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91 lines
5.2 KiB
Python
91 lines
5.2 KiB
Python
3 weeks ago
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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from clearml import Task
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# Connecting ClearML with the current process,
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# from here on everything is logged automatically
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from clearml.automation import HyperParameterOptimizer, UniformParameterRange
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from clearml.automation.optuna import OptimizerOptuna
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task = Task.init(
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project_name="Hyper-Parameter Optimization",
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task_name="YOLOv5",
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task_type=Task.TaskTypes.optimizer,
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reuse_last_task_id=False,
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)
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# Example use case:
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optimizer = HyperParameterOptimizer(
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# This is the experiment we want to optimize
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base_task_id="<your_template_task_id>",
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# here we define the hyper-parameters to optimize
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# Notice: The parameter name should exactly match what you see in the UI: <section_name>/<parameter>
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# For Example, here we see in the base experiment a section Named: "General"
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# under it a parameter named "batch_size", this becomes "General/batch_size"
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# If you have `argparse` for example, then arguments will appear under the "Args" section,
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# and you should instead pass "Args/batch_size"
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hyper_parameters=[
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UniformParameterRange("Hyperparameters/lr0", min_value=1e-5, max_value=1e-1),
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UniformParameterRange("Hyperparameters/lrf", min_value=0.01, max_value=1.0),
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UniformParameterRange("Hyperparameters/momentum", min_value=0.6, max_value=0.98),
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UniformParameterRange("Hyperparameters/weight_decay", min_value=0.0, max_value=0.001),
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UniformParameterRange("Hyperparameters/warmup_epochs", min_value=0.0, max_value=5.0),
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UniformParameterRange("Hyperparameters/warmup_momentum", min_value=0.0, max_value=0.95),
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UniformParameterRange("Hyperparameters/warmup_bias_lr", min_value=0.0, max_value=0.2),
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UniformParameterRange("Hyperparameters/box", min_value=0.02, max_value=0.2),
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UniformParameterRange("Hyperparameters/cls", min_value=0.2, max_value=4.0),
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UniformParameterRange("Hyperparameters/cls_pw", min_value=0.5, max_value=2.0),
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UniformParameterRange("Hyperparameters/obj", min_value=0.2, max_value=4.0),
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UniformParameterRange("Hyperparameters/obj_pw", min_value=0.5, max_value=2.0),
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UniformParameterRange("Hyperparameters/iou_t", min_value=0.1, max_value=0.7),
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UniformParameterRange("Hyperparameters/anchor_t", min_value=2.0, max_value=8.0),
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UniformParameterRange("Hyperparameters/fl_gamma", min_value=0.0, max_value=4.0),
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UniformParameterRange("Hyperparameters/hsv_h", min_value=0.0, max_value=0.1),
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UniformParameterRange("Hyperparameters/hsv_s", min_value=0.0, max_value=0.9),
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UniformParameterRange("Hyperparameters/hsv_v", min_value=0.0, max_value=0.9),
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UniformParameterRange("Hyperparameters/degrees", min_value=0.0, max_value=45.0),
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UniformParameterRange("Hyperparameters/translate", min_value=0.0, max_value=0.9),
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UniformParameterRange("Hyperparameters/scale", min_value=0.0, max_value=0.9),
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UniformParameterRange("Hyperparameters/shear", min_value=0.0, max_value=10.0),
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UniformParameterRange("Hyperparameters/perspective", min_value=0.0, max_value=0.001),
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UniformParameterRange("Hyperparameters/flipud", min_value=0.0, max_value=1.0),
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UniformParameterRange("Hyperparameters/fliplr", min_value=0.0, max_value=1.0),
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UniformParameterRange("Hyperparameters/mosaic", min_value=0.0, max_value=1.0),
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UniformParameterRange("Hyperparameters/mixup", min_value=0.0, max_value=1.0),
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UniformParameterRange("Hyperparameters/copy_paste", min_value=0.0, max_value=1.0),
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],
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# this is the objective metric we want to maximize/minimize
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objective_metric_title="metrics",
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objective_metric_series="mAP_0.5",
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# now we decide if we want to maximize it or minimize it (accuracy we maximize)
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objective_metric_sign="max",
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# let us limit the number of concurrent experiments,
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# this in turn will make sure we don't bombard the scheduler with experiments.
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# if we have an auto-scaler connected, this, by proxy, will limit the number of machine
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max_number_of_concurrent_tasks=1,
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# this is the optimizer class (actually doing the optimization)
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# Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
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optimizer_class=OptimizerOptuna,
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# If specified only the top K performing Tasks will be kept, the others will be automatically archived
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save_top_k_tasks_only=5, # 5,
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compute_time_limit=None,
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total_max_jobs=20,
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min_iteration_per_job=None,
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max_iteration_per_job=None,
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)
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# report every 10 seconds, this is way too often, but we are testing here
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optimizer.set_report_period(10 / 60)
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# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent
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# an_optimizer.start_locally(job_complete_callback=job_complete_callback)
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# set the time limit for the optimization process (2 hours)
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optimizer.set_time_limit(in_minutes=120.0)
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# Start the optimization process in the local environment
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optimizer.start_locally()
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# wait until process is done (notice we are controlling the optimization process in the background)
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optimizer.wait()
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# make sure background optimization stopped
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optimizer.stop()
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print("We are done, good bye")
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