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import os
import json
import numpy as np
from PIL import Image
from datetime import datetime
from eval import Eval
from env.thor_env import ThorEnv
class EvalTask(Eval):
'''
evaluate overall task performance
'''
@classmethod
def run(cls, model, resnet, task_queue, args, lock, successes, failures, results):
'''
evaluation loop
'''
# start THOR
env = ThorEnv()
while True:
if task_queue.qsize() == 0:
break
task = task_queue.get()
try:
traj = model.load_task_json(task)
r_idx = task['repeat_idx']
print("Evaluating: %s" % (traj['root']))
print("No. of trajectories left: %d" % (task_queue.qsize()))
cls.evaluate(env, model, r_idx, resnet, traj, args, lock, successes, failures, results)
except Exception as e:
import traceback
traceback.print_exc()
print("Error: " + repr(e))
# stop THOR
env.stop()
@classmethod
def evaluate(cls, env, model, r_idx, resnet, traj_data, args, lock, successes, failures, results):
# reset model
model.reset()
# # GT
# run_traj = [
# # valid_seen
# #'data/json_feat_2.1.0/pick_heat_then_place_in_recep-AppleSliced-None-Fridge-18/trial_T20190911_045200_839773', # heated apple -> (slice) -> (not heated) slices.
# #'data/json_feat_2.1.0/pick_and_place_with_movable_recep-Pen-Mug-Desk-309/trial_T20190909_023218_500741', # slippery issue
# #'data/json_feat_2.1.0/pick_and_place_simple-SprayBottle-None-Toilet-422/trial_T20190909_124835_952557', # invalid mask
# #'data/json_feat_2.1.0/pick_clean_then_place_in_recep-LettuceSliced-None-Fridge-19/trial_T20190909_010452_388275', # collision
# #'data/json_feat_2.1.0/pick_and_place_with_movable_recep-TomatoSliced-Pan-DiningTable-28/trial_T20190908_133804_285739', # planning error
# #'data/json_feat_2.1.0/pick_and_place_with_movable_recep-AppleSliced-Pot-CounterTop-4/trial_T20190906_180021_201134', # slippery issue, leading to nav fail
# #'data/json_feat_2.1.0/pick_cool_then_place_in_recep-AppleSliced-None-SideTable-21/trial_T20190907_013001_399963', # slippery issue
# #'data/json_feat_2.1.0/pick_and_place_simple-ToiletPaper-None-ToiletPaperHanger-415/trial_T20190908_050518_595510', # reran but it suceeded.
# #'data/json_feat_2.1.0/pick_and_place_simple-SprayBottle-None-Toilet-415/trial_T20190909_075955_678702', # reran but it succeeded.
#
# # valid_unseen
# #'data/json_feat_2.1.0/pick_heat_then_place_in_recep-AppleSliced-None-GarbageCan-10/trial_T20190908_073749_086690', # heated apple -> (slice) -> (not heated) slices.
# 'data/json_feat_2.1.0/pick_heat_then_place_in_recep-PotatoSliced-None-Fridge-10/trial_T20190909_123732_489327', # heated apple -> (slice) -> (not heated) slices.
# ]
# if traj_data['root'] not in run_traj:
# return
# setup scene
reward_type = 'dense'
cls.setup_scene(env, traj_data, r_idx, args, reward_type=reward_type)
# extract language features
#feat = model.featurize([traj_data], load_mask=False)
# traj_data_pddl = json.load(open(traj_data['root'].replace('json_feat_2.1.0', 'json_2.1.0_generated/'+traj_data['split']) + '/traj_data.json', 'r'))
traj_data_pddl = json.load(open(traj_data['root'].replace('json_feat_2.1.0', 'json_2.1.0_GT/'+traj_data['split']) + '/traj_data.json', 'r'))
# traj_data_pddl = json.load(open(traj_data['root'].replace('json_feat_2.1.0', 'json_2.1.0_predTaskArgs/'+traj_data['split']) + '/traj_data.json', 'r'))
gt_actions = traj_data_pddl['plan']['low_actions']
# aug28 turk_annotation template switch
new_traj = dict()
for i in list(traj_data.keys()):
if i != 'template':
new_traj[i] = traj_data[i]
else:
# new_traj['turk_annotations'] = {'anns': [traj_data[i]]}
new_traj['turk_annotations'] = {'anns': [{'high_descs': traj_data[i]['high_descs'][:-1], 'task_desc': traj_data[i]['task_desc']}]}
traj_data = new_traj
# goal instr
goal_instr = traj_data['turk_annotations']['anns'][r_idx]['task_desc']
done, success = False, False
fails = 0
t = 0
reward = 0
while not done:
# break if max_steps reached
if t >= args.max_steps or t >= len(gt_actions):
break
# extract visual features
#curr_image = Image.fromarray(np.uint8(env.last_event.frame))
#feat['frames'] = resnet.featurize([curr_image], batch=1).unsqueeze(0)
# forward model
#m_out = model.step(feat)
#m_pred = model.extract_preds(m_out, [traj_data], feat, clean_special_tokens=False)
#m_pred = list(m_pred.values())[0]
# check if <<stop>> was predicted
#if m_pred['action_low'] == cls.STOP_TOKEN:
# print("\tpredicted STOP")
# break
# get action and mask
#action, mask = m_pred['action_low'], m_pred['action_low_mask'][0]
#mask = np.squeeze(mask, axis=0) if model.has_interaction(action) else None
action = gt_actions[t]['discrete_action']['action']
mask = None
if model.has_interaction(action):
from gen.utils.image_util import decompress_mask
mask = decompress_mask(gt_actions[t]['discrete_action']['args']['mask'])
#mask = decompress_mask([gt_actions[t]['discrete_action']['args']['point']])
#mask = env.last_event.instance_masks[gt_actions[t]['api_action']['objectId']]
import matplotlib.pyplot as plt
rgb = np.array(env.last_event.frame, dtype=np.float32) / 255
plt.imsave('../tmp/{:05d}.png'.format(t), rgb/2 + mask[:,:,np.newaxis]/2)
# print action
if args.debug:
print(action)
# use predicted action and mask (if available) to interact with the env
t_success, _, _, err, _ = env.va_interact(action, interact_mask=mask, smooth_nav=args.smooth_nav, debug=args.debug)
if not t_success:
fails += 1
print("Interact API failed %d times" % fails + "; latest error '%s'" % err)
if fails >= args.max_fails:
print("Interact API failed %d times" % fails + "; latest error '%s'" % err)
break
#if t > 30:
# print(t)
# for o in env.last_event.metadata['objects']:
# if 'Apple' in o['name']:
# print(' -', o['name'], o['
# next time-step
t_reward, t_done = env.get_transition_reward()
reward += t_reward
t += 1
# check if goal was satisfied
goal_satisfied = env.get_goal_satisfied()
if goal_satisfied:
print("Goal Reached")
success = True
# goal_conditions
pcs = env.get_goal_conditions_met()
goal_condition_success_rate = pcs[0] / float(pcs[1])
# SPL
path_len_weight = len(traj_data['plan']['low_actions'])
s_spl = (1 if goal_satisfied else 0) * min(1., path_len_weight / float(t))
pc_spl = goal_condition_success_rate * min(1., path_len_weight / float(t))
# path length weighted SPL
plw_s_spl = s_spl * path_len_weight
plw_pc_spl = pc_spl * path_len_weight
# log success/fails
lock.acquire()
log_entry = {'trial': traj_data['task_id'],
'type': traj_data['task_type'],
'repeat_idx': int(r_idx),
'goal_instr': goal_instr,
'completed_goal_conditions': int(pcs[0]),
'total_goal_conditions': int(pcs[1]),
'goal_condition_success': float(goal_condition_success_rate),
'success_spl': float(s_spl),
'path_len_weighted_success_spl': float(plw_s_spl),
'goal_condition_spl': float(pc_spl),
'path_len_weighted_goal_condition_spl': float(plw_pc_spl),
'path_len_weight': int(path_len_weight),
'reward': float(reward)}
if success:
successes.append(log_entry)
else:
failures.append(log_entry)
# overall results
results['all'] = cls.get_metrics(successes, failures)
print("-------------")
print("SR: %d/%d = %.3f" % (results['all']['success']['num_successes'],
results['all']['success']['num_evals'],
results['all']['success']['success_rate']))
print("GC: %d/%d = %.3f" % (results['all']['goal_condition_success']['completed_goal_conditions'],
results['all']['goal_condition_success']['total_goal_conditions'],
results['all']['goal_condition_success']['goal_condition_success_rate']))
print("PLW SR: %.3f" % (results['all']['path_length_weighted_success_rate']))
print("PLW GC: %.3f" % (results['all']['path_length_weighted_goal_condition_success_rate']))
print("-------------")
# task type specific results
task_types = ['pick_and_place_simple', 'pick_clean_then_place_in_recep', 'pick_heat_then_place_in_recep',
'pick_cool_then_place_in_recep', 'pick_two_obj_and_place', 'look_at_obj_in_light',
'pick_and_place_with_movable_recep']
for task_type in task_types:
task_successes = [s for s in (list(successes)) if s['type'] == task_type]
task_failures = [f for f in (list(failures)) if f['type'] == task_type]
if len(task_successes) > 0 or len(task_failures) > 0:
results[task_type] = cls.get_metrics(task_successes, task_failures)
else:
results[task_type] = {}
lock.release()
@classmethod
def get_metrics(cls, successes, failures):
'''
compute overall succcess and goal_condition success rates along with path-weighted metrics
'''
# stats
num_successes, num_failures = len(successes), len(failures)
num_evals = len(successes) + len(failures)
total_path_len_weight = sum([entry['path_len_weight'] for entry in successes]) + \
sum([entry['path_len_weight'] for entry in failures])
completed_goal_conditions = sum([entry['completed_goal_conditions'] for entry in successes]) + \
sum([entry['completed_goal_conditions'] for entry in failures])
total_goal_conditions = sum([entry['total_goal_conditions'] for entry in successes]) + \
sum([entry['total_goal_conditions'] for entry in failures])
# metrics
sr = float(num_successes) / num_evals
pc = completed_goal_conditions / float(total_goal_conditions)
plw_sr = (float(sum([entry['path_len_weighted_success_spl'] for entry in successes]) +
sum([entry['path_len_weighted_success_spl'] for entry in failures])) /
total_path_len_weight)
plw_pc = (float(sum([entry['path_len_weighted_goal_condition_spl'] for entry in successes]) +
sum([entry['path_len_weighted_goal_condition_spl'] for entry in failures])) /
total_path_len_weight)
# result table
res = dict()
res['success'] = {'num_successes': num_successes,
'num_evals': num_evals,
'success_rate': sr}
res['goal_condition_success'] = {'completed_goal_conditions': completed_goal_conditions,
'total_goal_conditions': total_goal_conditions,
'goal_condition_success_rate': pc}
res['path_length_weighted_success_rate'] = plw_sr
res['path_length_weighted_goal_condition_success_rate'] = plw_pc
return res
def create_stats(self):
'''
storage for success, failure, and results info
'''
self.successes, self.failures = self.manager.list(), self.manager.list()
self.results = self.manager.dict()
def save_results(self):
results = {'successes': list(self.successes),
'failures': list(self.failures),
'results': dict(self.results)}
save_path = os.path.dirname(self.args.model_path)
save_path = os.path.join(save_path, 'task_results_' + self.args.eval_split + '_' + datetime.now().strftime("%Y%m%d_%H%M%S_%f") + '.json')
with open(save_path, 'w') as r:
json.dump(results, r, indent=4, sort_keys=True)
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