Spaces:
Runtime error
Runtime error
File size: 9,100 Bytes
fc16538 |
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 |
# TRI-VIDAR - Copyright 2022 Toyota Research Institute. All rights reserved.
import os
from datetime import datetime
import numpy as np
import torch
from vidar.utils.config import cfg_has
from vidar.utils.logging import pcolor
class ModelCheckpoint:
"""
Class for model checkpointing
Parameters
----------
cfg : Config
Configuration with parameters
verbose : Bool
Print information on screen if enabled
"""
def __init__(self, cfg, verbose=False):
super().__init__()
# Create checkpoint folder
self.folder = cfg_has(cfg, 'folder', None)
self.name = cfg_has(cfg, 'name', datetime.now().strftime("%Y-%m-%d_%Hh%Mm%Ss"))
if self.folder:
self.path = os.path.join(self.folder, self.name)
os.makedirs(self.path, exist_ok=True)
else:
self.path = None
# Exclude folders
self.excludes = ['sandbox']
# If there is no folder, only track metrics
self.tracking_only = self.path is None
# Store arguments
self.keep_top = cfg_has(cfg, 'keep_top', -1)
self.dataset = cfg_has(cfg, 'dataset', [])
self.monitor = cfg_has(cfg, 'monitor', [])
self.mode = cfg_has(cfg, 'mode', [])
# Number of metrics to track
self.num_tracking = len(self.mode)
# Prepare s3 bucket
if cfg_has(cfg, 's3_bucket'):
self.s3_path = f's3://{cfg.s3_bucket}/{self.name}'
self.s3_url = f'https://s3.console.aws.amazon.com/s3/buckets/{self.s3_path[5:]}'
else:
self.s3_path = self.s3_url = None
# Get starting information
self.torch_inf = torch.tensor(np.Inf)
mode_dict = {
'min': (self.torch_inf, 'min'),
'max': (-self.torch_inf, 'max'),
'auto': (-self.torch_inf, 'max') if \
'acc' in self.monitor or \
'a1' in self.monitor or \
'fmeasure' in self.monitor \
else (self.torch_inf, 'min'),
}
if self.mode:
self.top = [[] for _ in self.mode]
self.store_val = [[] for _ in self.mode]
self.previous = [0 for _ in self.mode]
self.best = [mode_dict[m][0] for m in self.mode]
self.mode = [mode_dict[m][1] for m in self.mode]
else:
self.top = []
# Print if requested
if verbose:
self.print()
# Save if requested
if cfg_has(cfg, 'save_code', False):
self.save_code()
if self.s3_url:
self.sync_s3(verbose=False)
def print(self):
"""Print information on screen"""
font_base = {'color': 'red', 'attrs': ('bold', 'dark')}
font_name = {'color': 'red', 'attrs': ('bold',)}
font_underline = {'color': 'red', 'attrs': ('underline',)}
print(pcolor('#' * 60, **font_base))
if self.path:
print(pcolor('### Checkpoint: ', **font_base) + \
pcolor('{}/{}'.format(self.folder, self.name), **font_name))
if self.s3_url:
print(pcolor('### ', **font_base) + \
pcolor('{}'.format(self.s3_url), **font_underline))
else:
print(pcolor('### Checkpoint: ', **font_base) + \
pcolor('Tracking only', **font_name))
print(pcolor('#' * 60, **font_base))
@staticmethod
def save_model(wrapper, name, epoch):
"""Save model"""
torch.save({
'config': wrapper.cfg, 'epoch': epoch,
'state_dict': wrapper.arch.state_dict(),
}, name)
@staticmethod
def del_model(name):
"""Delete model"""
if os.path.isfile(name):
os.remove(name)
def save_code(self):
"""Save code in the models folder"""
excludes = ' '.join([f'--exclude {exclude}' for exclude in self.excludes])
os.system(f"tar cfz {self.path}/{self.name}.tar.gz {excludes} *")
def sync_s3(self, verbose=True):
"""Sync saved models with the s3 bucket"""
font_base = {'color': 'magenta', 'attrs': ('bold', 'dark')}
font_name = {'color': 'magenta', 'attrs': ('bold',)}
if verbose:
print(pcolor('Syncing ', **font_base) +
pcolor('{}'.format(self.path), **font_name) +
pcolor(' -> ', **font_base) +
pcolor('{}'.format(self.s3_path), **font_name))
command = f'aws s3 sync {self.path} {self.s3_path} ' \
f'--acl bucket-owner-full-control --quiet --delete'
os.system(command)
def print_improvements(self, key, value, idx, is_best):
"""Print color-coded changes in tracked metrics"""
font1 = {'color': 'cyan', 'attrs':('dark', 'bold')}
font2 = {'color': 'cyan', 'attrs': ('bold',)}
font3 = {'color': 'yellow', 'attrs': ('bold',)}
font4 = {'color': 'green', 'attrs': ('bold',)}
font5 = {'color': 'red', 'attrs': ('bold',)}
current_inf = self.best[idx] == self.torch_inf or \
self.best[idx] == -self.torch_inf
print(
pcolor(f'{key}', **font2) + \
pcolor(f' ({self.mode[idx]}) : ', **font1) + \
('' if current_inf else
pcolor('%3.6f' % self.previous[idx], **font3) +
pcolor(f' -> ', **font1)) + \
(pcolor('%3.6f' % value, **font4) if is_best else
pcolor('%3.6f' % value, **font5)) +
('' if current_inf else
pcolor(' (%3.6f)' % self.best[idx], **font2))
)
def save(self, wrapper, epoch, verbose=True):
"""Save model"""
# Do nothing if no path is provided
if self.path:
name = '%03d.ckpt' % epoch
folder = os.path.join(self.path, 'models')
os.makedirs(folder, exist_ok=True)
folder_name = os.path.join(folder, name)
self.save_model(wrapper, folder_name, epoch)
self.top.append(folder_name)
if 0 < self.keep_top < len(self.top):
self.del_model(self.top.pop(0))
if self.s3_url:
self.sync_s3(verbose=False)
if verbose:
print()
def check_and_save(self, wrapper, metrics, prefixes, epoch, verbose=True):
"""Check if model should be saved and maybe save it"""
# Not tracking any metric, save every iteration
if self.num_tracking == 0:
# Do nothing if no path is provided
if self.path:
name = '%03d.ckpt' % epoch
folder = os.path.join(self.path, 'models')
os.makedirs(folder, exist_ok=True)
folder_name = os.path.join(folder, name)
self.save_model(wrapper, folder_name, epoch)
self.top.append(folder_name)
if 0 < self.keep_top < len(self.top):
self.del_model(self.top.pop(0))
if self.s3_url:
self.sync_s3(verbose=False)
# Check if saving for every metric
else:
for idx in range(self.num_tracking):
key = '{}-{}'.format(prefixes[self.dataset[idx]], self.monitor[idx])
value = metrics[key]
if self.mode[idx] == 'min':
is_best = value < self.best[idx]
will_store = len(self.store_val[idx]) < self.keep_top or \
value < np.max(self.store_val[idx])
store_idx = 0 if len(self.store_val[idx]) == 0 else int(np.argmax(self.store_val[idx]))
else:
is_best = value > self.best[idx]
will_store = len(self.store_val[idx]) < self.keep_top or \
value > np.min(self.store_val[idx])
store_idx = 0 if len(self.store_val[idx]) == 0 else int(np.argmin(self.store_val[idx]))
if verbose:
self.print_improvements(key, value, idx, is_best)
self.previous[idx] = value
if is_best:
self.best[idx] = value
if is_best or will_store:
if self.path:
name = '%03d_%3.6f.ckpt' % (epoch, value)
folder = os.path.join(self.path, key)
os.makedirs(folder, exist_ok=True)
folder_name = os.path.join(folder, name)
self.save_model(wrapper, folder_name, epoch)
self.top[idx].append(folder_name)
self.store_val[idx].append(value)
if 0 < self.keep_top < len(self.top[idx]):
self.del_model(self.top[idx].pop(store_idx))
self.store_val[idx].pop(store_idx)
if self.s3_url:
self.sync_s3(verbose=False)
if verbose:
print()
|