From 5a8859fe6666c096b75fa44db4f6c53d81a2ed62 Mon Sep 17 00:00:00 2001
From: Hongbin Sun <hongbin306@gmail.com>
Date: Fri, 3 Dec 2021 19:37:43 +0800
Subject: [PATCH] [Refactor] Refactor text detection config (#626)
* refactor textdet configs
* remove duplicate keys in _base_
* remove import from config
* syncbn to bn on cpu
* minimize change
---
configs/_base_/det_dataset/toy_dataset.py | 97 -----------
configs/_base_/det_datasets/ctw1500.py | 18 ++
configs/_base_/det_datasets/icdar2015.py | 18 ++
configs/_base_/det_datasets/icdar2017.py | 18 ++
configs/_base_/det_datasets/toy_data.py | 39 +++++
configs/_base_/det_models/dbnet_r18_fpnc.py | 21 +++
.../_base_/det_models/dbnet_r50dcnv2_fpnc.py | 23 +++
.../_base_/det_models/drrg_r50_fpn_unet.py | 21 +++
configs/_base_/det_models/fcenet_r50_fpn.py | 30 ++++
.../_base_/det_models/fcenet_r50dcnv2_fpn.py | 29 ++++
.../ocr_mask_rcnn_r50_fpn_ohem.py | 0
.../ocr_mask_rcnn_r50_fpn_ohem_poly.py | 0
.../_base_/det_models/panet_r18_fpem_ffm.py | 43 +++++
.../_base_/det_models/panet_r50_fpem_ffm.py | 20 +++
configs/_base_/det_models/psenet_r50_fpnf.py | 51 ++++++
.../det_models/textsnake_r50_fpn_unet.py | 21 +++
.../_base_/det_pipelines/dbnet_pipeline.py | 88 ++++++++++
configs/_base_/det_pipelines/drrg_pipeline.py | 60 +++++++
.../_base_/det_pipelines/fcenet_pipeline.py | 118 +++++++++++++
.../_base_/det_pipelines/maskrcnn_pipeline.py | 57 +++++++
.../_base_/det_pipelines/panet_pipeline.py | 156 ++++++++++++++++++
.../_base_/det_pipelines/psenet_pipeline.py | 70 ++++++++
.../det_pipelines/textsnake_pipeline.py | 65 ++++++++
.../schedules/schedule_adam_step_600e.py | 6 +
...chedule_1200e.py => schedule_sgd_1200e.py} | 0
.../_base_/schedules/schedule_sgd_1500e.py | 5 +
...{schedule_160e.py => schedule_sgd_160e.py} | 0
.../dbnet/dbnet_r18_fpnc_1200e_icdar2015.py | 105 +++---------
.../dbnet_r50dcnv2_fpnc_1200e_icdar2015.py | 113 +++----------
.../drrg/drrg_r50_fpn_unet_1200e_ctw1500.py | 109 ++----------
.../fcenet/fcenet_r50_fpn_1500e_icdar2015.py | 149 +++--------------
.../fcenet_r50dcnv2_fpn_1500e_ctw1500.py | 148 +++--------------
.../mask_rcnn_r50_fpn_160e_ctw1500.py | 76 +++------
.../mask_rcnn_r50_fpn_160e_icdar2015.py | 77 +++------
.../mask_rcnn_r50_fpn_160e_icdar2017.py | 76 +++------
.../panet/panet_r18_fpem_ffm_600e_ctw1500.py | 111 +++----------
.../panet_r18_fpem_ffm_600e_icdar2015.py | 111 +++----------
.../panet_r50_fpem_ffm_600e_icdar2017.py | 102 +++---------
.../psenet/psenet_r50_fpnf_600e_ctw1500.py | 119 +++----------
.../psenet/psenet_r50_fpnf_600e_icdar2015.py | 119 +++----------
.../psenet/psenet_r50_fpnf_600e_icdar2017.py | 106 ++----------
.../textsnake_r50_fpn_unet_1200e_ctw1500.py | 112 ++-----------
tests/test_apis/test_model_inference.py | 2 +
43 files changed, 1281 insertions(+), 1428 deletions(-)
delete mode 100644 configs/_base_/det_dataset/toy_dataset.py
create mode 100644 configs/_base_/det_datasets/ctw1500.py
create mode 100644 configs/_base_/det_datasets/icdar2015.py
create mode 100644 configs/_base_/det_datasets/icdar2017.py
create mode 100644 configs/_base_/det_datasets/toy_data.py
create mode 100644 configs/_base_/det_models/dbnet_r18_fpnc.py
create mode 100644 configs/_base_/det_models/dbnet_r50dcnv2_fpnc.py
create mode 100644 configs/_base_/det_models/drrg_r50_fpn_unet.py
create mode 100644 configs/_base_/det_models/fcenet_r50_fpn.py
create mode 100644 configs/_base_/det_models/fcenet_r50dcnv2_fpn.py
rename configs/_base_/{models => det_models}/ocr_mask_rcnn_r50_fpn_ohem.py (100%)
rename configs/_base_/{models => det_models}/ocr_mask_rcnn_r50_fpn_ohem_poly.py (100%)
create mode 100644 configs/_base_/det_models/panet_r18_fpem_ffm.py
create mode 100644 configs/_base_/det_models/panet_r50_fpem_ffm.py
create mode 100644 configs/_base_/det_models/psenet_r50_fpnf.py
create mode 100644 configs/_base_/det_models/textsnake_r50_fpn_unet.py
create mode 100644 configs/_base_/det_pipelines/dbnet_pipeline.py
create mode 100644 configs/_base_/det_pipelines/drrg_pipeline.py
create mode 100644 configs/_base_/det_pipelines/fcenet_pipeline.py
create mode 100644 configs/_base_/det_pipelines/maskrcnn_pipeline.py
create mode 100644 configs/_base_/det_pipelines/panet_pipeline.py
create mode 100644 configs/_base_/det_pipelines/psenet_pipeline.py
create mode 100644 configs/_base_/det_pipelines/textsnake_pipeline.py
create mode 100644 configs/_base_/schedules/schedule_adam_step_600e.py
rename configs/_base_/schedules/{schedule_1200e.py => schedule_sgd_1200e.py} (100%)
create mode 100644 configs/_base_/schedules/schedule_sgd_1500e.py
rename configs/_base_/schedules/{schedule_160e.py => schedule_sgd_160e.py} (100%)
diff --git a/configs/_base_/det_dataset/toy_dataset.py b/configs/_base_/det_dataset/toy_dataset.py
deleted file mode 100644
index 9789c511..00000000
--- a/configs/_base_/det_dataset/toy_dataset.py
+++ /dev/null
@@ -1,97 +0,0 @@
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-train_cfg = None
-test_cfg = None
-
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- 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),
- 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', color_type='color_ignore_orientation'),
- 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']),
- ])
-]
-
-dataset_type = 'TextDetDataset'
-img_prefix = 'tests/data/toy_dataset/imgs'
-train_anno_file = 'tests/data/toy_dataset/instances_test.txt'
-train1 = dict(
- type=dataset_type,
- img_prefix=img_prefix,
- ann_file=train_anno_file,
- loader=dict(
- type='HardDiskLoader',
- repeat=4,
- parser=dict(
- type='LineJsonParser',
- keys=['file_name', 'height', 'width', 'annotations'])),
- pipeline=train_pipeline,
- test_mode=False)
-
-data_root = 'tests/data/toy_dataset'
-train2 = dict(
- type='IcdarDataset',
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=train_pipeline)
-
-test_anno_file = 'tests/data/toy_dataset/instances_test.txt'
-test = dict(
- type=dataset_type,
- img_prefix=img_prefix,
- ann_file=test_anno_file,
- loader=dict(
- type='HardDiskLoader',
- repeat=1,
- parser=dict(
- type='LineJsonParser',
- keys=['file_name', 'height', 'width', 'annotations'])),
- pipeline=test_pipeline,
- test_mode=True)
-
-data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(type='ConcatDataset', datasets=[train1, train2]),
- val=dict(type='ConcatDataset', datasets=[test]),
- test=dict(type='ConcatDataset', datasets=[test]))
-
-evaluation = dict(interval=1, metric='hmean-iou')
diff --git a/configs/_base_/det_datasets/ctw1500.py b/configs/_base_/det_datasets/ctw1500.py
new file mode 100644
index 00000000..466ea7e1
--- /dev/null
+++ b/configs/_base_/det_datasets/ctw1500.py
@@ -0,0 +1,18 @@
+dataset_type = 'IcdarDataset'
+data_root = 'data/ctw1500'
+
+train = dict(
+ type=dataset_type,
+ ann_file=f'{data_root}/instances_training.json',
+ img_prefix=f'{data_root}/imgs',
+ pipeline=None)
+
+test = dict(
+ type=dataset_type,
+ ann_file=f'{data_root}/instances_test.json',
+ img_prefix=f'{data_root}/imgs',
+ pipeline=None)
+
+train_list = [train]
+
+test_list = [test]
diff --git a/configs/_base_/det_datasets/icdar2015.py b/configs/_base_/det_datasets/icdar2015.py
new file mode 100644
index 00000000..f711c06d
--- /dev/null
+++ b/configs/_base_/det_datasets/icdar2015.py
@@ -0,0 +1,18 @@
+dataset_type = 'IcdarDataset'
+data_root = 'data/icdar2015'
+
+train = dict(
+ type=dataset_type,
+ ann_file=f'{data_root}/instances_training.json',
+ img_prefix=f'{data_root}/imgs',
+ pipeline=None)
+
+test = dict(
+ type=dataset_type,
+ ann_file=f'{data_root}/instances_test.json',
+ img_prefix=f'{data_root}/imgs',
+ pipeline=None)
+
+train_list = [train]
+
+test_list = [test]
diff --git a/configs/_base_/det_datasets/icdar2017.py b/configs/_base_/det_datasets/icdar2017.py
new file mode 100644
index 00000000..446ea7ef
--- /dev/null
+++ b/configs/_base_/det_datasets/icdar2017.py
@@ -0,0 +1,18 @@
+dataset_type = 'IcdarDataset'
+data_root = 'data/icdar2017'
+
+train = dict(
+ type=dataset_type,
+ ann_file=f'{data_root}/instances_training.json',
+ img_prefix=f'{data_root}/imgs',
+ pipeline=None)
+
+test = dict(
+ type=dataset_type,
+ ann_file=f'{data_root}/instances_val.json',
+ img_prefix=f'{data_root}/imgs',
+ pipeline=None)
+
+train_list = [train]
+
+test_list = [test]
diff --git a/configs/_base_/det_datasets/toy_data.py b/configs/_base_/det_datasets/toy_data.py
new file mode 100644
index 00000000..11c55591
--- /dev/null
+++ b/configs/_base_/det_datasets/toy_data.py
@@ -0,0 +1,39 @@
+root = 'tests/data/toy_dataset'
+
+# dataset with type='TextDetDataset'
+train1 = dict(
+ type='TextDetDataset',
+ img_prefix=f'{root}/imgs',
+ ann_file=f'{root}/instances_test.txt',
+ loader=dict(
+ type='HardDiskLoader',
+ repeat=4,
+ parser=dict(
+ type='LineJsonParser',
+ keys=['file_name', 'height', 'width', 'annotations'])),
+ pipeline=None,
+ test_mode=False)
+
+# dataset with type='IcdarDataset'
+train2 = dict(
+ type='IcdarDataset',
+ ann_file=f'{root}/instances_test.json',
+ img_prefix=f'{root}/imgs',
+ pipeline=None)
+
+test = dict(
+ type='TextDetDataset',
+ img_prefix=f'{root}/imgs',
+ ann_file=f'{root}/instances_test.txt',
+ loader=dict(
+ type='HardDiskLoader',
+ repeat=1,
+ parser=dict(
+ type='LineJsonParser',
+ keys=['file_name', 'height', 'width', 'annotations'])),
+ pipeline=None,
+ test_mode=True)
+
+train_list = [train1, train2]
+
+test_list = [test]
diff --git a/configs/_base_/det_models/dbnet_r18_fpnc.py b/configs/_base_/det_models/dbnet_r18_fpnc.py
new file mode 100644
index 00000000..d06db5c2
--- /dev/null
+++ b/configs/_base_/det_models/dbnet_r18_fpnc.py
@@ -0,0 +1,21 @@
+model = dict(
+ type='DBNet',
+ backbone=dict(
+ type='mmdet.ResNet',
+ depth=18,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=-1,
+ norm_cfg=dict(type='BN', requires_grad=True),
+ init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18'),
+ norm_eval=False,
+ style='caffe'),
+ neck=dict(
+ type='FPNC', in_channels=[64, 128, 256, 512], lateral_channels=256),
+ bbox_head=dict(
+ type='DBHead',
+ text_repr_type='quad',
+ in_channels=256,
+ loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True)),
+ train_cfg=None,
+ test_cfg=None)
diff --git a/configs/_base_/det_models/dbnet_r50dcnv2_fpnc.py b/configs/_base_/det_models/dbnet_r50dcnv2_fpnc.py
new file mode 100644
index 00000000..452ab320
--- /dev/null
+++ b/configs/_base_/det_models/dbnet_r50dcnv2_fpnc.py
@@ -0,0 +1,23 @@
+model = dict(
+ type='DBNet',
+ backbone=dict(
+ type='mmdet.ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=-1,
+ norm_cfg=dict(type='BN', requires_grad=True),
+ norm_eval=False,
+ style='pytorch',
+ dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+ init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
+ stage_with_dcn=(False, True, True, True)),
+ neck=dict(
+ type='FPNC', in_channels=[256, 512, 1024, 2048], lateral_channels=256),
+ bbox_head=dict(
+ type='DBHead',
+ text_repr_type='quad',
+ in_channels=256,
+ loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True)),
+ train_cfg=None,
+ test_cfg=None)
diff --git a/configs/_base_/det_models/drrg_r50_fpn_unet.py b/configs/_base_/det_models/drrg_r50_fpn_unet.py
new file mode 100644
index 00000000..1c10c61a
--- /dev/null
+++ b/configs/_base_/det_models/drrg_r50_fpn_unet.py
@@ -0,0 +1,21 @@
+model = dict(
+ type='DRRG',
+ backbone=dict(
+ type='mmdet.ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=-1,
+ norm_cfg=dict(type='BN', requires_grad=True),
+ init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
+ norm_eval=True,
+ style='caffe'),
+ neck=dict(
+ type='FPN_UNet', in_channels=[256, 512, 1024, 2048], out_channels=32),
+ bbox_head=dict(
+ type='DRRGHead',
+ in_channels=32,
+ text_region_thr=0.3,
+ center_region_thr=0.4,
+ link_thr=0.80,
+ loss=dict(type='DRRGLoss')))
diff --git a/configs/_base_/det_models/fcenet_r50_fpn.py b/configs/_base_/det_models/fcenet_r50_fpn.py
new file mode 100644
index 00000000..90e92999
--- /dev/null
+++ b/configs/_base_/det_models/fcenet_r50_fpn.py
@@ -0,0 +1,30 @@
+model = dict(
+ type='FCENet',
+ backbone=dict(
+ type='mmdet.ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(1, 2, 3),
+ frozen_stages=-1,
+ norm_cfg=dict(type='BN', requires_grad=True),
+ init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
+ norm_eval=False,
+ style='pytorch'),
+ neck=dict(
+ type='mmdet.FPN',
+ in_channels=[512, 1024, 2048],
+ out_channels=256,
+ add_extra_convs='on_output',
+ 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=5,
+ ))
diff --git a/configs/_base_/det_models/fcenet_r50dcnv2_fpn.py b/configs/_base_/det_models/fcenet_r50dcnv2_fpn.py
new file mode 100644
index 00000000..27bb95e5
--- /dev/null
+++ b/configs/_base_/det_models/fcenet_r50dcnv2_fpn.py
@@ -0,0 +1,29 @@
+model = dict(
+ type='FCENet',
+ backbone=dict(
+ type='mmdet.ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(1, 2, 3),
+ frozen_stages=-1,
+ norm_cfg=dict(type='BN', requires_grad=True),
+ norm_eval=True,
+ style='pytorch',
+ dcn=dict(type='DCNv2', deform_groups=2, fallback_on_stride=False),
+ init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
+ stage_with_dcn=(False, True, True, True)),
+ neck=dict(
+ type='mmdet.FPN',
+ in_channels=[512, 1024, 2048],
+ out_channels=256,
+ add_extra_convs='on_output',
+ 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'),
+ fourier_degree=5,
+ ))
diff --git a/configs/_base_/models/ocr_mask_rcnn_r50_fpn_ohem.py b/configs/_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem.py
similarity index 100%
rename from configs/_base_/models/ocr_mask_rcnn_r50_fpn_ohem.py
rename to configs/_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem.py
diff --git a/configs/_base_/models/ocr_mask_rcnn_r50_fpn_ohem_poly.py b/configs/_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem_poly.py
similarity index 100%
rename from configs/_base_/models/ocr_mask_rcnn_r50_fpn_ohem_poly.py
rename to configs/_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem_poly.py
diff --git a/configs/_base_/det_models/panet_r18_fpem_ffm.py b/configs/_base_/det_models/panet_r18_fpem_ffm.py
new file mode 100644
index 00000000..56465126
--- /dev/null
+++ b/configs/_base_/det_models/panet_r18_fpem_ffm.py
@@ -0,0 +1,43 @@
+model_poly = dict(
+ type='PANet',
+ backbone=dict(
+ type='mmdet.ResNet',
+ depth=18,
+ 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://resnet18'),
+ 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)
+
+model_quad = dict(
+ type='PANet',
+ backbone=dict(
+ type='mmdet.ResNet',
+ depth=18,
+ 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://resnet18'),
+ norm_eval=True,
+ style='caffe'),
+ neck=dict(type='FPEM_FFM', in_channels=[64, 128, 256, 512]),
+ bbox_head=dict(
+ type='PANHead',
+ text_repr_type='quad',
+ in_channels=[128, 128, 128, 128],
+ out_channels=6,
+ loss=dict(type='PANLoss')),
+ train_cfg=None,
+ test_cfg=None)
diff --git a/configs/_base_/det_models/panet_r50_fpem_ffm.py b/configs/_base_/det_models/panet_r50_fpem_ffm.py
new file mode 100644
index 00000000..99a115e0
--- /dev/null
+++ b/configs/_base_/det_models/panet_r50_fpem_ffm.py
@@ -0,0 +1,20 @@
+model = dict(
+ type='PANet',
+ pretrained='torchvision://resnet50',
+ backbone=dict(
+ type='mmdet.ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=1,
+ norm_cfg=dict(type='BN', requires_grad=True),
+ norm_eval=True,
+ style='caffe'),
+ neck=dict(type='FPEM_FFM', in_channels=[256, 512, 1024, 2048]),
+ bbox_head=dict(
+ type='PANHead',
+ in_channels=[128, 128, 128, 128],
+ out_channels=6,
+ loss=dict(type='PANLoss', speedup_bbox_thr=32)),
+ train_cfg=None,
+ test_cfg=None)
diff --git a/configs/_base_/det_models/psenet_r50_fpnf.py b/configs/_base_/det_models/psenet_r50_fpnf.py
new file mode 100644
index 00000000..c9669d41
--- /dev/null
+++ b/configs/_base_/det_models/psenet_r50_fpnf.py
@@ -0,0 +1,51 @@
+model_poly = 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'),
+ bbox_head=dict(
+ type='PSEHead',
+ text_repr_type='poly',
+ in_channels=[256],
+ out_channels=7,
+ loss=dict(type='PSELoss')),
+ train_cfg=None,
+ test_cfg=None)
+
+model_quad = 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'),
+ bbox_head=dict(
+ type='PSEHead',
+ text_repr_type='quad',
+ in_channels=[256],
+ out_channels=7,
+ loss=dict(type='PSELoss')),
+ train_cfg=None,
+ test_cfg=None)
diff --git a/configs/_base_/det_models/textsnake_r50_fpn_unet.py b/configs/_base_/det_models/textsnake_r50_fpn_unet.py
new file mode 100644
index 00000000..81c3b6b2
--- /dev/null
+++ b/configs/_base_/det_models/textsnake_r50_fpn_unet.py
@@ -0,0 +1,21 @@
+model = dict(
+ type='TextSnake',
+ backbone=dict(
+ type='mmdet.ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=-1,
+ norm_cfg=dict(type='BN', requires_grad=True),
+ init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
+ norm_eval=True,
+ style='caffe'),
+ neck=dict(
+ type='FPN_UNet', in_channels=[256, 512, 1024, 2048], out_channels=32),
+ bbox_head=dict(
+ type='TextSnakeHead',
+ in_channels=32,
+ text_repr_type='poly',
+ loss=dict(type='TextSnakeLoss')),
+ train_cfg=None,
+ test_cfg=None)
diff --git a/configs/_base_/det_pipelines/dbnet_pipeline.py b/configs/_base_/det_pipelines/dbnet_pipeline.py
new file mode 100644
index 00000000..f243b91d
--- /dev/null
+++ b/configs/_base_/det_pipelines/dbnet_pipeline.py
@@ -0,0 +1,88 @@
+img_norm_cfg = dict(
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline_r18 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ 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='ImgAug',
+ args=[['Fliplr', 0.5],
+ dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]),
+ dict(type='EastRandomCrop', target_size=(640, 640)),
+ dict(type='DBNetTargets', shrink_ratio=0.4),
+ dict(type='Pad', size_divisor=32),
+ dict(
+ type='CustomFormatBundle',
+ keys=['gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'],
+ visualize=dict(flag=False, boundary_key='gt_shrink')),
+ dict(
+ type='Collect',
+ keys=['img', 'gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'])
+]
+
+test_pipeline_1333_736 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=(1333, 736),
+ flip=False,
+ transforms=[
+ dict(type='Resize', img_scale=(2944, 736), 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']),
+ ])
+]
+
+# for dbnet_r50dcnv2_fpnc
+img_norm_cfg_r50dcnv2 = dict(
+ mean=[122.67891434, 116.66876762, 104.00698793],
+ std=[58.395, 57.12, 57.375],
+ to_rgb=True)
+
+train_pipeline_r50dcnv2 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ 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_r50dcnv2),
+ dict(
+ type='ImgAug',
+ args=[['Fliplr', 0.5],
+ dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]),
+ dict(type='EastRandomCrop', target_size=(640, 640)),
+ dict(type='DBNetTargets', shrink_ratio=0.4),
+ dict(type='Pad', size_divisor=32),
+ dict(
+ type='CustomFormatBundle',
+ keys=['gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'],
+ visualize=dict(flag=False, boundary_key='gt_shrink')),
+ dict(
+ type='Collect',
+ keys=['img', 'gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'])
+]
+
+test_pipeline_4068_1024 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=(4068, 1024),
+ flip=False,
+ transforms=[
+ dict(type='Resize', img_scale=(2944, 736), keep_ratio=True),
+ dict(type='Normalize', **img_norm_cfg_r50dcnv2),
+ dict(type='Pad', size_divisor=32),
+ dict(type='ImageToTensor', keys=['img']),
+ dict(type='Collect', keys=['img']),
+ ])
+]
diff --git a/configs/_base_/det_pipelines/drrg_pipeline.py b/configs/_base_/det_pipelines/drrg_pipeline.py
new file mode 100644
index 00000000..2a169149
--- /dev/null
+++ b/configs/_base_/det_pipelines/drrg_pipeline.py
@@ -0,0 +1,60 @@
+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', color_type='color_ignore_orientation'),
+ 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='RandomScaling', size=800, scale=(0.75, 2.5)),
+ 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=60,
+ 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='DRRGTargets'),
+ dict(type='Pad', size_divisor=32),
+ dict(
+ type='CustomFormatBundle',
+ keys=[
+ 'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
+ 'gt_top_height_map', 'gt_bot_height_map', 'gt_sin_map',
+ 'gt_cos_map', 'gt_comp_attribs'
+ ],
+ visualize=dict(flag=False, boundary_key='gt_text_mask')),
+ dict(
+ type='Collect',
+ keys=[
+ 'img', 'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
+ 'gt_top_height_map', 'gt_bot_height_map', 'gt_sin_map',
+ 'gt_cos_map', 'gt_comp_attribs'
+ ])
+]
+
+test_pipeline = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=(1024, 640),
+ flip=False,
+ transforms=[
+ dict(type='Resize', img_scale=(1024, 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']),
+ ])
+]
diff --git a/configs/_base_/det_pipelines/fcenet_pipeline.py b/configs/_base_/det_pipelines/fcenet_pipeline.py
new file mode 100644
index 00000000..b1be6b22
--- /dev/null
+++ b/configs/_base_/det_pipelines/fcenet_pipeline.py
@@ -0,0 +1,118 @@
+img_norm_cfg = dict(
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# for icdar2015
+leval_prop_range_icdar2015 = ((0, 0.4), (0.3, 0.7), (0.6, 1.0))
+train_pipeline_icdar2015 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ 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=5,
+ level_proportion_range=leval_prop_range_icdar2015),
+ 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'])
+]
+
+img_scale_icdar2015 = (2260, 2260)
+test_pipeline_icdar2015 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=img_scale_icdar2015,
+ 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']),
+ ])
+]
+
+# for ctw1500
+leval_prop_range_ctw1500 = ((0, 0.25), (0.2, 0.65), (0.55, 1.0))
+train_pipeline_ctw1500 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ 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=5,
+ level_proportion_range=leval_prop_range_ctw1500),
+ 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'])
+]
+
+img_scale_ctw1500 = (1080, 736)
+test_pipeline_ctw1500 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=img_scale_ctw1500,
+ 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']),
+ ])
+]
diff --git a/configs/_base_/det_pipelines/maskrcnn_pipeline.py b/configs/_base_/det_pipelines/maskrcnn_pipeline.py
new file mode 100644
index 00000000..f9301025
--- /dev/null
+++ b/configs/_base_/det_pipelines/maskrcnn_pipeline.py
@@ -0,0 +1,57 @@
+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', color_type='color_ignore_orientation'),
+ dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+ dict(
+ type='ScaleAspectJitter',
+ img_scale=None,
+ keep_ratio=False,
+ resize_type='indep_sample_in_range',
+ scale_range=(640, 2560)),
+ dict(type='RandomFlip', flip_ratio=0.5),
+ dict(type='Normalize', **img_norm_cfg),
+ dict(
+ type='RandomCropInstances',
+ target_size=(640, 640),
+ mask_type='union_all',
+ instance_key='gt_masks'),
+ dict(type='Pad', size_divisor=32),
+ dict(type='DefaultFormatBundle'),
+ dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+
+# for ctw1500
+img_scale_ctw1500 = (1600, 1600)
+test_pipeline_ctw1500 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=img_scale_ctw1500,
+ flip=False,
+ transforms=[
+ dict(type='Resize', keep_ratio=True),
+ dict(type='RandomFlip'),
+ dict(type='Normalize', **img_norm_cfg),
+ dict(type='ImageToTensor', keys=['img']),
+ dict(type='Collect', keys=['img']),
+ ])
+]
+
+# for icdar2015
+img_scale_icdar2015 = (1920, 1920)
+test_pipeline_icdar2015 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=img_scale_icdar2015,
+ flip=False,
+ transforms=[
+ dict(type='Resize', keep_ratio=True),
+ dict(type='RandomFlip'),
+ dict(type='Normalize', **img_norm_cfg),
+ dict(type='ImageToTensor', keys=['img']),
+ dict(type='Collect', keys=['img']),
+ ])
+]
diff --git a/configs/_base_/det_pipelines/panet_pipeline.py b/configs/_base_/det_pipelines/panet_pipeline.py
new file mode 100644
index 00000000..36d239b4
--- /dev/null
+++ b/configs/_base_/det_pipelines/panet_pipeline.py
@@ -0,0 +1,156 @@
+img_norm_cfg = dict(
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# for ctw1500
+img_scale_train_ctw1500 = [(3000, 640)]
+shrink_ratio_train_ctw1500 = (1.0, 0.7)
+target_size_train_ctw1500 = (640, 640)
+train_pipeline_ctw1500 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ 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=img_scale_train_ctw1500,
+ 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=shrink_ratio_train_ctw1500),
+ dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
+ dict(type='RandomRotateTextDet'),
+ dict(
+ type='RandomCropInstances',
+ target_size=target_size_train_ctw1500,
+ instance_key='gt_kernels'),
+ dict(type='Pad', size_divisor=32),
+ 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'])
+]
+
+img_scale_test_ctw1500 = (3000, 640)
+test_pipeline_ctw1500 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=img_scale_test_ctw1500,
+ 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']),
+ ])
+]
+
+# for icdar2015
+img_scale_train_icdar2015 = [(3000, 736)]
+shrink_ratio_train_icdar2015 = (1.0, 0.5)
+target_size_train_icdar2015 = (736, 736)
+train_pipeline_icdar2015 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ 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=img_scale_train_icdar2015,
+ ratio_range=(0.7, 1.3),
+ aspect_ratio_range=(0.9, 1.1),
+ multiscale_mode='value',
+ keep_ratio=False),
+ dict(type='PANetTargets', shrink_ratio=shrink_ratio_train_icdar2015),
+ dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
+ dict(type='RandomRotateTextDet'),
+ dict(
+ type='RandomCropInstances',
+ target_size=target_size_train_icdar2015,
+ instance_key='gt_kernels'),
+ dict(type='Pad', size_divisor=32),
+ 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'])
+]
+
+img_scale_test_icdar2015 = (1333, 736)
+test_pipeline_icdar2015 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=img_scale_test_icdar2015,
+ 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']),
+ ])
+]
+
+# for icdar2017
+img_scale_train_icdar2017 = [(3000, 800)]
+shrink_ratio_train_icdar2017 = (1.0, 0.5)
+target_size_train_icdar2017 = (800, 800)
+train_pipeline_icdar2017 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ 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=img_scale_train_icdar2017,
+ ratio_range=(0.7, 1.3),
+ aspect_ratio_range=(0.9, 1.1),
+ multiscale_mode='value',
+ keep_ratio=False),
+ dict(type='PANetTargets', shrink_ratio=shrink_ratio_train_icdar2017),
+ dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
+ dict(type='RandomRotateTextDet'),
+ dict(
+ type='RandomCropInstances',
+ target_size=target_size_train_icdar2017,
+ instance_key='gt_kernels'),
+ dict(type='Pad', size_divisor=32),
+ 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'])
+]
+
+img_scale_test_icdar2017 = (1333, 800)
+test_pipeline_icdar2017 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=img_scale_test_icdar2017,
+ 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']),
+ ])
+]
diff --git a/configs/_base_/det_pipelines/psenet_pipeline.py b/configs/_base_/det_pipelines/psenet_pipeline.py
new file mode 100644
index 00000000..004dd63a
--- /dev/null
+++ b/configs/_base_/det_pipelines/psenet_pipeline.py
@@ -0,0 +1,70 @@
+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', color_type='color_ignore_orientation'),
+ 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, 736)],
+ ratio_range=(0.5, 3),
+ aspect_ratio_range=(1, 1),
+ multiscale_mode='value',
+ long_size_bound=1280,
+ short_size_bound=640,
+ resize_type='long_short_bound',
+ keep_ratio=False),
+ dict(type='PSENetTargets'),
+ 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),
+ 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'])
+]
+
+# for ctw1500
+img_scale_test_ctw1500 = (1280, 1280)
+test_pipeline_ctw1500 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=img_scale_test_ctw1500,
+ flip=False,
+ transforms=[
+ dict(type='Resize', img_scale=(1280, 1280), 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']),
+ ])
+]
+
+# for icdar2015
+img_scale_test_icdar2015 = (2240, 2240)
+test_pipeline_icdar2015 = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=img_scale_test_icdar2015,
+ flip=False,
+ transforms=[
+ dict(type='Resize', img_scale=(1280, 1280), 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']),
+ ])
+]
diff --git a/configs/_base_/det_pipelines/textsnake_pipeline.py b/configs/_base_/det_pipelines/textsnake_pipeline.py
new file mode 100644
index 00000000..583abec2
--- /dev/null
+++ b/configs/_base_/det_pipelines/textsnake_pipeline.py
@@ -0,0 +1,65 @@
+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', color_type='color_ignore_orientation'),
+ 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='RandomCropPolyInstances',
+ instance_key='gt_masks',
+ crop_ratio=0.65,
+ min_side_ratio=0.3),
+ dict(
+ type='RandomRotatePolyInstances',
+ rotate_ratio=0.5,
+ max_angle=20,
+ pad_with_fixed_color=False),
+ dict(
+ type='ScaleAspectJitter',
+ img_scale=[(3000, 736)], # unused
+ ratio_range=(0.7, 1.3),
+ aspect_ratio_range=(0.9, 1.1),
+ multiscale_mode='value',
+ long_size_bound=800,
+ short_size_bound=480,
+ resize_type='long_short_bound',
+ keep_ratio=False),
+ dict(type='SquareResizePad', target_size=800, pad_ratio=0.6),
+ dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
+ dict(type='TextSnakeTargets'),
+ dict(type='Pad', size_divisor=32),
+ dict(
+ type='CustomFormatBundle',
+ keys=[
+ 'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
+ 'gt_radius_map', 'gt_sin_map', 'gt_cos_map'
+ ],
+ visualize=dict(flag=False, boundary_key='gt_text_mask')),
+ dict(
+ type='Collect',
+ keys=[
+ 'img', 'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
+ 'gt_radius_map', 'gt_sin_map', 'gt_cos_map'
+ ])
+]
+
+test_pipeline = [
+ dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=(1333, 736),
+ flip=False,
+ transforms=[
+ dict(type='Resize', img_scale=(1333, 736), 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']),
+ ])
+]
diff --git a/configs/_base_/schedules/schedule_adam_step_600e.py b/configs/_base_/schedules/schedule_adam_step_600e.py
new file mode 100644
index 00000000..a861e821
--- /dev/null
+++ b/configs/_base_/schedules/schedule_adam_step_600e.py
@@ -0,0 +1,6 @@
+# optimizer
+optimizer = dict(type='Adam', lr=1e-4)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(policy='step', step=[200, 400])
+total_epochs = 600
diff --git a/configs/_base_/schedules/schedule_1200e.py b/configs/_base_/schedules/schedule_sgd_1200e.py
similarity index 100%
rename from configs/_base_/schedules/schedule_1200e.py
rename to configs/_base_/schedules/schedule_sgd_1200e.py
diff --git a/configs/_base_/schedules/schedule_sgd_1500e.py b/configs/_base_/schedules/schedule_sgd_1500e.py
new file mode 100644
index 00000000..63a1e2dd
--- /dev/null
+++ b/configs/_base_/schedules/schedule_sgd_1500e.py
@@ -0,0 +1,5 @@
+# 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
diff --git a/configs/_base_/schedules/schedule_160e.py b/configs/_base_/schedules/schedule_sgd_160e.py
similarity index 100%
rename from configs/_base_/schedules/schedule_160e.py
rename to configs/_base_/schedules/schedule_sgd_160e.py
diff --git a/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py b/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py
index 4c9ed225..997668f2 100644
--- a/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py
+++ b/configs/textdet/dbnet/dbnet_r18_fpnc_1200e_icdar2015.py
@@ -1,98 +1,33 @@
_base_ = [
- '../../_base_/schedules/schedule_1200e.py', '../../_base_/runtime_10e.py'
+ '../../_base_/runtime_10e.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'
]
-model = dict(
- type='DBNet',
- backbone=dict(
- type='mmdet.ResNet',
- depth=18,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18'),
- norm_eval=False,
- style='caffe'),
- neck=dict(
- type='FPNC', in_channels=[64, 128, 256, 512], lateral_channels=256),
- bbox_head=dict(
- type='DBHead',
- text_repr_type='quad',
- in_channels=256,
- loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True)),
- 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)
-# for visualizing img, pls uncomment it.
-# img_norm_cfg = dict(mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True)
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline_r18 = {{_base_.train_pipeline_r18}}
+test_pipeline_1333_736 = {{_base_.test_pipeline_1333_736}}
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- 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),
- # img aug
- dict(
- type='ImgAug',
- args=[['Fliplr', 0.5],
- dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]),
- # random crop
- dict(type='EastRandomCrop', target_size=(640, 640)),
- dict(type='DBNetTargets', shrink_ratio=0.4),
- dict(type='Pad', size_divisor=32),
- # for visualizing img and gts, pls set visualize = True
- dict(
- type='CustomFormatBundle',
- keys=['gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'],
- visualize=dict(flag=False, boundary_key='gt_shrink')),
- dict(
- type='Collect',
- keys=['img', 'gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 736),
- flip=False,
- transforms=[
- dict(type='Resize', img_scale=(2944, 736), 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=16,
workers_per_gpu=8,
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',
- # for debugging top k imgs
- # select_first_k=200,
- img_prefix=data_root + '/imgs',
- pipeline=train_pipeline),
+ type='UniformConcatDataset',
+ datasets=train_list,
+ pipeline=train_pipeline_r18),
val=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- # select_first_k=100,
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_1333_736),
test=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- # select_first_k=100,
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_1333_736))
+
evaluation = dict(interval=100, metric='hmean-iou')
diff --git a/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py b/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py
index 4ce70397..bd0b8c84 100644
--- a/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py
+++ b/configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py
@@ -1,104 +1,35 @@
_base_ = [
- '../../_base_/schedules/schedule_1200e.py', '../../_base_/runtime_10e.py'
+ '../../_base_/runtime_10e.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}}
+
load_from = 'checkpoints/textdet/dbnet/res50dcnv2_synthtext.pth'
-model = dict(
- type='DBNet',
- backbone=dict(
- type='mmdet.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=False,
- style='pytorch',
- dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
- stage_with_dcn=(False, True, True, True)),
- neck=dict(
- type='FPNC', in_channels=[256, 512, 1024, 2048], lateral_channels=256),
- bbox_head=dict(
- type='DBHead',
- text_repr_type='quad',
- in_channels=256,
- loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True)),
- train_cfg=None,
- test_cfg=None)
-
-dataset_type = 'IcdarDataset'
-data_root = 'data/icdar2015/'
-img_norm_cfg = dict(
- mean=[122.67891434, 116.66876762, 104.00698793],
- 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', color_type='color_ignore_orientation'),
- 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),
- # img aug
- dict(
- type='ImgAug',
- args=[['Fliplr', 0.5],
- dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]),
- # random crop
- dict(type='EastRandomCrop', target_size=(640, 640)),
- dict(type='DBNetTargets', shrink_ratio=0.4),
- dict(type='Pad', size_divisor=32),
- # for visualizing img and gts, pls set visualize = True
- dict(
- type='CustomFormatBundle',
- keys=['gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'],
- visualize=dict(flag=False, boundary_key='gt_shrink')),
- dict(
- type='Collect',
- keys=['img', 'gt_shrink', 'gt_shrink_mask', 'gt_thr', 'gt_thr_mask'])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(4068, 1024),
- flip=False,
- transforms=[
- dict(type='Resize', img_scale=(4068, 1024), 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=4,
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',
- # for debugging top k imgs
- # select_first_k=200,
- img_prefix=data_root + '/imgs',
- pipeline=train_pipeline),
+ type='UniformConcatDataset',
+ datasets=train_list,
+ pipeline=train_pipeline_r50dcnv2),
val=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- # select_first_k=100,
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_4068_1024),
test=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- # select_first_k=100,
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_4068_1024))
+
evaluation = dict(interval=100, metric='hmean-iou')
diff --git a/configs/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500.py b/configs/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500.py
index 2c1671d0..e30b1a74 100644
--- a/configs/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500.py
+++ b/configs/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500.py
@@ -1,112 +1,33 @@
_base_ = [
- '../../_base_/schedules/schedule_1200e.py',
- '../../_base_/default_runtime.py'
+ '../../_base_/schedules/schedule_sgd_1200e.py',
+ '../../_base_/default_runtime.py',
+ '../../_base_/det_models/drrg_r50_fpn_unet.py',
+ '../../_base_/det_datasets/ctw1500.py',
+ '../../_base_/det_pipelines/drrg_pipeline.py'
]
-model = dict(
- type='DRRG',
- backbone=dict(
- type='mmdet.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
- norm_eval=True,
- style='caffe'),
- neck=dict(
- type='FPN_UNet', in_channels=[256, 512, 1024, 2048], out_channels=32),
- bbox_head=dict(
- type='DRRGHead',
- in_channels=32,
- text_region_thr=0.3,
- center_region_thr=0.4,
- link_thr=0.80,
- loss=dict(type='DRRGLoss')))
-train_cfg = None
-test_cfg = None
-dataset_type = 'IcdarDataset'
-data_root = 'data/ctw1500/'
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = {{_base_.train_pipeline}}
+test_pipeline = {{_base_.test_pipeline}}
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- 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='RandomScaling', size=800, scale=(0.75, 2.5)),
- 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=60,
- 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='DRRGTargets'),
- dict(type='Pad', size_divisor=32),
- dict(
- type='CustomFormatBundle',
- keys=[
- 'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
- 'gt_top_height_map', 'gt_bot_height_map', 'gt_sin_map',
- 'gt_cos_map', 'gt_comp_attribs'
- ],
- visualize=dict(flag=False, boundary_key='gt_text_mask')),
- dict(
- type='Collect',
- keys=[
- 'img', 'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
- 'gt_top_height_map', 'gt_bot_height_map', 'gt_sin_map',
- 'gt_cos_map', 'gt_comp_attribs'
- ])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1024, 640),
- flip=False,
- transforms=[
- dict(type='Resize', img_scale=(1024, 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=4,
workers_per_gpu=4,
val_dataloader=dict(samples_per_gpu=1),
test_dataloader=dict(samples_per_gpu=1),
train=dict(
- type=dataset_type,
- ann_file=f'{data_root}/instances_training.json',
- img_prefix=f'{data_root}/imgs',
+ type='UniformConcatDataset',
+ datasets=train_list,
pipeline=train_pipeline),
val=dict(
- type=dataset_type,
- ann_file=f'{data_root}/instances_test.json',
- img_prefix=f'{data_root}/imgs',
+ type='UniformConcatDataset',
+ datasets=test_list,
pipeline=test_pipeline),
test=dict(
- type=dataset_type,
- ann_file=f'{data_root}/instances_test.json',
- img_prefix=f'{data_root}/imgs',
+ type='UniformConcatDataset',
+ datasets=test_list,
pipeline=test_pipeline))
evaluation = dict(interval=20, metric='hmean-iou')
diff --git a/configs/textdet/fcenet/fcenet_r50_fpn_1500e_icdar2015.py b/configs/textdet/fcenet/fcenet_r50_fpn_1500e_icdar2015.py
index 42864aba..c17f892c 100644
--- a/configs/textdet/fcenet/fcenet_r50_fpn_1500e_icdar2015.py
+++ b/configs/textdet/fcenet/fcenet_r50_fpn_1500e_icdar2015.py
@@ -1,136 +1,33 @@
-fourier_degree = 5
-model = dict(
- type='FCENet',
- backbone=dict(
- type='mmdet.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
- norm_eval=False,
- style='pytorch'),
- neck=dict(
- type='mmdet.FPN',
- in_channels=[512, 1024, 2048],
- out_channels=256,
- add_extra_convs='on_output',
- 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', color_type='color_ignore_orientation'),
- 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', color_type='color_ignore_orientation'),
- 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']),
- ])
+_base_ = [
+ '../../_base_/runtime_10e.py',
+ '../../_base_/schedules/schedule_sgd_1500e.py',
+ '../../_base_/det_models/fcenet_r50_fpn.py',
+ '../../_base_/det_datasets/icdar2015.py',
+ '../../_base_/det_pipelines/fcenet_pipeline.py'
]
+
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline_icdar2015 = {{_base_.train_pipeline_icdar2015}}
+test_pipeline_icdar2015 = {{_base_.test_pipeline_icdar2015}}
+
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),
+ type='UniformConcatDataset',
+ datasets=train_list,
+ pipeline=train_pipeline_icdar2015),
val=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015),
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')
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015))
-# 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)]
+evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500.py b/configs/textdet/fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500.py
index d6ceae2d..56ee4999 100644
--- a/configs/textdet/fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500.py
+++ b/configs/textdet/fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500.py
@@ -1,135 +1,33 @@
-fourier_degree = 5
-model = dict(
- type='FCENet',
- backbone=dict(
- type='mmdet.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch',
- dcn=dict(type='DCNv2', deform_groups=2, fallback_on_stride=False),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
- stage_with_dcn=(False, True, True, True)),
- neck=dict(
- type='mmdet.FPN',
- in_channels=[512, 1024, 2048],
- out_channels=256,
- add_extra_convs='on_output',
- 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'),
- fourier_degree=fourier_degree,
- ))
-
-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)
-
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- 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, 0.25), (0.2, 0.65), (0.55, 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', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1080, 736),
- 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']),
- ])
+_base_ = [
+ '../../_base_/runtime_10e.py',
+ '../../_base_/schedules/schedule_sgd_1500e.py',
+ '../../_base_/det_models/fcenet_r50dcnv2_fpn.py',
+ '../../_base_/det_datasets/ctw1500.py',
+ '../../_base_/det_pipelines/fcenet_pipeline.py'
]
+
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline_ctw1500 = {{_base_.train_pipeline_ctw1500}}
+test_pipeline_ctw1500 = {{_base_.test_pipeline_ctw1500}}
+
data = dict(
samples_per_gpu=6,
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),
+ type='UniformConcatDataset',
+ datasets=train_list,
+ pipeline=train_pipeline_ctw1500),
val=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_ctw1500),
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')
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_ctw1500))
-# 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)]
+evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500.py b/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500.py
index ace32ad4..42b7e7b8 100644
--- a/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500.py
+++ b/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500.py
@@ -1,69 +1,33 @@
_base_ = [
- '../../_base_/models/ocr_mask_rcnn_r50_fpn_ohem_poly.py',
- '../../_base_/schedules/schedule_160e.py', '../../_base_/runtime_10e.py'
+ '../../_base_/runtime_10e.py',
+ '../../_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem_poly.py',
+ '../../_base_/schedules/schedule_sgd_160e.py',
+ '../../_base_/det_datasets/ctw1500.py',
+ '../../_base_/det_pipelines/maskrcnn_pipeline.py'
]
-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)
-# img_norm_cfg = dict(mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(
- type='ScaleAspectJitter',
- img_scale=None,
- keep_ratio=False,
- resize_type='indep_sample_in_range',
- scale_range=(640, 2560)),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(
- type='RandomCropInstances',
- target_size=(640, 640),
- mask_type='union_all',
- instance_key='gt_masks'),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- # resize the long size to 1600
- img_scale=(1600, 1600),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- # no flip
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline = {{_base_.train_pipeline}}
+test_pipeline_ctw1500 = {{_base_.test_pipeline_ctw1500}}
+
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=dataset_type,
- ann_file=data_root + '/instances_training.json',
- img_prefix=data_root + '/imgs',
+ type='UniformConcatDataset',
+ datasets=train_list,
pipeline=train_pipeline),
val=dict(
- type=dataset_type,
- # select_first_k=1,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_ctw1500),
test=dict(
- type=dataset_type,
- # select_first_k=1,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_ctw1500))
+
evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015.py b/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015.py
index a1c31f8d..efffa12b 100644
--- a/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015.py
+++ b/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015.py
@@ -1,68 +1,33 @@
_base_ = [
- '../../_base_/models/ocr_mask_rcnn_r50_fpn_ohem.py',
- '../../_base_/schedules/schedule_160e.py', '../../_base_/runtime_10e.py'
-]
-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)
-# img_norm_cfg = dict(mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(
- type='ScaleAspectJitter',
- img_scale=None,
- keep_ratio=False,
- resize_type='indep_sample_in_range',
- scale_range=(640, 2560)),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(
- type='RandomCropInstances',
- target_size=(640, 640),
- mask_type='union_all',
- instance_key='gt_masks'),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- # resize the long size to 1600
- img_scale=(1920, 1920),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- # no flip
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
+ '../../_base_/runtime_10e.py',
+ '../../_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem.py',
+ '../../_base_/schedules/schedule_sgd_160e.py',
+ '../../_base_/det_datasets/icdar2015.py',
+ '../../_base_/det_pipelines/maskrcnn_pipeline.py'
]
+
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline = {{_base_.train_pipeline}}
+test_pipeline_icdar2015 = {{_base_.test_pipeline_icdar2015}}
+
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=dataset_type,
- ann_file=data_root + '/instances_training.json',
- img_prefix=data_root + '/imgs',
+ type='UniformConcatDataset',
+ datasets=train_list,
pipeline=train_pipeline),
val=dict(
- type=dataset_type,
- # select_first_k=1,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015),
test=dict(
- type=dataset_type,
- # select_first_k=1,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015))
+
evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017.py b/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017.py
index 088461af..b6b46ba4 100644
--- a/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017.py
+++ b/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017.py
@@ -1,69 +1,33 @@
_base_ = [
- '../../_base_/models/ocr_mask_rcnn_r50_fpn_ohem.py',
- '../../_base_/schedules/schedule_160e.py', '../../_base_/runtime_10e.py'
+ '../../_base_/runtime_10e.py',
+ '../../_base_/det_models/ocr_mask_rcnn_r50_fpn_ohem.py',
+ '../../_base_/schedules/schedule_sgd_160e.py',
+ '../../_base_/det_datasets/icdar2017.py',
+ '../../_base_/det_pipelines/maskrcnn_pipeline.py'
]
-dataset_type = 'IcdarDataset'
-data_root = 'data/icdar2017/'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-# img_norm_cfg = dict(mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(
- type='ScaleAspectJitter',
- img_scale=None,
- keep_ratio=False,
- resize_type='indep_sample_in_range',
- scale_range=(640, 2560)),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(
- type='RandomCropInstances',
- target_size=(640, 640),
- mask_type='union_all',
- instance_key='gt_masks'),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- # resize the long size to 1600
- img_scale=(1600, 1600),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- # no flip
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img']),
- ])
-]
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline = {{_base_.train_pipeline}}
+test_pipeline_icdar2015 = {{_base_.test_pipeline_icdar2015}}
+
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=dataset_type,
- ann_file=data_root + '/instances_training.json',
- img_prefix=data_root + '/imgs',
+ type='UniformConcatDataset',
+ datasets=train_list,
pipeline=train_pipeline),
val=dict(
- type=dataset_type,
- # select_first_k=1,
- ann_file=data_root + '/instances_val.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015),
test=dict(
- type=dataset_type,
- # select_first_k=1,
- ann_file=data_root + '/instances_val.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015))
+
evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/panet/panet_r18_fpem_ffm_600e_ctw1500.py b/configs/textdet/panet/panet_r18_fpem_ffm_600e_ctw1500.py
index 01573df1..b564a1aa 100644
--- a/configs/textdet/panet/panet_r18_fpem_ffm_600e_ctw1500.py
+++ b/configs/textdet/panet/panet_r18_fpem_ffm_600e_ctw1500.py
@@ -1,106 +1,35 @@
_base_ = [
'../../_base_/schedules/schedule_adam_600e.py',
- '../../_base_/runtime_10e.py'
+ '../../_base_/runtime_10e.py',
+ '../../_base_/det_models/panet_r18_fpem_ffm.py',
+ '../../_base_/det_datasets/ctw1500.py',
+ '../../_base_/det_pipelines/panet_pipeline.py'
]
-model = dict(
- type='PANet',
- backbone=dict(
- type='mmdet.ResNet',
- depth=18,
- 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://resnet18'),
- 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/'
+model = {{_base_.model_poly}}
-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_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline_ctw1500 = {{_base_.train_pipeline_ctw1500}}
+test_pipeline_ctw1500 = {{_base_.test_pipeline_ctw1500}}
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- 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', color_type='color_ignore_orientation'),
- 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,
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',
- # for debugging top k imgs
- # select_first_k=200,
- img_prefix=data_root + '/imgs',
- pipeline=train_pipeline),
+ type='UniformConcatDataset',
+ datasets=train_list,
+ pipeline=train_pipeline_ctw1500),
val=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- # select_first_k=100,
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_ctw1500),
test=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- # select_first_k=100,
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_ctw1500))
+
evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/panet/panet_r18_fpem_ffm_600e_icdar2015.py b/configs/textdet/panet/panet_r18_fpem_ffm_600e_icdar2015.py
index 02000fa9..e06fcd85 100644
--- a/configs/textdet/panet/panet_r18_fpem_ffm_600e_icdar2015.py
+++ b/configs/textdet/panet/panet_r18_fpem_ffm_600e_icdar2015.py
@@ -1,104 +1,35 @@
_base_ = [
'../../_base_/schedules/schedule_adam_600e.py',
- '../../_base_/runtime_10e.py'
+ '../../_base_/runtime_10e.py',
+ '../../_base_/det_models/panet_r18_fpem_ffm.py',
+ '../../_base_/det_datasets/icdar2015.py',
+ '../../_base_/det_pipelines/panet_pipeline.py'
]
-model = dict(
- type='PANet',
- backbone=dict(
- type='mmdet.ResNet',
- depth=18,
- 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://resnet18'),
- norm_eval=True,
- style='caffe'),
- neck=dict(type='FPEM_FFM', in_channels=[64, 128, 256, 512]),
- bbox_head=dict(
- type='PANHead',
- text_repr_type='quad',
- 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/icdar2015/'
-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)
+model = {{_base_.model_quad}}
+
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline_icdar2015 = {{_base_.train_pipeline_icdar2015}}
+test_pipeline_icdar2015 = {{_base_.test_pipeline_icdar2015}}
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- 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, 736)],
- ratio_range=(0.7, 1.3),
- aspect_ratio_range=(0.9, 1.1),
- multiscale_mode='value',
- keep_ratio=False),
- dict(type='PANetTargets', shrink_ratio=(1.0, 0.5), max_shrink=20),
- dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
- dict(type='RandomRotateTextDet'),
- dict(
- type='RandomCropInstances',
- target_size=(736, 736),
- 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', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 736),
- flip=False,
- transforms=[
- dict(type='Resize', img_scale=(1333, 736), 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',
- # for debugging top k imgs
- # select_first_k=200,
- img_prefix=data_root + '/imgs',
- pipeline=train_pipeline),
+ type='UniformConcatDataset',
+ datasets=train_list,
+ pipeline=train_pipeline_icdar2015),
val=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- # select_first_k=100,
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015),
test=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- # select_first_k=100,
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015))
+
evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/panet/panet_r50_fpem_ffm_600e_icdar2017.py b/configs/textdet/panet/panet_r50_fpem_ffm_600e_icdar2017.py
index 01486f42..9cb31143 100644
--- a/configs/textdet/panet/panet_r50_fpem_ffm_600e_icdar2017.py
+++ b/configs/textdet/panet/panet_r50_fpem_ffm_600e_icdar2017.py
@@ -1,95 +1,33 @@
_base_ = [
'../../_base_/schedules/schedule_adam_600e.py',
- '../../_base_/runtime_10e.py'
+ '../../_base_/runtime_10e.py',
+ '../../_base_/det_models/panet_r50_fpem_ffm.py',
+ '../../_base_/det_datasets/icdar2017.py',
+ '../../_base_/det_pipelines/panet_pipeline.py'
]
-model = dict(
- type='PANet',
- pretrained='torchvision://resnet50',
- backbone=dict(
- type='mmdet.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='caffe'),
- neck=dict(type='FPEM_FFM', in_channels=[256, 512, 1024, 2048]),
- bbox_head=dict(
- type='PANHead',
- in_channels=[128, 128, 128, 128],
- out_channels=6,
- loss=dict(type='PANLoss', speedup_bbox_thr=32)),
- train_cfg=None,
- test_cfg=None)
-dataset_type = 'IcdarDataset'
-data_root = 'data/icdar2017/'
-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', color_type='color_ignore_orientation'),
- 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, 800)],
- ratio_range=(0.7, 1.3),
- aspect_ratio_range=(0.9, 1.1),
- multiscale_mode='value',
- keep_ratio=False),
- dict(type='PANetTargets', shrink_ratio=(1.0, 0.5)),
- dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
- dict(type='RandomRotateTextDet'),
- dict(
- type='RandomCropInstances',
- target_size=(800, 800),
- 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', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', img_scale=(1333, 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']),
- ])
-]
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline_icdar2017 = {{_base_.train_pipeline_icdar2017}}
+test_pipeline_icdar2017 = {{_base_.test_pipeline_icdar2017}}
+
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
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),
+ type='UniformConcatDataset',
+ datasets=train_list,
+ pipeline=train_pipeline_icdar2017),
val=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_val.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2017),
test=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_val.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2017))
+
evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py b/configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py
index 12357fd9..483a2b2e 100644
--- a/configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py
+++ b/configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py
@@ -1,110 +1,35 @@
-_base_ = ['../../_base_/default_runtime.py']
-
-# optimizer
-optimizer = dict(type='Adam', lr=1e-4)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='step', step=[200, 400])
-total_epochs = 600
-
-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'),
- bbox_head=dict(
- type='PSEHead',
- text_repr_type='poly',
- in_channels=[256],
- out_channels=7,
- loss=dict(type='PSELoss')),
- 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)
-
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- 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, 736)],
- ratio_range=(0.5, 3),
- aspect_ratio_range=(1, 1),
- multiscale_mode='value',
- long_size_bound=1280,
- short_size_bound=640,
- resize_type='long_short_bound',
- keep_ratio=False),
- dict(type='PSENetTargets'),
- 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),
- 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', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1280, 1280),
- flip=False,
- transforms=[
- dict(type='Resize', img_scale=(1280, 1280), 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']),
- ])
+_base_ = [
+ '../../_base_/default_runtime.py',
+ '../../_base_/schedules/schedule_adam_step_600e.py',
+ '../../_base_/det_models/psenet_r50_fpnf.py',
+ '../../_base_/det_datasets/ctw1500.py',
+ '../../_base_/det_pipelines/psenet_pipeline.py'
]
+model = {{_base_.model_poly}}
+
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline = {{_base_.train_pipeline}}
+test_pipeline_ctw1500 = {{_base_.test_pipeline_ctw1500}}
+
data = dict(
samples_per_gpu=2,
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',
+ type='UniformConcatDataset',
+ datasets=train_list,
pipeline=train_pipeline),
val=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_ctw1500),
test=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_ctw1500))
evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py b/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py
index c5b495cc..f96d8a5d 100644
--- a/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py
+++ b/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py
@@ -1,110 +1,35 @@
-_base_ = ['../../_base_/runtime_10e.py']
-
-# optimizer
-optimizer = dict(type='Adam', lr=1e-4)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='step', step=[200, 400])
-total_epochs = 600
-
-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'),
- bbox_head=dict(
- type='PSEHead',
- text_repr_type='quad',
- in_channels=[256],
- out_channels=7,
- loss=dict(type='PSELoss')),
- 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', color_type='color_ignore_orientation'),
- 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, 736)], # unused
- ratio_range=(0.5, 3),
- aspect_ratio_range=(1, 1),
- multiscale_mode='value',
- long_size_bound=1280,
- short_size_bound=640,
- resize_type='long_short_bound',
- keep_ratio=False),
- dict(type='PSENetTargets'),
- 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),
- 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', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(2240, 2200),
- flip=False,
- transforms=[
- dict(type='Resize', img_scale=(2240, 2200), 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']),
- ])
+_base_ = [
+ '../../_base_/runtime_10e.py',
+ '../../_base_/schedules/schedule_adam_step_600e.py',
+ '../../_base_/det_models/psenet_r50_fpnf.py',
+ '../../_base_/det_datasets/icdar2015.py',
+ '../../_base_/det_pipelines/psenet_pipeline.py'
]
+model = {{_base_.model_quad}}
+
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline = {{_base_.train_pipeline}}
+test_pipeline_icdar2015 = {{_base_.test_pipeline_icdar2015}}
+
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',
+ type='UniformConcatDataset',
+ datasets=train_list,
pipeline=train_pipeline),
val=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015),
test=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_test.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015))
evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py b/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py
index 6e0e158d..acd40684 100644
--- a/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py
+++ b/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py
@@ -1,85 +1,18 @@
_base_ = [
'../../_base_/schedules/schedule_sgd_600e.py',
- '../../_base_/runtime_10e.py'
+ '../../_base_/runtime_10e.py',
+ '../../_base_/det_models/psenet_r50_fpnf.py',
+ '../../_base_/det_datasets/icdar2017.py',
+ '../../_base_/det_pipelines/psenet_pipeline.py'
]
-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='BN', 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'),
- bbox_head=dict(
- type='PSEHead',
- text_repr_type='quad',
- in_channels=[256],
- out_channels=7,
- loss=dict(type='PSELoss')),
- train_cfg=None,
- test_cfg=None)
-dataset_type = 'IcdarDataset'
-data_root = 'data/icdar2017/'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+model = {{_base_.model_quad}}
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- 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, 736)],
- ratio_range=(0.5, 3),
- aspect_ratio_range=(1, 1),
- multiscale_mode='value',
- long_size_bound=1280,
- short_size_bound=640,
- resize_type='long_short_bound',
- keep_ratio=False),
- dict(type='PSENetTargets'),
- 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),
- 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', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(2240, 2200),
- flip=False,
- transforms=[
- dict(type='Resize', img_scale=(2240, 2200), 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']),
- ])
-]
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
+
+train_pipeline = {{_base_.train_pipeline}}
+test_pipeline_icdar2015 = {{_base_.test_pipeline_icdar2015}}
data = dict(
samples_per_gpu=8,
@@ -87,19 +20,16 @@ data = dict(
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',
+ type='UniformConcatDataset',
+ datasets=train_list,
pipeline=train_pipeline),
val=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_val.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline),
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015),
test=dict(
- type=dataset_type,
- ann_file=data_root + '/instances_val.json',
- img_prefix=data_root + '/imgs',
- pipeline=test_pipeline))
+ type='UniformConcatDataset',
+ datasets=test_list,
+ pipeline=test_pipeline_icdar2015))
evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py b/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py
index 87983cad..0270b059 100644
--- a/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py
+++ b/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py
@@ -1,95 +1,16 @@
_base_ = [
- '../../_base_/schedules/schedule_1200e.py',
- '../../_base_/default_runtime.py'
+ '../../_base_/schedules/schedule_sgd_1200e.py',
+ '../../_base_/default_runtime.py',
+ '../../_base_/det_models/textsnake_r50_fpn_unet.py',
+ '../../_base_/det_datasets/ctw1500.py',
+ '../../_base_/det_pipelines/textsnake_pipeline.py'
]
-model = dict(
- type='TextSnake',
- backbone=dict(
- type='mmdet.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
- norm_eval=True,
- style='caffe'),
- neck=dict(
- type='FPN_UNet', in_channels=[256, 512, 1024, 2048], out_channels=32),
- bbox_head=dict(
- type='TextSnakeHead',
- in_channels=32,
- text_repr_type='poly',
- loss=dict(type='TextSnakeLoss')),
- 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)
+train_list = {{_base_.train_list}}
+test_list = {{_base_.test_list}}
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- 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='RandomCropPolyInstances',
- instance_key='gt_masks',
- crop_ratio=0.65,
- min_side_ratio=0.3),
- dict(
- type='RandomRotatePolyInstances',
- rotate_ratio=0.5,
- max_angle=20,
- pad_with_fixed_color=False),
- dict(
- type='ScaleAspectJitter',
- img_scale=[(3000, 736)], # unused
- ratio_range=(0.7, 1.3),
- aspect_ratio_range=(0.9, 1.1),
- multiscale_mode='value',
- long_size_bound=800,
- short_size_bound=480,
- resize_type='long_short_bound',
- keep_ratio=False),
- dict(type='SquareResizePad', target_size=800, pad_ratio=0.6),
- dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
- dict(type='TextSnakeTargets'),
- dict(type='Pad', size_divisor=32),
- dict(
- type='CustomFormatBundle',
- keys=[
- 'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
- 'gt_radius_map', 'gt_sin_map', 'gt_cos_map'
- ],
- visualize=dict(flag=False, boundary_key='gt_text_mask')),
- dict(
- type='Collect',
- keys=[
- 'img', 'gt_text_mask', 'gt_center_region_mask', 'gt_mask',
- 'gt_radius_map', 'gt_sin_map', 'gt_cos_map'
- ])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 736),
- flip=False,
- transforms=[
- dict(type='Resize', img_scale=(1333, 736), 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']),
- ])
-]
+train_pipeline = {{_base_.train_pipeline}}
+test_pipeline = {{_base_.test_pipeline}}
data = dict(
samples_per_gpu=4,
@@ -97,19 +18,16 @@ data = dict(
val_dataloader=dict(samples_per_gpu=1),
test_dataloader=dict(samples_per_gpu=1),
train=dict(
- type=dataset_type,
- ann_file=f'{data_root}/instances_training.json',
- img_prefix=f'{data_root}/imgs',
+ type='UniformConcatDataset',
+ datasets=train_list,
pipeline=train_pipeline),
val=dict(
- type=dataset_type,
- ann_file=f'{data_root}/instances_test.json',
- img_prefix=f'{data_root}/imgs',
+ type='UniformConcatDataset',
+ datasets=test_list,
pipeline=test_pipeline),
test=dict(
- type=dataset_type,
- ann_file=f'{data_root}/instances_test.json',
- img_prefix=f'{data_root}/imgs',
+ type='UniformConcatDataset',
+ datasets=test_list,
pipeline=test_pipeline))
evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/tests/test_apis/test_model_inference.py b/tests/test_apis/test_model_inference.py
index 3cb2e864..4476b35e 100644
--- a/tests/test_apis/test_model_inference.py
+++ b/tests/test_apis/test_model_inference.py
@@ -7,11 +7,13 @@ from mmcv.image import imread
from mmocr.apis.inference import init_detector, model_inference
from mmocr.datasets import build_dataset # noqa: F401
from mmocr.models import build_detector # noqa: F401
+from mmocr.utils import revert_sync_batchnorm
def build_model(config_file):
device = 'cpu'
model = init_detector(config_file, checkpoint=None, device=device)
+ model = revert_sync_batchnorm(model)
if model.cfg.data.test['type'] == 'ConcatDataset':
model.cfg.data.test.pipeline = model.cfg.data.test['datasets'][