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evaluate.py
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evaluate.py
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import os
import gc
import hydra
from hydra.utils import get_original_cwd
from tqdm import tqdm
from functools import partial
from composuite import make
import torch
from utils.data_utils import get_task_list, get_partial_task_list
from utils.model_utils import (
load_model,
get_latest_model_path,
create_trainer,
try_get_load_path,
)
from utils.env_utils import DummyVecEnv
from algos.cp_iql import create_cp_encoderfactory
# fmt: off
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# fmt: on
DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
GLOBAL_SUBTASK_KWARGS = {
"has_renderer": False,
"has_offscreen_renderer": False,
"reward_shaping": True,
"use_camera_obs": False,
"use_task_id_obs": True,
"env_horizon": 500,
}
def rollout_envs(env, model, num_steps: int, num_trajs: int, save_path: str):
"""Rollout a fixed number of trajectories of length num_steps using
the given model and environment.
Args:
env (gym.Env): Environment to rollout.
model (nn.Module): Model to rollout.
num_steps (int, optional): Number of steps to rollout. Defaults to 500.
num_trajs (int, optional): Number of trajectories to rollout. Defaults to 1.
save_path (str, optional): Path to save the rollout data. Defaults to None.
Returns:
float: Average sum of rewards across all trajectories.
float: Average success rate across all trajectories.
"""
obs = env.reset()
for i in tqdm(range(num_trajs)):
for s in tqdm(range(num_steps)):
with torch.no_grad():
action = model.predict(torch.from_numpy(obs).to(DEVICE))
assert action.shape == (obs.shape[0], env.action_space.shape[0])
obs, reward, dones, _ = env.step(list(action))
# Save the rewards per env
with open(save_path, "a") as f:
f.write(f"{i},{s}")
for r in reward:
f.write(f",{r}")
f.write("\n")
# Reset the envs that are done
for j, done in enumerate(dones):
if done:
obs[j] = env.envs[j].reset()[0]
obs = env.reset()
return True
def evaluate_tasklist(
task_list, trainer, model_path, algo, trainer_kwargs, n_steps, n_trajs, save_loc
):
env_fns = [
partial(
make,
robot=robot,
obj=obj,
obstacle=obst,
task=subtask,
**GLOBAL_SUBTASK_KWARGS,
)
for robot, obj, obst, subtask in task_list
]
logger.info(f"Creating environment for {len(env_fns)} tasks")
env = DummyVecEnv(env_fns)
trainer = create_trainer(algo, trainer_kwargs)
logger.info(f"Attempting to load model from {model_path} for {algo}")
trainer = load_model(trainer, model_path, env=env.envs[0])
logger.info(f"Loaded model from {model_path} for {algo}")
logger.info(f"Saving returns to {save_loc}")
with open(save_loc, "w") as f:
f.write("traj,step")
for task in task_list:
task_string = f"{task[0]}_{task[1]}_{task[2]}_{task[3]}"
f.write(f",{task_string}")
f.write("\n")
logger.info(f"Rolling out {n_trajs} trajectories of length {n_steps}")
rollout_envs(env, trainer, n_steps, n_trajs, save_loc)
del env
gc.collect()
return trainer
@hydra.main(config_path="_configs", config_name="evaluate")
def main(cfg):
# ${dataset.type}/${dataset.split}/${algo}/${dataset.seed}
cfg.base_path = (
cfg.base_path
if os.path.isabs(cfg.base_path)
else os.path.join(get_original_cwd(), cfg.base_path)
)
path = os.path.join(
cfg.base_path,
cfg.dataset.type,
cfg.dataset.split,
cfg.exp,
cfg.algo,
str(cfg.dataset.seed),
)
logger.info(f"Finetuning from {path}")
# load the task list
_, train_task_list, expert_task_list, test_task_list = get_task_list(
(
cfg.dataset.task_list_path
if os.path.isabs(cfg.dataset.task_list_path)
else os.path.join(get_original_cwd(), cfg.dataset.task_list_path)
),
cfg.dataset.type,
cfg.dataset.split,
cfg.dataset.holdout_elem,
cfg.dataset.seed,
)
logger.info(
f"Found train task list of length {len(train_task_list)} and test task list of length {len(test_task_list)}"
)
if expert_task_list:
logger.info(f"Found expert task list of length {len(expert_task_list)}")
if cfg.dataset.partial.use:
if cfg.dataset.split == "compositional":
logger.warning(
"Careful, you specified compositional training but partial loading. "
+ "You may not get any expert tasks."
)
task_list = train_task_list + test_task_list
_, test_task_list = get_partial_task_list(
task_list, cfg.dataset.partial.remove_elems, cfg.dataset.partial.n_tasks
)
if cfg.task_id != -1:
test_task_list = [test_task_list[cfg.task_id]]
logger.info(
f"After partial loading, train task list has length {len(train_task_list)}"
)
if expert_task_list:
logger.info(
f"After partial loading, expert task list has length {len(expert_task_list)}"
)
logger.info(
f"After partial loading, test task list has length {len(test_task_list)}"
)
# load the model
load_path = try_get_load_path(
os.path.join(get_original_cwd(), cfg.base_path),
cfg.dataset.type,
cfg.dataset.split,
cfg.exp,
cfg.algo,
cfg.dataset.seed,
)
_, model_path = get_latest_model_path(load_path)
trainer_kwargs = {}
if cfg.algo == "cp_iql":
trainer_kwargs["actor_encoder_factory"] = create_cp_encoderfactory()
trainer_kwargs["critic_encoder_factory"] = create_cp_encoderfactory(
with_action=True, output_dim=1
)
trainer_kwargs["value_encoder_factory"] = create_cp_encoderfactory(
with_action=False, output_dim=1
)
if cfg.algo == "cp_bc":
trainer_kwargs["encoder_factory"] = create_cp_encoderfactory()
trainer = None
# evaluate the model
if cfg.get_train_results:
logger.info("Evaluating train task list")
trainer = evaluate_tasklist(
train_task_list,
trainer,
model_path,
cfg.algo,
trainer_kwargs,
cfg.n_steps,
cfg.n_trajs,
"train_returns.csv",
)
else:
logger.info("get_train_results is False, skipping Train evaluation")
if expert_task_list:
logger.info("Evaluating expert task list")
trainer = evaluate_tasklist(
expert_task_list,
trainer,
model_path,
cfg.algo,
trainer_kwargs,
cfg.n_steps,
cfg.n_trajs,
"expert_returns.csv",
)
else:
logger.info("No expert task list found, skipping Expert evaluation")
logger.info("Evaluating test task list")
trainer = evaluate_tasklist(
test_task_list,
trainer,
model_path,
cfg.algo,
trainer_kwargs,
cfg.n_steps,
cfg.n_trajs,
"test_returns.csv",
)
if __name__ == "__main__":
main()