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import einops
import os
import random
from collections import deque
from pathlib import Path
import hydra
import numpy as np
import torch
import tqdm
from omegaconf import OmegaConf
import wandb
from utils.video import VideoRecorder
import pickle
from datasets.core import TrajectoryEmbeddingDataset, split_traj_datasets
from datasets.vqbet_repro import TrajectorySlicerDataset
if "MUJOCO_GL" not in os.environ:
os.environ["MUJOCO_GL"] = "egl"
def seed_everything(random_seed: int):
np.random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed_all(random_seed)
random.seed(random_seed)
@hydra.main(config_path="eval_configs", version_base="1.2")
def main(cfg):
print(OmegaConf.to_yaml(cfg))
seed_everything(cfg.seed)
encoder = hydra.utils.instantiate(cfg.encoder)
encoder = encoder.to(cfg.device).eval()
dataset = hydra.utils.instantiate(cfg.dataset)
train_data, test_data = split_traj_datasets(
dataset,
train_fraction=cfg.train_fraction,
random_seed=cfg.seed,
)
use_libero_goal = cfg.data.get("use_libero_goal", False)
train_data = TrajectoryEmbeddingDataset(
encoder, train_data, device=cfg.device, embed_goal=use_libero_goal
)
test_data = TrajectoryEmbeddingDataset(
encoder, test_data, device=cfg.device, embed_goal=use_libero_goal
)
traj_slicer_kwargs = {
"window": cfg.data.window_size,
"action_window": cfg.data.action_window_size,
"vqbet_get_future_action_chunk": cfg.data.vqbet_get_future_action_chunk,
"future_conditional": (cfg.data.goal_conditional == "future"),
"min_future_sep": cfg.data.action_window_size,
"future_seq_len": cfg.data.future_seq_len,
"use_libero_goal": use_libero_goal,
}
train_data = TrajectorySlicerDataset(train_data, **traj_slicer_kwargs)
test_data = TrajectorySlicerDataset(test_data, **traj_slicer_kwargs)
train_loader = torch.utils.data.DataLoader(
train_data, batch_size=cfg.batch_size, shuffle=True, pin_memory=False
)
test_loader = torch.utils.data.DataLoader(
test_data, batch_size=cfg.batch_size, shuffle=False, pin_memory=False
)
for param in encoder.parameters():
param.requires_grad = False
encoder.eval()
cbet_model = hydra.utils.instantiate(cfg.model).to(cfg.device)
optimizer = cbet_model.configure_optimizers(
weight_decay=cfg.optim.weight_decay,
learning_rate=cfg.optim.lr,
betas=cfg.optim.betas,
)
env = hydra.utils.instantiate(cfg.env.gym)
if "use_libero_goal" in cfg.data:
with torch.no_grad():
# calculate goal embeddings for each task
goals_cache = []
for i in range(10):
idx = i * 50
last_obs, _, _ = dataset.get_frames(idx, [-1]) # 1 V C H W
last_obs = last_obs.to(cfg.device)
embd = encoder(last_obs)[0] # V E
embd = einops.rearrange(embd, "V E -> (V E)")
goals_cache.append(embd)
def goal_fn(goal_idx):
return goals_cache[goal_idx]
else:
empty_tensor = torch.zeros(1)
def goal_fn(goal_idx):
return empty_tensor
run = wandb.init(
project=cfg.wandb.project,
entity=cfg.wandb.entity,
config=OmegaConf.to_container(cfg, resolve=True),
)
run_name = run.name or "Offline"
save_path = Path(cfg.save_path) / run_name
save_path.mkdir(parents=True, exist_ok=False)
video = VideoRecorder(dir_name=save_path)
@torch.no_grad()
def eval_on_env(
cfg,
num_evals=cfg.num_env_evals,
num_eval_per_goal=1,
videorecorder=None,
epoch=None,
):
def embed(enc, obs):
obs = (
torch.as_tensor(obs, dtype=torch.float32).unsqueeze(0).to(cfg.device)
) # 1 V C H W
result = enc(obs)
result = einops.rearrange(result, "1 V E -> (V E)")
return result
avg_reward = 0
action_list = []
completion_id_list = []
avg_max_coverage = []
avg_final_coverage = []
env.seed(cfg.seed)
for goal_idx in range(num_evals):
if videorecorder is not None:
videorecorder.init(enabled=True)
for i in range(num_eval_per_goal):
obs_stack = deque(maxlen=cfg.eval_window_size)
this_obs = env.reset(goal_idx=goal_idx) # V C H W
assert (
this_obs.min() >= 0 and this_obs.max() <= 1
), "expect 0-1 range observation"
this_obs_enc = embed(encoder, this_obs)
obs_stack.append(this_obs_enc)
done, step, total_reward = False, 0, 0
goal = goal_fn(goal_idx) # V C H W
while not done:
obs = torch.stack(tuple(obs_stack)).float().to(cfg.device)
goal = torch.as_tensor(goal, dtype=torch.float32, device=cfg.device)
# goal = embed(encoder, goal)
goal = goal.unsqueeze(0).repeat(cfg.eval_window_size, 1)
action, _, _ = cbet_model(obs.unsqueeze(0), goal.unsqueeze(0), None)
action = action[0] # remove batch dim; always 1
if cfg.action_window_size > 1:
action_list.append(action[-1].cpu().detach().numpy())
if len(action_list) > cfg.action_window_size:
action_list = action_list[1:]
curr_action = np.array(action_list)
curr_action = (
np.sum(curr_action, axis=0)[0] / curr_action.shape[0]
)
new_action_list = []
for a_chunk in action_list:
new_action_list.append(
np.concatenate(
(a_chunk[1:], np.zeros((1, a_chunk.shape[1])))
)
)
action_list = new_action_list
else:
curr_action = action[-1, 0, :].cpu().detach().numpy()
this_obs, reward, done, info = env.step(curr_action)
this_obs_enc = embed(encoder, this_obs)
obs_stack.append(this_obs_enc)
if videorecorder.enabled:
videorecorder.record(info["image"])
step += 1
total_reward += reward
goal = goal_fn(goal_idx)
avg_reward += total_reward
if cfg.env.gym.id == "pusht":
env.env._seed += 1
avg_max_coverage.append(info["max_coverage"])
avg_final_coverage.append(info["final_coverage"])
elif cfg.env.gym.id == "blockpush":
avg_max_coverage.append(info["moved"])
avg_final_coverage.append(info["entered"])
completion_id_list.append(info["all_completions_ids"])
videorecorder.save("eval_{}_{}.mp4".format(epoch, goal_idx))
return (
avg_reward / (num_evals * num_eval_per_goal),
completion_id_list,
avg_max_coverage,
avg_final_coverage,
)
metrics_history = []
reward_history = []
for epoch in tqdm.trange(cfg.epochs):
cbet_model.eval()
if epoch % cfg.eval_on_env_freq == 0:
avg_reward, completion_id_list, max_coverage, final_coverage = eval_on_env(
cfg,
videorecorder=video,
epoch=epoch,
num_eval_per_goal=cfg.num_final_eval_per_goal,
)
reward_history.append(avg_reward)
with open("{}/completion_idx_{}.json".format(save_path, epoch), "wb") as fp:
pickle.dump(completion_id_list, fp)
wandb.log({"eval_on_env": avg_reward})
if cfg.env.gym.id in ["pusht", "blockpush"]:
metric_final = (
"final coverage" if cfg.env.gym.id == "pusht" else "entered"
)
metric_max = "max coverage" if cfg.env.gym.id == "pusht" else "moved"
metrics = {
f"{metric_final} mean": sum(final_coverage) / len(final_coverage),
f"{metric_final} max": max(final_coverage),
f"{metric_final} min": min(final_coverage),
f"{metric_max} mean": sum(max_coverage) / len(max_coverage),
f"{metric_max} max": max(max_coverage),
f"{metric_max} min": min(max_coverage),
}
wandb.log(metrics)
metrics_history.append(metrics)
if epoch % cfg.eval_freq == 0:
total_loss = 0
action_diff = 0
action_diff_tot = 0
action_diff_mean_res1 = 0
action_diff_mean_res2 = 0
action_diff_max = 0
with torch.no_grad():
for data in test_loader:
obs, act, goal = (x.to(cfg.device) for x in data)
assert obs.ndim == 4, "expect N T V E here"
obs = einops.rearrange(obs, "N T V E -> N T (V E)")
goal = einops.rearrange(goal, "N T V E -> N T (V E)")
predicted_act, loss, loss_dict = cbet_model(obs, goal, act)
total_loss += loss.item()
wandb.log({"eval/{}".format(x): y for (x, y) in loss_dict.items()})
action_diff += loss_dict["action_diff"]
action_diff_tot += loss_dict["action_diff_tot"]
action_diff_mean_res1 += loss_dict["action_diff_mean_res1"]
action_diff_mean_res2 += loss_dict["action_diff_mean_res2"]
action_diff_max += loss_dict["action_diff_max"]
print(f"Test loss: {total_loss / len(test_loader)}")
wandb.log({"eval/epoch_wise_action_diff": action_diff})
wandb.log({"eval/epoch_wise_action_diff_tot": action_diff_tot})
wandb.log({"eval/epoch_wise_action_diff_mean_res1": action_diff_mean_res1})
wandb.log({"eval/epoch_wise_action_diff_mean_res2": action_diff_mean_res2})
wandb.log({"eval/epoch_wise_action_diff_max": action_diff_max})
cbet_model.train()
for data in tqdm.tqdm(train_loader):
optimizer.zero_grad()
obs, act, goal = (x.to(cfg.device) for x in data)
obs = einops.rearrange(obs, "N T V E -> N T (V E)")
goal = einops.rearrange(goal, "N T V E -> N T (V E)")
predicted_act, loss, loss_dict = cbet_model(obs, goal, act)
wandb.log({"train/{}".format(x): y for (x, y) in loss_dict.items()})
loss.backward()
optimizer.step()
avg_reward, completion_id_list, max_coverage, final_coverage = eval_on_env(
cfg,
num_evals=cfg.num_final_evals,
num_eval_per_goal=cfg.num_final_eval_per_goal,
videorecorder=video,
epoch=cfg.epochs,
)
reward_history.append(avg_reward)
if cfg.env.gym.id in ["pusht", "blockpush"]:
metric_final = "final coverage" if cfg.env.gym.id == "pusht" else "entered"
metric_max = "max coverage" if cfg.env.gym.id == "pusht" else "moved"
metrics = {
f"{metric_final} mean": sum(final_coverage) / len(final_coverage),
f"{metric_final} max": max(final_coverage),
f"{metric_final} min": min(final_coverage),
f"{metric_max} mean": sum(max_coverage) / len(max_coverage),
f"{metric_max} max": max(max_coverage),
f"{metric_max} min": min(max_coverage),
}
wandb.log(metrics)
metrics_history.append(metrics)
with open("{}/completion_idx_final.json".format(save_path), "wb") as fp:
pickle.dump(completion_id_list, fp)
if cfg.env.gym.id == "pusht":
final_eval_on_env = max([x["final coverage mean"] for x in metrics_history])
elif cfg.env.gym.id == "blockpush":
final_eval_on_env = max([x["entered mean"] for x in metrics_history])
elif cfg.env.gym.id == "libero_goal":
final_eval_on_env = max(reward_history)
elif cfg.env.gym.id == "kitchen-v0":
final_eval_on_env = avg_reward
wandb.log({"final_eval_on_env": final_eval_on_env})
return final_eval_on_env
if __name__ == "__main__":
main()
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