# Ultralytics YOLOv5 🚀, AGPL-3.0 license """Main Logger class for ClearML experiment tracking.""" import glob import re from pathlib import Path import matplotlib.image as mpimg import matplotlib.pyplot as plt import numpy as np import yaml from ultralytics.utils.plotting import Annotator, colors try: import clearml from clearml import Dataset, Task assert hasattr(clearml, "__version__") # verify package import not local dir except (ImportError, AssertionError): clearml = None def construct_dataset(clearml_info_string): """Load in a clearml dataset and fill the internal data_dict with its contents.""" dataset_id = clearml_info_string.replace("clearml://", "") dataset = Dataset.get(dataset_id=dataset_id) dataset_root_path = Path(dataset.get_local_copy()) # We'll search for the yaml file definition in the dataset yaml_filenames = list(glob.glob(str(dataset_root_path / "*.yaml")) + glob.glob(str(dataset_root_path / "*.yml"))) if len(yaml_filenames) > 1: raise ValueError( "More than one yaml file was found in the dataset root, cannot determine which one contains " "the dataset definition this way." ) elif not yaml_filenames: raise ValueError( "No yaml definition found in dataset root path, check that there is a correct yaml file " "inside the dataset root path." ) with open(yaml_filenames[0]) as f: dataset_definition = yaml.safe_load(f) assert set( dataset_definition.keys() ).issuperset( {"train", "test", "val", "nc", "names"} ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')" data_dict = { "train": ( str((dataset_root_path / dataset_definition["train"]).resolve()) if dataset_definition["train"] else None ) } data_dict["test"] = ( str((dataset_root_path / dataset_definition["test"]).resolve()) if dataset_definition["test"] else None ) data_dict["val"] = ( str((dataset_root_path / dataset_definition["val"]).resolve()) if dataset_definition["val"] else None ) data_dict["nc"] = dataset_definition["nc"] data_dict["names"] = dataset_definition["names"] return data_dict class ClearmlLogger: """ Log training runs, datasets, models, and predictions to ClearML. This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, this information includes hyperparameters, system configuration and metrics, model metrics, code information and basic data metrics and analyses. By providing additional command line arguments to train.py, datasets, models and predictions can also be logged. """ def __init__(self, opt, hyp): """ - Initialize ClearML Task, this object will capture the experiment - Upload dataset version to ClearML Data if opt.upload_dataset is True. Arguments: opt (namespace) -- Commandline arguments for this run hyp (dict) -- Hyperparameters for this run """ self.current_epoch = 0 # Keep tracked of amount of logged images to enforce a limit self.current_epoch_logged_images = set() # Maximum number of images to log to clearML per epoch self.max_imgs_to_log_per_epoch = 16 # Get the interval of epochs when bounding box images should be logged # Only for detection task though! if "bbox_interval" in opt: self.bbox_interval = opt.bbox_interval self.clearml = clearml self.task = None self.data_dict = None if self.clearml: self.task = Task.init( project_name="YOLOv5" if str(opt.project).startswith("runs/") else opt.project, task_name=opt.name if opt.name != "exp" else "Training", tags=["YOLOv5"], output_uri=True, reuse_last_task_id=opt.exist_ok, auto_connect_frameworks={"pytorch": False, "matplotlib": False}, # We disconnect pytorch auto-detection, because we added manual model save points in the code ) # ClearML's hooks will already grab all general parameters # Only the hyperparameters coming from the yaml config file # will have to be added manually! self.task.connect(hyp, name="Hyperparameters") self.task.connect(opt, name="Args") # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent self.task.set_base_docker( "ultralytics/yolov5:latest", docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"', docker_setup_bash_script="pip install clearml", ) # Get ClearML Dataset Version if requested if opt.data.startswith("clearml://"): # data_dict should have the following keys: # names, nc (number of classes), test, train, val (all three relative paths to ../datasets) self.data_dict = construct_dataset(opt.data) # Set data to data_dict because wandb will crash without this information and opt is the best way # to give it to them opt.data = self.data_dict def log_scalars(self, metrics, epoch): """ Log scalars/metrics to ClearML. Arguments: metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...} epoch (int) iteration number for the current set of metrics """ for k, v in metrics.items(): title, series = k.split("/") self.task.get_logger().report_scalar(title, series, v, epoch) def log_model(self, model_path, model_name, epoch=0): """ Log model weights to ClearML. Arguments: model_path (PosixPath or str) Path to the model weights model_name (str) Name of the model visible in ClearML epoch (int) Iteration / epoch of the model weights """ self.task.update_output_model( model_path=str(model_path), name=model_name, iteration=epoch, auto_delete_file=False ) def log_summary(self, metrics): """ Log final metrics to a summary table. Arguments: metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...} """ for k, v in metrics.items(): self.task.get_logger().report_single_value(k, v) def log_plot(self, title, plot_path): """ Log image as plot in the plot section of ClearML. Arguments: title (str) Title of the plot plot_path (PosixPath or str) Path to the saved image file """ img = mpimg.imread(plot_path) fig = plt.figure() ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect="auto", xticks=[], yticks=[]) # no ticks ax.imshow(img) self.task.get_logger().report_matplotlib_figure(title, "", figure=fig, report_interactive=False) def log_debug_samples(self, files, title="Debug Samples"): """ Log files (images) as debug samples in the ClearML task. Arguments: files (List(PosixPath)) a list of file paths in PosixPath format title (str) A title that groups together images with the same values """ for f in files: if f.exists(): it = re.search(r"_batch(\d+)", f.name) iteration = int(it.groups()[0]) if it else 0 self.task.get_logger().report_image( title=title, series=f.name.replace(f"_batch{iteration}", ""), local_path=str(f), iteration=iteration ) def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25): """ Draw the bounding boxes on a single image and report the result as a ClearML debug sample. Arguments: image_path (PosixPath) the path the original image file boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] class_names (dict): dict containing mapping of class int to class name image (Tensor): A torch tensor containing the actual image data """ if ( len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch and self.current_epoch >= 0 and (self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images) ): im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2)) annotator = Annotator(im=im, pil=True) for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])): color = colors(i) class_name = class_names[int(class_nr)] confidence_percentage = round(float(conf) * 100, 2) label = f"{class_name}: {confidence_percentage}%" if conf > conf_threshold: annotator.rectangle(box.cpu().numpy(), outline=color) annotator.box_label(box.cpu().numpy(), label=label, color=color) annotated_image = annotator.result() self.task.get_logger().report_image( title="Bounding Boxes", series=image_path.name, iteration=self.current_epoch, image=annotated_image ) self.current_epoch_logged_images.add(image_path)