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Clean and bump dvc callback: settings, stacked images (#4343)

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Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Ivan Shcheklein 2023-08-13 12:50:35 -07:00 committed by GitHub
parent b5d1af42d8
commit d704507217
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4 changed files with 21 additions and 17 deletions
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2
docs/quickstart.md
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@ -259,7 +259,7 @@ The table below provides an overview of the settings available for adjustment wi
| `api_key` | `''` | `str` | Ultralytics HUB [API Key](https://hub.ultralytics.com/settings?tab=api+keys) |
| `clearml` | `True` | `bool` | Whether to use ClearML logging |
| `comet` | `True` | `bool` | Whether to use [Comet ML](https://bit.ly/yolov8-readme-comet) for experiment tracking and visualization |
| `dvc` | `True` | `bool` | Whether to use DVC for version control |
| `dvc` | `True` | `bool` | Whether to use [DVC for experiment tracking](https://dvc.org/doc/dvclive/ml-frameworks/yolo) and version control |
| `hub` | `True` | `bool` | Whether to use [Ultralytics HUB](https://hub.ultralytics.com) integration |
| `mlflow` | `True` | `bool` | Whether to use MLFlow for experiment tracking |
| `neptune` | `True` | `bool` | Whether to use Neptune for experiment tracking |

3
ultralytics/engine/trainer.py
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@ -543,7 +543,8 @@ class BaseTrainer:
def on_plot(self, name, data=None):
"""Registers plots (e.g. to be consumed in callbacks)"""
self.plots[name] = {'data': data, 'timestamp': time.time()}
path = Path(name)
self.plots[path] = {'data': data, 'timestamp': time.time()}
def final_eval(self):
"""Performs final evaluation and validation for object detection YOLO model."""

3
ultralytics/engine/validator.py
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@ -303,7 +303,8 @@ class BaseValidator:
def on_plot(self, name, data=None):
"""Registers plots (e.g. to be consumed in callbacks)"""
self.plots[name] = {'data': data, 'timestamp': time.time()}
path = Path(name)
self.plots[path] = {'data': data, 'timestamp': time.time()}
# TODO: may need to put these following functions into callback
def plot_val_samples(self, batch, ni):

30
ultralytics/utils/callbacks/dvc.py
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@ -1,6 +1,8 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
import os
import re
from pathlib import Path
import pkg_resources as pkg
@ -32,13 +34,17 @@ _processed_plots = {}
_training_epoch = False
def _logger_disabled():
return os.getenv('ULTRALYTICS_DVC_DISABLED', 'false').lower() == 'true'
def _log_images(image_path, prefix=''):
def _log_images(path, prefix=''):
if live:
live.log_image(os.path.join(prefix, image_path.name), image_path)
name = path.name
# Group images by batch to enable sliders in UI
if m := re.search(r'_batch(\d+)', name):
ni = m.group(1)
new_stem = re.sub(r'_batch(\d+)', '_batch', path.stem)
name = (Path(new_stem) / ni).with_suffix(path.suffix)
live.log_image(os.path.join(prefix, name), path)
def _log_plots(plots, prefix=''):
@ -68,14 +74,10 @@ def _log_confusion_matrix(validator):
def on_pretrain_routine_start(trainer):
try:
global live
if not _logger_disabled():
live = dvclive.Live(save_dvc_exp=True, cache_images=True)
LOGGER.info(
'DVCLive is detected and auto logging is enabled (can be disabled with `ULTRALYTICS_DVC_DISABLED=true`).'
)
else:
LOGGER.debug('DVCLive is detected and auto logging is disabled via `ULTRALYTICS_DVC_DISABLED`.')
live = None
live = dvclive.Live(save_dvc_exp=True, cache_images=True)
LOGGER.info(
f'DVCLive is detected and auto logging is enabled (can be disabled in the {SETTINGS.file} with `dvc: false`).'
)
except Exception as e:
LOGGER.warning(f'WARNING ⚠️ DVCLive installed but not initialized correctly, not logging this run. {e}')