panet_r18_fpem_ffm_600e_ctw1500.py

_base_ = [
    '../../_base_/schedules/schedule_adam_600e.py',
    '../../_base_/runtime_10e.py'
]
model = dict(
    type='PANet',
    pretrained='torchvision://resnet18',
    backbone=dict(
        type='ResNet',
        depth=18,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=-1,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=True,
        style='caffe'),
    neck=dict(type='FPEM_FFM', in_channels=[64, 128, 256, 512]),
    bbox_head=dict(
        type='PANHead',
        text_repr_type='poly',
        in_channels=[128, 128, 128, 128],
        out_channels=6,
        loss=dict(type='PANLoss')),
    train_cfg=None,
    test_cfg=None)

dataset_type = 'IcdarDataset'
data_root = 'data/ctw1500/'

img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# for visualizing img, pls uncomment it.
# img_norm_cfg = dict(
#    mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True)

train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='LoadTextAnnotations',
        with_bbox=True,
        with_mask=True,
        poly2mask=False),
    dict(type='ColorJitter', brightness=32.0 / 255, saturation=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(
        type='ScaleAspectJitter',
        img_scale=[(3000, 640)],
        ratio_range=(0.7, 1.3),
        aspect_ratio_range=(0.9, 1.1),
        multiscale_mode='value',
        keep_ratio=False),
    # shrink_ratio is from big to small. The 1st must be 1.0
    dict(type='PANetTargets', shrink_ratio=(1.0, 0.7)),
    dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
    dict(type='RandomRotateTextDet'),
    dict(
        type='RandomCropInstances',
        target_size=(640, 640),
        instance_key='gt_kernels'),
    dict(type='Pad', size_divisor=32),
    # for visualizing img and gts, pls set visualize = True
    dict(
        type='CustomFormatBundle',
        keys=['gt_kernels', 'gt_mask'],
        visualize=dict(flag=False, boundary_key='gt_kernels')),
    dict(type='Collect', keys=['img', 'gt_kernels', 'gt_mask'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(3000, 640),
        flip=False,
        transforms=[
            dict(type='Resize', img_scale=(3000, 640), keep_ratio=True),
            dict(type='Normalize', **img_norm_cfg),
            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=dataset_type,
        ann_file=data_root + '/instances_training.json',
        # for debugging top k imgs
        # select_first_k=200,
        img_prefix=data_root + '/imgs',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        ann_file=data_root + '/instances_test.json',
        img_prefix=data_root + '/imgs',
        # select_first_k=100,
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + '/instances_test.json',
        img_prefix=data_root + '/imgs',
        # select_first_k=100,
        pipeline=test_pipeline))
evaluation = dict(interval=10, metric='hmean-iou')