From 151c953d5e5a744ed90e1ce59bda4cd7ad7f38b0 Mon Sep 17 00:00:00 2001 From: Victor Sonck Date: Wed, 3 Jan 2024 08:14:39 +0100 Subject: [PATCH] Add segmentation and classification support for ClearML (#10752) * Added ClearML instance segmentation and classification support * Cleaned up ClearML plot output * typos * Log results as plots instead of debug samples * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Glenn Jocher Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher --- utils/loggers/__init__.py | 49 +++++++++++++++---- utils/loggers/clearml/clearml_utils.py | 65 ++++++++++++++++++++++++-- 2 files changed, 101 insertions(+), 13 deletions(-) diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py index aa6eae4c6f8c..381d477d127c 100644 --- a/utils/loggers/__init__.py +++ b/utils/loggers/__init__.py @@ -173,10 +173,11 @@ def on_pretrain_routine_end(self, labels, names): 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) + if self.clearml: + for path in paths: + self.clearml.log_plot(title=path.stem, plot_path=path) def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): log_dict = dict(zip(self.keys[:3], vals)) @@ -255,9 +256,7 @@ def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): 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) + self.clearml.log_scalars(x, epoch) if self.wandb: if best_fitness == fi: @@ -311,9 +310,10 @@ def on_train_end(self, last, best, epoch, results): 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) + self.clearml.log_summary(dict(zip(self.keys[3:10], results))) + [self.clearml.log_plot(title=f.stem, plot_path=f) for f in files] + self.clearml.log_model(str(best if best.exists() else last), + "Best Model" if best.exists() else "Last Model", epoch) if self.comet_logger: final_results = dict(zip(self.keys[3:10], results)) @@ -325,6 +325,8 @@ def on_params_update(self, params: dict): self.wandb.wandb_run.config.update(params, allow_val_change=True) if self.comet_logger: self.comet_logger.on_params_update(params) + if self.clearml: + self.clearml.task.connect(params) class GenericLogger: @@ -337,7 +339,7 @@ class GenericLogger: include: loggers to include """ - def __init__(self, opt, console_logger, include=('tb', 'wandb')): + def __init__(self, opt, console_logger, include=('tb', 'wandb', 'clearml')): # init default loggers self.save_dir = Path(opt.save_dir) self.include = include @@ -356,6 +358,22 @@ def __init__(self, opt, console_logger, include=('tb', 'wandb')): else: self.wandb = None + if clearml and 'clearml' in self.include: + try: + # Hyp is not available in classification mode + if 'hyp' not in opt: + hyp = {} + else: + hyp = opt.hyp + self.clearml = ClearmlLogger(opt, hyp) + except Exception: + self.clearml = None + prefix = colorstr('ClearML: ') + LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.' + f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme') + else: + self.clearml = None + def log_metrics(self, metrics, epoch): # Log metrics dictionary to all loggers if self.csv: @@ -372,6 +390,9 @@ def log_metrics(self, metrics, epoch): if self.wandb: self.wandb.log(metrics, step=epoch) + if self.clearml: + self.clearml.log_scalars(metrics, 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 @@ -384,6 +405,12 @@ def log_images(self, files, name='Images', epoch=0): if self.wandb: self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch) + if self.clearml: + if name == 'Results': + [self.clearml.log_plot(f.stem, f) for f in files] + else: + self.clearml.log_debug_samples(files, title=name) + def log_graph(self, model, imgsz=(640, 640)): # Log model graph to all loggers if self.tb: @@ -395,11 +422,15 @@ def log_model(self, model_path, epoch=0, metadata={}): art = wandb.Artifact(name=f'run_{wandb.run.id}_model', type='model', metadata=metadata) art.add_file(str(model_path)) wandb.log_artifact(art) + if self.clearml: + self.clearml.log_model(model_path=model_path, model_name=model_path.stem) def update_params(self, params): # Update the parameters logged if self.wandb: wandb.run.config.update(params, allow_val_change=True) + if self.clearml: + self.clearml.task.connect(params) def log_tensorboard_graph(tb, model, imgsz=(640, 640)): diff --git a/utils/loggers/clearml/clearml_utils.py b/utils/loggers/clearml/clearml_utils.py index 4e999bfee5db..c7627a261186 100644 --- a/utils/loggers/clearml/clearml_utils.py +++ b/utils/loggers/clearml/clearml_utils.py @@ -3,6 +3,8 @@ 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 @@ -78,18 +80,22 @@ def __init__(self, opt, hyp): # 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 + # 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=opt.project if opt.project != 'runs/train' else 'YOLOv5', + project_name=opt.project if not str(opt.project).startswith('runs/') else 'YOLOv5', 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} + 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 @@ -112,6 +118,57 @@ def __init__(self, opt, hyp): # 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. @@ -125,7 +182,7 @@ def log_debug_samples(self, files, title='Debug Samples'): 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(), ''), + series=f.name.replace(f"_batch{iteration}", ''), local_path=str(f), iteration=iteration)