# dataset settings for SSL metrics
val_data_source_cfg = dict(type='ImageNet')
# ImageNet dataset for SSL metrics
val_data_train_list = 'data/meta/ImageNet/train_labeled_full.txt'
val_data_train_root = 'data/ImageNet/train'
val_data_test_list = 'data/meta/ImageNet/val_labeled.txt'
val_data_test_root = 'data/ImageNet/val/'

test_pipeline = [
    dict(type='Resize', size=256),
    dict(type='CenterCrop', size=224),
    dict(type='ToTensor'),
    dict(type='Normalize', mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
val_data = dict(
    train=dict(
        type='ClassificationDataset',
        data_source=dict(
            list_file=val_data_train_list, root=val_data_train_root,
            **val_data_source_cfg),
        pipeline=test_pipeline,
        prefetch=False,
    ),
    val=dict(
        type='ClassificationDataset',
        data_source=dict(
            list_file=val_data_test_list, root=val_data_test_root,
            **val_data_source_cfg),
        pipeline=test_pipeline,
        prefetch=False,
    ))

# additional hooks
custom_hooks = [
    dict(type='SSLMetricHook',
        val_dataset=val_data['val'],
        train_dataset=val_data['train'],  # remove it if metric_mode is None
        forward_mode='vis',
        metric_mode='knn',  # linear metric (take a bit long time on imagenet)
        metric_args=dict(knn=20, temperature=0.07, chunk_size=256),
        visual_mode='umap',  # 'tsne' or 'umap'
        visual_args=dict(n_epochs=200, plot_backend='seaborn'),
        save_val=False,  # whether to save results
        initial=True,
        interval=25,
        imgs_per_gpu=256,
        workers_per_gpu=6,
        eval_param=dict(topk=(1, 5))),
]
