-
Notifications
You must be signed in to change notification settings - Fork 21
/
ray_get_lfve.py
140 lines (118 loc) · 3.93 KB
/
ray_get_lfve.py
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
"""
posthoc script to create a directory for "best lfve"
"""
from typing import List, Union
from os import path
import json
import argparse
import ray, yaml, shutil
from ray import tune
import torch
from tune_models import tuneNDT
from defaults import DEFAULT_CONFIG_DIR
from src.config.default import flatten
PBT_HOME = path.expanduser('~/ray_results/ndt/gridsearch')
OVERWRITE = True
PBT_METRIC = 'smth_masked_loss'
BEST_MODEL_METRIC = 'best_masked_loss'
LOGGED_COLUMNS = ['smth_masked_loss', 'masked_loss', 'r2', 'unmasked_loss']
DEFAULT_HP_DICT = {
'TRAIN.WEIGHT_DECAY': tune.loguniform(1e-8, 1e-3),
'TRAIN.MASK_RATIO': tune.uniform(0.1, 0.4)
}
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument(
"--exp-config", "-e",
type=str,
required=True,
help="path to config yaml containing info about experiment",
)
parser.add_argument('--eval-only', '-ev', dest='eval_only', action='store_true')
parser.add_argument('--no-eval-only', '-nev', dest='eval_only', action='store_false')
parser.set_defaults(eval_only=False)
parser.add_argument(
"--name", "-n",
type=str,
default="",
help="defaults to exp filename"
)
parser.add_argument(
"--gpus-per-worker", "-g",
type=float,
default=0.5
)
parser.add_argument(
"--cpus-per-worker", "-c",
type=float,
default=3.0
)
parser.add_argument(
"--workers", "-w",
type=int,
default=-1,
help="-1 indicates -- use max possible workers on machine (assuming 0.5 GPUs per trial)"
)
parser.add_argument(
"--samples", "-s",
type=int,
default=20,
help="samples for random search"
)
parser.add_argument(
"--seed", "-d",
type=int,
default=-1,
help="seed for config"
)
return parser
def main():
parser = get_parser()
args = parser.parse_args()
launch_search(**vars(args))
def build_hp_dict(raw_json: dict):
hp_dict = {}
for key in raw_json:
info: dict = raw_json[key]
sample_fn = info.get("sample_fn", "uniform")
assert hasattr(tune, sample_fn)
if sample_fn == "choice":
hp_dict[key] = tune.choice(info['opts'])
else:
assert "low" in info, "high" in info
sample_fn = getattr(tune, sample_fn)
hp_dict[key] = sample_fn(info['low'], info['high'])
return hp_dict
def launch_search(exp_config: Union[List[str], str], name: str, workers: int, gpus_per_worker: float, cpus_per_worker: float, eval_only: bool, samples: int, seed: int) -> None:
# ---------- PBT I/O CONFIGURATION ----------
# the directory to save PBT runs (usually '~/ray_results')
if len(path.split(exp_config)[0]) > 0:
CFG_PATH = exp_config
else:
CFG_PATH = path.join(DEFAULT_CONFIG_DIR, exp_config)
variant_name = path.split(CFG_PATH)[1].split('.')[0]
if seed > 0:
variant_name = f"{variant_name}-s{seed}"
if name == "":
name = variant_name
pbt_dir = path.join(PBT_HOME, name)
# the name of this PBT run (run will be stored at `pbt_dir`)
# ---------------------------------------------
# * No train step
# load the results dataframe for this run
df = tune.Analysis(
pbt_dir
).dataframe()
df = df[df.logdir.apply(lambda path: not 'best_model' in path)]
lfves = []
for logdir in df.logdir:
ckpt = torch.load(path.join(logdir, f'ckpts/{variant_name}.lfve.pth'), map_location='cpu')
lfves.append(ckpt['best_unmasked_val']['value'])
df['best_unmasked_val'] = lfves
best_model_logdir = df.loc[df['best_unmasked_val'].idxmin()].logdir
best_model_dest = path.join(pbt_dir, 'best_model_unmasked')
if path.exists(best_model_dest):
shutil.rmtree(best_model_dest)
shutil.copytree(best_model_logdir, best_model_dest)
if __name__ == "__main__":
main()