def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency_step,
agent=cfg.agent.name,
action_repeat=cfg.action_repeat)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.env = make_env(cfg)
cfg.agent.params.obs_shape = self.env.observation_space.shape
print(self.env.action_space.shape)
cfg.agent.params.action_shape = (self.env.action_space.n,)
cfg.agent.params.action_range = [
0,
12
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = ReplayBuffer(self.env.observation_space.shape,
self.env.action_space.shape,
cfg.replay_buffer_capacity,
self.cfg.image_pad, self.device)
self.video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = 0
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.observation_space_shape = (16, 16)
self.device = device
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency,
agent=cfg.agent.name)
utils.set_seed_everywhere(cfg.seed)
self.env = make_env(cfg.env)
self.max_episode_steps = cfg.max_episode_steps
cfg.agent.params.obs_dim = self.observation_space_shape
# SET action_dim = env.action_space.n
cfg.agent.params.action_dim = (self.env.action_space.n)
cfg.agent.params.action_range = [
float(0), float(self.env.action_space.n)
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = ReplayBuffer(self.observation_space_shape,
(self.env.action_space.n),
int(cfg.replay_buffer_capacity),
self.device)
'''
self.video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
'''
self.step = 0
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency,
agent=cfg.agent.name)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.env = DummyWrapper({'steps': 100})
cfg.agent.params.obs_dim = 1152
cfg.agent.params.action_dim = self.env.action_space.shape[0]
cfg.agent.params.action_range = [
float(self.env.action_space.low.min()),
float(self.env.action_space.high.max())
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = ReplayMemoryFast(memory_size=1024)
self.step = 0
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency,
agent=cfg.agent.name)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.env = utils.make_env(cfg)
cfg.agent.params.obs_dim = self.env.observation_space.shape[0]
cfg.agent.params.action_dim = self.env.action_space.shape[0]
cfg.agent.params.action_range = [
float(self.env.action_space.low.min()),
float(self.env.action_space.high.max())
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = ReplayBuffer(self.env.observation_space.shape,
self.env.action_space.shape,
int(cfg.replay_buffer_capacity),
self.device)
self.video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = 0
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency,
agent=cfg.agent.name)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.train_envs, self.test_envs = utils.make_env(cfg)
cfg.agent.params.obs_dim = self.train_envs[0].observation_space.shape[0] + cfg.noise_dims
cfg.agent.params.action_dim = self.train_envs[0].action_space.shape[0]
if cfg.agent.name != 'sac':
cfg.agent.params.num_envs = cfg.num_train_envs
cfg.agent.params.action_range = [
float(self.train_envs[0].action_space.low.min()),
float(self.train_envs[0].action_space.high.max())
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.agent.seq_len = cfg.seq_len
self.replay_buffer = MultiEnvReplayBuffer((cfg.agent.params.obs_dim,), # hard coded
self.train_envs[0].action_space.shape,
int(cfg.replay_buffer_capacity),
self.device, num_envs=cfg.num_train_envs, seq_len=cfg.seq_len)
self.video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = [0] * cfg.num_train_envs
def load_static(args):
device, n_gpu = setup_device()
set_seed_everywhere(args.seed, n_gpu)
schemas_raw, schemas_dict = spider_utils.load_schema(args.data_dir)
grammar = semQL.Grammar()
model = IRNet(args, device, grammar)
model.to(device)
# load the pre-trained parameters
model.load_state_dict(
torch.load(args.model_to_load, map_location=torch.device('cpu')))
model.eval()
print("Load pre-trained model from '{}'".format(args.model_to_load))
nlp = English()
tokenizer = nlp.Defaults.create_tokenizer(nlp)
with open(os.path.join(args.conceptNet, 'english_RelatedTo.pkl'),
'rb') as f:
related_to_concept = pickle.load(f)
with open(os.path.join(args.conceptNet, 'english_IsA.pkl'), 'rb') as f:
is_a_concept = pickle.load(f)
return args, grammar, model, nlp, tokenizer, related_to_concept, is_a_concept, schemas_raw, schemas_dict
def run(args):
import hyperparameters
cfg = hyperparameters.get_config(args)
cfg.layers_per_scale = 1
# cfg.layers_per_scale=1
# cfg.num_keypoints=32
# cfg.batch_size = 25
utils.set_seed_everywhere(args.seed)
imgs_to_keyp_model = ImagesToKeypEncoder(cfg, (8, 3, 64, 64), debug=True)
keyp_to_imgs_model = KeypToImagesDecoder(cfg, (8, 3, 64, 64), debug=True)
keyp_pred_net = KeypPredictor(cfg)
keyp_inverse_net = KeypInverseModel(cfg)
print(imgs_to_keyp_model)
print(keyp_to_imgs_model)
print(keyp_pred_net)
# summary(model, input_size=(2, 3, 64, 64))
img = 0.5 * torch.ones((1, 4, 3, 64, 64))
action = 0.4 * torch.ones((1, 4, 4))
k, h = imgs_to_keyp_model(img)
r = keyp_to_imgs_model(k, img[:, 0], k[:, 0])
print(k.shape, h.shape, r.shape)
pred_k = keyp_pred_net(k[Ellipsis, :2], action)
pred_action = keyp_inverse_net(k[Ellipsis, :2])
print("Pred_k: ", pred_k.shape, "Pred_action:", pred_action.shape)
b = sum([
np.prod(list(params.size()))
for params in imgs_to_keyp_model.parameters()
])
print("Encodeer params: ", b)
c = sum([
np.prod(list(params.size()))
for params in keyp_to_imgs_model.parameters()
])
print("Decoder params: ", c)
d = sum([
np.prod(list(params.size())) for params in keyp_pred_net.parameters()
])
print("Keyp Predictor params: ", d)
print("Model parameters: ", b + c + d)
for n in range(k.shape[1]):
print(pred_k[0, 2, n, :2], k[0, 2, n, :2])
print(
F.mse_loss(pred_k, k[:, 1:, :, :2], reduction='sum') /
(pred_k.shape[0] * pred_k.shape[1]))
def __init__(self, cfg):
self.cfg = cfg
seeds = torch.randint(0, 2**32, torch.Size([4]))
set_seed_everywhere(seeds[0])
self.save_dir = os.path.join(self.cfg.gen.base_dir, 'results/unsup_cl',
self.cfg.gen.target_dir,
str(seeds[0].item()))
self.log_dir = os.path.join(self.save_dir, 'logs')
def init_env(args):
utils.set_seed_everywhere(args.seed)
return make_pad_env(domain_name=args.domain_name,
task_name=args.task_name,
seed=args.seed,
episode_length=args.episode_length,
action_repeat=args.action_repeat,
mode=args.mode)
def main(args):
utils.set_seed_everywhere(args.seed)
cfg = hyperparameters.get_config(args)
cfg.seed = args.seed
args.cuda = not args.no_cuda and torch.cuda.is_available()
time_str = datetime.now(
timezone('US/Eastern')).strftime("%Y-%m-%d-%H-%M-%S")
exp_dir = os.path.join(cfg.base_dir, time_str)
checkpoint_dir = os.path.join(exp_dir, cfg.checkpoint_dir)
log_dir = os.path.join(exp_dir, cfg.log_dir)
save_config(cfg, exp_dir, "config.json")
print("Log path: ", log_dir, "Checkpoint Dir: ", checkpoint_dir)
num_timsteps = cfg.observed_steps + cfg.predicted_steps
data_shape = {'image': (None, num_timsteps, 3, 64, 64)}
cfg.data_shapes = data_shape
model = KeypointModel(cfg)
cp_callback = ModelCheckpoint(filepath=os.path.join(
checkpoint_dir, "model_"),
period=25,
save_top_k=-1)
logger = TensorBoardLogger(log_dir, name="", version=None)
gpus = 1 if args.cuda else None
if args.pretrained_path:
checkpoint_path = get_latest_checkpoint(args.pretrained_path)
import json
model = KeypointModel.load_from_checkpoint(checkpoint_path)
print(json.dumps(model.cfg, indent=4))
print("On GPU Device: ", gpus)
trainer = Trainer(
max_epochs=args.num_epochs,
logger=logger,
checkpoint_callback=cp_callback,
gpus=gpus,
#distributed_backend='dp',
progress_bar_refresh_rate=1,
#gradient_clip_val=cfg.clipnorm,
fast_dev_run=False,
#train_percent_check=0.1,val_percent_check=0.0,
#val_percent_check=0.3,
track_grad_norm=2,
show_progress_bar=True)
trainer.fit(model)
save_path = os.path.join(checkpoint_dir,
"model_final_" + str(args.num_epochs) + ".ckpt")
print("Saving model finally:")
trainer.save_checkpoint(save_path)
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.model_dir = utils.make_dir(self.work_dir, 'model')
self.buffer_dir = utils.make_dir(self.work_dir, 'buffer')
self.cfg = cfg
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency_step,
action_repeat=cfg.action_repeat,
agent=cfg.agent.name)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.env = dmc.make(cfg.env, cfg.frame_stack, cfg.action_repeat,
cfg.seed)
self.eval_env = dmc.make(cfg.env, cfg.frame_stack, cfg.action_repeat,
cfg.seed + 1)
obs_spec = self.env.observation_spec()['pixels']
action_spec = self.env.action_spec()
cfg.agent.params.obs_shape = obs_spec.shape
cfg.agent.params.action_shape = action_spec.shape
cfg.agent.params.action_range = [
float(action_spec.minimum.min()),
float(action_spec.maximum.max())
]
# exploration agent uses intrinsic reward
self.expl_agent = hydra.utils.instantiate(cfg.agent,
task_agnostic=True)
# task agent uses extr extrinsic reward
self.task_agent = hydra.utils.instantiate(cfg.agent,
task_agnostic=False)
self.task_agent.assign_modules_from(self.expl_agent)
if cfg.load_pretrained:
pretrained_path = utils.find_pretrained_agent(
cfg.pretrained_dir, cfg.env, cfg.seed, cfg.pretrained_step)
print(f'snapshot is taken from: {pretrained_path}')
pretrained_agent = utils.load(pretrained_path)
self.task_agent.assign_modules_from(pretrained_agent)
# buffer for the task-agnostic phase
self.expl_buffer = ReplayBuffer(obs_spec.shape, action_spec.shape,
cfg.replay_buffer_capacity,
self.device)
# buffer for task-specific phase
self.task_buffer = ReplayBuffer(obs_spec.shape, action_spec.shape,
cfg.replay_buffer_capacity,
self.device)
self.eval_video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = 0
def __init__(self, cfg):
self.cfg = cfg
seeds = torch.randint(0, 2 ** 32, torch.Size([4]))
set_seed_everywhere(seeds[0])
self.save_dir = os.path.join(self.cfg.gen.base_dir, 'results/unsup_cl_rag', self.cfg.gen.target_dir, str(seeds[0].item()))
self.log_dir = os.path.join(self.save_dir, 'logs')
print("embeddings are on sphere")
print(f"save dir: {self.save_dir}")
print(f"log dir: {self.log_dir}")
def init_env(args):
utils.set_seed_everywhere(args.seed)
return make_locomotion_env(env_name=args.env_name,
seed=args.seed,
episode_length=args.episode_length,
from_pixels=args.pixel_obs,
action_repeat=args.action_repeat,
obs_height=args.obs_height,
obs_width=args.obs_width,
camera_id=args.env_camera_id,
mode=args.mode)
def run_final_test(args):
utils.set_seed_everywhere(args.seed)
cfg = hyperparameters.get_config(args)
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if args.cuda else "cpu")
l_dir = cfg.train_dir if args.is_train else args.test_dir
print("Data loader: ", l_dir)
loader, data_shapes = datasets.get_sequence_dataset(
data_dir=os.path.join(cfg.data_dir, l_dir),
batch_size=cfg.batch_size,
num_timesteps=2 * args.timesteps,
shuffle=True)
cfg.log_training = args.log_training
cfg.log_training_path = os.path.join(args.log_training_path)
cfg.data_shapes = data_shapes
if args.no_first:
if args.keyp_pred:
print("Loding keyp pred")
model = train_keyp_pred.KeypointModel(cfg).to(device)
elif args.keyp_inverse:
print("Loding Inverse Model")
model = train_keyp_inverse.KeypointModel(cfg).to(device)
else:
pass
else:
model = train.KeypointModel(cfg).to(device)
if args.pretrained_path:
if args.ckpt:
checkpoint_path = os.path.join(
args.pretrained_path, "_ckpt_epoch_" + args.ckpt + ".ckpt")
else:
print("Loading latest")
checkpoint_path = get_latest_checkpoint(args.pretrained_path)
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
print("Loading model from: ", checkpoint_path)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
print("Load complete")
trainer = Trainer(gpus=1,
progress_bar_refresh_rate=1,
show_progress_bar=True)
trainer.test(model)
def __init__(self, cfg):
self.work_dir = '/media/trevor/mariadb/thesis/'
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency_step,
agent=cfg.agent.name,
action_repeat=cfg.action_repeat)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.env = make_env(cfg)
cfg.agent.params.obs_shape = self.env.observation_space.shape
cfg.agent.params.action_shape = self.env.action_space.shape
cfg.agent.params.action_range = [
float(self.env.action_space.low.min()),
float(self.env.action_space.high.max())
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = ReplayBuffer(self.env.observation_space.shape,
self.env.action_space.shape,
cfg.replay_buffer_capacity,
self.cfg.image_pad, self.device,
self.cfg.env)
# obs_shape = (3 * 3, 84, 84)
# pre_aug_obs_shape = (3 * 3, 100, 100)
#
# self.replay_buffer = ReplayBuffer(
# obs_shape=pre_aug_obs_shape,
# action_shape=self.env.action_space.shape,
# capacity=cfg.replay_buffer_capacity,
# batch_size=cfg.batch_size,
# device=self.device,
# image_size=84,
# pre_image_size=100,
# )
self.video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = 0
def __init__(self, cfg):
self.work_dir = os.getcwd()
"""Hack to adjust action_repeat"""
adjust_action_repeat_hack(cfg)
print(f"CFG:\n{'-'*100}\n{cfg}\n{'-'*100}")
self.cfg = cfg
experiment_name = f"{cfg.full_title}_{cfg.run_id}"
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
save_wb=cfg.log_save_wandb,
log_frequency=cfg.log_frequency_step,
agent=cfg.agent.name,
action_repeat=cfg.action_repeat,
cfg=dict(flatten_cfg(cfg)),
plot_project="drqtest",
experiment=experiment_name)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.env = make_env(cfg)
cfg.agent.params.obs_shape = self.env.observation_space.shape
cfg.agent.params.action_shape = self.env.action_space.shape
cfg.agent.params.action_range = [
float(self.env.action_space.low.min()),
float(self.env.action_space.high.max())
]
self.agent = hydra.utils.instantiate(cfg.agent)
print(f"ACTOR:\n{'-'*100}\n{self.agent.actor}\n{'-'*100}")
print(f"CRITIC:\n{'-'*100}\n{self.agent.critic}\n{'-'*100}")
self.replay_buffer = ReplayBuffer(
self.env.observation_space.shape,
self.env.action_space.shape,
cfg.replay_buffer_capacity,
self.cfg.image_pad,
self.device,
use_aug=cfg.replay_buffer_augmentation)
self.video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = 0
def __init__(self, cfg, env_maker, phi, work_dir):
self.env_maker = env_maker
self.env = env_maker()
self.phi = phi
# self.env = dmc2gym.make(
# domain_name='CarIntersect',
# task_name=task_name,
# seed=cfg.seed,
# visualize_reward=False,
# from_pixels=True,
# frame_skip=cfg.action_repeat,
# )
self.work_dir = work_dir
print(f'workspace: {self.work_dir}')
self.cfg = cfg
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
cfg.agent.params.obs_shape = self.phi(self.env.reset()).shape
print(f'DRQ: get observation shape : {cfg.agent.params.obs_shape}')
cfg.agent.params.action_shape = self.env.action_space.shape
print(f'DRQ: get action shape : {cfg.agent.params.obs_shape}')
cfg.agent.params.action_range = [-1, +1]
self.agent: DRQAgent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = ReplayBuffer(
obs_shape=cfg.agent.params.obs_shape,
action_shape=self.env.action_space.shape,
capacity=cfg.replay_buffer_capacity,
image_pad=self.cfg.image_pad,
device=self.device,
phi=self.phi,
)
self.step = 0
self.total_env_step = 0
self.total_updates = 0
self.total_episodes = 0
def __init__(
self,
log_save_tb=True,
log_frequency_step=10000,
agent_name='drq',
# device='cuda',
device='cpu',
env='cartpole_swingup',
seed=1,
image_size=84,
action_repeat=8,
frame_stack=3,
replay_buffer_capacity=100000,
image_pad=4,
save_video=True):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.logger = Logger(self.work_dir,
save_tb=log_save_tb,
log_frequency=log_frequency_step,
agent=agent_name,
action_repeat=action_repeat)
utils.set_seed_everywhere(seed)
self.device = torch.device(device)
self.env = make_env(env, seed, image_size, action_repeat, frame_stack)
self.agent = DRQAgent(
obs_shape=self.env.observation_space.shape,
action_shape=self.env.action_space.shape,
action_range=(float(self.env.action_space.low.min()),
float(self.env.action_space.high.max())),
device=self.device)
self.replay_buffer = ReplayBuffer(self.env.observation_space.shape,
self.env.action_space.shape,
replay_buffer_capacity, image_pad,
self.device)
self.video_recorder = VideoRecorder(
self.work_dir if save_video else None)
self.step = 0
def set_envs(args):
if not torch.cuda.is_available():
args.cuda = False
args.fp16 = False
if 'CUDA_VISIBLE_DEVICES' in os.environ.keys():
args.device_ids = eval(f"[{os.environ['CUDA_VISIBLE_DEVICES']}]")
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
torch.backends.cudnn.benchmark = True
if not args.device:
args.device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
if args.expand_filepaths_to_save_dir:
args.model_state_file = os.path.join(args.save_dir,args.model_state_file)
set_seed_everywhere(args.seed, args.cuda)
handle_dirs(args.save_dir)
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f"workspace: {self.work_dir}")
self.cfg = cfg
self.logger = Logger(
self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency_step,
agent=cfg.agent.name,
action_repeat=cfg.action_repeat,
)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.env = make_env(cfg, eval=False)
cfg.agent.params.obs_shape = self.env.observation_space.shape
cfg.agent.params.action_shape = self.env.action_space.shape
cfg.agent.params.action_range = [
float(self.env.action_space.low.min()),
float(self.env.action_space.high.max()),
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = ReplayBuffer(
self.env.observation_space.shape,
self.env.action_space.shape,
self.env.state_space.shape,
cfg.replay_buffer_capacity,
self.cfg.image_size,
self.agent.random_encoder,
self.cfg.aug_type,
self.cfg.use_drq,
self.device,
)
self.video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = 0
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency_step,
agent=cfg.agent.name,
action_repeat=cfg.action_repeat)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.env = make_env(cfg, self.logger)
self.eval_env = gym.make(cfg.env)
if "img_only" not in cfg.env:
self.eval_env = DictToBoxWrapper(DictTransposeImage(self.eval_env))
else:
self.eval_env = TransposeImage(self.eval_env)
# env = utils.FrameStack(env, k=cfg.frame_stack)
self.eval_env.seed(cfg.seed + 111)
cfg.agent.params.obs_shape = self.env.observation_space.shape
cfg.agent.params.action_shape = self.env.action_space.shape
cfg.agent.params.action_range = [
float(self.env.action_space.low.min()),
float(self.env.action_space.high.max())
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = ReplayBuffer(self.env.observation_space.shape,
self.env.action_space.shape,
cfg.replay_buffer_capacity,
self.cfg.image_pad, self.device)
self.video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = 0
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency_step,
agent=cfg.agent.name,
action_repeat=cfg.action_repeat)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
gibson_config_filename = os.path.join(
os.path.dirname(gibson2.__file__),
'../examples/configs/hand_drawer.yaml')
self.env = HandDrawerEnv(config_file=gibson_config_filename,
mode='headless')
self.env = utils.FrameStack(self.env, k=cfg.frame_stack)
cfg.agent.params.obs_shape = self.env.observation_space.shape
cfg.agent.params.action_shape = self.env.action_space.shape
cfg.agent.params.action_range = [
float(self.env.action_space.low.min()),
float(self.env.action_space.high.max())
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = ReplayBuffer(self.env.observation_space.shape,
self.env.action_space.shape,
cfg.replay_buffer_capacity,
self.cfg.image_pad, self.device)
self.video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = 0
def load_model(args):
utils.set_seed_everywhere(args.seed)
cfg = hyperparameters.get_config(args)
cfg.data_shapes = {'image': (None, 16, 3, 64, 64)}
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if args.cuda else "cpu")
if not args.inv_fwd:
model = train_keyp_pred.KeypointModel(cfg).to(device)
else:
model = train_keyp_inverse_forward.KeypointModel(cfg).to(device)
checkpoint_path = os.path.join(args.pretrained_path,
"_ckpt_epoch_" + args.ckpt + ".ckpt")
checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
print("Loading model from: ", checkpoint_path)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
print("Load complete")
return model
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.logger = Logger(self.work_dir + "_" + self.cfg.env +
"_eval2k_effective_{}_seed_{}".format(
self.cfg.effective_aug, self.cfg.seed),
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency_step,
agent=cfg.agent.name,
action_repeat=cfg.action_repeat)
self.effective_aug = self.cfg.effective_aug
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.env = make_env(cfg)
cfg.agent.params.obs_shape = self.env.observation_space.shape
cfg.agent.params.action_shape = self.env.action_space.shape
cfg.agent.params.action_range = [
float(self.env.action_space.low.min()),
float(self.env.action_space.high.max())
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = ReplayBuffer(self.env.observation_space.shape,
self.env.action_space.shape,
cfg.replay_buffer_capacity,
self.cfg.image_pad, self.device,
self.effective_aug)
self.video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = 0
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency_step,
agent=cfg.agent.name)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.env = dmc.make_meta(cfg.env, cfg.episode_length, cfg.seed)
self.eval_env = dmc.make_meta(cfg.env, cfg.episode_length,
cfg.seed + 1)
obs_spec = self.env.observation_spec()['features']
action_spec = self.env.action_spec()
cfg.agent.params.obs_shape = obs_spec.shape
cfg.agent.params.action_shape = action_spec.shape
cfg.agent.params.action_range = [
float(action_spec.minimum.min()),
float(action_spec.maximum.max())
]
self.agent = hydra.utils.instantiate(cfg.agent)
self.replay_buffer = MetaReplayBuffer(cfg.train_tasks, obs_spec.shape,
action_spec.shape,
cfg.replay_buffer_capacity,
self.device)
self.eval_video_recorder = VideoRecorder(
self.work_dir if cfg.save_video else None)
self.step = 0
def _remove_spaces(sentence):
s = sentence.strip().split()
s = " ".join(s)
return s
def _find_nums(question):
nums = re.findall('\d*\.?\d+', question)
return nums
if __name__ == '__main__':
args = read_arguments_manual_inference()
device, n_gpu = setup_device()
set_seed_everywhere(args.seed, n_gpu)
schemas_raw, schemas_dict = spider_utils.load_schema(args.data_dir)
grammar = semQL.Grammar()
model = IRNet(args, device, grammar)
model.to(device)
# load the pre-trained parameters
model.load_state_dict(torch.load(args.model_to_load))
# to use cpu instead of gpu , uncomment this code
# model.load_state_dict(torch.load(args.model_to_load,map_location=torch.device('cpu')))
model.eval()
print("Load pre-trained model from '{}'".format(args.model_to_load))
args.model_state_file = os.path.join(args.save_dir,
args.model_state_file)
print("Expanded filepaths: ")
print("\t{}".format(args.vectorizer_file))
print("\t{}".format(args.model_state_file))
# Check CUDA
if not torch.cuda.is_available():
args.cuda = False
print("Using CUDA: {}".format(args.cuda))
args.device = torch.device("cuda" if args.cuda else "cpu")
set_seed_everywhere(args.seed, args.cuda)
dataset = args.dataset
vectorizer = args.vectorizer
classifier = args.classifier
classifier = classifier.to(args.device)
loss_func = nn.CrossEntropyLoss()
optimizer = optim.Adam(classifier.parameters(), lr=args.learning_rate)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,
mode='min',
factor=0.5,
patience=1)
train_state = make_train_state(args)
def main():
args = parse_args()
if args.seed == -1:
args.__dict__["seed"] = np.random.randint(1, 1000000)
utils.set_seed_everywhere(args.seed)
pre_transform_image_size = args.pre_transform_image_size if 'crop' in args.data_augs else args.image_size
pre_image_size = args.pre_transform_image_size # record the pre transform image size for translation
env = dmc2gym.make(domain_name=args.domain_name,
task_name=args.task_name,
seed=args.seed,
visualize_reward=False,
from_pixels=(args.encoder_type == 'pixel'),
height=pre_transform_image_size,
width=pre_transform_image_size,
frame_skip=args.action_repeat)
env.seed(args.seed)
# stack several consecutive frames together
if args.encoder_type == 'pixel':
env = utils.FrameStack(env, k=args.frame_stack)
# make directory
ts = time.gmtime()
ts = time.strftime("%m-%d", ts)
env_name = args.domain_name + '-' + args.task_name
exp_name = env_name + '-' + ts + '-im' + str(args.image_size) +'-b' \
+ str(args.batch_size) + '-s' + str(args.seed) + '-' + args.encoder_type
args.work_dir = args.work_dir + '/' + exp_name
utils.make_dir(args.work_dir)
video_dir = utils.make_dir(os.path.join(args.work_dir, 'video'))
model_dir = utils.make_dir(os.path.join(args.work_dir, 'model'))
buffer_dir = utils.make_dir(os.path.join(args.work_dir, 'buffer'))
video = VideoRecorder(video_dir if args.save_video else None)
with open(os.path.join(args.work_dir, 'args.json'), 'w') as f:
json.dump(vars(args), f, sort_keys=True, indent=4)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
action_shape = env.action_space.shape
if args.encoder_type == 'pixel':
obs_shape = (3 * args.frame_stack, args.image_size, args.image_size)
pre_aug_obs_shape = (3 * args.frame_stack, pre_transform_image_size,
pre_transform_image_size)
else:
obs_shape = env.observation_space.shape
pre_aug_obs_shape = obs_shape
replay_buffer = utils.ReplayBuffer(
obs_shape=pre_aug_obs_shape,
action_shape=action_shape,
capacity=args.replay_buffer_capacity,
batch_size=args.batch_size,
device=device,
image_size=args.image_size,
pre_image_size=pre_image_size,
)
agent = make_agent(obs_shape=obs_shape,
action_shape=action_shape,
args=args,
device=device)
L = Logger(args.work_dir, use_tb=args.save_tb)
episode, episode_reward, done = 0, 0, True
start_time = time.time()
for step in range(args.num_train_steps):
# evaluate agent periodically
if step % args.eval_freq == 0:
L.log('eval/episode', episode, step)
evaluate(env, agent, video, args.num_eval_episodes, L, step, args)
if args.save_model:
agent.save_curl(model_dir, step)
if args.save_buffer:
replay_buffer.save(buffer_dir)
if done:
if step > 0:
if step % args.log_interval == 0:
L.log('train/duration', time.time() - start_time, step)
L.dump(step)
start_time = time.time()
if step % args.log_interval == 0:
L.log('train/episode_reward', episode_reward, step)
obs = env.reset()
done = False
episode_reward = 0
episode_step = 0
episode += 1
if step % args.log_interval == 0:
L.log('train/episode', episode, step)
# sample action for data collection
if step < args.init_steps:
action = env.action_space.sample()
else:
with utils.eval_mode(agent):
action = agent.sample_action(obs / 255.)
# run training update
if step >= args.init_steps:
num_updates = 1
for _ in range(num_updates):
agent.update(replay_buffer, L, step)
next_obs, reward, done, _ = env.step(action)
# allow infinit bootstrap
done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(
done)
episode_reward += reward
replay_buffer.add(obs, action, reward, next_obs, done_bool)
obs = next_obs
episode_step += 1
class Experiment(object):
def __init__(
self,
# environment
env_id,
# visual?
from_images=False,
# reproducibility
seed=1,
# env
fix_goals=False,
# compute
device='cuda' if torch.cuda.is_available() else 'cpu',
# replay buffer
num_resampled_goals=1,
capacity=1_000_000,
# agent
feature_dim=128,
hidden_sizes=[512, 512, 512],
log_std_bounds=[-10, 2],
discount=0.95,
init_temperature=0.1,
lr=0.0006,
actor_update_frequency=2,
critic_tau=0.005,
critic_target_update_frequency=2,
batch_size=128,
# evaluation
num_eval_episodes=5,
# training
gradient_steps=1, # better for wall clock time. increase for better performance.
num_timesteps=20_000, # maximum time steps
num_seed_steps=1_000, # random actions to improve exploration
update_after=1_000, # when to start updating (off-policy still learns from seed steps)
eval_every=20, # episodic frequency for evaluation
save_every=5_000, # how often to save the experiment progress in time steps
**kwargs, # lazily absorb extra args
):
self.observation_key = 'image_observation' if from_images else 'observation'
self.achieved_goal_key = 'image_achieved_goal' if from_images else 'achieved_goal'
self.desired_goal_key = 'image_desired_goal' if from_images else 'desired_goal'
# Seed
utils.set_seed_everywhere(seed)
# # Create env
self.env_id = env_id
self.seed = seed
self.from_images = from_images
self.fix_goals = fix_goals
self.env = gym.make(self.env_id)
if 'Kinova' in self.env_id:
self.env = wrappers.KinovaWrapper(self.env, self.seed,
self.from_images, self.fix_goals)
else:
self.env = wrappers.MultiWrapper(self.env, self.seed,
self.from_images, self.fix_goals)
# Create agent
self.agent = Agent(from_images,
self.env.observation_space,
self.env.action_space,
device=device,
feature_dim=feature_dim,
hidden_sizes=hidden_sizes,
log_std_bounds=log_std_bounds,
discount=discount,
init_temperature=init_temperature,
lr=lr,
critic_tau=critic_tau,
batch_size=batch_size)
# update env to use agent encoder for images if necessary
if self.from_images:
self.env.set_agent(
self.agent) # set the conv encoder for latent distance rewards
# Create replay buffer
self.replay_buffer = HindsightReplayBuffer(
from_images=from_images,
env=self.env,
num_resampled_goals=num_resampled_goals,
observation_space=self.env.observation_space,
action_space=self.env.action_space,
capacity=capacity,
device=device,
)
self.step = 0
self.num_eval_episodes = num_eval_episodes
self.gradient_steps = gradient_steps
self.num_timesteps = num_timesteps
self.num_seed_steps = num_seed_steps
self.update_after = update_after
self.eval_every = eval_every
self.save_every = save_every
def main(args):
# Set seed
utils.set_seed_everywhere(args.seed)
# Initialize environments
gym.logger.set_level(40)
env = make_env(domain_name=args.domain_name,
task_name=args.task_name,
seed=args.seed + 42,
episode_length=args.episode_length,
action_repeat=args.action_repeat,
mode=args.eval_mode)
# Set working directory
work_dir = os.path.join(args.log_dir,
args.domain_name + '_' + args.task_name,
args.algorithm, str(args.seed))
print('Working directory:', work_dir)
assert os.path.exists(
work_dir), 'specified working directory does not exist'
model_dir = utils.make_dir(os.path.join(work_dir, 'model'))
video_dir = utils.make_dir(os.path.join(work_dir, 'video'))
video = VideoRecorder(video_dir if args.save_video else None,
height=448,
width=448)
# Check if evaluation has already been run
results_fp = os.path.join(work_dir, args.eval_mode + '.pt')
assert not os.path.exists(
results_fp), f'{args.eval_mode} results already exist for {work_dir}'
# Prepare agent
assert torch.cuda.is_available(), 'must have cuda enabled'
cropped_obs_shape = (3 * args.frame_stack, 84, 84)
agent = make_agent(obs_shape=cropped_obs_shape,
action_shape=env.action_space.shape,
args=args)
agent = torch.load(os.path.join(model_dir, str(args.train_steps) + '.pt'))
agent.train(False)
print(
f'\nEvaluating {work_dir} for {args.eval_episodes} episodes (mode: {args.eval_mode})'
)
reward = evaluate(env, agent, video, args.eval_episodes, args.eval_mode)
print('Reward:', int(reward))
adapt_reward = None
if args.algorithm == 'pad':
env = make_env(domain_name=args.domain_name,
task_name=args.task_name,
seed=args.seed + 42,
episode_length=args.episode_length,
action_repeat=args.action_repeat,
mode=args.eval_mode)
adapt_reward = evaluate(env,
agent,
video,
args.eval_episodes,
args.eval_mode,
adapt=True)
print('Adapt reward:', int(adapt_reward))
# Save results
torch.save({
'args': args,
'reward': reward,
'adapt_reward': adapt_reward
}, results_fp)
print('Saved results to', results_fp)