File size: 10,436 Bytes
315fa91 |
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 |
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Logging utils
"""
import os
import warnings
import pkg_resources as pkg
import torch
from torch.utils.tensorboard import SummaryWriter
from utils.general import colorstr, cv2
from utils.loggers.clearml.clearml_utils import ClearmlLogger
from utils.loggers.wandb.wandb_utils import WandbLogger
from utils.plots import plot_images, plot_results
from utils.torch_utils import de_parallel
LOGGERS = ('csv', 'tb', 'wandb', 'clearml') # *.csv, TensorBoard, Weights & Biases, ClearML
RANK = int(os.getenv('RANK', -1))
try:
import wandb
assert hasattr(wandb, '__version__') # verify package import not local dir
if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}:
try:
wandb_login_success = wandb.login(timeout=30)
except wandb.errors.UsageError: # known non-TTY terminal issue
wandb_login_success = False
if not wandb_login_success:
wandb = None
except (ImportError, AssertionError):
wandb = None
try:
import clearml
assert hasattr(clearml, '__version__') # verify package import not local dir
except (ImportError, AssertionError):
clearml = None
class Loggers():
# YOLOv5 Loggers class
def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
self.save_dir = save_dir
self.weights = weights
self.opt = opt
self.hyp = hyp
self.logger = logger # for printing results to console
self.include = include
self.keys = [
'train/box_loss',
'train/obj_loss',
'train/cls_loss', # train loss
'metrics/precision',
'metrics/recall',
'metrics/mAP_0.5',
'metrics/mAP_0.5:0.95', # metrics
'val/box_loss',
'val/obj_loss',
'val/cls_loss', # val loss
'x/lr0',
'x/lr1',
'x/lr2'] # params
self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95']
for k in LOGGERS:
setattr(self, k, None) # init empty logger dictionary
self.csv = True # always log to csv
# Messages
if not wandb:
prefix = colorstr('Weights & Biases: ')
s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases"
self.logger.info(s)
if not clearml:
prefix = colorstr('ClearML: ')
s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 runs in ClearML"
self.logger.info(s)
# TensorBoard
s = self.save_dir
if 'tb' in self.include and not self.opt.evolve:
prefix = colorstr('TensorBoard: ')
self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/")
self.tb = SummaryWriter(str(s))
# W&B
if wandb and 'wandb' in self.include:
wandb_artifact_resume = isinstance(self.opt.resume, str) and self.opt.resume.startswith('wandb-artifact://')
run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume and not wandb_artifact_resume else None
self.opt.hyp = self.hyp # add hyperparameters
self.wandb = WandbLogger(self.opt, run_id)
# temp warn. because nested artifacts not supported after 0.12.10
if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.11'):
s = "YOLOv5 temporarily requires wandb version 0.12.10 or below. Some features may not work as expected."
self.logger.warning(s)
else:
self.wandb = None
# ClearML
if clearml and 'clearml' in self.include:
self.clearml = ClearmlLogger(self.opt, self.hyp)
else:
self.clearml = None
def on_train_start(self):
# Callback runs on train start
pass
def on_pretrain_routine_end(self):
# Callback runs on pre-train routine end
paths = self.save_dir.glob('*labels*.jpg') # training labels
if self.wandb:
self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
if self.clearml:
pass # ClearML saves these images automatically using hooks
def on_train_batch_end(self, ni, model, imgs, targets, paths, plots):
# Callback runs on train batch end
# ni: number integrated batches (since train start)
if plots:
if ni == 0:
if self.tb and not self.opt.sync_bn: # --sync known issue https://github.com/ultralytics/yolov5/issues/3754
with warnings.catch_warnings():
warnings.simplefilter('ignore') # suppress jit trace warning
self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
if ni < 3:
f = self.save_dir / f'train_batch{ni}.jpg' # filename
plot_images(imgs, targets, paths, f)
if (self.wandb or self.clearml) and ni == 10:
files = sorted(self.save_dir.glob('train*.jpg'))
if self.wandb:
self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
if self.clearml:
self.clearml.log_debug_samples(files, title='Mosaics')
def on_train_epoch_end(self, epoch):
# Callback runs on train epoch end
if self.wandb:
self.wandb.current_epoch = epoch + 1
def on_val_image_end(self, pred, predn, path, names, im):
# Callback runs on val image end
if self.wandb:
self.wandb.val_one_image(pred, predn, path, names, im)
if self.clearml:
self.clearml.log_image_with_boxes(path, pred, names, im)
def on_val_end(self):
# Callback runs on val end
if self.wandb or self.clearml:
files = sorted(self.save_dir.glob('val*.jpg'))
if self.wandb:
self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
if self.clearml:
self.clearml.log_debug_samples(files, title='Validation')
def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
# Callback runs at the end of each fit (train+val) epoch
x = dict(zip(self.keys, vals))
if self.csv:
file = self.save_dir / 'results.csv'
n = len(x) + 1 # number of cols
s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header
with open(file, 'a') as f:
f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
if self.tb:
for k, v in x.items():
self.tb.add_scalar(k, v, epoch)
elif self.clearml: # log to ClearML if TensorBoard not used
for k, v in x.items():
title, series = k.split('/')
self.clearml.task.get_logger().report_scalar(title, series, v, epoch)
if self.wandb:
if best_fitness == fi:
best_results = [epoch] + vals[3:7]
for i, name in enumerate(self.best_keys):
self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary
self.wandb.log(x)
self.wandb.end_epoch(best_result=best_fitness == fi)
if self.clearml:
self.clearml.current_epoch_logged_images = set() # reset epoch image limit
self.clearml.current_epoch += 1
def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
# Callback runs on model save event
if self.wandb:
if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
if self.clearml:
if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
self.clearml.task.update_output_model(model_path=str(last),
model_name='Latest Model',
auto_delete_file=False)
def on_train_end(self, last, best, plots, epoch, results):
# Callback runs on training end
if plots:
plot_results(file=self.save_dir / 'results.csv') # save results.png
files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter
self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles
for f in files:
self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
if self.wandb:
self.wandb.log(dict(zip(self.keys[3:10], results)))
self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
# Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
if not self.opt.evolve:
wandb.log_artifact(str(best if best.exists() else last),
type='model',
name=f'run_{self.wandb.wandb_run.id}_model',
aliases=['latest', 'best', 'stripped'])
self.wandb.finish_run()
if self.clearml:
# Save the best model here
if not self.opt.evolve:
self.clearml.task.update_output_model(model_path=str(best if best.exists() else last),
name='Best Model')
def on_params_update(self, params):
# Update hyperparams or configs of the experiment
# params: A dict containing {param: value} pairs
if self.wandb:
self.wandb.wandb_run.config.update(params, allow_val_change=True)
|