yolov5_s_mask-refine-v61_syncbn_fast_8xb16-300e_coco.py

_base_ = '../yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'

# This config will refine bbox by mask while loading annotations and
# transforming after `YOLOv5RandomAffine`

# ========================modified parameters======================
use_mask2refine = True
min_area_ratio = 0.01  # YOLOv5RandomAffine

# ===============================Unmodified in most cases====================
pre_transform = [
    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
    dict(
        type='LoadAnnotations',
        with_bbox=True,
        with_mask=True,
        mask2bbox=use_mask2refine)
]

last_transform = [
    # Delete gt_masks to avoid more computation
    dict(type='RemoveDataElement', keys=['gt_masks']),
    dict(
        type='mmdet.Albu',
        transforms=_base_.albu_train_transforms,
        bbox_params=dict(
            type='BboxParams',
            format='pascal_voc',
            label_fields=['gt_bboxes_labels', 'gt_ignore_flags']),
        keymap={
            'img': 'image',
            'gt_bboxes': 'bboxes'
        }),
    dict(type='YOLOv5HSVRandomAug'),
    dict(type='mmdet.RandomFlip', prob=0.5),
    dict(
        type='mmdet.PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
                   'flip_direction'))
]

train_pipeline = [
    *pre_transform,
    dict(
        type='Mosaic',
        img_scale=_base_.img_scale,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(
        type='YOLOv5RandomAffine',
        max_rotate_degree=0.0,
        max_shear_degree=0.0,
        scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale),
        # img_scale is (width, height)
        border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
        border_val=(114, 114, 114),
        min_area_ratio=min_area_ratio,
        use_mask_refine=use_mask2refine),
    *last_transform
]

train_dataloader = dict(dataset=dict(pipeline=train_pipeline))