File size: 12,197 Bytes
6789f6f |
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 |
import os, argparse, re, json, copy, math
from collections import OrderedDict
import numpy as np
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('base', help='base log path')
parser.add_argument('--file_name', default='train.log', help='the log file name')
parser.add_argument('--target', default='valid_loss', help='target metric')
parser.add_argument('--last', type=int, default=999999999, help='print last n matches')
parser.add_argument('--last_files', type=int, default=None, help='print last x files')
parser.add_argument('--everything', action='store_true', help='print everything instead of only last match')
parser.add_argument('--path_contains', help='only consider matching file pattern')
parser.add_argument('--group_on', help='if set, groups by this metric and shows table of differences')
parser.add_argument('--epoch', help='epoch for comparison', type=int)
parser.add_argument('--skip_empty', action='store_true', help='skip empty results')
parser.add_argument('--skip_containing', help='skips entries containing this attribute')
parser.add_argument('--unique_epochs', action='store_true', help='only consider the last line fore each epoch')
parser.add_argument('--best', action='store_true', help='print the last best result')
parser.add_argument('--avg_params', help='average these params through entire log')
parser.add_argument('--extract_prev', help='extracts this metric from previous line')
parser.add_argument('--remove_metric', help='extracts this metric from previous line')
parser.add_argument('--compact', action='store_true', help='if true, just prints checkpoint <tab> best val')
parser.add_argument('--hydra', action='store_true', help='if true, uses hydra param conventions')
parser.add_argument('--best_biggest', action='store_true', help='if true, best is the biggest number, not smallest')
parser.add_argument('--key_len', type=int, default=10, help='max length of key')
parser.add_argument('--best_only', action='store_true', help='if set, only prints the best value')
parser.add_argument('--flat', action='store_true', help='just print the best results')
def main(args, print_output):
ret = {}
entries = []
def extract_metric(s, metric):
try:
j = json.loads(s)
except:
return None
if args.epoch is not None and ('epoch' not in j or j['epoch'] != args.epoch):
return None
return j[metric] if metric in j else None
def extract_params(s):
s = s.replace(args.base, '', 1)
if args.path_contains is not None:
s = s.replace(args.path_contains, '', 1)
if args.hydra:
num_matches = re.findall(r'(?:/|__)([^/:]+):(\d+\.?\d*)', s)
# str_matches = re.findall(r'(?:/|__)([^/:]+):([^\.]*[^\d\.]+)(?:/|__)', s)
str_matches = re.findall(r'(?:/|__)?((?:(?!(?:\:|__)).)+):([^\.]*[^\d\.]+\d*)(?:/|__)', s)
lr_matches = re.findall(r'optimization.(lr):\[([\d\.,]+)\]', s)
task_matches = re.findall(r'.*/(\d+)$', s)
else:
num_matches = re.findall(r'\.?([^\.]+?)(\d+(e\-\d+)?(?:\.\d+)?)(\.|$)', s)
str_matches = re.findall(r'[/\.]([^\.]*[^\d\.]+\d*)(?=\.)', s)
lr_matches = []
task_matches = []
cp_matches = re.findall(r'checkpoint(?:_\d+)?_(\d+).pt', s)
items = OrderedDict()
for m in str_matches:
if isinstance(m, tuple):
if 'checkpoint' not in m[0]:
items[m[0]] = m[1]
else:
items[m] = ''
for m in num_matches:
items[m[0]] = m[1]
for m in lr_matches:
items[m[0]] = m[1]
for m in task_matches:
items["hydra_task"] = m
for m in cp_matches:
items['checkpoint'] = m
return items
abs_best = None
sources = []
for root, _, files in os.walk(args.base):
if args.path_contains is not None and not args.path_contains in root:
continue
for f in files:
if f.endswith(args.file_name):
sources.append((root, f))
if args.last_files is not None:
sources = sources[-args.last_files:]
for root, file in sources:
with open(os.path.join(root, file), 'r') as fin:
found = []
avg = {}
prev = None
for line in fin:
line = line.rstrip()
if line.find(args.target) != -1 and (
args.skip_containing is None or line.find(args.skip_containing) == -1):
try:
idx = line.index("{")
line = line[idx:]
line_json = json.loads(line)
except:
continue
if prev is not None:
try:
prev.update(line_json)
line_json = prev
except:
pass
if args.target in line_json:
found.append(line_json)
if args.avg_params:
avg_params = args.avg_params.split(',')
for p in avg_params:
m = extract_metric(line, p)
if m is not None:
prev_v, prev_c = avg.get(p, (0, 0))
avg[p] = prev_v + float(m), prev_c + 1
if args.extract_prev:
try:
prev = json.loads(line)
except:
pass
best = None
if args.best:
curr_best = None
for i in range(len(found)):
cand_best = found[i][args.target] if args.target in found[i] else None
def cmp(a, b):
a = float(a)
b = float(b)
if args.best_biggest:
return a > b
return a < b
if cand_best is not None and not math.isnan(float(cand_best)) and (
curr_best is None or cmp(cand_best, curr_best)):
curr_best = cand_best
if abs_best is None or cmp(curr_best, abs_best):
abs_best = curr_best
best = found[i]
if args.unique_epochs or args.epoch:
last_found = []
last_epoch = None
for i in reversed(range(len(found))):
epoch = found[i]['epoch']
if args.epoch and args.epoch != epoch:
continue
if epoch != last_epoch:
last_epoch = epoch
last_found.append(found[i])
found = list(reversed(last_found))
if len(found) == 0:
if print_output and (args.last_files is not None or not args.skip_empty):
# print(root.split('/')[-1])
print(root[len(args.base):])
print('Nothing')
else:
if not print_output:
ret[root[len(args.base):]] = best
continue
if args.compact:
# print('{}\t{}'.format(root.split('/')[-1], curr_best))
print('{}\t{}'.format(root[len(args.base)+1:], curr_best))
continue
if args.group_on is None and not args.best_only:
# print(root.split('/')[-1])
print(root[len(args.base):])
if not args.everything:
if best is not None and args.group_on is None and not args.best_only and not args.flat:
print(best, '(best)')
if args.group_on is None and args.last and not args.best_only and not args.flat:
for f in found[-args.last:]:
if args.extract_prev is not None:
try:
print('{}\t{}'.format(f[args.extract_prev], f[args.target]))
except Exception as e:
print('Exception!', e)
else:
print(f)
try:
metric = found[-1][args.target] if not args.best or best is None else best[args.target]
except:
print(found[-1])
raise
if metric is not None:
entries.append((extract_params(root), metric))
else:
for f in found:
print(f)
if not args.group_on and print_output:
print()
if len(avg) > 0:
for k, (v, c) in avg.items():
print(f'{k}: {v/c}')
if args.best_only:
print(abs_best)
if args.flat:
print("\t".join(m for _, m in entries))
if args.group_on is not None:
by_val = OrderedDict()
for e, m in entries:
k = args.group_on
if k not in e:
m_keys = [x for x in e.keys() if x.startswith(k)]
if len(m_keys) == 0:
val = "False"
else:
assert len(m_keys) == 1
k = m_keys[0]
val = m_keys[0]
else:
val = e[args.group_on]
if val == "":
val = "True"
scrubbed_entry = copy.deepcopy(e)
if k in scrubbed_entry:
del scrubbed_entry[k]
if args.remove_metric and args.remove_metric in scrubbed_entry:
val += '_' + scrubbed_entry[args.remove_metric]
del scrubbed_entry[args.remove_metric]
by_val.setdefault(tuple(scrubbed_entry.items()), dict())[val] = m
distinct_vals = set()
for v in by_val.values():
distinct_vals.update(v.keys())
try:
distinct_vals = {int(d) for d in distinct_vals}
except:
print(distinct_vals)
print()
print("by_val", len(by_val))
for k,v in by_val.items():
print(k, '=>', v)
print()
# , by_val, entries)
raise
from natsort import natsorted
svals = list(map(str, natsorted(distinct_vals)))
print('{}\t{}'.format(args.group_on, '\t'.join(svals)))
sums = OrderedDict({n:[] for n in svals})
for k, v in by_val.items():
kstr = '.'.join(':'.join(x) for x in k)
vstr = ''
for mv in svals:
x = v[mv] if mv in v else ''
vstr += '\t{}'.format(round(x, 5) if isinstance(x, float) else x)
try:
sums[mv].append(float(x))
except:
pass
print('{}{}'.format(kstr[:args.key_len], vstr))
if any(len(x) > 0 for x in sums.values()):
print('min:', end='')
for v in sums.values():
min = np.min(v)
print(f'\t{round(min, 5)}', end='')
print()
print('max:', end='')
for v in sums.values():
max = np.max(v)
print(f'\t{round(max, 5)}', end='')
print()
print('avg:', end='')
for v in sums.values():
mean = np.mean(v)
print(f'\t{round(mean, 5)}', end='')
print()
print('median:', end='')
for v in sums.values():
median = np.median(v)
print(f'\t{round(median, 5)}', end='')
print()
return ret
if __name__ == "__main__":
args = parser.parse_args()
main(args, print_output=True) |