_base_ = '../../base.py'
# model settings
model = dict(
    type='DenseCL',
    pretrained=None,
    queue_len=65536,
    feat_dim=128,
    momentum=0.999,
    loss_lambda=0.5,
    backbone=dict(
        type='ResNet',
        depth=50,
        in_channels=3,
        out_indices=[4],  # 0: conv-1, x: stage-x
        norm_cfg=dict(type='BN')),
    neck=dict(
        type='DenseCLNeck',
        in_channels=2048,
        hid_channels=2048,
        out_channels=128,
        num_grid=None),
    head=dict(type='ContrastiveHead', temperature=0.2))
# dataset settings
data_source_cfg = dict(
    type='CocoLMDB')
data_train_list = 'train2017.lmdb'
data_train_root = 'data/coco'
dataset_type = 'ContrastiveDataset'
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_pipeline = [
    dict(type='RandomResizedCrop', size=224, scale=(0.2, 1.)),
    dict(
        type='RandomAppliedTrans',
        transforms=[
            dict(
                type='ColorJitter',
                brightness=0.4,
                contrast=0.4,
                saturation=0.4,
                hue=0.1)
        ],
        p=0.8),
    dict(type='RandomGrayscale', p=0.2),
    dict(
        type='RandomAppliedTrans',
        transforms=[
            dict(
                type='GaussianBlur',
                sigma_min=0.1,
                sigma_max=2.0)
        ],
        p=0.5),
    dict(type='RandomHorizontalFlip'),
    dict(type='ToTensor'),
    dict(type='Normalize', **img_norm_cfg),
]
data = dict(
    imgs_per_gpu=32,  # total 32*8=256
    workers_per_gpu=4,
    drop_last=True,
    train=dict(
        type=dataset_type,
        data_source=dict(
            list_file=data_train_list, root=data_train_root,
            **data_source_cfg),
        pipeline=train_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.3, weight_decay=0.0001, momentum=0.9)
# learning policy
lr_config = dict(policy='CosineAnnealing', min_lr=0.)
checkpoint_config = dict(interval=40)
# runtime settings
total_epochs = 800
