models/vision/detection/configs/retinanet/SM/1/retinanet_r50_fpn_1x_coco.py [2:111]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
base_files = ['../../../common/sagemaker_1x8.py',
              '../../../common/datasets/coco.py',
              '../../../common/lr_policy.py',
              '../../../common/runtime.py',
              '../../../common/models/retinanet_fpn.py']

# dataset settings
dataset_type = 'CocoDataset'
data_root = '/data/COCO/'
preproc_mode = 'caffe'
image_mean = (123.68, 116.78, 103.94)
image_std = (1., 1., 1.)
data = dict(
    _overwrite_ = True,
    imgs_per_gpu=4,
    train=dict(
        type=dataset_type,
        train=True,
        dataset_dir=data_root,
        subset='train',
        flip_ratio=0.5,
        pad_mode='fixed',
        preproc_mode=preproc_mode,
        mean=image_mean,
        std=image_std,
        scale=(800, 1333)),
    val=dict(
        type=dataset_type,
        train=False,
        dataset_dir=data_root,
        subset='val',
        flip_ratio=0,
        pad_mode='fixed',
        preproc_mode=preproc_mode,
        mean=image_mean,
        std=image_std,
        scale=(800, 1333)),
    test=dict(
        type=dataset_type,
        train=False,
        dataset_dir=data_root,
        subset='val',
        flip_ratio=0,
        pad_mode='fixed',
        preproc_mode=preproc_mode,
        mean=image_mean,
        std=image_std,
        scale=(800, 1333)),
)

# overwrite train cfg to indicate sagemaker training
train_cfg = dict(
    _overwrite_=True,
    freeze_patterns=['^conv[12]_*', '_bn$'],
    weight_decay=1e-4,
    sagemaker=True,
)


# overwrite default optimizer
optimizer = dict(
    _overwrite_=True,
    type='SGD',
    learning_rate=5e-3,
    momentum=0.9,
    nesterov=False,
)

# extra options related to optimizers
optimizer_config = dict(
    _overwrite_=True,
    amp_enabled=True,
    gradient_clip=5.0,
)

# learning policy
lr_config = dict(
    _overwrite_=True,
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=1.0 / 10,
    step=[8, 11])

# log, tensorboard configuration with s3 path for logs
log_config=dict(
    _overwrite_=True,
    interval=50,
    hooks=[
        dict(
            type='TextLoggerHook'
        ),
        dict(
            type='TensorboardLoggerHook',
            log_dir=None,
            image_interval=100,
            s3_dir='', # set dynamically
        ),
        dict(
            type='Visualizer',
            dataset_cfg=None, # set dynamically
            interval=100,
            top_k=10,
            run_on_sagemaker=True,
        ),
    ]
)


work_dir = './work_dirs/retinanet_r50_fpn_1x_coco'
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



models/vision/detection/configs/retinanet/SM/8/retinanet_r50_fpn_1x_coco.py [2:111]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
base_files = ['../../../common/sagemaker_1x8.py',
              '../../../common/datasets/coco.py',
              '../../../common/lr_policy.py',
              '../../../common/runtime.py',
              '../../../common/models/retinanet_fpn.py']

# dataset settings
dataset_type = 'CocoDataset'
data_root = '/data/COCO/'
preproc_mode = 'caffe'
image_mean = (123.68, 116.78, 103.94)
image_std = (1., 1., 1.)
data = dict(
    _overwrite_ = True,
    imgs_per_gpu=4,
    train=dict(
        type=dataset_type,
        train=True,
        dataset_dir=data_root,
        subset='train',
        flip_ratio=0.5,
        pad_mode='fixed',
        preproc_mode=preproc_mode,
        mean=image_mean,
        std=image_std,
        scale=(800, 1333)),
    val=dict(
        type=dataset_type,
        train=False,
        dataset_dir=data_root,
        subset='val',
        flip_ratio=0,
        pad_mode='fixed',
        preproc_mode=preproc_mode,
        mean=image_mean,
        std=image_std,
        scale=(800, 1333)),
    test=dict(
        type=dataset_type,
        train=False,
        dataset_dir=data_root,
        subset='val',
        flip_ratio=0,
        pad_mode='fixed',
        preproc_mode=preproc_mode,
        mean=image_mean,
        std=image_std,
        scale=(800, 1333)),
)

# overwrite train cfg to indicate sagemaker training
train_cfg = dict(
    _overwrite_=True,
    freeze_patterns=['^conv[12]_*', '_bn$'],
    weight_decay=1e-4,
    sagemaker=True,
)


# overwrite default optimizer
optimizer = dict(
    _overwrite_=True,
    type='SGD',
    learning_rate=5e-3,
    momentum=0.9,
    nesterov=False,
)

# extra options related to optimizers
optimizer_config = dict(
    _overwrite_=True,
    amp_enabled=True,
    gradient_clip=5.0,
)

# learning policy
lr_config = dict(
    _overwrite_=True,
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=1.0 / 10,
    step=[8, 11])

# log, tensorboard configuration with s3 path for logs
log_config=dict(
    _overwrite_=True,
    interval=50,
    hooks=[
        dict(
            type='TextLoggerHook'
        ),
        dict(
            type='TensorboardLoggerHook',
            log_dir=None,
            image_interval=100,
            s3_dir='', # set dynamically
        ),
        dict(
            type='Visualizer',
            dataset_cfg=None, # set dynamically
            interval=100,
            top_k=10,
            run_on_sagemaker=True,
        ),
    ]
)


work_dir = './work_dirs/retinanet_r50_fpn_1x_coco'
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -