configs/faster_rcnn_x101_64x4d_fpn_1x.py

# model settings
model = dict(
    type="FasterRCNN",
    pretrained="open-mmlab://resnext101_64x4d",
    backbone=dict(
        type="ResNeXt",
        depth=101,
        groups=64,
        base_width=4,
        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, num_outs=5
    ),
    rpn_head=dict(
        type="RPNHead",
        in_channels=256,
        feat_channels=256,
        anchor_scales=[8],
        anchor_ratios=[0.5, 1.0, 2.0],
        anchor_strides=[4, 8, 16, 32, 64],
        target_means=[0.0, 0.0, 0.0, 0.0],
        target_stds=[1.0, 1.0, 1.0, 1.0],
        loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type="SmoothL1Loss", beta=1.0 / 9.0, loss_weight=1.0),
    ),
    bbox_roi_extractor=dict(
        type="SingleRoIExtractor",
        roi_layer=dict(type="RoIAlign", out_size=7, sample_num=2),
        out_channels=256,
        featmap_strides=[4, 8, 16, 32],
    ),
    bbox_head=dict(
        type="SharedFCBBoxHead",
        num_fcs=2,
        in_channels=256,
        fc_out_channels=1024,
        roi_feat_size=7,
        num_classes=81,
        target_means=[0.0, 0.0, 0.0, 0.0],
        target_stds=[0.1, 0.1, 0.2, 0.2],
        reg_class_agnostic=False,
        loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=False, loss_weight=1.0),
        loss_bbox=dict(type="SmoothL1Loss", beta=1.0, loss_weight=1.0),
    ),
)
# model training and testing settings
train_cfg = dict(
    rpn=dict(
        assigner=dict(
            type="MaxIoUAssigner",
            pos_iou_thr=0.7,
            neg_iou_thr=0.3,
            min_pos_iou=0.3,
            ignore_iof_thr=-1,
        ),
        sampler=dict(
            type="RandomSampler",
            num=256,
            pos_fraction=0.5,
            neg_pos_ub=-1,
            add_gt_as_proposals=False,
        ),
        allowed_border=0,
        pos_weight=-1,
        debug=False,
    ),
    rpn_proposal=dict(
        nms_across_levels=False,
        nms_pre=2000,
        nms_post=2000,
        max_num=2000,
        nms_thr=0.7,
        min_bbox_size=0,
    ),
    rcnn=dict(
        assigner=dict(
            type="MaxIoUAssigner",
            pos_iou_thr=0.5,
            neg_iou_thr=0.5,
            min_pos_iou=0.5,
            ignore_iof_thr=-1,
        ),
        sampler=dict(
            type="RandomSampler",
            num=512,
            pos_fraction=0.25,
            neg_pos_ub=-1,
            add_gt_as_proposals=True,
        ),
        pos_weight=-1,
        debug=False,
    ),
)
test_cfg = dict(
    rpn=dict(
        nms_across_levels=False,
        nms_pre=1000,
        nms_post=1000,
        max_num=1000,
        nms_thr=0.7,
        min_bbox_size=0,
    ),
    rcnn=dict(score_thr=0.05, nms=dict(type="nms", iou_thr=0.5), max_per_img=100)
    # soft-nms is also supported for rcnn testing
    # e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05)
)
# 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=2,
    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=0.02, 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=50,
    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/faster_rcnn_x101_64x4d_fpn_1x"
load_from = None
resume_from = None
workflow = [("train", 1)]