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eval_agent.py
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eval_agent.py
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import argparse
import json
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from enum import IntEnum, auto
from home_robot.core.interfaces import DiscreteNavigationAction, Observations, ContinuousNavigationAction
import numpy as np
from omegaconf import DictConfig, OmegaConf
from agent.ovmm.ovmm import OVMMAgent
from utils.ovmm_env_visualizer import Visualizer
HOME_ROBOT_BASE_DIR = str(Path(__file__).resolve().parent.parent / "home-robot") + "/"
sys.path.insert(
0,
HOME_ROBOT_BASE_DIR + "src/home_robot"
)
sys.path.insert(
0,
HOME_ROBOT_BASE_DIR + "src/home_robot_sim"
)
import cv2
from habitat import make_dataset
from habitat.core.environments import get_env_class
from habitat.core.vector_env import VectorEnv
from habitat.utils.gym_definitions import _get_env_name
from habitat_baselines.rl.ppo.ppo_trainer import PPOTrainer
# from home_robot.agent.ovmm_agent.ovmm_agent import OpenVocabManipAgent
from home_robot_sim.env.habitat_ovmm_env.habitat_ovmm_env import (
HabitatOpenVocabManipEnv,
)
from typing import Optional, Tuple
from habitat_baselines.config.default import get_config as get_habitat_config
from omegaconf import DictConfig
from utils.viewer import OpenCVViewer
from utils.visualization import (
display_grayscale,
display_rgb,
plot_image,
save_image,
draw_top_down_map,
Recording,
visualize_gt,
visualize_pred,
save_img_tensor)
import torch
import random
# NON_SCALAR_METRICS = {"top_down_map", "collisions.is_collision"}
# METRICS = ['OVMMDistToPickGoal', # distance to pick goal
# 'ovmm_nav_to_pick_succ' # success of navigation to pick goal
# ]
# def extract_scalars_from_info(cls, info):
# result = {}
# for k, v in info.items():
# if not isinstance(k, str) or k in NON_SCALAR_METRICS:
# continue
# if isinstance(v, dict):
# result.update(
# {
# k + "." + subk: subv
# for subk, subv in cls._extract_scalars_from_info(
# v
# ).items()
# if isinstance(subk, str)
# and k + "." + subk not in NON_SCALAR_METRICS
# }
# )
# # Things that are scalar-like will have an np.size of 1.
# # Strings also have an np.size of 1, so explicitly ban those
# elif np.size(v) == 1 and not isinstance(v, str):
# result[k] = float(v)
# return result
def get_total_navigable_area(env):
"""
Returns the total navigable area in the environment
"""
sim = env.habitat_env.env._env.habitat_env.sim
pf = sim.pathfinder
return pf.navigable_area
def create_ovmm_env_fn(config,args):
"""Create habitat environment using configsand wrap HabitatOpenVocabManipEnv around it. This function is used by VectorEnv for creating the individual environments"""
if args.collect_data:
splits = ['train','val','test']
OmegaConf.set_readonly(config, False)
config.habitat.dataset.split = 'train'
config.habitat.task.episode_init=False
OmegaConf.set_readonly(config, True)
habitat_config = config.habitat
dataset = make_dataset(habitat_config.dataset.type, config=habitat_config.dataset)
# we select a subset of episodes to generate the dataset
eps_select = {}
eps_list = []
skip = 22
eps_per_scene = 12
for eps in dataset.episodes:
scene_id = eps.scene_id
if scene_id not in eps_select:
eps_select[scene_id] = 0
eps_select[scene_id] += 1
if eps_select[scene_id] < skip:
continue
if eps_select[scene_id] < skip + eps_per_scene:
eps_list.append(eps)
dataset.episodes = eps_list
else:
habitat_config = config.habitat
dataset = make_dataset(habitat_config.dataset.type, config=habitat_config.dataset)
if args.eval_eps is not None:
eval_eps = [f'{e}' for e in args.eval_eps]
eps_list = [eps for eps in dataset.episodes if eps.episode_id in eval_eps]
dataset.episodes = eps_list
if args.eval_eps_total_num is not None:
dataset.episodes = dataset.episodes[:args.eval_eps_total_num]
env_class_name = _get_env_name(config)
env_class = get_env_class(env_class_name)
habitat_env = env_class(config=habitat_config, dataset=dataset)
habitat_env.seed(habitat_config.seed)
env = HabitatOpenVocabManipEnv(habitat_env, config, dataset=dataset)
return env
class InteractiveEvaluator():
"""class for interactive evaluation of OpenVocabManipAgent on an episode dataset"""
def __init__(self, config, gpu_id: int = 0, args=None):
self.visualize = config.VISUALIZE or config.PRINT_IMAGES
episode_ids_range = config.habitat.dataset.episode_indices_range
if episode_ids_range is not None:
config.EXP_NAME = os.path.join(
config.EXP_NAME, f"{episode_ids_range[0]}_{episode_ids_range[1]}"
)
OmegaConf.set_readonly(config, True)
self.config = config
self.results_dir = os.path.join(
self.config.DUMP_LOCATION, "results", self.config.EXP_NAME
)
self.videos_dir = self.config.habitat_baselines.video_dir
os.makedirs(self.results_dir, exist_ok=True)
os.makedirs(self.videos_dir, exist_ok=True)
self.agent = None
self.env = None
self.gpu_id = gpu_id
self.args = args
self.viewer = OpenCVViewer() if not args.no_render else None
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.env.close()
def eval(self, num_episodes_per_env=10):
self.env = create_ovmm_env_fn(self.config,self.args)
visualize=self.args.save_video or (not self.args.no_render) or (not self.args.no_interactive)
print(f'Env created')
agent = OVMMAgent(
config=self.config,
device_id=self.gpu_id,
obs_spaces=self.env.observation_space,
action_spaces=self.env.action_space,
collect_data=self.args.collect_data,
eval_rl_nav=(config.AGENT.SKILLS.NAV_TO_OBJ.type == "rl"),
use_FBE_policy=self.args.eval_policy == 'fbe',
visualize=visualize,
)
print(f'Agent created')
self.play(
agent,
self.env,
num_episodes_per_env=num_episodes_per_env,
episode_keys=None,
)
def write_results(self, episode_metrics):
aggregated_metrics = defaultdict(list)
metrics = set(
[
k
for metrics_per_episode in episode_metrics.values()
for k in metrics_per_episode
if k != "goal_name"
]
)
for v in episode_metrics.values():
for k in metrics:
if k in v:
aggregated_metrics[f"{k}/total"].append(v[k])
aggregated_metrics = dict(
sorted(
{
k2: v2
for k1, v1 in aggregated_metrics.items()
for k2, v2 in {
f"{k1}/mean": np.mean(v1),
f"{k1}/min": np.min(v1),
f"{k1}/max": np.max(v1),
}.items()
}.items()
)
)
with open(f"{self.results_dir}/aggregated_results.json", "w") as f:
json.dump(aggregated_metrics, f, indent=4)
with open(f"{self.results_dir}/episode_results.json", "w") as f:
json.dump(episode_metrics, f, indent=4)
def play(
self,
agent: OVMMAgent,
env: HabitatOpenVocabManipEnv,
num_episodes_per_env=None,
episode_keys=None,
):
# The stopping condition is either specified through
# num_episodes_per_env (stop after each environment
# finishes a certain number of episodes)
print(f"Running eval on {env.number_of_episodes} episodes")
# detic_perception = OvmmPerception(self.config,self.gpu_id)
episode_metrics = {}
print(f'Resetting...')
ob = env.reset()
# update_detic_perception_vocab(ob, detic_perception)
visualizer = Visualizer(self.config,self.env._dataset,self.args)
visualizer.reset()
agent.reset_vectorized([self.env.current_episode()])
agent_info = None
print('*'*20)
print(f'Goal: {ob.task_observations["goal_name"]}')
pre_entropy = 0
ep_idx = 0
coverage_log_interval = 10
########################################
# init evaluation metrics
# only used for object navigation task
########################################
visualize=self.args.save_video or (not self.args.no_render) or (not self.args.no_interactive)
results = []
recorder = Recording()
result_dir = f'datadump/exp_results/{self.args.exp_name}/'
os.makedirs(result_dir, exist_ok=True)
tested_episodes = []
for f in os.listdir(result_dir):
if f.endswith('.json'):
r = json.load(open(os.path.join(result_dir,f),'r'))
tested_episodes.append(r['episode_id'])
util_img = np.zeros((640,640,3),dtype=np.uint8)
want_terminate = False
forward_steps = 0
init_dts = env.habitat_env.env._env.habitat_env.get_metrics()['ovmm_dist_to_pick_goal']
exp_coverage_list = []
checking_area_list = []
entropy_list = []
close_coverage_list = []
eps_step = 0
pre_pose = np.zeros(2)
total_dist = 0
total_planning_time = 0
total_ig_time = []
while ep_idx < env.number_of_episodes:
if self.args.skip_existing:
current_episodes_id = self.env.current_episode().episode_id
if current_episodes_id in tested_episodes:
print(f'Skip existing episode: {current_episodes_id}')
ob = env.reset()
continue
eps_step += 1
# print(f'Current pose: {ob.gps*100}, theta: {ob.compass*180/np.pi}')
start_time = time.time()
action, agent_info, _ = agent.act(ob)
total_planning_time += time.time() - start_time
# print(f'Entropy: {agent_info["entropy"]}, change: {agent_info["entropy"] - pre_entropy}')
# pre_entropy = agent_info["entropy"]
# print(f'exp_area: {agent_info["exp_coverage"]}, checking_area: {agent_info["checking_area"]}')
exp_coverage_list.append(agent_info["exp_coverage"])
checking_area_list.append(agent_info["checking_area"]+checking_area_list[-1] \
if len(checking_area_list) > 0 else agent_info["checking_area"])
entropy_list.append(agent_info["entropy"])
close_coverage_list.append(agent_info["close_coverage"])
if agent_info["ig_time"] is not None:
total_ig_time.append(agent_info["ig_time"])
if visualize:
# first visualize thrid person
images = {}
images['third_person'] = ob.third_person_image # 640 x 640 x 3
# visualize detected instances
images['rgb_detection'] = ob.task_observations['semantic_frame']
# visualize depth
images['depth'] = ob.depth.copy()
images['depth'] = (images['depth'] / 10.0 * 255).astype(np.uint8)
# visualize semantic map
vis = visualizer.visualize(**agent_info)
images['semantic_map_vis'] = vis['semantic_map']
# visualize probabilistic map
# IGNORE THIS FOR NOW
# if "probabilistic_map" in agent_info and agent_info['probabilistic_map'] is not None:
# prob_map = agent_info['probabilistic_map']
# prob_map = np.flipud(prob_map)
# visualize info_gain map for ur policy only
if self.args.eval_policy == 'ur':
ig_vis = agent_info['ig_vis']
if ig_vis is not None:
util_img = ig_vis['utility']
util_img = np.flipud(util_img)
images['utility'] = util_img
vis_type = 'video'
if vis_type == 'paper':
draw_ob = np.zeros((640,640*4,3),dtype=np.uint8)
for k in ['third_person','rgb_detection','semantic_map_vis','utility']:
img = images[k]
img = cv2.resize(
img,
(640, 640),
interpolation=cv2.INTER_NEAREST,
)
draw_ob[:,640*images[k].shape[1]:640*(images[k].shape[1]+1),:3] = img
elif vis_type == 'video':
draw_ob = np.zeros((640,1280,3),dtype=np.uint8)
draw_ob[:,0:640,:] = images['third_person']
for i, k in enumerate(['rgb_detection','depth', 'semantic_map_vis','utility']):
img = images[k]
img = cv2.resize(
img,
(320, 320),
interpolation=cv2.INTER_NEAREST,
)
row = i // 2
col = i % 2
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
draw_ob[row*320:(row+1)*320,640+col*320:640+(col+1)*320,:] = img[:,:,:3]
if self.args.save_video:
recorder.add_frame(draw_ob)
if not self.args.no_render:
user_action = self.viewer.imshow(
draw_ob, delay=0 if not self.args.no_interactive else 2
)
if not self.args.no_interactive and user_action is not None:
if user_action['info'] == "plan_high":
agent.force_update_high_goal(0)
action = user_action['action']
outputs = env.apply_action(action, agent_info)
ob, done, info = outputs
if agent_info["skill_done"] != '':
want_terminate = True
done = True
dist = np.linalg.norm(ob.gps - pre_pose)
total_dist += dist
pre_pose = ob.gps
if done:
print(f"Episode {ep_idx} finished.")
current_episodes_info = self.env.current_episode()
# save evaluation results
total_nav_area = get_total_navigable_area(env) # in m2
eps_result = {
'episode_id': current_episodes_info.episode_id,
'scene_id': current_episodes_info.scene_id,
'success': info['ovmm_nav_to_pick_succ'],
'distance_to_goal': info['ovmm_dist_to_pick_goal'],
'travelled_distance': total_dist,
'steps': info['num_steps'],
'want_terminate': want_terminate,
'goal_object': ob.task_observations["goal_name"],
'spl': init_dts / max(total_dist, init_dts),
'total_nav_area': total_nav_area,
'exp_coverage': exp_coverage_list,
'checking_area': checking_area_list,
'entropy': entropy_list,
'close_coverage': close_coverage_list,
'total_time': total_planning_time,
'ig_times': total_ig_time,
}
results.append(eps_result)
fname = f'{ob.task_observations["goal_name"]}_{info["ovmm_nav_to_pick_succ"]}'
with open(f'{result_dir}/{fname}.json', 'w') as f:
json.dump(eps_result, f)
if self.args.save_video:
recorder.save_video(fname,result_dir)
# reset env and agent
ob = env.reset()
agent.reset_vectorized_for_env(
0, self.env.current_episode()
)
visualizer.reset()
ep_idx += 1
# reset eval metrics
want_terminate = False
forward_steps = 0
init_dts = env.habitat_env.env._env.habitat_env.get_metrics()['ovmm_dist_to_pick_goal']
exp_coverage_list = []
checking_area_list = []
entropy_list = []
close_coverage_list = []
eps_step = 0
total_dist = 0
pre_pose = np.zeros(2)
total_planning_time = 0
total_ig_time = []
util_img = np.zeros((640,640,3),dtype=np.uint8)
print("*"*20)
print(f'Goal: {ob.task_observations["goal_name"]}')
env.close()
self.write_results(episode_metrics)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--habitat_config_path",
type=str,
default="ovmm/ovmm_eval.yaml",
help="Path to config yaml",
)
parser.add_argument(
"--baseline_config_path",
type=str,
default="configs/agent/hssd_eval.yaml",
help="Path to config yaml",
)
parser.add_argument(
"--gpu_id",
type=int,
default=1,
help="GPU id to use for evaluation",
)
parser.add_argument(
"opts",
default=None,
nargs=argparse.REMAINDER,
help="Modify config options from command line",
)
parser.add_argument(
"--no_render",
action="store_true",
help="Whether to render the environment or not",
default=False,
)
parser.add_argument(
"--no_interactive",
action="store_true",
help="Whether to render the environment or not",
default=False,
)
parser.add_argument(
"--eval_eps",
help="evaluate a subset of episodes",
nargs="+",
default=None,
)
parser.add_argument(
"--eval_eps_total_num",
help="evaluate a subset of episodes",
type=int,
default=1000,
)
parser.add_argument(
"--collect_data",
help="wheter to collect data for training",
action="store_true",
default=False,
)
parser.add_argument(
"--exp_name",
help="experiment name",
type=str,
default='debug',
)
parser.add_argument(
"--save_video",
help="Save video",
action="store_true",
default=False,
)
parser.add_argument(
"--eval_policy",
help="policy to evaluate: fbe | rl | ur",
type=str,
default='ur',
)
parser.add_argument(
"--seed",
help="random seed",
type=int,
default=0,
)
parser.add_argument(
"--gt_semantic",
help="whether to use ground truth semantic map",
action="store_true",
default=False,
)
parser.add_argument(
"--no_use_prob_map",
help="whether to use probability map",
action="store_true",
default=False,
)
parser.add_argument(
"--skip_existing",
help="whether to skip existing results",
action="store_true",
default=False,
)
parser.add_argument(
"--allow_sliding",
help="whether to allow sliding",
action="store_true",
default=False,
)
print("Arguments:")
args = parser.parse_args()
print(json.dumps(vars(args), indent=4))
print("-" * 100)
print("Configs:")
config = get_habitat_config(args.habitat_config_path, overrides=[])
baseline_config = OmegaConf.load(args.baseline_config_path)
extra_config = OmegaConf.from_cli(args.opts)
baseline_config = OmegaConf.merge(baseline_config, extra_config)
print(OmegaConf.to_yaml(baseline_config))
config = DictConfig({**config, **baseline_config})
evaluator = InteractiveEvaluator(config,args.gpu_id,args=args)
print("-" * 100)
seed = args.seed
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
OmegaConf.set_readonly(config, False)
config.habitat.seed = seed
if args.collect_data:
config.AGENT.IG_PLANNER.ig_predictor_type='rendering'
args.eval_policy = 'ur'
if args.eval_policy == 'rl':
config.AGENT.SKILLS.NAV_TO_OBJ.type = "rl"
elif args.eval_policy == 'fbe':
config.AGENT.SKILLS.NAV_TO_OBJ.type = "heuristic"
elif args.eval_policy == 'ur':
config.AGENT.SKILLS.NAV_TO_OBJ.type = "heuristic"
else:
raise ValueError(f'Unknown policy type: {args.eval_policy}')
config.GROUND_TRUTH_SEMANTICS = 1 if args.gt_semantic else 0
config.habitat.simulator.habitat_sim_v0.allow_sliding=args.allow_sliding
# if args.eval_policy == 'ur' and not args.no_use_prob_map and not args.gt_semantic:
# config.AGENT.SEMANTIC_MAP.use_probability_map = True
# else:
# config.AGENT.SEMANTIC_MAP.use_probability_map = False
visualize = args.save_video or (not args.no_render) or (not args.no_interactive)
OmegaConf.set_readonly(config, True)
evaluator.eval(
num_episodes_per_env=config.EVAL_VECTORIZED.num_episodes_per_env,
)