Add docstrings to YOLOv5 functions (#12760)
* Add docstrings to top level files * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Add docstrings * Auto-format by https://ultralytics.com/actions * Update activations.py Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Auto-format by https://ultralytics.com/actions --------- Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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@@ -73,6 +73,7 @@ def _json_default(value):
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class Loggers:
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# YOLOv5 Loggers class
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def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
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"""Initializes loggers for YOLOv5 training and validation metrics, paths, and options."""
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self.save_dir = save_dir
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self.weights = weights
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self.opt = opt
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@@ -150,7 +151,7 @@ class Loggers:
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@property
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def remote_dataset(self):
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# Get data_dict if custom dataset artifact link is provided
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"""Fetches dataset dictionary from remote logging services like ClearML, Weights & Biases, or Comet ML."""
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data_dict = None
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if self.clearml:
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data_dict = self.clearml.data_dict
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@@ -162,15 +163,17 @@ class Loggers:
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return data_dict
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def on_train_start(self):
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"""Initializes the training process for Comet ML logger if it's configured."""
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if self.comet_logger:
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self.comet_logger.on_train_start()
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def on_pretrain_routine_start(self):
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"""Invokes pre-training routine start hook for Comet ML logger if available."""
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if self.comet_logger:
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self.comet_logger.on_pretrain_routine_start()
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def on_pretrain_routine_end(self, labels, names):
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# Callback runs on pre-train routine end
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"""Callback that runs at the end of pre-training routine, logging label plots if enabled."""
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if self.plots:
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plot_labels(labels, names, self.save_dir)
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paths = self.save_dir.glob("*labels*.jpg") # training labels
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@@ -183,6 +186,7 @@ class Loggers:
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self.clearml.log_plot(title=path.stem, plot_path=path)
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def on_train_batch_end(self, model, ni, imgs, targets, paths, vals):
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"""Logs training batch end events, plots images, and updates external loggers with batch-end data."""
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log_dict = dict(zip(self.keys[:3], vals))
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# Callback runs on train batch end
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# ni: number integrated batches (since train start)
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@@ -203,7 +207,7 @@ class Loggers:
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self.comet_logger.on_train_batch_end(log_dict, step=ni)
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def on_train_epoch_end(self, epoch):
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# Callback runs on train epoch end
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"""Callback that updates the current epoch in Weights & Biases at the end of a training epoch."""
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if self.wandb:
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self.wandb.current_epoch = epoch + 1
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@@ -211,22 +215,24 @@ class Loggers:
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self.comet_logger.on_train_epoch_end(epoch)
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def on_val_start(self):
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"""Callback that signals the start of a validation phase to the Comet logger."""
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if self.comet_logger:
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self.comet_logger.on_val_start()
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def on_val_image_end(self, pred, predn, path, names, im):
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# Callback runs on val image end
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"""Callback that logs a validation image and its predictions to WandB or ClearML."""
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if self.wandb:
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self.wandb.val_one_image(pred, predn, path, names, im)
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if self.clearml:
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self.clearml.log_image_with_boxes(path, pred, names, im)
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def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out):
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"""Logs validation batch results to Comet ML during training at the end of each validation batch."""
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if self.comet_logger:
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self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out)
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def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
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# Callback runs on val end
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"""Logs validation results to WandB or ClearML at the end of the validation process."""
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if self.wandb or self.clearml:
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files = sorted(self.save_dir.glob("val*.jpg"))
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if self.wandb:
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@@ -238,7 +244,7 @@ class Loggers:
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self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
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def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
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# Callback runs at the end of each fit (train+val) epoch
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"""Callback that logs metrics and saves them to CSV or NDJSON at the end of each fit (train+val) epoch."""
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x = dict(zip(self.keys, vals))
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if self.csv:
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file = self.save_dir / "results.csv"
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@@ -277,7 +283,7 @@ class Loggers:
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self.comet_logger.on_fit_epoch_end(x, epoch=epoch)
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def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
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# Callback runs on model save event
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"""Callback that handles model saving events, logging to Weights & Biases or ClearML if enabled."""
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if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1:
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if self.wandb:
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self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
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@@ -290,7 +296,7 @@ class Loggers:
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self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi)
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def on_train_end(self, last, best, epoch, results):
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# Callback runs on training end, i.e. saving best model
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"""Callback that runs at the end of training to save plots and log results."""
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if self.plots:
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plot_results(file=self.save_dir / "results.csv") # save results.png
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files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))]
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@@ -326,7 +332,7 @@ class Loggers:
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self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results)
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def on_params_update(self, params: dict):
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# Update hyperparams or configs of the experiment
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"""Updates experiment hyperparameters or configurations in WandB, Comet, or ClearML."""
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if self.wandb:
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self.wandb.wandb_run.config.update(params, allow_val_change=True)
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if self.comet_logger:
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@@ -346,7 +352,7 @@ class GenericLogger:
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"""
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def __init__(self, opt, console_logger, include=("tb", "wandb", "clearml")):
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# init default loggers
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"""Initializes a generic logger with optional TensorBoard, W&B, and ClearML support."""
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self.save_dir = Path(opt.save_dir)
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self.include = include
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self.console_logger = console_logger
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@@ -381,7 +387,7 @@ class GenericLogger:
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self.clearml = None
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def log_metrics(self, metrics, epoch):
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# Log metrics dictionary to all loggers
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"""Logs metrics to CSV, TensorBoard, W&B, and ClearML; `metrics` is a dict, `epoch` is an int."""
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if self.csv:
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keys, vals = list(metrics.keys()), list(metrics.values())
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n = len(metrics) + 1 # number of cols
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@@ -400,7 +406,7 @@ class GenericLogger:
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self.clearml.log_scalars(metrics, epoch)
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def log_images(self, files, name="Images", epoch=0):
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# Log images to all loggers
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"""Logs images to all loggers with optional naming and epoch specification."""
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files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path
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files = [f for f in files if f.exists()] # filter by exists
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@@ -418,11 +424,12 @@ class GenericLogger:
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self.clearml.log_debug_samples(files, title=name)
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def log_graph(self, model, imgsz=(640, 640)):
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# Log model graph to all loggers
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"""Logs model graph to all configured loggers with specified input image size."""
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if self.tb:
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log_tensorboard_graph(self.tb, model, imgsz)
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def log_model(self, model_path, epoch=0, metadata=None):
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"""Logs the model to all configured loggers with optional epoch and metadata."""
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if metadata is None:
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metadata = {}
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# Log model to all loggers
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@@ -434,7 +441,7 @@ class GenericLogger:
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self.clearml.log_model(model_path=model_path, model_name=model_path.stem)
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def update_params(self, params):
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# Update the parameters logged
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"""Updates logged parameters in WandB and/or ClearML if enabled."""
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if self.wandb:
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wandb.run.config.update(params, allow_val_change=True)
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if self.clearml:
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@@ -442,7 +449,7 @@ class GenericLogger:
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def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
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# Log model graph to TensorBoard
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"""Logs the model graph to TensorBoard with specified image size and model."""
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try:
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p = next(model.parameters()) # for device, type
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imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand
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@@ -455,7 +462,7 @@ def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
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def web_project_name(project):
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# Convert local project name to web project name
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"""Converts a local project name to a standardized web project name with optional suffixes."""
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if not project.startswith("runs/train"):
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return project
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suffix = "-Classify" if project.endswith("-cls") else "-Segment" if project.endswith("-seg") else ""
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