RelationNet2/configs/bvr/retinanet_r50_fpn_relation_coco_1x.py at main · microsoft/RelationNet2 · GitHub

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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),
        norm_eval=True,
        style='pytorch'),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_input',
        num_outs=5,
        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)),
    bbox_head=dict(
        type='BVRHead',
        bbox_head_cfg=dict(
            type='RetinaHead',
            num_classes=80,
            in_channels=256,
            stacked_convs=4,
            feat_channels=256,
            anchor_generator=dict(
                type='AnchorGenerator',
                octave_base_scale=4,
                scales_per_octave=3,
                ratios=[0.5, 1.0, 2.0],
                strides=[8, 16, 32, 64, 128]),
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[1.0, 1.0, 1.0, 1.0]),
            loss_cls=dict(
                type='FocalLoss',
                use_sigmoid=True,
                gamma=2.0,
                alpha=0.25,
                loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0),
            norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)),
        keypoint_pos='input',
        keypoint_head_cfg=dict(
            type='KeypointHead',
            num_classes=80,
            in_channels=256,
            stacked_convs=2,
            strides=[8, 16, 32, 64, 128],
            shared_stacked_convs=0,
            logits_convs=1,
            head_types=['top_left_corner', 'bottom_right_corner', 'center'],
            corner_pooling=False,
            loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0),
            loss_cls=dict(type='GaussianFocalLoss', loss_weight=0.25),
            norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)),
        cls_keypoint_cfg=dict(
            keypoint_types=['center'],
            with_key_score=False,
            with_relation=True),
        reg_keypoint_cfg=dict(
            keypoint_types=['top_left_corner', 'bottom_right_corner'],
            with_key_score=False,
            with_relation=True),
        keypoint_cfg=dict(max_keypoint_num=20, keypoint_score_thr=0.0),
        feature_selection_cfg=dict(
            selection_method='index',
            cross_level_topk=50,
            cross_level_selection=True),
        num_attn_heads=8,
        scale_position=False,
        pos_cfg=dict(base_size=[400, 400], log_scale=True)))
train_cfg = dict(
    bbox=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),
    keypoint=dict(
        assigner=dict(type='PointKptAssigner'),
        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_threshold=0.5),
    max_per_img=100)
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', file_client_args=dict(backend='zip')),
    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',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile', file_client_args=dict(backend='zip')),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='CocoDataset',
        ann_file='data/coco/annotations/instances_train2017.json',
        img_prefix='data/coco/train2017/',
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='zip')),
            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',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ]),
    val=dict(
        type='CocoDataset',
        ann_file='data/coco/annotations/instances_val2017.json',
        img_prefix='data/coco/val2017/',
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='zip')),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='CocoDataset',
        ann_file='data/coco/annotations/instances_val2017.json',
        img_prefix='data/coco/val2017/',
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='zip')),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[8, 11])
total_epochs = 12
checkpoint_config = dict(interval=1, create_symlink=False)
log_config = dict(
    interval=50,
    hooks=[
        dict(type='TextLoggerHook'),
        dict(
            type='GlobalWandbLoggerHook',
            init_kwargs=dict(project='keypoint', entity='det'))
    ])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
gpu_ids = range(0, 8)