"""Main Logger class for ClearML experiment tracking.""" import glob import re from pathlib import Path import numpy as np import yaml from utils.plots 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 len(yaml_filenames) == 0: 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 = dict() 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 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=opt.project if opt.project != 'runs/train' else 'YOLOv5', task_name=opt.name if opt.name != 'exp' else 'Training', tags=['YOLOv5'], output_uri=True, auto_connect_frameworks={'pytorch': 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') # 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_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(it.group(), ''), 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: # Log every bbox_interval times and deduplicate for any intermittend extra eval runs if 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)