,
use_text=use_text)
self.device = device
self.argmax = argmax
self.num_envs = num_envs
if self.acmodel.recurrent:
self.memories = torch.zeros(self.num_envs,
self.acmodel.memory_size)
self.acmodel.load_state_dict(utils.get_model_state(model_dir))
self.acmodel.to(self.device)
self.acmodel.eval()
if hasattr(self.preprocess_obss, "vocab"):
self.preprocess_obss.vocab.load_vocab(utils.get_vocab(model_dir))
def __init__(self, obs_space, action_space, model_dir, device=None, argmax=False, num_envs=1):
obs_space, self.preprocess_obss = utils.get_obss_preprocessor(obs_space)
self.acmodel = ACModel(obs_space, action_space)
self.device = device
self.argmax = argmax
self.num_envs = num_envs
self.acmodel.load_state_dict(utils.get_model_state(model_dir))
self.acmodel.to(self.device)
self.acmodel.eval()
def __init__(self, obs_space, action_space, model_dir, device=None, argmax=False, num_envs=1):
obs_space, self.preprocess_obss = utils.get_obss_preprocessor(obs_space)
self.acmodel = ACModel(obs_space, action_space)
self.device = device
self.argmax = argmax
self.num_envs = num_envs
if self.acmodel.recurrent:
self.memories = torch.zeros(self.num_envs, self.acmodel.memory_size)
self.acmodel.load_state_dict(utils.get_model_state(model_dir))
self.acmodel.to(self.device)
self.acmodel.eval()
def __init__(self,
obs_space,
action_space,
model_dir,
device=None,
argmax=False,
num_envs=1):
obs_space, self.preprocess_obss = utils.get_obss_preprocessor(
obs_space)
self.model = QModel(obs_space, action_space)
self.device = device
self.argmax = argmax
self.num_envs = num_envs
self.model.load_state_dict(utils.get_model_state(model_dir))
self.model.to(self.device)
self.model.eval()
if hasattr(self.preprocess_obss, "vocab"):
self.preprocess_obss.vocab.load_vocab(utils.get_vocab(model_dir))
def __init__(self,
env,
obs_space,
action_space,
model_dir,
ignoreLTL,
progression_mode,
gnn,
recurrence=1,
dumb_ac=False,
device=None,
argmax=False,
num_envs=1):
try:
print(model_dir)
status = utils.get_status(model_dir)
except OSError:
status = {"num_frames": 0, "update": 0}
using_gnn = (gnn != "GRU" and gnn != "LSTM")
obs_space, self.preprocess_obss = utils.get_obss_preprocessor(
env, using_gnn, progression_mode)
if "vocab" in status and self.preprocess_obss.vocab is not None:
self.preprocess_obss.vocab.load_vocab(status["vocab"])
if recurrence > 1:
self.acmodel = RecurrentACModel(env, obs_space, action_space,
ignoreLTL, gnn, dumb_ac, True)
self.memories = torch.zeros(num_envs,
self.acmodel.memory_size,
device=device)
else:
self.acmodel = ACModel(env, obs_space, action_space, ignoreLTL,
gnn, dumb_ac, True)
self.device = device
self.argmax = argmax
self.num_envs = num_envs
self.acmodel.load_state_dict(utils.get_model_state(model_dir))
self.acmodel.to(self.device)
self.acmodel.eval()
def __init__(self,
obs_space,
action_space,
model_dir,
model_name='AC',
device=None,
argmax=False,
num_envs=1,
use_memory=False,
use_text=False,
input_type="image",
feature_learn="curiosity"):
obs_space, self.preprocess_obss = utils.get_obss_preprocessor(
obs_space)
if model_name == 'ac':
self.acmodel = ACModel(obs_space,
action_space,
use_memory=use_memory,
use_text=use_text)
elif model_name == 'sr':
self.acmodel = SRModel(obs_space,
action_space,
input_type=input_type,
use_memory=use_memory,
use_text=use_text,
feature_learn=feature_learn)
self.model_name = model_name
self.device = device
self.argmax = argmax
self.num_envs = num_envs
if self.acmodel.recurrent:
self.memories = torch.zeros(self.num_envs,
self.acmodel.memory_size)
self.acmodel.load_state_dict(utils.get_model_state(model_dir))
self.acmodel.to(self.device)
self.acmodel.eval()
if hasattr(self.preprocess_obss, "vocab"):
self.preprocess_obss.vocab.load_vocab(utils.get_vocab(model_dir))
type=int,
default=0,
help="random seed (default: 0)")
args = parser.parse_args()
# Set seed for all randomness sources
utils.seed(args.seed)
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Device: {device}\n")
# Load generator
gen = utils.make_gen(args.gen)
print("Generator loaded\n")
# Load model
model_dir = utils.get_model_dir(args.model)
model = AdditionModel()
model.load_state_dict(utils.get_model_state(model_dir))
model.to(device)
print("Model loaded\n")
# Evaluate model
print("Accuracy: {}".format(gen.evaluate(model, args.examples, device=device)))
