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Update Actions with Lychee and GitHub Token (#12592)

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* Add Ultralytics Actions

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* Update format.yml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Paula DerrengerUltralyticsAssistantGlenn Jocher
 authored and GitHub committed 2024-01-08 01:29:14 +01:00
1 parent f56355d321
commit ad97dc6a6d
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+4217 -3462
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utils/loggers/clearml/hpo.py
+41 -37
View File
@@ -1,18 +1,21 @@
from clearml import Task
# Connecting ClearML with the current process,
# from here on everything is logged automatically
from clearml.automation import HyperParameterOptimizer, UniformParameterRange
from clearml.automation.optuna import OptimizerOptuna
task = Task.init(project_name='Hyper-Parameter Optimization',
task_name='YOLOv5',
task_type=Task.TaskTypes.optimizer,
reuse_last_task_id=False)
task = Task.init(
project_name="Hyper-Parameter Optimization",
task_name="YOLOv5",
task_type=Task.TaskTypes.optimizer,
reuse_last_task_id=False,
)
# Example use case:
optimizer = HyperParameterOptimizer(
# This is the experiment we want to optimize
base_task_id='<your_template_task_id>',
base_task_id="<your_template_task_id>",
# here we define the hyper-parameters to optimize
# Notice: The parameter name should exactly match what you see in the UI: <section_name>/<parameter>
# For Example, here we see in the base experiment a section Named: "General"
@@ -20,39 +23,40 @@ optimizer = HyperParameterOptimizer(
# If you have `argparse` for example, then arguments will appear under the "Args" section,
# and you should instead pass "Args/batch_size"
hyper_parameters=[
UniformParameterRange('Hyperparameters/lr0', min_value=1e-5, max_value=1e-1),
UniformParameterRange('Hyperparameters/lrf', min_value=0.01, max_value=1.0),
UniformParameterRange('Hyperparameters/momentum', min_value=0.6, max_value=0.98),
UniformParameterRange('Hyperparameters/weight_decay', min_value=0.0, max_value=0.001),
UniformParameterRange('Hyperparameters/warmup_epochs', min_value=0.0, max_value=5.0),
UniformParameterRange('Hyperparameters/warmup_momentum', min_value=0.0, max_value=0.95),
UniformParameterRange('Hyperparameters/warmup_bias_lr', min_value=0.0, max_value=0.2),
UniformParameterRange('Hyperparameters/box', min_value=0.02, max_value=0.2),
UniformParameterRange('Hyperparameters/cls', min_value=0.2, max_value=4.0),
UniformParameterRange('Hyperparameters/cls_pw', min_value=0.5, max_value=2.0),
UniformParameterRange('Hyperparameters/obj', min_value=0.2, max_value=4.0),
UniformParameterRange('Hyperparameters/obj_pw', min_value=0.5, max_value=2.0),
UniformParameterRange('Hyperparameters/iou_t', min_value=0.1, max_value=0.7),
UniformParameterRange('Hyperparameters/anchor_t', min_value=2.0, max_value=8.0),
UniformParameterRange('Hyperparameters/fl_gamma', min_value=0.0, max_value=4.0),
UniformParameterRange('Hyperparameters/hsv_h', min_value=0.0, max_value=0.1),
UniformParameterRange('Hyperparameters/hsv_s', min_value=0.0, max_value=0.9),
UniformParameterRange('Hyperparameters/hsv_v', min_value=0.0, max_value=0.9),
UniformParameterRange('Hyperparameters/degrees', min_value=0.0, max_value=45.0),
UniformParameterRange('Hyperparameters/translate', min_value=0.0, max_value=0.9),
UniformParameterRange('Hyperparameters/scale', min_value=0.0, max_value=0.9),
UniformParameterRange('Hyperparameters/shear', min_value=0.0, max_value=10.0),
UniformParameterRange('Hyperparameters/perspective', min_value=0.0, max_value=0.001),
UniformParameterRange('Hyperparameters/flipud', min_value=0.0, max_value=1.0),
UniformParameterRange('Hyperparameters/fliplr', min_value=0.0, max_value=1.0),
UniformParameterRange('Hyperparameters/mosaic', min_value=0.0, max_value=1.0),
UniformParameterRange('Hyperparameters/mixup', min_value=0.0, max_value=1.0),
UniformParameterRange('Hyperparameters/copy_paste', min_value=0.0, max_value=1.0)],
UniformParameterRange("Hyperparameters/lr0", min_value=1e-5, max_value=1e-1),
UniformParameterRange("Hyperparameters/lrf", min_value=0.01, max_value=1.0),
UniformParameterRange("Hyperparameters/momentum", min_value=0.6, max_value=0.98),
UniformParameterRange("Hyperparameters/weight_decay", min_value=0.0, max_value=0.001),
UniformParameterRange("Hyperparameters/warmup_epochs", min_value=0.0, max_value=5.0),
UniformParameterRange("Hyperparameters/warmup_momentum", min_value=0.0, max_value=0.95),
UniformParameterRange("Hyperparameters/warmup_bias_lr", min_value=0.0, max_value=0.2),
UniformParameterRange("Hyperparameters/box", min_value=0.02, max_value=0.2),
UniformParameterRange("Hyperparameters/cls", min_value=0.2, max_value=4.0),
UniformParameterRange("Hyperparameters/cls_pw", min_value=0.5, max_value=2.0),
UniformParameterRange("Hyperparameters/obj", min_value=0.2, max_value=4.0),
UniformParameterRange("Hyperparameters/obj_pw", min_value=0.5, max_value=2.0),
UniformParameterRange("Hyperparameters/iou_t", min_value=0.1, max_value=0.7),
UniformParameterRange("Hyperparameters/anchor_t", min_value=2.0, max_value=8.0),
UniformParameterRange("Hyperparameters/fl_gamma", min_value=0.0, max_value=4.0),
UniformParameterRange("Hyperparameters/hsv_h", min_value=0.0, max_value=0.1),
UniformParameterRange("Hyperparameters/hsv_s", min_value=0.0, max_value=0.9),
UniformParameterRange("Hyperparameters/hsv_v", min_value=0.0, max_value=0.9),
UniformParameterRange("Hyperparameters/degrees", min_value=0.0, max_value=45.0),
UniformParameterRange("Hyperparameters/translate", min_value=0.0, max_value=0.9),
UniformParameterRange("Hyperparameters/scale", min_value=0.0, max_value=0.9),
UniformParameterRange("Hyperparameters/shear", min_value=0.0, max_value=10.0),
UniformParameterRange("Hyperparameters/perspective", min_value=0.0, max_value=0.001),
UniformParameterRange("Hyperparameters/flipud", min_value=0.0, max_value=1.0),
UniformParameterRange("Hyperparameters/fliplr", min_value=0.0, max_value=1.0),
UniformParameterRange("Hyperparameters/mosaic", min_value=0.0, max_value=1.0),
UniformParameterRange("Hyperparameters/mixup", min_value=0.0, max_value=1.0),
UniformParameterRange("Hyperparameters/copy_paste", min_value=0.0, max_value=1.0),
],
# this is the objective metric we want to maximize/minimize
objective_metric_title='metrics',
objective_metric_series='mAP_0.5',
objective_metric_title="metrics",
objective_metric_series="mAP_0.5",
# now we decide if we want to maximize it or minimize it (accuracy we maximize)
objective_metric_sign='max',
objective_metric_sign="max",
# let us limit the number of concurrent experiments,
# this in turn will make sure we do dont bombard the scheduler with experiments.
# if we have an auto-scaler connected, this, by proxy, will limit the number of machine
@@ -81,4 +85,4 @@ optimizer.wait()
# make sure background optimization stopped
optimizer.stop()
print('We are done, good bye')
print("We are done, good bye")