forked from ultralytics/yolov5
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Refactor train.py and val.py
loggers (ultralytics#4137)
* Update loggers * Config * Update val.py * cleanup * fix1 * fix2 * fix3 and reformat * format sweep.py * Logger() class * cleanup * cleanup2 * wandb package import fix * wandb package import fix2 * txt fix * fix4 * fix5 * fix6 * drop wandb into utils/loggers * fix 7 * rename loggers/wandb_logging to loggers/wandb * Update message * Update message * Update message * cleanup * Fix x axis bug * fix rank 0 issue * cleanup
- Loading branch information
1 parent
1b0840e
commit 1a8d97f
Showing
9 changed files
with
172 additions
and
91 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,129 @@ | ||
| # YOLOv5 experiment logging utils | ||
|
|
||
| import warnings | ||
|
|
||
| import torch | ||
| from torch.utils.tensorboard import SummaryWriter | ||
|
|
||
| from utils.general import colorstr, emojis | ||
| from utils.loggers.wandb.wandb_utils import WandbLogger | ||
| from utils.torch_utils import de_parallel | ||
|
|
||
| LOGGERS = ('txt', 'tb', 'wandb') # text-file, TensorBoard, Weights & Biases | ||
|
|
||
| try: | ||
| import wandb | ||
|
|
||
| assert hasattr(wandb, '__version__') # verify package import not local dir | ||
| except (ImportError, AssertionError): | ||
| wandb = None | ||
|
|
||
|
|
||
| class Loggers(): | ||
| # YOLOv5 Loggers class | ||
| def __init__(self, save_dir=None, results_file=None, weights=None, opt=None, hyp=None, | ||
| data_dict=None, logger=None, include=LOGGERS): | ||
| self.save_dir = save_dir | ||
| self.results_file = results_file | ||
| self.weights = weights | ||
| self.opt = opt | ||
| self.hyp = hyp | ||
| self.data_dict = data_dict | ||
| self.logger = logger # for printing results to console | ||
| self.include = include | ||
| for k in LOGGERS: | ||
| setattr(self, k, None) # init empty logger dictionary | ||
|
|
||
| def start(self): | ||
| self.txt = True # always log to txt | ||
|
|
||
| # Message | ||
| try: | ||
| import wandb | ||
| except ImportError: | ||
| prefix = colorstr('Weights & Biases: ') | ||
| s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)" | ||
| print(emojis(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 | ||
| try: | ||
| assert 'wandb' in self.include and wandb | ||
| run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume else None | ||
| self.opt.hyp = self.hyp # add hyperparameters | ||
| self.wandb = WandbLogger(self.opt, s.stem, run_id, self.data_dict) | ||
| except: | ||
| self.wandb = None | ||
|
|
||
| return self | ||
|
|
||
| def on_train_batch_end(self, ni, model, imgs): | ||
| # Callback runs on train batch end | ||
| if ni == 0: | ||
| 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 self.wandb and ni == 10: | ||
| files = sorted(self.save_dir.glob('train*.jpg')) | ||
| self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]}) | ||
|
|
||
| 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_batch_end(self, pred, predn, path, names, im): | ||
| # Callback runs on train batch end | ||
| if self.wandb: | ||
| self.wandb.val_one_image(pred, predn, path, names, im) | ||
|
|
||
| def on_val_end(self): | ||
| # Callback runs on val end | ||
| if self.wandb: | ||
| files = sorted(self.save_dir.glob('val*.jpg')) | ||
| self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]}) | ||
|
|
||
| def on_train_val_end(self, mloss, results, lr, epoch, s, best_fitness, fi): | ||
| # Callback runs on validation end during training | ||
| vals = list(mloss[:-1]) + list(results) + lr | ||
| tags = ['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', | ||
| 'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss | ||
| 'x/lr0', 'x/lr1', 'x/lr2'] # params | ||
| if self.txt: | ||
| with open(self.results_file, 'a') as f: | ||
| f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss | ||
| if self.tb: | ||
| for x, tag in zip(vals, tags): | ||
| self.tb.add_scalar(tag, x, epoch) # TensorBoard | ||
| if self.wandb: | ||
| self.wandb.log({k: v for k, v in zip(tags, vals)}) | ||
| self.wandb.end_epoch(best_result=best_fitness == fi) | ||
|
|
||
| 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) | ||
|
|
||
| def on_train_end(self, last, best): | ||
| # Callback runs on training end | ||
| 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 | ||
| if self.wandb: | ||
| wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]}) | ||
| wandb.log_artifact(str(best if best.exists() else last), type='model', | ||
| name='run_' + self.wandb.wandb_run.id + '_model', | ||
| aliases=['latest', 'best', 'stripped']) | ||
| self.wandb.finish_run() | ||
|
|
||
| def log_images(self, paths): | ||
| # Log images | ||
| if self.wandb: | ||
| self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]}) |
File renamed without changes.
File renamed without changes.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.