_base_ = '../../base.py'
# model settings
model = dict(
    type='RelativeLoc',
    pretrained=None,
    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='RelativeLocNeck',
        in_channels=2048,
        out_channels=4096,
        with_avg_pool=True),
    head=dict(
        type='ClsHead',
        with_avg_pool=False,
        in_channels=4096,
        num_classes=8))
# dataset settings
data_source_cfg = dict(
    type='ImageNet',
    memcached=True,
    mclient_path='/mnt/lustre/share/memcached_client')
data_train_list = 'data/imagenet/meta/train.txt'
data_train_root = 'data/imagenet/train'
data_test_list = 'data/imagenet/meta/val.txt'
data_test_root = 'data/imagenet/val'
dataset_type = 'RelativeLocDataset'
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_pipeline = [
    dict(type='Resize', size=292),
    dict(type='RandomCrop', size=255),
    dict(type='RandomGrayscale', p=0.66),
]
test_pipeline = [
    dict(type='Resize', size=292),
    dict(type='CenterCrop', size=255),
]
format_pipeline = [
    dict(type='ToTensor'),
    dict(type='Normalize', **img_norm_cfg),
]
data = dict(
    imgs_per_gpu=64,  # 64 x 8 = 512
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        data_source=dict(
            list_file=data_train_list, root=data_train_root,
            **data_source_cfg),
        pipeline=train_pipeline,
        format_pipeline=format_pipeline),
    val=dict(
        type=dataset_type,
        data_source=dict(
            list_file=data_test_list, root=data_test_root, **data_source_cfg),
        pipeline=test_pipeline,
        format_pipeline=format_pipeline))
# optimizer
optimizer = dict(
    type='SGD', lr=0.2, momentum=0.9, weight_decay=0.0001,
    nesterov=False,
    paramwise_options={
        '\Aneck.': dict(weight_decay=0.0005),
        '\Ahead.': dict(weight_decay=0.0005)})
# learning policy
lr_config = dict(
    policy='step',
    step=[30, 50],
    warmup='linear',
    warmup_iters=5,  # 5 ep
    warmup_ratio=0.1,
    warmup_by_epoch=True)
checkpoint_config = dict(interval=10)
# runtime settings
total_epochs = 70

0bf71e68bc6abee72d29688bb65757721d188eed
<mask token>

_base_ = '../../base.py'
model = dict(type='RelativeLoc', pretrained=None, backbone=dict(type=
    'ResNet', depth=50, in_channels=3, out_indices=[4], norm_cfg=dict(type=
    'BN')), neck=dict(type='RelativeLocNeck', in_channels=2048,
    out_channels=4096, with_avg_pool=True), head=dict(type='ClsHead',
    with_avg_pool=False, in_channels=4096, num_classes=8))
data_source_cfg = dict(type='ImageNet', memcached=True, mclient_path=
    '/mnt/lustre/share/memcached_client')
data_train_list = 'data/imagenet/meta/train.txt'
data_train_root = 'data/imagenet/train'
data_test_list = 'data/imagenet/meta/val.txt'
data_test_root = 'data/imagenet/val'
dataset_type = 'RelativeLocDataset'
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_pipeline = [dict(type='Resize', size=292), dict(type='RandomCrop',
    size=255), dict(type='RandomGrayscale', p=0.66)]
test_pipeline = [dict(type='Resize', size=292), dict(type='CenterCrop',
    size=255)]
format_pipeline = [dict(type='ToTensor'), dict(type='Normalize', **
    img_norm_cfg)]
data = dict(imgs_per_gpu=64, workers_per_gpu=2, train=dict(type=
    dataset_type, data_source=dict(list_file=data_train_list, root=
    data_train_root, **data_source_cfg), pipeline=train_pipeline,
    format_pipeline=format_pipeline), val=dict(type=dataset_type,
    data_source=dict(list_file=data_test_list, root=data_test_root, **
    data_source_cfg), pipeline=test_pipeline, format_pipeline=format_pipeline))
optimizer = dict(type='SGD', lr=0.2, momentum=0.9, weight_decay=0.0001,
    nesterov=False, paramwise_options={'\\Aneck.': dict(weight_decay=0.0005
    ), '\\Ahead.': dict(weight_decay=0.0005)})
lr_config = dict(policy='step', step=[30, 50], warmup='linear',
    warmup_iters=5, warmup_ratio=0.1, warmup_by_epoch=True)
checkpoint_config = dict(interval=10)
total_epochs = 70

_base_ = '../../base.py'
# model settings
model = dict(
    type='RelativeLoc',
    pretrained=None,
    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='RelativeLocNeck',
        in_channels=2048,
        out_channels=4096,
        with_avg_pool=True),
    head=dict(
        type='ClsHead',
        with_avg_pool=False,
        in_channels=4096,
        num_classes=8))
# dataset settings
data_source_cfg = dict(
    type='ImageNet',
    memcached=True,
    mclient_path='/mnt/lustre/share/memcached_client')
data_train_list = 'data/imagenet/meta/train.txt'
data_train_root = 'data/imagenet/train'
data_test_list = 'data/imagenet/meta/val.txt'
data_test_root = 'data/imagenet/val'
dataset_type = 'RelativeLocDataset'
img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
train_pipeline = [
    dict(type='Resize', size=292),
    dict(type='RandomCrop', size=255),
    dict(type='RandomGrayscale', p=0.66),
]
test_pipeline = [
    dict(type='Resize', size=292),
    dict(type='CenterCrop', size=255),
]
format_pipeline = [
    dict(type='ToTensor'),
    dict(type='Normalize', **img_norm_cfg),
]
data = dict(
    imgs_per_gpu=64,  # 64 x 8 = 512
    workers_per_gpu=2,
    train=dict(
        type=dataset_type,
        data_source=dict(
            list_file=data_train_list, root=data_train_root,
            **data_source_cfg),
        pipeline=train_pipeline,
        format_pipeline=format_pipeline),
    val=dict(
        type=dataset_type,
        data_source=dict(
            list_file=data_test_list, root=data_test_root, **data_source_cfg),
        pipeline=test_pipeline,
        format_pipeline=format_pipeline))
# optimizer
optimizer = dict(
    type='SGD', lr=0.2, momentum=0.9, weight_decay=0.0001,
    nesterov=False,
    paramwise_options={
        '\Aneck.': dict(weight_decay=0.0005),
        '\Ahead.': dict(weight_decay=0.0005)})
# learning policy
lr_config = dict(
    policy='step',
    step=[30, 50],
    warmup='linear',
    warmup_iters=5,  # 5 ep
    warmup_ratio=0.1,
    warmup_by_epoch=True)
checkpoint_config = dict(interval=10)
# runtime settings
total_epochs = 70
