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 @property 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}'