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import numpy as np
import torch
import collections
import pathlib
import tqdm
import dill
import math
import wandb.sdk.data_types.video as wv
from diffusion_policy.env.pusht.pusht_image_env import PushTImageEnv
from diffusion_policy.gym_util.async_vector_env import AsyncVectorEnv
# from diffusion_policy.gym_util.sync_vector_env import SyncVectorEnv
from diffusion_policy.gym_util.multistep_wrapper import MultiStepWrapper
from diffusion_policy.gym_util.video_recording_wrapper import VideoRecordingWrapper, VideoRecorder
from diffusion_policy.policy.base_image_policy import BaseImagePolicy
from diffusion_policy.common.pytorch_util import dict_apply
from diffusion_policy.env_runner.base_image_runner import BaseImageRunner
class PushTImageRunner(BaseImageRunner):
def __init__(self,
output_dir,
n_train=10,
n_train_vis=3,
train_start_seed=0,
n_test=22,
n_test_vis=6,
legacy_test=False,
test_start_seed=10000,
max_steps=200,
n_obs_steps=8,
n_action_steps=8,
fps=10,
crf=22,
render_size=96,
past_action=False,
tqdm_interval_sec=5.0,
n_envs=None
):
super().__init__(output_dir)
if n_envs is None:
n_envs = n_train + n_test
steps_per_render = max(10 // fps, 1)
def env_fn():
return MultiStepWrapper(
VideoRecordingWrapper(
PushTImageEnv(
legacy=legacy_test,
render_size=render_size
),
video_recoder=VideoRecorder.create_h264(
fps=fps,
codec='h264',
input_pix_fmt='rgb24',
crf=crf,
thread_type='FRAME',
thread_count=1
),
file_path=None,
steps_per_render=steps_per_render
),
n_obs_steps=n_obs_steps,
n_action_steps=n_action_steps,
max_episode_steps=max_steps
)
env_fns = [env_fn] * n_envs
env_seeds = list()
env_prefixs = list()
env_init_fn_dills = list()
# train
for i in range(n_train):
seed = train_start_seed + i
enable_render = i < n_train_vis
def init_fn(env, seed=seed, enable_render=enable_render):
# setup rendering
# video_wrapper
assert isinstance(env.env, VideoRecordingWrapper)
env.env.video_recoder.stop()
env.env.file_path = None
if enable_render:
filename = pathlib.Path(output_dir).joinpath(
'media', wv.util.generate_id() + ".mp4")
filename.parent.mkdir(parents=False, exist_ok=True)
filename = str(filename)
env.env.file_path = filename
# set seed
assert isinstance(env, MultiStepWrapper)
env.seed(seed)
env_seeds.append(seed)
env_prefixs.append('train/')
env_init_fn_dills.append(dill.dumps(init_fn))
# test
for i in range(n_test):
seed = test_start_seed + i
enable_render = i < n_test_vis
def init_fn(env, seed=seed, enable_render=enable_render):
# setup rendering
# video_wrapper
assert isinstance(env.env, VideoRecordingWrapper)
env.env.video_recoder.stop()
env.env.file_path = None
if enable_render:
filename = pathlib.Path(output_dir).joinpath(
'media', wv.util.generate_id() + ".mp4")
filename.parent.mkdir(parents=False, exist_ok=True)
filename = str(filename)
env.env.file_path = filename
# set seed
assert isinstance(env, MultiStepWrapper)
env.seed(seed)
env_seeds.append(seed)
env_prefixs.append('test/')
env_init_fn_dills.append(dill.dumps(init_fn))
env = AsyncVectorEnv(env_fns)
# test env
# env.reset(seed=env_seeds)
# x = env.step(env.action_space.sample())
# imgs = env.call('render')
# import pdb; pdb.set_trace()
self.env = env
self.env_fns = env_fns
self.env_seeds = env_seeds
self.env_prefixs = env_prefixs
self.env_init_fn_dills = env_init_fn_dills
self.fps = fps
self.crf = crf
self.n_obs_steps = n_obs_steps
self.n_action_steps = n_action_steps
self.past_action = past_action
self.max_steps = max_steps
self.tqdm_interval_sec = tqdm_interval_sec
def run(self, policy: BaseImagePolicy):
device = policy.device
dtype = policy.dtype
env = self.env
# plan for rollout
n_envs = len(self.env_fns)
n_inits = len(self.env_init_fn_dills)
n_chunks = math.ceil(n_inits / n_envs)
# allocate data
all_video_paths = [None] * n_inits
all_rewards = [None] * n_inits
for chunk_idx in range(n_chunks):
start = chunk_idx * n_envs
end = min(n_inits, start + n_envs)
this_global_slice = slice(start, end)
this_n_active_envs = end - start
this_local_slice = slice(0,this_n_active_envs)
this_init_fns = self.env_init_fn_dills[this_global_slice]
n_diff = n_envs - len(this_init_fns)
if n_diff > 0:
this_init_fns.extend([self.env_init_fn_dills[0]]*n_diff)
assert len(this_init_fns) == n_envs
# init envs
env.call_each('run_dill_function',
args_list=[(x,) for x in this_init_fns])
# start rollout
obs = env.reset()
past_action = None
policy.reset()
pbar = tqdm.tqdm(total=self.max_steps, desc=f"Eval PushtImageRunner {chunk_idx+1}/{n_chunks}",
leave=False, mininterval=self.tqdm_interval_sec)
done = False
while not done:
# create obs dict
np_obs_dict = dict(obs)
if self.past_action and (past_action is not None):
# TODO: not tested
np_obs_dict['past_action'] = past_action[
:,-(self.n_obs_steps-1):].astype(np.float32)
# device transfer
obs_dict = dict_apply(np_obs_dict,
lambda x: torch.from_numpy(x).to(
device=device))
# run policy
with torch.no_grad():
action_dict = policy.predict_action(obs_dict)
# device_transfer
np_action_dict = dict_apply(action_dict,
lambda x: x.detach().to('cpu').numpy())
action = np_action_dict['action']
# step env
obs, reward, done, info = env.step(action)
done = np.all(done)
past_action = action
# update pbar
pbar.update(action.shape[1])
pbar.close()
all_video_paths[this_global_slice] = env.render()[this_local_slice]
all_rewards[this_global_slice] = env.call('get_attr', 'reward')[this_local_slice]
# clear out video buffer
_ = env.reset()
# log
max_rewards = collections.defaultdict(list)
log_data = dict()
# results reported in the paper are generated using the commented out line below
# which will only report and average metrics from first n_envs initial condition and seeds
# fortunately this won't invalidate our conclusion since
# 1. This bug only affects the variance of metrics, not their mean
# 2. All baseline methods are evaluated using the same code
# to completely reproduce reported numbers, uncomment this line:
# for i in range(len(self.env_fns)):
# and comment out this line
for i in range(n_inits):
seed = self.env_seeds[i]
prefix = self.env_prefixs[i]
max_reward = np.max(all_rewards[i])
max_rewards[prefix].append(max_reward)
log_data[prefix+f'sim_max_reward_{seed}'] = max_reward
# visualize sim
video_path = all_video_paths[i]
if video_path is not None:
sim_video = wandb.Video(video_path)
log_data[prefix+f'sim_video_{seed}'] = sim_video
# log aggregate metrics
for prefix, value in max_rewards.items():
name = prefix+'mean_score'
value = np.mean(value)
log_data[name] = value
return log_data
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