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| import os | |
| import warnings | |
| from pathlib import Path | |
| import pkg_resources as pkg | |
| import torch | |
| from torch.utils.tensorboard import SummaryWriter | |
| from utils.general import LOGGER, 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_labels, plot_results | |
| from utils.torch_utils import de_parallel | |
| LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet') # *.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 | |
| try: | |
| if RANK not in [0, -1]: | |
| comet_ml = None | |
| else: | |
| import comet_ml | |
| assert hasattr(comet_ml, '__version__') # verify package import not local dir | |
| from utils.loggers.comet import CometLogger | |
| except (ModuleNotFoundError, ImportError, AssertionError): | |
| comet_ml = None | |
| class Loggers(): | |
| # YOLO 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.plots = not opt.noplots # plot results | |
| self.logger = logger # for printing results to console | |
| self.include = include | |
| self.keys = [ | |
| 'train/box_loss', | |
| 'train/cls_loss', | |
| 'train/dfl_loss', # train loss | |
| 'metrics/precision', | |
| 'metrics/recall', | |
| 'metrics/mAP_0.5', | |
| 'metrics/mAP_0.5:0.95', # metrics | |
| 'val/box_loss', | |
| 'val/cls_loss', | |
| 'val/dfl_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 YOLO 🚀 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 YOLO 🚀 in ClearML" | |
| self.logger.info(s) | |
| if not comet_ml: | |
| prefix = colorstr('Comet: ') | |
| s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLO 🚀 runs in Comet" | |
| 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 = "YOLO 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 | |
| # Comet | |
| if comet_ml and 'comet' in self.include: | |
| if isinstance(self.opt.resume, str) and self.opt.resume.startswith("comet://"): | |
| run_id = self.opt.resume.split("/")[-1] | |
| self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id) | |
| else: | |
| self.comet_logger = CometLogger(self.opt, self.hyp) | |
| else: | |
| self.comet_logger = None | |
| def remote_dataset(self): | |
| # Get data_dict if custom dataset artifact link is provided | |
| data_dict = None | |
| if self.clearml: | |
| data_dict = self.clearml.data_dict | |
| if self.wandb: | |
| data_dict = self.wandb.data_dict | |
| if self.comet_logger: | |
| data_dict = self.comet_logger.data_dict | |
| return data_dict | |
| def on_train_start(self): | |
| if self.comet_logger: | |
| self.comet_logger.on_train_start() | |
| def on_pretrain_routine_start(self): | |
| if self.comet_logger: | |
| self.comet_logger.on_pretrain_routine_start() | |
| def on_pretrain_routine_end(self, labels, names): | |
| # Callback runs on pre-train routine end | |
| if self.plots: | |
| plot_labels(labels, names, self.save_dir) | |
| 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 | |
| if self.comet_logger: | |
| self.comet_logger.on_pretrain_routine_end(paths) | |
| def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): | |
| log_dict = dict(zip(self.keys[0:3], vals)) | |
| # Callback runs on train batch end | |
| # ni: number integrated batches (since train start) | |
| if self.plots: | |
| if ni < 3: | |
| f = self.save_dir / f'train_batch{ni}.jpg' # filename | |
| plot_images(imgs, targets, paths, f) | |
| if ni == 0 and self.tb and not self.opt.sync_bn: | |
| log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz)) | |
| if ni == 10 and (self.wandb or self.clearml): | |
| 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') | |
| if self.comet_logger: | |
| self.comet_logger.on_train_batch_end(log_dict, step=ni) | |
| def on_train_epoch_end(self, epoch): | |
| # Callback runs on train epoch end | |
| if self.wandb: | |
| self.wandb.current_epoch = epoch + 1 | |
| if self.comet_logger: | |
| self.comet_logger.on_train_epoch_end(epoch) | |
| def on_val_start(self): | |
| if self.comet_logger: | |
| self.comet_logger.on_val_start() | |
| 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_batch_end(self, batch_i, im, targets, paths, shapes, out): | |
| if self.comet_logger: | |
| self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out) | |
| def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): | |
| # 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') | |
| if self.comet_logger: | |
| self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) | |
| 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 | |
| if self.comet_logger: | |
| self.comet_logger.on_fit_epoch_end(x, epoch=epoch) | |
| def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): | |
| # Callback runs on model save event | |
| if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1: | |
| if self.wandb: | |
| self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) | |
| if self.clearml: | |
| self.clearml.task.update_output_model(model_path=str(last), | |
| model_name='Latest Model', | |
| auto_delete_file=False) | |
| if self.comet_logger: | |
| self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi) | |
| def on_train_end(self, last, best, epoch, results): | |
| # Callback runs on training end, i.e. saving best model | |
| if self.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 and not self.opt.evolve: | |
| self.clearml.task.update_output_model(model_path=str(best if best.exists() else last), | |
| name='Best Model', | |
| auto_delete_file=False) | |
| if self.comet_logger: | |
| final_results = dict(zip(self.keys[3:10], results)) | |
| self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results) | |
| def on_params_update(self, params: dict): | |
| # Update hyperparams or configs of the experiment | |
| if self.wandb: | |
| self.wandb.wandb_run.config.update(params, allow_val_change=True) | |
| if self.comet_logger: | |
| self.comet_logger.on_params_update(params) | |
| class GenericLogger: | |
| """ | |
| YOLO General purpose logger for non-task specific logging | |
| Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...) | |
| Arguments | |
| opt: Run arguments | |
| console_logger: Console logger | |
| include: loggers to include | |
| """ | |
| def __init__(self, opt, console_logger, include=('tb', 'wandb')): | |
| # init default loggers | |
| self.save_dir = Path(opt.save_dir) | |
| self.include = include | |
| self.console_logger = console_logger | |
| self.csv = self.save_dir / 'results.csv' # CSV logger | |
| if 'tb' in self.include: | |
| prefix = colorstr('TensorBoard: ') | |
| self.console_logger.info( | |
| f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/") | |
| self.tb = SummaryWriter(str(self.save_dir)) | |
| if wandb and 'wandb' in self.include: | |
| self.wandb = wandb.init(project=web_project_name(str(opt.project)), | |
| name=None if opt.name == "exp" else opt.name, | |
| config=opt) | |
| else: | |
| self.wandb = None | |
| def log_metrics(self, metrics, epoch): | |
| # Log metrics dictionary to all loggers | |
| if self.csv: | |
| keys, vals = list(metrics.keys()), list(metrics.values()) | |
| n = len(metrics) + 1 # number of cols | |
| s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # header | |
| with open(self.csv, 'a') as f: | |
| f.write(s + ('%23.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') | |
| if self.tb: | |
| for k, v in metrics.items(): | |
| self.tb.add_scalar(k, v, epoch) | |
| if self.wandb: | |
| self.wandb.log(metrics, step=epoch) | |
| def log_images(self, files, name='Images', epoch=0): | |
| # Log images to all loggers | |
| files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path | |
| files = [f for f in files if f.exists()] # filter by exists | |
| if self.tb: | |
| for f in files: | |
| self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC') | |
| if self.wandb: | |
| self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch) | |
| def log_graph(self, model, imgsz=(640, 640)): | |
| # Log model graph to all loggers | |
| if self.tb: | |
| log_tensorboard_graph(self.tb, model, imgsz) | |
| def log_model(self, model_path, epoch=0, metadata={}): | |
| # Log model to all loggers | |
| if self.wandb: | |
| art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata) | |
| art.add_file(str(model_path)) | |
| wandb.log_artifact(art) | |
| def update_params(self, params): | |
| # Update the paramters logged | |
| if self.wandb: | |
| wandb.run.config.update(params, allow_val_change=True) | |
| def log_tensorboard_graph(tb, model, imgsz=(640, 640)): | |
| # Log model graph to TensorBoard | |
| try: | |
| p = next(model.parameters()) # for device, type | |
| imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand | |
| im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty) | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter('ignore') # suppress jit trace warning | |
| tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), []) | |
| except Exception as e: | |
| LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}') | |
| def web_project_name(project): | |
| # Convert local project name to web project name | |
| if not project.startswith('runs/train'): | |
| return project | |
| suffix = '-Classify' if project.endswith('-cls') else '-Segment' if project.endswith('-seg') else '' | |
| return f'YOLO{suffix}' | |