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)

# Example use case:

optimizer = HyperParameterOptimizer(

    # This is the experiment we want to optimize

    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"

    # under it a parameter named "batch_size", this becomes "General/batch_size"

    # 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)],

    # this is the objective metric we want to maximize/minimize

    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',

    # 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

    max_number_of_concurrent_tasks=1,

    # this is the optimizer class (actually doing the optimization)

    # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)

    optimizer_class=OptimizerOptuna,

    # If specified only the top K performing Tasks will be kept, the others will be automatically archived

    save_top_k_tasks_only=5,  # 5,

    compute_time_limit=None,

    total_max_jobs=20,

    min_iteration_per_job=None,

    max_iteration_per_job=None,

)

# report every 10 seconds, this is way too often, but we are testing here

optimizer.set_report_period(10 / 60)

# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent

# an_optimizer.start_locally(job_complete_callback=job_complete_callback)

# set the time limit for the optimization process (2 hours)

optimizer.set_time_limit(in_minutes=120.0)

# Start the optimization process in the local environment

optimizer.start_locally()

# wait until process is done (notice we are controlling the optimization process in the background)

optimizer.wait()

# make sure background optimization stopped

optimizer.stop()

print('We are done, good bye')