def gen_rand_policy(num_policy, num_op):
op_list = augment_list(False)
policies = []
for i in range(num_policy):
ops = []
for j in range(num_op):
op_idx = random.randint(0, len(op_list) - 1)
op_prob = random.random()
op_level = random.random()
ops.append((op_list[op_idx][0].__name__, op_prob, op_level))
policies.append(ops)
return policies
def batch_policy_decoder(augment): # augment: [batch, num_policy, n_op, 3]
op_list = augment_list(False)
batch_policies = []
for policy in augment: # policy: [num_policy, n_op, 3]
policies = []
for subpolicies in policy: # subpolicies: [n_op, 3]
ops = []
for op in subpolicies:
op_idx, op_prob, op_level = op
op_prob = op_prob / 10.
op_level = op_level / 10.0 + 0.1
assert (0.0 <= op_prob <= 1.0) and (0.0 <= op_level <=
1.0), err_txt
ops.append((op_list[op_idx][0].__name__, op_prob, op_level))
policies.append(ops)
batch_policies.append(policies)
return batch_policies # (list:list:list:tuple) [batch, num_policy, n_op, 3]
def main():
w = PyStopwatch()
parser = ConfigArgumentParser(conflict_handler="resolve")
parser.add_argument(
"--dataroot",
type=str,
default="/data/private/pretrainedmodels",
help="torchvision data folder",
)
parser.add_argument("--until", type=int, default=5)
parser.add_argument("--num-op", type=int, default=2)
parser.add_argument("--num-policy", type=int, default=5)
parser.add_argument("--num-search", type=int, default=200)
parser.add_argument("--cv-ratio", type=float, default=0.4)
parser.add_argument("--decay", type=float, default=-1)
parser.add_argument("--redis", type=str, default="gpu-cloud-vnode30.dakao.io:23655")
parser.add_argument("--per-class", action="store_true")
parser.add_argument("--resume", action="store_true")
parser.add_argument("--smoke-test", action="store_true")
args = parser.parse_args()
if args.decay > 0:
logger.info("decay=%.4f" % args.decay)
C.get()["optimizer"]["decay"] = args.decay
add_filehandler(
logger,
os.path.join(
"models",
"%s_%s_cv%.1f.log"
% (C.get()["dataset"], C.get()["model"]["type"], args.cv_ratio),
),
)
logger.info("configuration...")
logger.info(json.dumps(C.get().conf, sort_keys=True, indent=4))
logger.info("initialize ray...")
ray.init(address=args.redis)
num_result_per_cv = 10
cv_num = 5
copied_c = copy.deepcopy(C.get().conf)
logger.info(
"search augmentation policies, dataset=%s model=%s"
% (C.get()["dataset"], C.get()["model"]["type"])
)
logger.info(
"----- Train without Augmentations cv=%d ratio(test)=%.1f -----"
% (cv_num, args.cv_ratio)
)
w.start(tag="train_no_aug")
paths = [
_get_path(
C.get()["dataset"],
C.get()["model"]["type"],
"ratio%.1f_fold%d" % (args.cv_ratio, i),
)
for i in range(cv_num)
]
print(paths)
reqs = [
train_model.remote(
copy.deepcopy(copied_c),
args.dataroot,
C.get()["aug"],
args.cv_ratio,
i,
save_path=paths[i],
skip_exist=True,
)
for i in range(cv_num)
]
tqdm_epoch = tqdm(range(C.get()["epoch"]))
is_done = False
for epoch in tqdm_epoch:
while True:
epochs_per_cv = OrderedDict()
for cv_idx in range(cv_num):
try:
latest_ckpt = torch.load(paths[cv_idx])
if "epoch" not in latest_ckpt:
epochs_per_cv["cv%d" % (cv_idx + 1)] = C.get()["epoch"]
continue
epochs_per_cv["cv%d" % (cv_idx + 1)] = latest_ckpt["epoch"]
except Exception as e:
continue
tqdm_epoch.set_postfix(epochs_per_cv)
if (
len(epochs_per_cv) == cv_num
and min(epochs_per_cv.values()) >= C.get()["epoch"]
):
is_done = True
if len(epochs_per_cv) == cv_num and min(epochs_per_cv.values()) >= epoch:
break
time.sleep(10)
if is_done:
break
logger.info("getting results...")
pretrain_results = ray.get(reqs)
for r_model, r_cv, r_dict in pretrain_results:
logger.info(
"model=%s cv=%d top1_train=%.4f top1_valid=%.4f"
% (r_model, r_cv + 1, r_dict["top1_train"], r_dict["top1_valid"])
)
logger.info("processed in %.4f secs" % w.pause("train_no_aug"))
if args.until == 1:
sys.exit(0)
logger.info("----- Search Test-Time Augmentation Policies -----")
w.start(tag="search")
ops = augment_list(False)
space = {}
for i in range(args.num_policy):
for j in range(args.num_op):
space["policy_%d_%d" % (i, j)] = hp.choice(
"policy_%d_%d" % (i, j), list(range(0, len(ops)))
)
space["prob_%d_%d" % (i, j)] = hp.uniform("prob_%d_ %d" % (i, j), 0.0, 1.0)
space["level_%d_%d" % (i, j)] = hp.uniform(
"level_%d_ %d" % (i, j), 0.0, 1.0
)
final_policy_set = []
total_computation = 0
reward_attr = "top1_valid" # top1_valid or minus_loss
for _ in range(1): # run multiple times.
for cv_fold in range(cv_num):
name = "search_%s_%s_fold%d_ratio%.1f" % (
C.get()["dataset"],
C.get()["model"]["type"],
cv_fold,
args.cv_ratio,
)
print(name)
# def train(augs, rpt):
def train(config, reporter):
return eval_tta(
copy.deepcopy(copied_c), config, reporter, num_class, get_model, get_dataloaders
)
register_trainable(name, train)
algo = HyperOptSearch(
space, max_concurrent=4 * 20, metric=reward_attr, mode="max"
)
results = run(
train,
name=name,
config={
"dataroot": args.dataroot,
"save_path": paths[cv_fold],
"cv_ratio_test": args.cv_ratio,
"cv_fold": cv_fold,
"num_op": args.num_op,
"num_policy": args.num_policy,
},
num_samples=4 if args.smoke_test else args.num_search,
resources_per_trial={"gpu": 1},
stop={"training_iteration": args.num_policy},
search_alg=algo,
scheduler=None,
verbose=0,
queue_trials=True,
resume=args.resume,
raise_on_failed_trial=False,
)
print()
df = results.results_df
import pickle
with open("results.pickle", "wb") as fp:
pickle.dump(results, fp)
df.to_csv("df.csv")
results = df.sort_values(by=reward_attr, ascending=False)
# results = [x for x in results if x.last_result is not None]
# results = sorted(results, key=lambda x: x.last_result[reward_attr], reverse=True)
# calculate computation usage
for _, result in results.iterrows():
total_computation += result["elapsed_time"]
for _, result in results.iloc[:num_result_per_cv].iterrows():
final_policy = policy_decoder(
result, args.num_policy, args.num_op, prefix="config."
)
logger.info(
"loss=%.12f top1_valid=%.4f %s"
% (result["minus_loss"], result["top1_valid"], final_policy)
)
final_policy = remove_deplicates(final_policy)
final_policy_set.extend(final_policy)
logger.info(json.dumps(final_policy_set))
logger.info("final_policy=%d" % len(final_policy_set))
logger.info(
"processed in %.4f secs, gpu hours=%.4f"
% (w.pause("search"), total_computation / 3600.0)
)
logger.info(
"----- Train with Augmentations model=%s dataset=%s aug=%s ratio(test)=%.1f -----"
% (C.get()["model"]["type"], C.get()["dataset"], C.get()["aug"], args.cv_ratio)
)
w.start(tag="train_aug")
num_experiments = 5
default_path = [
_get_path(
C.get()["dataset"],
C.get()["model"]["type"],
"ratio%.1f_default%d" % (args.cv_ratio, _),
)
for _ in range(num_experiments)
]
augment_path = [
_get_path(
C.get()["dataset"],
C.get()["model"]["type"],
"ratio%.1f_augment%d" % (args.cv_ratio, _),
)
for _ in range(num_experiments)
]
reqs = [
train_model.remote(
copy.deepcopy(copied_c),
args.dataroot,
C.get()["aug"],
0.0,
0,
save_path=default_path[_],
skip_exist=True,
)
for _ in range(num_experiments)
] + [
train_model.remote(
copy.deepcopy(copied_c),
args.dataroot,
final_policy_set,
0.0,
0,
save_path=augment_path[_],
)
for _ in range(num_experiments)
]
tqdm_epoch = tqdm(range(C.get()["epoch"]))
is_done = False
for epoch in tqdm_epoch:
while True:
epochs = OrderedDict()
for exp_idx in range(num_experiments):
try:
if os.path.exists(default_path[exp_idx]):
latest_ckpt = torch.load(default_path[exp_idx])
epochs["default_exp%d" % (exp_idx + 1)] = latest_ckpt["epoch"]
except:
pass
try:
if os.path.exists(augment_path[exp_idx]):
latest_ckpt = torch.load(augment_path[exp_idx])
epochs["augment_exp%d" % (exp_idx + 1)] = latest_ckpt["epoch"]
except:
pass
tqdm_epoch.set_postfix(epochs)
if (
len(epochs) == num_experiments * 2
and min(epochs.values()) >= C.get()["epoch"]
):
is_done = True
if len(epochs) == num_experiments * 2 and min(epochs.values()) >= epoch:
break
time.sleep(10)
if is_done:
break
logger.info("getting results...")
final_results = ray.get(reqs)
for train_mode in ["default", "augment"]:
avg = 0.0
for _ in range(num_experiments):
r_model, r_cv, r_dict = final_results.pop(0)
logger.info(
"[%s] top1_train=%.4f top1_test=%.4f"
% (train_mode, r_dict["top1_train"], r_dict["top1_test"])
)
avg += r_dict["top1_test"]
avg /= num_experiments
logger.info(
"[%s] top1_test average=%.4f (#experiments=%d)"
% (train_mode, avg, num_experiments)
)
logger.info("processed in %.4f secs" % w.pause("train_aug"))
logger.info(w)
logger.info(
'model=%s cv=%d top1_train=%.4f top1_valid=%.4f' %
(r_model, r_cv + 1, r_dict['top1_train'], r_dict['top1_valid']))
#print('watch is ', w.pause('train_no_aug'))
w.pause('train_no_aug')
# ! watch的结果是none，需要查阅api
#logger.info('processed in %.4f secs' % w)
#sys.exit(0)
if args.until == 1: #? 不知道的参数
sys.exit(0)
logger.info('----- Search Test-Time Augmentation Policies -----')
w.start(tag='search')
ops = augment_list(False) # op操作列表，false去掉了最后几个op
space = {} # 用于超参数搜索，结合hp包使用
# ? 5个policy会不会太少
# hp.choice或者hp.uniform只是代表了一个搜索空间
for i in range(args.num_policy): # args.num_policy 5
for j in range(args.num_op): # args.num_policy 2
space['policy_%d_%d' % (i, j)] = hp.choice(
'policy_%d_%d' % (i, j), list(range(0, len(ops))))
space['prob_%d_%d' % (i, j)] = hp.uniform('prob_%d_ %d' % (i, j),
0.0, 1.0)
space['level_%d_%d' % (i, j)] = hp.uniform('level_%d_ %d' % (i, j),
0.0, 1.0)
print('policy is , ', space['policy_0_0'])
print('prob is , ', space['prob_0_0'])
print('level is , ', space['level_0_0'])
skip_exist=True) for i in range(cv_num)
]
for r_model, r_cv, r_dict in pretrain_results:
logger.info(
'model=%s cv=%d top1_train=%.4f top1_valid=%.4f' %
(r_model, r_cv + 1, r_dict['top1_train'], r_dict['top1_valid']))
logger.info('processed in %.4f secs' % w.pause('train_no_aug'))
if args.until == 1:
sys.exit(0)
logger.info('----- Search Test-Time Augmentation Policies -----')
w.start(tag='search')
ops = augment_list(False)
space = {}
for i in range(args.num_policy):
for j in range(args.num_op):
space['policy_%d_%d' % (i, j)] = hp.choice(
'policy_%d_%d' % (i, j), list(range(0, len(ops))))
space['prob_%d_%d' % (i, j)] = hp.uniform('prob_%d_ %d' % (i, j),
0.0, 1.0)
space['level_%d_%d' % (i, j)] = hp.uniform('level_%d_ %d' % (i, j),
0.0, 1.0)
def eval_t(augs):
print(augs)
return eval_tta(copy.deepcopy(copied_c), augs)
final_policy_set = []
def search_aug_policy(copied_conf, cv_ratio, num_fold, num_result_per_fold,
num_policy, num_op, smoke_test, num_search,
resume) -> list:
global MODEL_PATHS, DATASET_ROOT
global logger, watcher
logger.info(
'----- [Phase 2.] Search Test-Time Augmentation Policies -----')
watcher.start(tag='search')
ops = augment_list(False)
space = {}
for i in range(num_policy):
for j in range(num_op):
space['policy_%d_%d' % (i, j)] = hp.choice(
'policy_%d_%d' % (i, j), list(range(0, len(ops))))
space['prob_%d_%d' % (i, j)] = hp.uniform('prob_%d_ %d' % (i, j),
0.0, 1.0)
space['level_%d_%d' % (i, j)] = hp.uniform('level_%d_ %d' % (i, j),
0.0, 1.0)
final_policy_set = []
total_computation = 0
reward_attr = 'top1_valid' # top1_valid or minus_loss
for _ in range(1): # run multiple times.
for cv_fold in range(num_fold):
name = "search_%s_%s_fold%d_ratio%.1f" % (Config.get(
)['dataset'], Config.get()['model']['type'], cv_fold, cv_ratio)
print(name)
register_trainable(name, lambda augs, rpt: eval_tta(
copy.deepcopy(copied_conf), augs, rpt)
) # augs: a dict, just like the 'exp_config'
algo = HyperOptSearch(space,
max_concurrent=4 * 20,
reward_attr=reward_attr)
exp_config = {
name: {
'run': name,
'num_samples': 4 if smoke_test else num_search,
'resources_per_trial': {
'gpu': 1
},
'stop': {
'training_iteration': num_policy
},
'config': {
'dataroot': DATASET_ROOT,
'save_path': MODEL_PATHS[cv_fold],
'cv_ratio_test': cv_ratio,
'cv_fold': cv_fold,
'num_op': num_op,
'num_policy': num_policy
},
}
}
results = run_experiments(exp_config,
search_alg=algo,
scheduler=None,
verbose=0,
queue_trials=True,
resume=resume,
raise_on_failed_trial=False)
print()
results = [x for x in results if x.last_result is not None]
results = sorted(results,
key=lambda x: x.last_result[reward_attr],
reverse=True)
# calculate computation usage
for result in results:
total_computation += result.last_result['elapsed_time']
for result in results[:num_result_per_fold]:
final_policy = policy_decoder(result.config, num_policy,
num_op)
logger.info('loss=%.12f top1_valid=%.4f %s' %
(result.last_result['minus_loss'],
result.last_result['top1_valid'], final_policy))
final_policy = remove_deplicates(final_policy)
final_policy_set.extend(final_policy)
logger.info(json.dumps(final_policy_set))
logger.info('final_policy=%d' % len(final_policy_set))
logger.info('processed in %.4f secs, gpu hours=%.4f' %
(watcher.pause('search'), total_computation / 3600.))
return final_policy_set
def __init__(self, args=None, paths_ls=None):
if args is None:
d = yaml.load(open(
'/home/noam/ZazuML/augmentations_tuner/fastautoaugment/confs/resnet50.yaml'
),
Loader=yaml.FullLoader)
from argparse import Namespace
args = Namespace(**d)
args.redis = 'gpu-cloud-vnode30.dakao.io:23655'
args.per_class = True
args.resume = True
args.smoke_test = True
if args.decay > 0:
logger.info('decay=%.4f' % args.decay)
C.get()['optimizer']['decay'] = args.decay
add_filehandler(
logger,
os.path.join(
'FastAutoAugment/models', '%s_%s_cv%.1f.log' %
(C.get()['dataset'], C.get()['model']['type'], args.cv_ratio)))
logger.info('initialize ray...')
ray.init(num_cpus=1, num_gpus=1)
num_result_per_cv = 10 if not args.smoke_test else 2
cv_num = 5 if paths_ls is None else len(paths_ls)
args.version = 1
args._timestamp = '2020/08/30 20:40:10'
args.config = '/home/noam/ZazuML/augmentations_tuner/fastautoaugment/confs/resnet50.yaml'
copied_c = copy.deepcopy(args)
self.copied_c = copied_c
logger.info('search augmentation policies, dataset=%s model=%s' %
(C.get()['dataset'], C.get()['model']['type']))
logger.info(
'----- Train without Augmentations ratio(test)=%.1f -----' %
(args.cv_ratio))
w.start(tag='train_no_aug')
if paths_ls is None:
paths_ls = [
_get_path(C.get()['dataset'],
C.get()['model']['type'],
'ratio%.1f_fold%d' % (args.cv_ratio, i))
for i in range(cv_num)
]
print(paths_ls)
logger.info('getting results...')
pretrain_results = [
train_model(copy.deepcopy(copied_c),
args.dataroot,
C.get()['aug'],
args.cv_ratio,
i,
save_path=paths_ls[i],
skip_exist=args.smoke_test) for i in range(cv_num)
]
for r_model, r_cv, r_dict in pretrain_results:
logger.info('model=%s cv=%d top1_train=%.4f top1_valid=%.4f' %
(r_model, r_cv + 1, r_dict['top1_train'],
r_dict['top1_valid']))
logger.info('processed in %.4f secs' % w.pause('train_no_aug'))
if args.until == 1:
sys.exit(0)
logger.info('----- Search Test-Time Augmentation Policies -----')
w.start(tag='search')
ops = augment_list(False)
space = {}
for i in range(args.num_policy):
for j in range(args.num_op):
space['policy_%d_%d' % (i, j)] = hp.choice(
'policy_%d_%d' % (i, j), list(range(0, len(ops))))
space['prob_%d_%d' % (i, j)] = hp.uniform(
'prob_%d_ %d' % (i, j), 0.0, 1.0)
space['level_%d_%d' % (i, j)] = hp.uniform(
'level_%d_ %d' % (i, j), 0.0, 1.0)
def eval_t(augs):
print(augs)
return eval_tta(copy.deepcopy(copied_c), augs)
final_policy_set = []
total_computation = 0
reward_attr = 'top1_valid' # top1_valid or minus_loss
for _ in range(1): # run multiple times.
for cv_fold in range(cv_num):
name = "search_%s_%s_fold%d_ratio%.1f" % (C.get(
)['dataset'], C.get()['model']['type'], cv_fold, args.cv_ratio)
print(name)
algo = HyperOptSearch(space,
max_concurrent=1,
metric=reward_attr)
aug_config = {
'dataroot': args.dataroot,
'save_path': paths_ls[cv_fold],
'cv_ratio_test': args.cv_ratio,
'cv_fold': cv_fold,
'num_op': args.num_op,
'num_policy': args.num_policy
}
num_samples = 4 if args.smoke_test else args.num_search
print(aug_config)
eval_t(aug_config)
results = run(eval_t,
search_alg=algo,
config=aug_config,
num_samples=num_samples,
resources_per_trial={'gpu': 1},
stop={'training_iteration': args.num_policy})
dataframe = results.dataframe().sort_values(reward_attr,
ascending=False)
total_computation = dataframe['elapsed_time'].sum()
for i in range(num_result_per_cv):
config_dict = dataframe.loc[i].filter(
like='config').to_dict()
new_keys = [
x.replace('config/', '') for x in config_dict.keys()
]
new_config_dict = {}
for key in new_keys:
new_config_dict[key] = config_dict['config/' + key]
final_policy = policy_decoder(new_config_dict,
args.num_policy, args.num_op)
logger.info(
'loss=%.12f top1_valid=%.4f %s' %
(dataframe.loc[i]['minus_loss'].item(),
dataframe.loc[i]['top1_valid'].item(), final_policy))
final_policy = remove_deplicates(final_policy)
final_policy_set.extend(final_policy)
logger.info(json.dumps(final_policy_set))
logger.info('final_policy=%d' % len(final_policy_set))
logger.info('processed in %.4f secs, gpu hours=%.4f' %
(w.pause('search'), total_computation / 3600.))
logger.info(
'----- Train with Augmentations model=%s dataset=%s aug=%s ratio(test)=%.1f -----'
% (C.get()['model']['type'], C.get()['dataset'], C.get()['aug'],
args.cv_ratio))
w.start(tag='train_aug')
self.final_policy_set = final_policy_set
self.args = args
self.paths_ls = paths_ls
def search(args, paths=None):
args.redis = 'gpu-cloud-vnode30.dakao.io:23655'
args.per_class = True
args.resume = True
args.smoke_test = True
if args.decay > 0:
logger.info('decay=%.4f' % args.decay)
C.get()['optimizer']['decay'] = args.decay
add_filehandler(
logger,
os.path.join(
'FastAutoAugment/models', '%s_%s_cv%.1f.log' %
(C.get()['dataset'], C.get()['model']['type'], args.cv_ratio)))
logger.info('configuration...')
logger.info(json.dumps(C.get().conf, sort_keys=True, indent=4))
logger.info('initialize ray...')
ray.init(num_cpus=1, num_gpus=1)
num_result_per_cv = 10 if not args.smoke_test else 2
cv_num = 5
copied_c = copy.deepcopy(C.get().conf)
logger.info('search augmentation policies, dataset=%s model=%s' %
(C.get()['dataset'], C.get()['model']['type']))
logger.info(
'----- Train without Augmentations cv=%d ratio(test)=%.1f -----' %
(cv_num, args.cv_ratio))
w.start(tag='train_no_aug')
if paths == None:
paths = [
_get_path(C.get()['dataset'],
C.get()['model']['type'],
'ratio%.1f_fold%d' % (args.cv_ratio, i))
for i in range(cv_num)
]
print(paths)
tqdm_epoch = tqdm(range(C.get()['epoch']))
logger.info('getting results...')
# pretrain_results = [
# train_model(copy.deepcopy(copied_c), args.dataroot, C.get()['aug'], args.cv_ratio, i, save_path=paths[i],
# skip_exist=True) for i in range(cv_num)]
pretrain_results = [
train_model(copy.deepcopy(copied_c),
args.dataroot,
C.get()['aug'],
args.cv_ratio,
i,
save_path=paths[i]) for i in range(cv_num)
]
for r_model, r_cv, r_dict in pretrain_results:
logger.info(
'model=%s cv=%d top1_train=%.4f top1_valid=%.4f' %
(r_model, r_cv + 1, r_dict['top1_train'], r_dict['top1_valid']))
logger.info('processed in %.4f secs' % w.pause('train_no_aug'))
if args.until == 1:
sys.exit(0)
logger.info('----- Search Test-Time Augmentation Policies -----')
w.start(tag='search')
ops = augment_list(False)
space = {}
for i in range(args.num_policy):
for j in range(args.num_op):
space['policy_%d_%d' % (i, j)] = hp.choice(
'policy_%d_%d' % (i, j), list(range(0, len(ops))))
space['prob_%d_%d' % (i, j)] = hp.uniform('prob_%d_ %d' % (i, j),
0.0, 1.0)
space['level_%d_%d' % (i, j)] = hp.uniform('level_%d_ %d' % (i, j),
0.0, 1.0)
def eval_t(augs):
print(augs)
return eval_tta(copy.deepcopy(copied_c), augs)
final_policy_set = []
total_computation = 0
reward_attr = 'top1_valid' # top1_valid or minus_loss
for _ in range(1): # run multiple times.
for cv_fold in range(cv_num):
name = "search_%s_%s_fold%d_ratio%.1f" % (C.get()['dataset'],
C.get()['model']['type'],
cv_fold, args.cv_ratio)
print(name)
algo = HyperOptSearch(space, max_concurrent=1, metric=reward_attr)
aug_config = {
'dataroot': args.dataroot,
'save_path': paths[cv_fold],
'cv_ratio_test': args.cv_ratio,
'cv_fold': cv_fold,
'num_op': args.num_op,
'num_policy': args.num_policy
}
num_samples = 4 if args.smoke_test else args.num_search
print(aug_config)
eval_t(aug_config)
results = run(eval_t,
search_alg=algo,
config=aug_config,
num_samples=num_samples,
resources_per_trial={'gpu': 1},
stop={'training_iteration': args.num_policy})
dataframe = results.dataframe().sort_values(reward_attr,
ascending=False)
total_computation = dataframe['elapsed_time'].sum()
for i in range(num_result_per_cv):
config_dict = dataframe.loc[i].filter(like='config').to_dict()
new_keys = [
x.replace('config/', '') for x in config_dict.keys()
]
new_config_dict = {}
for key in new_keys:
new_config_dict[key] = config_dict['config/' + key]
final_policy = policy_decoder(new_config_dict, args.num_policy,
args.num_op)
logger.info(
'loss=%.12f top1_valid=%.4f %s' %
(dataframe.loc[i]['minus_loss'].item(),
dataframe.loc[i]['top1_valid'].item(), final_policy))
final_policy = remove_deplicates(final_policy)
final_policy_set.extend(final_policy)
logger.info(json.dumps(final_policy_set))
logger.info('final_policy=%d' % len(final_policy_set))
logger.info('processed in %.4f secs, gpu hours=%.4f' %
(w.pause('search'), total_computation / 3600.))
logger.info(
'----- Train with Augmentations model=%s dataset=%s aug=%s ratio(test)=%.1f -----'
% (C.get()['model']['type'], C.get()['dataset'], C.get()['aug'],
args.cv_ratio))
w.start(tag='train_aug')
num_experiments = 5
default_path = [
_get_path(C.get()['dataset'],
C.get()['model']['type'],
'ratio%.1f_default%d' % (args.cv_ratio, _))
for _ in range(num_experiments)
]
augment_path = [
_get_path(C.get()['dataset'],
C.get()['model']['type'],
'ratio%.1f_augment%d' % (args.cv_ratio, _))
for _ in range(num_experiments)
]
tqdm_epoch = tqdm(range(C.get()['epoch']))
is_done = False
for epoch in tqdm_epoch:
while True:
epochs = OrderedDict()
for exp_idx in range(num_experiments):
try:
if os.path.exists(default_path[exp_idx]):
latest_ckpt = torch.load(default_path[exp_idx])
if 'epoch' not in latest_ckpt:
epochs['default_exp%d' %
(exp_idx + 1)] = C.get()['epoch']
else:
epochs['default_exp%d' %
(exp_idx + 1)] = latest_ckpt['epoch']
except Exception as e:
pass
try:
if os.path.exists(augment_path[exp_idx]):
latest_ckpt = torch.load(augment_path[exp_idx])
if 'epoch' not in latest_ckpt:
epochs['augment_exp%d' %
(exp_idx + 1)] = C.get()['epoch']
else:
epochs['augment_exp%d' %
(exp_idx + 1)] = latest_ckpt['epoch']
except:
pass
tqdm_epoch.set_postfix(epochs)
if len(epochs) == num_experiments * 2 and min(
epochs.values()) >= C.get()['epoch']:
is_done = True
if len(epochs) == num_experiments * 2 and min(
epochs.values()) >= epoch:
break
time.sleep(10, '-- sleeping for 10 seconds --')
if is_done:
break
logger.info('getting results...')
final_results = [train_model(copy.deepcopy(copied_c), args.dataroot, C.get()['aug'], 0.0, 0,
save_path=default_path[_], skip_exist=True) for _ in range(num_experiments)] + \
[train_model(copy.deepcopy(copied_c), args.dataroot, final_policy_set, 0.0, 0,
save_path=augment_path[_]) for _ in range(num_experiments)]
for train_mode in ['default', 'augment']:
avg = 0.
for _ in range(num_experiments):
r_model, r_cv, r_dict = final_results.pop(0)
logger.info(
'[%s] top1_train=%.4f top1_test=%.4f' %
(train_mode, r_dict['top1_train'], r_dict['top1_test']))
avg += r_dict['top1_test']
avg /= num_experiments
logger.info('[%s] top1_test average=%.4f (#experiments=%d)' %
(train_mode, avg, num_experiments))
logger.info('processed in %.4f secs' % w.pause('train_aug'))
logger.info(w)
