fcenet_r50_fpn_1500e_icdar2015.py

fourier_degree = 5
model = dict(
    type='FCENet',
    pretrained='torchvision://resnet50',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(1, 2, 3),
        frozen_stages=-1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=False,
        style='pytorch'),
    neck=dict(
        type='FPN',
        in_channels=[512, 1024, 2048],
        out_channels=256,
        add_extra_convs=True,
        extra_convs_on_inputs=False,  # use P5
        num_outs=3,
        relu_before_extra_convs=True,
        act_cfg=None),
    bbox_head=dict(
        type='FCEHead',
        in_channels=256,
        scales=(8, 16, 32),
        loss=dict(type='FCELoss'),
        alpha=1.2,
        beta=1.0,
        text_repr_type='quad',
        fourier_degree=fourier_degree,
    ))

train_cfg = None
test_cfg = None

dataset_type = 'IcdarDataset'
data_root = 'data/icdar2015/'

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='LoadTextAnnotations',
        with_bbox=True,
        with_mask=True,
        poly2mask=False),
    dict(
        type='ColorJitter',
        brightness=32.0 / 255,
        saturation=0.5,
        contrast=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='RandomScaling', size=800, scale=(3. / 4, 5. / 2)),
    dict(
        type='RandomCropFlip', crop_ratio=0.5, iter_num=1, min_area_ratio=0.2),
    dict(
        type='RandomCropPolyInstances',
        instance_key='gt_masks',
        crop_ratio=0.8,
        min_side_ratio=0.3),
    dict(
        type='RandomRotatePolyInstances',
        rotate_ratio=0.5,
        max_angle=30,
        pad_with_fixed_color=False),
    dict(type='SquareResizePad', target_size=800, pad_ratio=0.6),
    dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
    dict(type='Pad', size_divisor=32),
    dict(
        type='FCENetTargets',
        fourier_degree=fourier_degree,
        level_proportion_range=((0, 0.4), (0.3, 0.7), (0.6, 1.0))),
    dict(
        type='CustomFormatBundle',
        keys=['p3_maps', 'p4_maps', 'p5_maps'],
        visualize=dict(flag=False, boundary_key=None)),
    dict(type='Collect', keys=['img', 'p3_maps', 'p4_maps', 'p5_maps'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(2260, 2260),
        flip=False,
        transforms=[
            dict(type='Resize', img_scale=(1280, 800), 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=8,
    workers_per_gpu=2,
    val_dataloader=dict(samples_per_gpu=1),
    test_dataloader=dict(samples_per_gpu=1),
    train=dict(
        type=dataset_type,
        ann_file=data_root + '/instances_training.json',
        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',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        ann_file=data_root + '/instances_test.json',
        img_prefix=data_root + '/imgs',
        pipeline=test_pipeline))
evaluation = dict(interval=5, metric='hmean-iou')

# optimizer
optimizer = dict(type='SGD', lr=1e-3, momentum=0.90, weight_decay=5e-4)
optimizer_config = dict(grad_clip=None)
lr_config = dict(policy='poly', power=0.9, min_lr=1e-7, by_epoch=True)
total_epochs = 1500

checkpoint_config = dict(interval=5)
# yapf:disable
log_config = dict(
    interval=20,
    hooks=[
        dict(type='TextLoggerHook')

    ])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]