# model settings
model = dict(
    type='RetinaNet',
    pretrained='torchvision://resnet50',
    backbone=dict(type='ResNet',
                  depth=50,
                  num_stages=4,
                  out_indices=(0, 1, 2, 3),
                  frozen_stages=1,
                  norm_cfg=dict(type='BN', requires_grad=True),
                  style='pytorch'),
    neck=[
        dict(type='FPN',
             in_channels=[256, 512, 1024, 2048],
             out_channels=256,
             start_level=1,
             add_extra_convs=True,
             num_outs=5),
        dict(
            type='SEPC',
            out_channels=256,
            Pconv_num=4,
            pconv_deform=True,
            lcconv_deform=True,
            iBN=True,  # when open, please set imgs/gpu >= 4
        )
    ],
    bbox_head=dict(type='SepcFreeAnchorRetinaHead',
                   num_classes=81,
                   in_channels=256,
                   stacked_convs=0,
                   feat_channels=256,
                   octave_base_scale=4,
                   scales_per_octave=3,
                   anchor_ratios=[0.5, 1.0, 2.0],
                   anchor_strides=[8, 16, 32, 64, 128],
                   target_means=[.0, .0, .0, .0],
                   target_stds=[0.1, 0.1, 0.2, 0.2],
                   loss_bbox=dict(type='SmoothL1Loss',
                                  beta=0.11,
                                  loss_weight=0.75)))
# training and testing settings
train_cfg = dict(assigner=dict(type='MaxIoUAssigner',
                               pos_iou_thr=0.5,
                               neg_iou_thr=0.4,
                               min_pos_iou=0,
                               ignore_iof_thr=-1),
                 allowed_border=-1,
                 pos_weight=-1,
                 debug=False)
test_cfg = dict(nms_pre=1000,
                min_bbox_size=0,
                score_thr=0.05,
                nms=dict(type='nms', iou_thr=0.5),
                max_per_img=100)
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
                    std=[58.395, 57.12, 57.375],
                    to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='MultiScaleFlipAug',
         img_scale=(1333, 800),
         flip=False,
         transforms=[
             dict(type='Resize', keep_ratio=True),
             dict(type='RandomFlip'),
             dict(type='Normalize', **img_norm_cfg),
             dict(type='Pad', size_divisor=32),
             dict(type='ImageToTensor', keys=['img']),
             dict(type='Collect', keys=['img']),
         ])
]
data = dict(
    imgs_per_gpu=4,
    workers_per_gpu=2,
    train=dict(type=dataset_type,
               ann_file=data_root + 'annotations/instances_train2017.json',
               img_prefix=data_root + 'train2017/',
               pipeline=train_pipeline),
    val=dict(type=dataset_type,
             ann_file=data_root + 'annotations/instances_val2017.json',
             img_prefix=data_root + 'val2017/',
             pipeline=test_pipeline),
    test=dict(type=dataset_type,
              ann_file=data_root + 'annotations/instances_val2017.json',
              img_prefix=data_root + 'val2017/',
              pipeline=test_pipeline))
evaluation = dict(interval=1, metric='bbox')
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(policy='step',
                 warmup='linear',
                 warmup_iters=500,
                 warmup_ratio=1.0 / 3,
                 step=[8, 11])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
    interval=1,
    hooks=[
        dict(type='TextLoggerHook'),
        # dict(type='TensorboardLoggerHook')
    ])
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/retinanet_free_anchor_r50_fpn_1x'
load_from = None
resume_from = None
workflow = [('train', 1)]
