forked from rllab-snu/TopologicalSemanticGraphMemory
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathevaluate.py
More file actions
555 lines (527 loc) · 27.4 KB
/
evaluate.py
File metadata and controls
555 lines (527 loc) · 27.4 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
import sys
import argparse
import imageio
import gzip
from copy import deepcopy
import datetime
import torch
from env_utils.make_env_utils import add_panoramic_camera
from utils.debug_utils import get_remain_time
import habitat
from habitat import make_dataset
from env_utils import *
from configs.default import get_config, CN
import time
import cv2
import os
import json
from runner import *
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=1)
parser.add_argument("--ep-per-env", type=int, default=1000)
parser.add_argument("--config", type=str, default="./configs/TSGM.yaml", help="path to config yaml containing info about experiment")
parser.add_argument("--gpu", type=str, default="0")
parser.add_argument("--version", type=str, required=True)
parser.add_argument("--diff", choices=['random', 'easy', 'medium', 'hard'], default='')
parser.add_argument("--split", choices=['val', 'train', 'val_mini', 'test'], default='val')
parser.add_argument('--eval-ckpt', type=str, default='data/checkpoints')
parser.add_argument('--record', choices=['0','1','2','3'], default='0') # 0: no record 1: env.render 2: pose + action numerical traj 3: features
parser.add_argument('--project-dir', default='.', type=str)
parser.add_argument('--dataset', default='gibson' , type=str)
parser.add_argument('--task', default='imggoalnav', type=str)
parser.add_argument('--record-dir', type=str, default='data/video_dir')
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--use-detector', action='store_true', default=False)
parser.add_argument('--num-object', default=10, type=int)
parser.add_argument('--detector-th', default=0.1, type=float)
parser.add_argument('--wandb', action='store_true', default=False)
parser.add_argument('--mode', default='eval', type=str)
parser.add_argument('--episode_name', default='VGM', type=str) #[VGM, NRNS_curved, NRNS_straight]
parser.add_argument('--policy', default='TSGMPolicy', type=str)
parser.add_argument('--coverage', action='store_true', default=False)
parser.add_argument('--fd', action='store_true', default=False, help="use finetuned detector")
parser.add_argument('--obj-score-th', default=0.1, type=float)
parser.add_argument('--img-node-th', type=str, default='0.75')
parser.add_argument('--obj-node-th', type=str, default='0.8')
parser.add_argument('--debug', action='store_true', default=False)
# Noise settings
parser.add_argument('--depth_noise', action='store_true', default=False)
parser.add_argument('--actua_noise', action='store_true', default=False)
parser.add_argument('--sensor_noise', action='store_true', default=False)
parser.add_argument('--depth-noise-level', default=4.0, type=float)
parser.add_argument('--actua-noise-level', default=4.0, type=float)
parser.add_argument('--sensor-noise-level', default=4.0, type=float)
args = parser.parse_args()
args.record = int(args.record)
args.img_node_th = float(args.img_node_th)
args.obj_node_th = float(args.obj_node_th)
os.environ['GLOG_minloglevel'] = "3"
os.environ['MAGNUM_LOG'] = "quiet"
os.environ['HABITAT_SIM_LOG'] = "quiet"
import numpy as np
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.gpu != 'cpu':
torch.cuda.manual_seed(args.seed)
torch.set_num_threads(5)
torch.backends.cudnn.enabled = True
device = 'cpu' if args.gpu == '-1' else 'cuda:{}'.format(args.gpu)
def eval_config(args):
config = get_config(args.config, base_task_config_path="./configs/{}_{}.yaml".format(args.task, args.dataset), arguments=vars(args))
config.defrost()
config.POLICY = args.policy
config.use_depth = True
config.USE_DETECTOR = True
config.DIFFICULTY = args.diff
config.scene_data = args.dataset
habitat_api_path = os.path.join(os.path.dirname(habitat.__file__), '../')
# print(args.config)
config.TASK_CONFIG = add_panoramic_camera(config.TASK_CONFIG, normalize_depth=True)
config.TASK_CONFIG.DATASET.SPLIT = args.split
config.TASK_CONFIG.DATASET.SCENES_DIR = os.path.join(habitat_api_path, config.TASK_CONFIG.DATASET.SCENES_DIR)
config.TASK_CONFIG.DATASET.DATA_PATH = os.path.join(habitat_api_path, config.TASK_CONFIG.DATASET.DATA_PATH)
config.TASK_CONFIG.DATASET.DATASET_NAME = args.dataset
config.TASK_CONFIG.TASK.MEASUREMENTS = ["GOAL_INDEX"] + config.TASK_CONFIG.TASK.MEASUREMENTS
config.TASK_CONFIG.TASK.GOAL_INDEX = config.TASK_CONFIG.TASK.SPL.clone()
config.TASK_CONFIG.TASK.GOAL_INDEX.TYPE = 'GoalIndex'
if 'COLLISIONS' not in config.TASK_CONFIG.TASK.MEASUREMENTS:
config.TASK_CONFIG.TASK.MEASUREMENTS += ['COLLISIONS']
dataset = make_dataset(config.TASK_CONFIG.DATASET.TYPE)
if config.TASK_CONFIG.DATASET.CONTENT_SCENES == ['*']:
print("*"*100)
print(config.TASK_CONFIG.DATASET)
print("*"*100)
scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)
else:
scenes = config.TASK_CONFIG.DATASET.CONTENT_SCENES
config.TASK_CONFIG.DATASET.CONTENT_SCENES = scenes
config.TASK_CONFIG.ENVIRONMENT.ITERATOR_OPTIONS.MAX_SCENE_REPEAT_EPISODES = args.ep_per_env
config.ACTION_DIM = 4
config.TASK_CONFIG.TASK.POSSIBLE_ACTIONS= ["STOP", "MOVE_FORWARD", "TURN_LEFT", "TURN_RIGHT"]
config.TASK_CONFIG.PROC_ID = 0
config.freeze()
return config
def load(ckpt):
if ckpt != 'none':
sd = torch.load(ckpt, map_location=torch.device('cpu'))
state_dict = sd['state_dict']
new_state_dict = {}
for key in state_dict.keys():
if 'actor_critic' in key:
new_state_dict[key[len('actor_critic.'):]] = state_dict[key]
else:
new_state_dict[key] = state_dict[key]
if 'config' in sd.keys():
return (new_state_dict, sd['config'])
return (new_state_dict,None)
else:
return (None, None)
def evaluate(eval_config, state_dict, ckpt_config):
if ckpt_config is not None:
task_config = eval_config.TASK_CONFIG
ckpt_config.defrost()
task_config.defrost()
ckpt_config.TASK_CONFIG = task_config
ckpt_config.runner = eval_config.runner
ckpt_config.iter_num = eval_config.iter_num
ckpt_config.AGENT_TASK = 'search'
ckpt_config.USE_DETECTOR = ckpt_config.TASK_CONFIG.USE_DETECTOR = eval_config.USE_DETECTOR
ckpt_config.detector_th = ckpt_config.TASK_CONFIG.detector_th = args.detector_th
ckpt_config.use_depth = ckpt_config.TASK_CONFIG.use_depth = eval_config.use_depth
try:
ckpt_config['ARGS']['project_dir'] = args.project_dir
except:
ckpt_config['ARGS'] = vars(args)
ckpt_config.POLICY = eval_config.POLICY
ckpt_config.DIFFICULTY = eval_config.DIFFICULTY
ckpt_config.ACTION_DIM = eval_config.ACTION_DIM
ckpt_config.memory = eval_config.memory
ckpt_config.scene_data = eval_config.scene_data
ckpt_config.WRAPPER = eval_config.WRAPPER
ckpt_config.REWARD_METHOD = eval_config.REWARD_METHOD
ckpt_config.ENV_NAME = eval_config.ENV_NAME
for k, v in eval_config.items():
if k not in ckpt_config:
ckpt_config.update({k:v})
if isinstance(v, CN):
for kk, vv in v.items():
if kk not in ckpt_config[k]:
ckpt_config[k].update({kk: vv})
ckpt_config.freeze()
eval_config = ckpt_config
eval_config.defrost()
eval_config.img_node_th = args.img_node_th
eval_config.TASK_CONFIG.img_node_th = args.img_node_th
eval_config.TASK_CONFIG.obj_node_th = args.obj_node_th
eval_config.record = args.record > 0
eval_config.render_map = args.record > 0 or args.render or 'hand' in args.config
eval_config.noisy_actuation = True
eval_config.memory.num_objects = args.num_object
eval_config.OBJECTGRAPH.SPARSE = False
eval_config.features.object_category_num = 80
eval_config.gpu = args.gpu.split(',')
if len(args.gpu) > 1:
eval_config.TORCH_GPU_ID = int(args.gpu[0])
eval_config.SIMULATOR_GPU_ID = int(args.gpu[1])
eval_config.TASK_CONFIG.DETECTOR_GPU_ID = int(args.gpu[1])
else:
eval_config.TORCH_GPU_ID = int(args.gpu[0])
eval_config.SIMULATOR_GPU_ID = int(args.gpu[0])
eval_config.TASK_CONFIG.DETECTOR_GPU_ID = int(args.gpu[0])
eval_config.TASK_CONFIG['ARGS'] = vars(args)
eval_config['ARGS'] = vars(args)
eval_config.freeze()
runner = eval(eval_config.runner)(args, eval_config, return_features=True)
print(eval_config.memory)
print('====================================')
print('Version Name: ', args.version)
print('Dataset Name: ', args.dataset)
print('Evaluating: ', eval_config.iter_num)
print('Runner : ', eval_config.runner)
print('Policy : ', eval_config.POLICY)
print('Difficulty: ', eval_config.DIFFICULTY)
print('Use Detector: ', eval_config.USE_DETECTOR)
print('Detector threshold: ', eval_config.detector_th)
print('Stop action: ', 'True' if eval_config.ACTION_DIM==4 else 'False')
print('====================================')
curr_hostname = os.uname()[1]
version_name = eval_config.saving.name if args.version == 'none' else args.version
version_name += '_{}'.format(args.dataset)
version_name += '_{}'.format(args.task)
if args.wandb:
import wandb
from wandb import AlertLevel
wandb.init(project="(eval)TSGM_{}".format(args.task), config=eval_config, name=version_name + '_{}'.format(curr_hostname), tags=[curr_hostname])
runner.eval()
if torch.cuda.device_count() > 0:
runner.to(device)
try:
runner.load(state_dict)
print('Loaded model from checkpoint')
except:
agent_dict = runner.agent.state_dict()
new_sd = {k: v for k, v in state_dict.items() if k in agent_dict.keys() and (v.shape == agent_dict[k].shape)}
agent_dict.update(new_sd)
runner.load(agent_dict)
print('Loaded partial model')
eval_config.defrost()
tot_episodes = 0
if args.episode_name == "VGM":
for scene in eval_config.TASK_CONFIG.DATASET.CONTENT_SCENES:
json_file = os.path.join(args.project_dir, 'data/episodes/{}/{}/{}_{}.json'.format(args.episode_name, args.dataset.split("_")[0], scene, args.diff))
with open(json_file, 'r') as f:
episodes = json.load(f)
tot_episodes += len(episodes)
elif args.episode_name.split("_")[0] == "NRNS":
json_file = os.path.join(args.project_dir, 'data/episodes/{}/{}/{}/test_{}.json.gz'.format(args.episode_name.split("_")[0], args.dataset.split("_")[0], args.episode_name.split("_")[1], args.diff))
with gzip.open(json_file, "r") as fin:
episodes = json.loads(fin.read().decode("utf-8"))['episodes']
tot_episodes = len(episodes)
elif args.episode_name == "MARL":
for scene in eval_config.TASK_CONFIG.DATASET.CONTENT_SCENES:
json_file = os.path.join(args.project_dir, 'data/episodes/{}/{}/{}.json.gz'.format(args.episode_name, args.dataset.split("_")[0], scene))
with gzip.open(json_file, "r") as fin:
episodes = json.loads(fin.read().decode("utf-8"))
episodes = [episode for episode in episodes if episode['info']['difficulty'] == args.diff]
tot_episodes += len(episodes)
# 573/214->505/205
eval_config.freeze()
env = eval(eval_config.ENV_NAME)(eval_config)
env.habitat_env._sim.seed(args.seed)
if runner.need_env_wrapper:
env = runner.wrap_env(env, eval_config)
result = {}
result['config'] = eval_config
result['args'] = args
result['version'] = str(args.version)
datetime_now = str(datetime.datetime.today()).split(".")[0].replace(" ","_")
result['start_time'] = datetime_now
result['noisy_action'] = bool(env.noise)
scene_dict = {}
render_check = False
start_time = time.time()
with torch.no_grad():
ep_list = []
total_success, total_spl, total_dtg, total_softspl, total_localize_success, total_node_dists, total_success_timesteps = [], [], [], [], [], [], []
for episode_id in range(tot_episodes):
obs = env.reset()
if render_check == False:
if obs['panoramic_rgb'].sum() == 0 :
print('NO RENDERING!!!!!!!!!!!!!!!!!! YOU SHOULD CHECK YOUT DISPLAY SETTING')
else:
render_check=True
runner.reset()
scene_name = env.current_episode.scene_id.split('/')[-1][:-4]
if scene_name not in scene_dict.keys():
scene_dict[scene_name] = {'success': [], 'spl': [], 'dtg': [], 'softspl': []}
done = True
reward = None
info = None
if args.record > 0:
img = env.render('rgb')
imgs = [img]
step = 0
records = []
record_graphs = []
record_maps = []
record_features = []
record_objects = []
localize_success = []
while True:
action = runner.step(obs, reward, done, info, env)
if action == 100: # handcrafted navigation mode
paths = env.get_navigation_path(obs)
obs['planned_path'] = paths[0]
action = runner.step(obs, reward, done, info, env)
if 'curr_attn' in runner.features.keys():
dist_nodes = []
for iii in range(len(env.imggraph.node_position_list)):
dist_nodes.append(env.env._env.sim.geodesic_distance(env.imggraph.node_position_list[iii], env.current_position))
if np.argmin(dist_nodes) == runner.features['curr_attn'].squeeze().argmax().item():
localize_success.append(1)
else:
localize_success.append(0)
if args.record > 1:
records.append([env.get_agent_state().position, env.get_agent_state().rotation.components, action])
if hasattr(env.mapper, 'node_list'):
num_img_node = env.imggraph.num_node()
num_obj_node = env.objgraph.num_node()
img_memory_dict = {
'img_memory_feat': env.imggraph.graph_memory[:num_img_node].copy(),
'img_memory_pose': np.stack(env.imggraph.node_position_list).copy(),
'img_memory_mask': env.imggraph.graph_mask[:num_img_node].copy(),
'img_memory_A': env.imggraph.A[:num_img_node, :num_img_node].copy(),
'img_memory_idx': env.imggraph.last_localized_node_idx,
'img_memory_time': env.imggraph.graph_time[:num_img_node].copy()
}
obj_memory_dict = {
'obj_memory_feat': env.objgraph.graph_memory[:num_obj_node].copy(),
'obj_memory_pose': np.stack(env.objgraph.node_position_list[0]),
'obj_memory_score': env.objgraph.graph_score[:num_obj_node].copy(),
'obj_memory_category': env.objgraph.graph_category[:num_obj_node].copy(),
'obj_memory_mask': env.objgraph.graph_mask[:num_obj_node].copy(),
'obj_memory_A_OV': env.objgraph.A_OV[:num_obj_node, :num_img_node].copy(),
'obj_memory_time': env.objgraph.graph_time[:num_obj_node].copy()
}
img_memory_dict.update(obj_memory_dict)
record_graphs.append(img_memory_dict)
record_objects.append({
"object": deepcopy(obs['object'][0][:, 1:].cpu().detach().numpy()),
"object_score": deepcopy(obs['object_score'][0].cpu().detach().numpy()),
"object_category": deepcopy(obs['object_category'][0].cpu().detach().numpy()),
"object_pose": deepcopy(obs['object_pose'][0].cpu().detach().numpy()),
})
if info is not None:
record_maps.append({'agent_angle': deepcopy(info['ortho_map']['agent_rot']),
'agent_loc': deepcopy(info['ortho_map']['agent_loc']),
})
else:
lower_bound, upper_bound = env.habitat_env._sim.pathfinder.get_bounds()
record_maps.append({
'ortho_map': deepcopy(env.ortho_rgb),
'P': deepcopy(np.array(env.P)),
'target_loc': np.array(env.habitat_env._current_episode.goals[0].position),
'lower_bound': deepcopy(lower_bound),
'upper_bound': deepcopy(upper_bound),
'WIDTH': env.habitat_env._config.SIMULATOR.ORTHO_RGB_SENSOR.WIDTH,
'HEIGHT': env.habitat_env._config.SIMULATOR.ORTHO_RGB_SENSOR.HEIGHT
})
if args.record > 2:
record_features.append(runner.features)
obs, reward, done, info = env.step(action)
step += 1
if args.record > 2:
img = env.render(mode='rgb', attns=runner.features)
imgs.append(img)
elif args.record > 0:
img = env.render('rgb')
imgs.append(img)
if args.render:
env.render('human')
if done: break
spl = info['spl']
if np.isnan(spl):
spl = 0.0
print('spl nan!', env.habitat_env._sim.geodesic_distance(env.current_episode.start_position, env.current_episode.goals[0].position))
if np.isinf(spl):
spl = 0.0
scene_dict[scene_name]['success'].append(info['success'])
scene_dict[scene_name]['spl'].append(spl)
scene_dict[scene_name]['dtg'].append(info['distance_to_goal'])
scene_dict[scene_name]['softspl'].append(info['softspl'])
total_success.append(info['success'])
total_spl.append(spl)
total_dtg.append(info['distance_to_goal'])
total_softspl.append(info['softspl'])
total_localize_success.append(np.array(localize_success).mean())
if info['success']:
total_success_timesteps.append(step)
#total_node_dists.append(np.array(node_dists).mean())
ep_list.append({'house': scene_name,
'ep_id': env.current_episode.episode_id,
'start_pose': [list(env.current_episode.start_position), list(env.current_episode.start_rotation)],
'target_pose': [env.current_episode.goals[0].position , env.current_episode.goals[0].rotation],
'total_step': step,
'collision': info['collisions']['count'] if isinstance(info['collisions'], dict) else info['collisions'],
'success': info['success'],
'spl': spl,
'distance_to_goal': info['distance_to_goal'],
'target_distance': env.habitat_env._sim.geodesic_distance(env.habitat_env.current_episode.goals[0].position,env.current_episode.start_position),
'localize_success': localize_success})
if args.record > 0:
video_name = os.path.join(VIDEO_DIR,'%04d_%s_success=%.1f_spl=%.1f.mp4'%(episode_id, scene_name, info['success'], spl))
with imageio.get_writer(video_name, fps=30) as writer:
im_shape = imgs[-1].shape
for im in imgs:
if (im.shape[0] != im_shape[0]) or (im.shape[1] != im_shape[1]):
im = cv2.resize(im, (im_shape[1], im_shape[0]))
writer.append_data(im.astype(np.uint8))
writer.close()
if args.record > 1:
file_name = os.path.join(OTHER_DIR, '%04d_%s_data_success=%.1f_spl=%.1f.dat.gz' % (episode_id, scene_name, info['success'], spl))
data = {'position': records, 'graph': record_graphs, 'map': record_maps, 'episode': ep_list[-1]}
joblib.dump(data, file_name)
del data
if args.record > 2:
file_name = os.path.join(OTHER_DIR, '%04d_%s_features_success=%.1f_spl=%.1f.dat.gz' % (episode_id, scene_name, info['success'], spl))
joblib.dump(record_features, file_name)
del record_features
remain_time = get_remain_time((time.time() - start_time) / (episode_id+1), (tot_episodes - episode_id))
print(remain_time + ' [%04d/%04d] %s success %.4f, spl %.4f, softspl %.4f, dtg %.4f, localize %.4f, total success %.4f, spl %.4f, softspl %.4f, dtg %.4f, localize %.4f, success time step %.4f'
%(episode_id,
tot_episodes,
scene_name,
np.array(scene_dict[scene_name]['success']).mean(),
np.array(scene_dict[scene_name]['spl']).mean(),
np.array(scene_dict[scene_name]['softspl']).mean(),
np.array(scene_dict[scene_name]['dtg']).mean(),
np.array(localize_success).mean(),
np.array(total_success).mean(),
np.array(total_spl).mean(),
np.array(total_softspl).mean(),
np.array(total_dtg).mean(),
np.array(total_localize_success).mean(),
np.array(total_success_timesteps).mean()))
result['detailed_info'] = ep_list
result['each_house_result'] = {}
success = []
spl = []
dtg = []
softspl = []
for scene_name in scene_dict.keys():
mean_success = np.array(scene_dict[scene_name]['success']).mean()
mean_spl = np.array(scene_dict[scene_name]['spl']).mean()
result['each_house_result'][scene_name] = {'success': mean_success, 'spl': mean_spl}
print('SCENE %s: success %.4f, spl %.4f'%(scene_name, mean_success,mean_spl))
success.extend(scene_dict[scene_name]['success'])
spl.extend(scene_dict[scene_name]['spl'])
softspl.extend(scene_dict[scene_name]['softspl'])
dtg.extend(scene_dict[scene_name]['dtg'])
result['total_success'] = np.array(success).mean()
result['total_spl'] = np.array(spl).mean()
result['total_softspl'] = np.array(softspl).mean()
result['total_dtg'] = np.array(dtg).mean()
result['total_timesteps'] = np.array(total_success_timesteps)
result['iter'] = str(eval_config.iter_num)
print('================================================')
print('total success : %.4f'%(np.array(success).mean()))
print('total spl : %.4f'%(np.array(spl).mean()))
print('total softspl : %.4f'%(np.array(softspl).mean()))
print('total dtg : %.4f'%(np.array(dtg).mean()))
print('total timesteps : %.3f'%(np.array(total_success_timesteps).mean()))
# env.close()
if args.wandb:
wandb.alert(
title="Performance",
text="Success %3f SPL %3f Soft-SPL %3f DTG %2f on model %s iter %s"
%(np.array(success).mean(), np.array(spl).mean(), np.array(softspl).mean(), np.array(dtg).mean(), args.version, result['iter']),
level=AlertLevel.INFO
)
return result
if __name__=='__main__':
import joblib
import glob
cfg = eval_config(args)
curr_hostname = os.uname()[1]
# eval_data_name = os.path.join(args.project_dir, 'results', 'eval_result_{}.dat.gz'.format(curr_hostname))
args.eval_ckpt = os.path.join(args.project_dir, args.eval_ckpt)
os.makedirs(os.path.join(args.project_dir, 'results'), exist_ok=True)
# Load checked ckpts
checked_ckpt = []
loaded_dict = False
cnt = 0
cnt_name = 0
while not loaded_dict:
cnt += 1
try:
eval_data_name = os.path.join(args.project_dir, 'results', 'eval_result_{}_v{}.dat.gz'.format(curr_hostname, cnt_name))
if os.path.isfile(eval_data_name):
try:
result_dict = joblib.load(eval_data_name)
loaded_dict = True
except:
cnt_name += 1
except:
pass
if cnt > 10:
result_dict = {}
break
if args.version in result_dict:
result_dict = result_dict[args.version]
for i in result_dict.keys():
if "/".join(i.split("/")[:-1]) == args.eval_ckpt:
checked_ckpt.append(i.split(".pt")[0]+ ".pt")
elif "/".join(i.split("/")[:-1]).replace(args.project_dir, ".") == args.eval_ckpt:
checked_ckpt.append(i.split(".pt")[0]+ ".pt")
print("The code has been evaluated at: ", checked_ckpt)
while True:
try:
if os.path.isfile(args.eval_ckpt):
ckpts = [args.eval_ckpt]
elif os.path.isdir(args.eval_ckpt):
# print('eval_ckpt ', args.eval_ckpt, ' is directory')
ckpts = [os.path.join(args.eval_ckpt, x) for x in sorted(os.listdir(args.eval_ckpt))]
ckpts.reverse()
elif os.path.exists(args.eval_ckpt):
ckpts = args.eval_ckpt.split(",")
else:
ckpts = [x for x in sorted(glob.glob(args.eval_ckpt + '*'))]
ckpts.reverse()
last_ckpt = ckpts[0]
except:
time.sleep(1000)
continue
iter_num = last_ckpt.split("/")[-1]
if last_ckpt not in checked_ckpt:
ckpt_dir = last_ckpt
print('start evaluate {} '.format(ckpt_dir))
state_dict, ckpt_config = load(ckpt_dir)
# while True:
# try:
# break
# except:
# continue
if args.record > 0:
if not os.path.exists(os.path.join(args.project_dir, args.record_dir, args.version)):
os.mkdir(os.path.join(args.project_dir, args.record_dir, args.version))
VIDEO_DIR = os.path.join(args.project_dir, args.record_dir, args.version + '_video_' + ckpt_dir.split('/')[-1] + '_' + str(time.ctime()))
if not os.path.exists(VIDEO_DIR): os.mkdir(VIDEO_DIR)
if args.record > 1:
OTHER_DIR = os.path.join(args.project_dir, args.record_dir, args.version + '_other_' + ckpt_dir.split('/')[-1] + '_' + str(time.ctime()))
if not os.path.exists(OTHER_DIR): os.mkdir(OTHER_DIR)
print('='*30, iter_num, '='*30)
cfg.defrost()
cfg.iter_num = iter_num
cfg.freeze()
result = evaluate(cfg, state_dict, ckpt_config)
datetime_now = str(datetime.datetime.today()).split(".")[0].replace(" ","_")
if os.path.exists(eval_data_name):
data = joblib.load(eval_data_name)
if args.version in data.keys():
data[args.version].update({ckpt_dir + '_{}'.format(datetime_now): result})
else:
data.update({args.version: {ckpt_dir + '_{}'.format(datetime_now): result}})
else:
data = {args.version: {ckpt_dir + '_{}'.format(datetime_now): result}}
# joblib.dump(data, eval_data_name)
checked_ckpt.append(ckpt_dir)