diff --git a/configs/_base_/det_models/dbnet_r18_fpnc.py b/configs/_base_/det_models/dbnet_r18_fpnc.py index 7507605d..3d47f628 100644 --- a/configs/_base_/det_models/dbnet_r18_fpnc.py +++ b/configs/_base_/det_models/dbnet_r18_fpnc.py @@ -1,3 +1,9 @@ +preprocess_cfg = dict( + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + pad_size_divisor=32) + model = dict( type='DBNet', backbone=dict( @@ -12,10 +18,9 @@ model = dict( style='caffe'), neck=dict( type='FPNC', in_channels=[64, 128, 256, 512], lateral_channels=256), - bbox_head=dict( + det_head=dict( type='DBHead', in_channels=256, - loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True), + loss=dict(type='DBLoss'), postprocessor=dict(type='DBPostprocessor', text_repr_type='quad')), - train_cfg=None, - test_cfg=None) + preprocess_cfg=preprocess_cfg) diff --git a/configs/_base_/det_models/dbnet_r50dcnv2_fpnc.py b/configs/_base_/det_models/dbnet_r50dcnv2_fpnc.py index 1cd1f1ba..3f085d42 100644 --- a/configs/_base_/det_models/dbnet_r50dcnv2_fpnc.py +++ b/configs/_base_/det_models/dbnet_r50dcnv2_fpnc.py @@ -1,3 +1,9 @@ +preprocess_cfg = dict( + mean=[122.67891434, 116.66876762, 104.00698793], + std=[58.395, 57.12, 57.375], + to_rgb=True, + pad_size_divisor=32) + model = dict( type='DBNet', backbone=dict( @@ -14,10 +20,9 @@ model = dict( stage_with_dcn=(False, True, True, True)), neck=dict( type='FPNC', in_channels=[256, 512, 1024, 2048], lateral_channels=256), - bbox_head=dict( + det_head=dict( type='DBHead', in_channels=256, - loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True), + loss=dict(type='DBLoss'), postprocessor=dict(type='DBPostprocessor', text_repr_type='quad')), - train_cfg=None, - test_cfg=None) + preprocess_cfg=preprocess_cfg) diff --git a/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py b/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py index 467903fe..d9a5222e 100644 --- a/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py +++ b/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py @@ -2,32 +2,78 @@ _base_ = [ '../../_base_/default_runtime.py', '../../_base_/schedules/schedule_sgd_1200e.py', '../../_base_/det_models/dbnet_r18_fpnc.py', - '../../_base_/det_datasets/icdar2015.py', - '../../_base_/det_pipelines/dbnet_pipeline.py' ] -train_list = {{_base_.train_list}} -test_list = {{_base_.test_list}} +default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=20), ) -train_pipeline_r18 = {{_base_.train_pipeline_r18}} -test_pipeline_1333_736 = {{_base_.test_pipeline_1333_736}} +train_pipeline_r18 = [ + dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), + dict( + type='LoadOCRAnnotations', + with_polygon=True, + with_bbox=True, + with_label=True, + ), + dict( + type='TorchVisionWrapper', + op='ColorJitter', + brightness=32.0 / 255, + saturation=0.5), + dict( + type='ImgAug', + args=[['Fliplr', 0.5], + dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]), + dict(type='RandomCrop', min_side_ratio=0.1), + dict(type='Resize', scale=(640, 640), keep_ratio=True), + dict(type='Pad', size=(640, 640)), + dict( + type='PackTextDetInputs', + meta_keys=('img_path', 'ori_shape', 'img_shape')) +] -data = dict( - samples_per_gpu=16, - workers_per_gpu=8, - val_dataloader=dict(samples_per_gpu=1), - test_dataloader=dict(samples_per_gpu=1), - train=dict( - type='UniformConcatDataset', - datasets=train_list, - pipeline=train_pipeline_r18), - val=dict( - type='UniformConcatDataset', - datasets=test_list, - pipeline=test_pipeline_1333_736), - test=dict( - type='UniformConcatDataset', - datasets=test_list, - pipeline=test_pipeline_1333_736)) +test_pipeline_1333_736 = [ + dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), + dict(type='Resize', scale=(1333, 736), keep_ratio=True), + dict( + type='PackTextDetInputs', + meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor', + 'instances')) +] -evaluation = dict(interval=100, metric='hmean-iou') +dataset_type = 'OCRDataset' +data_root = 'data/icdar2015' + +train_dataset = dict( + type=dataset_type, + data_root=data_root, + ann_file='instances_training.json', + data_prefix=dict(img_path='imgs/'), + filter_cfg=dict(filter_empty_gt=True, min_size=32), + pipeline=train_pipeline_r18) + +test_dataset = dict( + type=dataset_type, + data_root=data_root, + ann_file='instances_test.json', + data_prefix=dict(img_path='imgs/'), + test_mode=True, + pipeline=test_pipeline_1333_736) + +train_dataloader = dict( + batch_size=16, + num_workers=8, + persistent_workers=False, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=train_dataset) +val_dataloader = dict( + batch_size=16, + num_workers=8, + persistent_workers=False, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=test_dataset) +test_dataloader = val_dataloader + +val_evaluator = dict(type='HmeanIOUMetric') +test_evaluator = val_evaluator + +visualizer = dict(type='TextDetLocalVisualizer', name='visualizer') diff --git a/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py b/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py index 251b7bc2..b84028f8 100644 --- a/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py +++ b/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py @@ -2,34 +2,80 @@ _base_ = [ '../../_base_/default_runtime.py', '../../_base_/schedules/schedule_sgd_1200e.py', '../../_base_/det_models/dbnet_r50dcnv2_fpnc.py', - '../../_base_/det_datasets/icdar2015.py', - '../../_base_/det_pipelines/dbnet_pipeline.py' ] -train_list = {{_base_.train_list}} -test_list = {{_base_.test_list}} - -train_pipeline_r50dcnv2 = {{_base_.train_pipeline_r50dcnv2}} -test_pipeline_4068_1024 = {{_base_.test_pipeline_4068_1024}} +default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=20), ) load_from = 'checkpoints/textdet/dbnet/res50dcnv2_synthtext.pth' -data = dict( - samples_per_gpu=8, - workers_per_gpu=4, - val_dataloader=dict(samples_per_gpu=1), - test_dataloader=dict(samples_per_gpu=1), - train=dict( - type='UniformConcatDataset', - datasets=train_list, - pipeline=train_pipeline_r50dcnv2), - val=dict( - type='UniformConcatDataset', - datasets=test_list, - pipeline=test_pipeline_4068_1024), - test=dict( - type='UniformConcatDataset', - datasets=test_list, - pipeline=test_pipeline_4068_1024)) +train_pipeline_r50dcnv2 = [ + dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), + dict( + type='LoadOCRAnnotations', + with_bbox=True, + with_polygon=True, + with_label=True, + ), + dict( + type='TorchVisionWrapper', + op='ColorJitter', + brightness=32.0 / 255, + saturation=0.5), + dict( + type='ImgAug', + args=[['Fliplr', 0.5], + dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]), + dict(type='RandomCrop', min_side_ratio=0.1), + dict(type='Resize', scale=(640, 640), keep_ratio=True), + dict(type='Pad', size=(640, 640)), + dict( + type='PackTextDetInputs', + meta_keys=('img_path', 'ori_shape', 'img_shape')) +] -evaluation = dict(interval=100, metric='hmean-iou') +test_pipeline_4068_1024 = [ + dict(type='LoadImageFromFile', color_type='color_ignore_orientation'), + dict(type='Resize', scale=(4068, 1024), keep_ratio=True), + dict( + type='PackTextDetInputs', + meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor', + 'instances')) +] + +dataset_type = 'OCRDataset' +data_root = 'data/icdar2015' + +train_dataset = dict( + type=dataset_type, + data_root=data_root, + ann_file='instances_training.json', + data_prefix=dict(img_path='imgs/'), + filter_cfg=dict(filter_empty_gt=True, min_size=32), + pipeline=train_pipeline_r50dcnv2) + +test_dataset = dict( + type=dataset_type, + data_root=data_root, + ann_file='instances_test.json', + data_prefix=dict(img_path='imgs/'), + test_mode=True, + pipeline=test_pipeline_4068_1024) + +train_dataloader = dict( + batch_size=16, + num_workers=8, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=train_dataset) +val_dataloader = dict( + batch_size=16, + num_workers=8, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=False), + dataset=test_dataset) +test_dataloader = val_dataloader + +val_evaluator = dict(type='HmeanIOUMetric') +test_evaluator = val_evaluator + +visualizer = dict(type='TextDetLocalVisualizer', name='visualizer')