model/text-det/psenet.py

file_client_args = dict(backend='disk')
model = dict(
    type='PSENet',
    backbone=dict(
        type='mmdet.ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=-1,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
        norm_eval=True,
        style='caffe'),
    neck=dict(
        type='FPNF',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        fusion_type='concat'),
    det_head=dict(
        type='PSEHead',
        in_channels=[256],
        hidden_dim=256,
        out_channel=7,
        module_loss=dict(type='PSEModuleLoss'),
        postprocessor=dict(type='PSEPostprocessor', text_repr_type='poly')),
    data_preprocessor=dict(
        type='TextDetDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32))
default_scope = 'mmocr'
env_cfg = dict(
    cudnn_benchmark=True,
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    dist_cfg=dict(backend='nccl'))
randomness = dict(seed=None)
default_hooks = dict(
    timer=dict(type='IterTimerHook'),
    logger=dict(type='LoggerHook', interval=100),
    param_scheduler=dict(type='ParamSchedulerHook'),
    checkpoint=dict(type='CheckpointHook', interval=5),
    sampler_seed=dict(type='DistSamplerSeedHook'),
    sync_buffer=dict(type='SyncBuffersHook'),
    visualization=dict(
        type='VisualizationHook',
        interval=1,
        enable=False,
        show=False,
        draw_gt=False,
        draw_pred=False))
log_level = 'INFO'
log_processor = dict(type='LogProcessor', window_size=100, by_epoch=True)
load_from = None
resume = True
val_evaluator = dict(type='HmeanIOUMetric')
test_evaluator = dict(type='HmeanIOUMetric')
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
    type='TextDetLocalVisualizer',
    name='visualizer',
    vis_backends=[dict(type='LocalVisBackend')])
max_epochs = 50
optim_wrapper = dict(
    type='OptimWrapper', optimizer=dict(type='Adam', lr=0.001))
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=50, val_interval=20)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [dict(type='PolyLR', power=0.9, end=50)]
train_dataloader = dict(
    batch_size=10,
    num_workers=16,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type='OCRDataset',
        data_root='data/det/vl+vc-textdet',
        ann_file='textdet_train.json',
        data_prefix=dict(img_path='imgs/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk'),
                color_type='color_ignore_orientation'),
            dict(
                type='LoadOCRAnnotations',
                with_polygon=True,
                with_bbox=True,
                with_label=True),
            dict(
                type='TorchVisionWrapper',
                op='ColorJitter',
                brightness=0.12549019607843137,
                saturation=0.5),
            dict(type='FixInvalidPolygon'),
            dict(
                type='ShortScaleAspectJitter',
                short_size=736,
                scale_divisor=32),
            dict(type='RandomRotate', max_angle=10),
            dict(type='TextDetRandomCrop', target_size=(736, 736)),
            dict(type='Pad', size=(736, 736)),
            dict(
                type='PackTextDetInputs',
                meta_keys=('img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))
val_dataloader = dict(
    batch_size=4,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='OCRDataset',
        data_root='data/det/textdet-thvote',
        ann_file='textdet_test.json',
        data_prefix=dict(img_path='imgs/'),
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk'),
                color_type='color_ignore_orientation'),
            dict(type='Resize', scale=(2240, 2240), keep_ratio=True),
            dict(
                type='LoadOCRAnnotations',
                with_polygon=True,
                with_bbox=True,
                with_label=True),
            dict(
                type='PackTextDetInputs',
                meta_keys=('img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))
test_dataloader = dict(
    batch_size=4,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='OCRDataset',
        data_root='data/det/textdet-thvote',
        ann_file='textdet_test.json',
        data_prefix=dict(img_path='imgs/'),
        test_mode=True,
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk'),
                color_type='color_ignore_orientation'),
            dict(type='Resize', scale=(2240, 2240), keep_ratio=True),
            dict(
                type='LoadOCRAnnotations',
                with_polygon=True,
                with_bbox=True,
                with_label=True),
            dict(
                type='PackTextDetInputs',
                meta_keys=('img_path', 'ori_shape', 'img_shape',
                           'scale_factor'))
        ]))
auto_scale_lr = dict(base_batch_size=32)
launcher = 'none'
work_dir = './work_dirs/psenet_resnet50_fpnf_votecount'