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[Refactor] rename configs #560

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Oct 24, 2022
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[Refactor] rename configs #560

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e8aa62d
update
zytx121 Oct 21, 2022
a7ae239
update
zytx121 Oct 21, 2022
a042f00
fix lint
zytx121 Oct 21, 2022
203cc66
update
zytx121 Oct 21, 2022
01abe27
update
zytx121 Oct 22, 2022
efd481e
Update gliding-vertex-rbox_r50_fpn_1x_dota.py
zytx121 Oct 23, 2022
d2a4f95
update ms configs
zytx121 Oct 23, 2022
20da1a8
update hrsc configs
zytx121 Oct 23, 2022
f4a5935
fix lint
zytx121 Oct 23, 2022
51bf540
Update configs/redet/README.md
zytx121 Oct 24, 2022
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94 changes: 94 additions & 0 deletions configs/_base_/datasets/dota_ms.py
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@@ -0,0 +1,94 @@
# dataset settings
dataset_type = 'DOTADataset'
data_root = 'data/split_ms_dota/'
file_client_args = dict(backend='disk')

train_pipeline = [
dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args),
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
dict(
type='mmdet.RandomFlip',
prob=0.75,
direction=['horizontal', 'vertical', 'diagonal']),
dict(
type='RandomRotate',
prob=0.5,
angle_range=180,
rect_obj_labels=[9, 11]),
dict(type='mmdet.PackDetInputs')
]
val_pipeline = [
dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
# avoid bboxes being resized
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
test_pipeline = [
dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args),
dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
# avoid bboxes being resized
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=None,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='trainval/annfiles/',
data_prefix=dict(img_path='trainval/images/'),
img_shape=(1024, 1024),
filter_cfg=dict(filter_empty_gt=True),
pipeline=train_pipeline))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='trainval/annfiles/',
data_prefix=dict(img_path='trainval/images/'),
img_shape=(1024, 1024),
test_mode=True,
pipeline=val_pipeline))
test_dataloader = val_dataloader

val_evaluator = dict(type='DOTAMetric', metric='mAP')
test_evaluator = val_evaluator

# inference on test dataset and format the output results
# for submission. Note: the test set has no annotation.
# test_dataloader = dict(
# batch_size=1,
# num_workers=2,
# persistent_workers=True,
# drop_last=False,
# sampler=dict(type='DefaultSampler', shuffle=False),
# dataset=dict(
# type=dataset_type,
# data_root=data_root,
# data_prefix=dict(img_path='test/images/'),
# img_shape=(1024, 1024),
# test_mode=True,
# pipeline=test_pipeline))
# test_evaluator = dict(
# type='DOTAMetric',
# format_only=True,
# merge_patches=True,
# outfile_prefix='./work_dirs/dota/Task1')
102 changes: 56 additions & 46 deletions configs/_base_/datasets/hrsc.py
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@@ -1,56 +1,66 @@
# dataset settings
dataset_type = 'HRSCDataset'
data_root = 'data/hrsc/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
file_client_args = dict(backend='disk')

train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RResize', img_scale=(800, 800)),
dict(type='RRandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args),
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
dict(type='mmdet.Resize', scale=(800, 512), keep_ratio=True),
dict(
type='mmdet.RandomFlip',
prob=0.75,
direction=['horizontal', 'vertical', 'diagonal']),
dict(type='mmdet.PackDetInputs')
]
val_pipeline = [
dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args),
dict(type='mmdet.Resize', scale=(800, 512), keep_ratio=True),
# avoid bboxes being resized
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args),
dict(type='mmdet.Resize', scale=(800, 512), keep_ratio=True),
# avoid bboxes being resized
dict(
type='MultiScaleFlipAug',
img_scale=(800, 800),
flip=False,
transforms=[
dict(type='RResize'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
classwise=False,
ann_file=data_root + 'ImageSets/trainval.txt',
ann_subdir=data_root + 'FullDataSet/Annotations/',
img_subdir=data_root + 'FullDataSet/AllImages/',
img_prefix=data_root + 'FullDataSet/AllImages/',
pipeline=train_pipeline),
val=dict(
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=None,
dataset=dict(
type=dataset_type,
classwise=False,
ann_file=data_root + 'ImageSets/test.txt',
ann_subdir=data_root + 'FullDataSet/Annotations/',
img_subdir=data_root + 'FullDataSet/AllImages/',
img_prefix=data_root + 'FullDataSet/AllImages/',
pipeline=test_pipeline),
test=dict(
data_root=data_root,
ann_file='ImageSets/trainval.txt',
data_prefix=dict(sub_data_root='FullDataSet/'),
filter_cfg=dict(filter_empty_gt=True),
pipeline=train_pipeline))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
classwise=False,
ann_file=data_root + 'ImageSets/test.txt',
ann_subdir=data_root + 'FullDataSet/Annotations/',
img_subdir=data_root + 'FullDataSet/AllImages/',
img_prefix=data_root + 'FullDataSet/AllImages/',
pipeline=test_pipeline))
data_root=data_root,
ann_file='ImageSets/test.txt',
data_prefix=dict(sub_data_root='FullDataSet/'),
test_mode=True,
pipeline=val_pipeline))
test_dataloader = val_dataloader

val_evaluator = dict(type='DOTAMetric', metric='mAP')
test_evaluator = val_evaluator
10 changes: 5 additions & 5 deletions configs/cfa/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -16,11 +16,11 @@ Detecting oriented and densely packed objects remains challenging for spatial fe

DOTA1.0

| Backbone | mAP | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
| :----------------------: | :---: | :---: | :-----: | :------: | :------------: | :-: | :--------: | :--------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ResNet50 (1024,1024,200) | 59.44 | oc | 1x | 3.45 | 15.6 | - | 2 | [rotated_reppoints_r50_fpn_1x_dota_oc](../rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc-d38ce217.pth) \| [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc_20220205_145010.log.json) |
| ResNet50 (1024,1024,200) | 69.63 | le135 | 1x | 3.45 | 16.1 | - | 2 | [cfa_r50_fpn_1x_dota_le135](./cfa_r50_fpn_1x_dota_le135.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_1x_dota_le135/cfa_r50_fpn_1x_dota_le135-aed1cbc6.pth) \| [log](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_1x_dota_le135/cfa_r50_fpn_1x_dota_le135_20220205_144859.log.json) |
| ResNet50 (1024,1024,200) | 73.45 | oc | 40e | 3.45 | 16.1 | - | 2 | [cfa_r50_fpn_40e_dota_oc](./cfa_r50_fpn_40e_dota_oc.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_40e_dota_oc/cfa_r50_fpn_40e_dota_oc-2f387232.pth) \| [log](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_40e_dota_oc/cfa_r50_fpn_40e_dota_oc_20220209_171237.log.json) |
| Backbone | mAP | Angle | lr schd | Mem (GB) | Inf Time (fps) | Aug | Batch Size | Configs | Download |
| :----------------------: | :---: | :---: | :-----: | :------: | :------------: | :-: | :--------: | :------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ResNet50 (1024,1024,200) | 59.44 | oc | 1x | 3.45 | 15.6 | - | 2 | [rotated-reppoints-qbox_r50_fpn_1x_dota](../rotated_reppoints/rotated-reppoints-qbox_r50_fpn_1x_dota.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc-d38ce217.pth) \| [log](https://download.openmmlab.com/mmrotate/v0.1.0/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc/rotated_reppoints_r50_fpn_1x_dota_oc_20220205_145010.log.json) |
| ResNet50 (1024,1024,200) | 69.63 | le135 | 1x | 3.45 | 16.1 | - | 2 | [cfa-qbox_r50_fpn_1x_dota](./cfa-qbox_r50_fpn_1x_dota.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_1x_dota_le135/cfa_r50_fpn_1x_dota_le135-aed1cbc6.pth) \| [log](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_1x_dota_le135/cfa_r50_fpn_1x_dota_le135_20220205_144859.log.json) |
| ResNet50 (1024,1024,200) | 73.45 | oc | 40e | 3.45 | 16.1 | - | 2 | [cfa-qbox_r50_fpn_40e_dota](./cfa-qbox_r50_fpn_40e_dota.py) | [model](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_40e_dota_oc/cfa_r50_fpn_40e_dota_oc-2f387232.pth) \| [log](https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_40e_dota_oc/cfa_r50_fpn_40e_dota_oc_20220209_171237.log.json) |

## Citation

Expand Down
0 configs/cfa/cfa_r50_fpn_1x_dota_qbox.py → configs/cfa/cfa-qbox_r50_fpn_1x_dota.py
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0 configs/cfa/cfa_r50_fpn_40e_dota_qbox.py → configs/cfa/cfa-qbox_r50_fpn_40e_dota.py
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File renamed without changes.
8 changes: 4 additions & 4 deletions configs/cfa/metafile.yml
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Expand Up @@ -14,9 +14,9 @@ Collections:
README: configs/cfa/README.md

Models:
- Name: cfa_r50_fpn_1x_dota_le135
- Name: cfa-qbox_r50_fpn_1x_dota
In Collection: cfa
Config: configs/cfa/cfa_r50_fpn_1x_dota_le135.py
Config: configs/cfa/cfa-qbox_r50_fpn_1x_dota.py
Metadata:
Training Data: DOTAv1.0
Results:
Expand All @@ -26,9 +26,9 @@ Models:
mAP: 69.63
Weights: https://download.openmmlab.com/mmrotate/v0.1.0/cfa/cfa_r50_fpn_1x_dota_le135/cfa_r50_fpn_1x_dota_le135-aed1cbc6.pth

- Name: cfa_r50_fpn_40e_dota_oc
- Name: cfa-qbox_r50_fpn_40e_dota
In Collection: cfa
Config: configs/cfa/cfa_r50_fpn_40e_dota_oc.py
Config: configs/cfa/cfa-qbox_r50_fpn_40e_dota.py
Metadata:
Training Data: DOTAv1.0
Results:
Expand Down
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