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Commit b12850fe authored by dengjb's avatar dengjb
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update codes

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configs/_base_/datasets/coco_instance.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'datasets/coco_mini/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
# If you don't have a gt annotation, delete the pipeline
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='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=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args))
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='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/instances_val2017.json',
metric=['bbox', 'segm'],
format_only=False,
backend_args=backend_args)
test_evaluator = val_evaluator
# inference on test dataset and
# format the output results for submission.
# 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,
# ann_file=data_root + 'annotations/image_info_test-dev2017.json',
# data_prefix=dict(img='test2017/'),
# test_mode=True,
# pipeline=test_pipeline))
# test_evaluator = dict(
# type='CocoMetric',
# metric=['bbox', 'segm'],
# format_only=True,
# ann_file=data_root + 'annotations/image_info_test-dev2017.json',
# outfile_prefix='./work_dirs/coco_instance/test')
configs/_base_/datasets/coco_instance_semantic.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(
type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
# If you don't have a gt annotation, delete the pipeline
dict(
type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
dict(
type='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=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/', seg='stuffthingmaps/train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args))
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='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/instances_val2017.json',
metric=['bbox', 'segm'],
format_only=False,
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/coco_panoptic.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadPanopticAnnotations', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='LoadPanopticAnnotations', backend_args=backend_args),
dict(
type='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=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/panoptic_train2017.json',
data_prefix=dict(
img='train2017/', seg='annotations/panoptic_train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args))
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='annotations/panoptic_val2017.json',
data_prefix=dict(img='val2017/', seg='annotations/panoptic_val2017/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoPanopticMetric',
ann_file=data_root + 'annotations/panoptic_val2017.json',
seg_prefix=data_root + 'annotations/panoptic_val2017/',
backend_args=backend_args)
test_evaluator = val_evaluator
# inference on test dataset and
# format the output results for submission.
# test_dataloader = dict(
# batch_size=1,
# num_workers=1,
# persistent_workers=True,
# drop_last=False,
# sampler=dict(type='DefaultSampler', shuffle=False),
# dataset=dict(
# type=dataset_type,
# data_root=data_root,
# ann_file='annotations/panoptic_image_info_test-dev2017.json',
# data_prefix=dict(img='test2017/'),
# test_mode=True,
# pipeline=test_pipeline))
# test_evaluator = dict(
# type='CocoPanopticMetric',
# format_only=True,
# ann_file=data_root + 'annotations/panoptic_image_info_test-dev2017.json',
# outfile_prefix='./work_dirs/coco_panoptic/test')
configs/_base_/datasets/coco_semantic.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'CocoSegDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(
type='LoadAnnotations',
with_bbox=False,
with_label=False,
with_seg=True),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='LoadAnnotations',
with_bbox=False,
with_label=False,
with_seg=True),
dict(
type='PackDetInputs',
meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
]
# For stuffthingmaps_semseg, please refer to
# `docs/en/user_guides/dataset_prepare.md`
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type=dataset_type,
data_root=data_root,
data_prefix=dict(
img_path='train2017/',
seg_map_path='stuffthingmaps_semseg/train2017/'),
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,
data_prefix=dict(
img_path='val2017/',
seg_map_path='stuffthingmaps_semseg/val2017/'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='SemSegMetric', iou_metrics=['mIoU'])
test_evaluator = val_evaluator
configs/_base_/datasets/deepfashion.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', scale=(750, 1101), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(750, 1101), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='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=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='Anno/segmentation/DeepFashion_segmentation_train.json',
data_prefix=dict(img='Img/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args)))
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='Anno/segmentation/DeepFashion_segmentation_query.json',
data_prefix=dict(img='Img/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
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,
ann_file='Anno/segmentation/DeepFashion_segmentation_gallery.json',
data_prefix=dict(img='Img/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root +
'Anno/segmentation/DeepFashion_segmentation_query.json',
metric=['bbox', 'segm'],
format_only=False,
backend_args=backend_args)
test_evaluator = dict(
type='CocoMetric',
ann_file=data_root +
'Anno/segmentation/DeepFashion_segmentation_gallery.json',
metric=['bbox', 'segm'],
format_only=False,
backend_args=backend_args)
configs/_base_/datasets/dsdl.py 0 → 100644
View file @ b12850fe
dataset_type = 'DSDLDetDataset'
data_root = 'path to dataset folder'
train_ann = 'path to train yaml file'
val_ann = 'path to val yaml file'
backend_args = None
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': "s3://open_data/",
# 'data/': "s3://open_data/"
# }))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
# If you don't have a gt annotation, delete the pipeline
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'instances'))
]
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=train_ann,
filter_cfg=dict(filter_empty_gt=True, min_size=32, bbox_min_size=32),
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=val_ann,
test_mode=True,
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='CocoMetric', metric='bbox')
# val_evaluator = dict(type='VOCMetric', metric='mAP', eval_mode='11points')
test_evaluator = val_evaluator
configs/_base_/datasets/isaid_instance.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'iSAIDDataset'
data_root = 'data/iSAID/'
backend_args = None
# Please see `projects/iSAID/README.md` for data preparation
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', scale=(800, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(800, 800), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='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=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='train/instancesonly_filtered_train.json',
data_prefix=dict(img='train/images/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args))
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='val/instancesonly_filtered_val.json',
data_prefix=dict(img='val/images/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'val/instancesonly_filtered_val.json',
metric=['bbox', 'segm'],
format_only=False,
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/lvis_v0.5_instance.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'LVISV05Dataset'
data_root = 'data/lvis_v0.5/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/lvis_v0.5/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='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=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/lvis_v0.5_train.json',
data_prefix=dict(img='train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args)))
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='annotations/lvis_v0.5_val.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='LVISMetric',
ann_file=data_root + 'annotations/lvis_v0.5_val.json',
metric=['bbox', 'segm'],
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/lvis_v1_instance.py 0 → 100644
View file @ b12850fe
# dataset settings
_base_ = 'lvis_v0.5_instance.py'
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
train_dataloader = dict(
dataset=dict(
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/lvis_v1_train.json',
data_prefix=dict(img=''))))
val_dataloader = dict(
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/lvis_v1_val.json',
data_prefix=dict(img='')))
test_dataloader = val_dataloader
val_evaluator = dict(ann_file=data_root + 'annotations/lvis_v1_val.json')
test_evaluator = val_evaluator
configs/_base_/datasets/mot_challenge.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'MOTChallengeDataset'
data_root = 'data/MOT17/'
img_scale = (1088, 1088)
backend_args = None
# data pipeline
train_pipeline = [
dict(
type='UniformRefFrameSample',
num_ref_imgs=1,
frame_range=10,
filter_key_img=True),
dict(
type='TransformBroadcaster',
share_random_params=True,
transforms=[
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadTrackAnnotations'),
dict(
type='RandomResize',
scale=img_scale,
ratio_range=(0.8, 1.2),
keep_ratio=True,
clip_object_border=False),
dict(type='PhotoMetricDistortion')
]),
dict(
type='TransformBroadcaster',
# different cropped positions for different frames
share_random_params=False,
transforms=[
dict(
type='RandomCrop', crop_size=img_scale, bbox_clip_border=False)
]),
dict(
type='TransformBroadcaster',
share_random_params=True,
transforms=[
dict(type='RandomFlip', prob=0.5),
]),
dict(type='PackTrackInputs')
]
test_pipeline = [
dict(
type='TransformBroadcaster',
transforms=[
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=img_scale, keep_ratio=True),
dict(type='LoadTrackAnnotations')
]),
dict(type='PackTrackInputs')
]
# dataloader
train_dataloader = dict(
batch_size=2,
num_workers=2,
persistent_workers=True,
sampler=dict(type='TrackImgSampler'), # image-based sampling
dataset=dict(
type=dataset_type,
data_root=data_root,
visibility_thr=-1,
ann_file='annotations/half-train_cocoformat.json',
data_prefix=dict(img_path='train'),
metainfo=dict(classes=('pedestrian', )),
pipeline=train_pipeline))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
# Now we support two ways to test, image_based and video_based
# if you want to use video_based sampling, you can use as follows
# sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
sampler=dict(type='TrackImgSampler'), # image-based sampling
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/half-val_cocoformat.json',
data_prefix=dict(img_path='train'),
test_mode=True,
pipeline=test_pipeline))
test_dataloader = val_dataloader
# evaluator
val_evaluator = dict(
type='MOTChallengeMetric', metric=['HOTA', 'CLEAR', 'Identity'])
test_evaluator = val_evaluator
configs/_base_/datasets/mot_challenge_det.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/MOT17/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args, to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=(1088, 1088),
ratio_range=(0.8, 1.2),
keep_ratio=True,
clip_object_border=False),
dict(type='PhotoMetricDistortion'),
dict(type='RandomCrop', crop_size=(1088, 1088), bbox_clip_border=False),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1088, 1088), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='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=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/half-train_cocoformat.json',
data_prefix=dict(img='train/'),
metainfo=dict(classes=('pedestrian', )),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
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='annotations/half-val_cocoformat.json',
data_prefix=dict(img='train/'),
metainfo=dict(classes=('pedestrian', )),
test_mode=True,
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/half-val_cocoformat.json',
metric='bbox',
format_only=False)
test_evaluator = val_evaluator
configs/_base_/datasets/mot_challenge_reid.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'ReIDDataset'
data_root = 'data/MOT17/'
backend_args = None
# data pipeline
train_pipeline = [
dict(
type='TransformBroadcaster',
share_random_params=False,
transforms=[
dict(
type='LoadImageFromFile',
backend_args=backend_args,
to_float32=True),
dict(
type='Resize',
scale=(128, 256),
keep_ratio=False,
clip_object_border=False),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
]),
dict(type='PackReIDInputs', meta_keys=('flip', 'flip_direction'))
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args, to_float32=True),
dict(type='Resize', scale=(128, 256), keep_ratio=False),
dict(type='PackReIDInputs')
]
# dataloader
train_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
triplet_sampler=dict(num_ids=8, ins_per_id=4),
data_prefix=dict(img_path='reid/imgs'),
ann_file='reid/meta/train_80.txt',
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,
triplet_sampler=None,
data_prefix=dict(img_path='reid/imgs'),
ann_file='reid/meta/val_20.txt',
pipeline=test_pipeline))
test_dataloader = val_dataloader
# evaluator
val_evaluator = dict(type='ReIDMetrics', metric=['mAP', 'CMC'])
test_evaluator = val_evaluator
configs/_base_/datasets/objects365v1_detection.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'Objects365V1Dataset'
data_root = 'data/Objects365/Obj365_v1/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
# If you don't have a gt annotation, delete the pipeline
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='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=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/objects365_train.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args))
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='annotations/objects365_val.json',
data_prefix=dict(img='val/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/objects365_val.json',
metric='bbox',
sort_categories=True,
format_only=False,
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/objects365v2_detection.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'Objects365V2Dataset'
data_root = 'data/Objects365/Obj365_v2/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
# If you don't have a gt annotation, delete the pipeline
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='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=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/zhiyuan_objv2_train.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args))
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='annotations/zhiyuan_objv2_val.json',
data_prefix=dict(img='val/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/zhiyuan_objv2_val.json',
metric='bbox',
format_only=False,
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/openimages_detection.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'OpenImagesDataset'
data_root = 'data/OpenImages/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', scale=(1024, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1024, 800), keep_ratio=True),
# avoid bboxes being resized
dict(type='LoadAnnotations', with_bbox=True),
# TODO: find a better way to collect image_level_labels
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'instances', 'image_level_labels'))
]
train_dataloader = dict(
batch_size=2,
num_workers=0, # workers_per_gpu > 0 may occur out of memory
persistent_workers=False,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/oidv6-train-annotations-bbox.csv',
data_prefix=dict(img='OpenImages/train/'),
label_file='annotations/class-descriptions-boxable.csv',
hierarchy_file='annotations/bbox_labels_600_hierarchy.json',
meta_file='annotations/train-image-metas.pkl',
pipeline=train_pipeline,
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=0,
persistent_workers=False,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/validation-annotations-bbox.csv',
data_prefix=dict(img='OpenImages/validation/'),
label_file='annotations/class-descriptions-boxable.csv',
hierarchy_file='annotations/bbox_labels_600_hierarchy.json',
meta_file='annotations/validation-image-metas.pkl',
image_level_ann_file='annotations/validation-'
'annotations-human-imagelabels-boxable.csv',
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='OpenImagesMetric',
iou_thrs=0.5,
ioa_thrs=0.5,
use_group_of=True,
get_supercategory=True)
test_evaluator = val_evaluator
configs/_base_/datasets/refcoco+.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'RefCocoDataset'
data_root = 'data/coco/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='LoadAnnotations',
with_mask=True,
with_bbox=False,
with_seg=False,
with_label=False),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'gt_masks', 'text'))
]
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,
data_prefix=dict(img_path='train2014/'),
ann_file='refcoco+/instances.json',
split_file='refcoco+/refs(unc).p',
split='val',
text_mode='select_first',
pipeline=test_pipeline))
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='train2014/'),
ann_file='refcoco+/instances.json',
split_file='refcoco+/refs(unc).p',
split='testA', # or 'testB'
text_mode='select_first',
pipeline=test_pipeline))
val_evaluator = dict(type='RefSegMetric', metric=['cIoU', 'mIoU'])
test_evaluator = val_evaluator
configs/_base_/datasets/refcoco.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'RefCocoDataset'
data_root = 'data/coco/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='LoadAnnotations',
with_mask=True,
with_bbox=False,
with_seg=False,
with_label=False),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'gt_masks', 'text'))
]
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,
data_prefix=dict(img_path='train2014/'),
ann_file='refcoco/instances.json',
split_file='refcoco/refs(unc).p',
split='val',
text_mode='select_first',
pipeline=test_pipeline))
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='train2014/'),
ann_file='refcoco/instances.json',
split_file='refcoco/refs(unc).p',
split='testA', # or 'testB'
text_mode='select_first',
pipeline=test_pipeline))
val_evaluator = dict(type='RefSegMetric', metric=['cIoU', 'mIoU'])
test_evaluator = val_evaluator
configs/_base_/datasets/refcocog.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'RefCocoDataset'
data_root = 'data/coco/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='LoadAnnotations',
with_mask=True,
with_bbox=False,
with_seg=False,
with_label=False),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'gt_masks', 'text'))
]
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,
data_prefix=dict(img_path='train2014/'),
ann_file='refcocog/instances.json',
split_file='refcocog/refs(umd).p',
split='val',
text_mode='select_first',
pipeline=test_pipeline))
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='train2014/'),
ann_file='refcocog/instances.json',
split_file='refcocog/refs(umd).p',
split='test',
text_mode='select_first',
pipeline=test_pipeline))
val_evaluator = dict(type='RefSegMetric', metric=['cIoU', 'mIoU'])
test_evaluator = val_evaluator
configs/_base_/datasets/semi_coco_detection.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
color_space = [
[dict(type='ColorTransform')],
[dict(type='AutoContrast')],
[dict(type='Equalize')],
[dict(type='Sharpness')],
[dict(type='Posterize')],
[dict(type='Solarize')],
[dict(type='Color')],
[dict(type='Contrast')],
[dict(type='Brightness')],
]
geometric = [
[dict(type='Rotate')],
[dict(type='ShearX')],
[dict(type='ShearY')],
[dict(type='TranslateX')],
[dict(type='TranslateY')],
]
scale = [(1333, 400), (1333, 1200)]
branch_field = ['sup', 'unsup_teacher', 'unsup_student']
# pipeline used to augment labeled data,
# which will be sent to student model for supervised training.
sup_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomResize', scale=scale, keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='RandAugment', aug_space=color_space, aug_num=1),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
dict(
type='MultiBranch',
branch_field=branch_field,
sup=dict(type='PackDetInputs'))
]
# pipeline used to augment unlabeled data weakly,
# which will be sent to teacher model for predicting pseudo instances.
weak_pipeline = [
dict(type='RandomResize', scale=scale, keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'flip', 'flip_direction',
'homography_matrix')),
]
# pipeline used to augment unlabeled data strongly,
# which will be sent to student model for unsupervised training.
strong_pipeline = [
dict(type='RandomResize', scale=scale, keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='RandomOrder',
transforms=[
dict(type='RandAugment', aug_space=color_space, aug_num=1),
dict(type='RandAugment', aug_space=geometric, aug_num=1),
]),
dict(type='RandomErasing', n_patches=(1, 5), ratio=(0, 0.2)),
dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'flip', 'flip_direction',
'homography_matrix')),
]
# pipeline used to augment unlabeled data into different views
unsup_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadEmptyAnnotations'),
dict(
type='MultiBranch',
branch_field=branch_field,
unsup_teacher=weak_pipeline,
unsup_student=strong_pipeline,
)
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
]
batch_size = 5
num_workers = 5
# There are two common semi-supervised learning settings on the coco dataset：
# (1) Divide the train2017 into labeled and unlabeled datasets
# by a fixed percentage, such as 1%, 2%, 5% and 10%.
# The format of labeled_ann_file and unlabeled_ann_file are
# instances_train2017.{fold}@{percent}.json, and
# instances_train2017.{fold}@{percent}-unlabeled.json
# `fold` is used for cross-validation, and `percent` represents
# the proportion of labeled data in the train2017.
# (2) Choose the train2017 as the labeled dataset
# and unlabeled2017 as the unlabeled dataset.
# The labeled_ann_file and unlabeled_ann_file are
# instances_train2017.json and image_info_unlabeled2017.json
# We use this configuration by default.
labeled_dataset = dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=sup_pipeline,
backend_args=backend_args)
unlabeled_dataset = dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instances_unlabeled2017.json',
data_prefix=dict(img='unlabeled2017/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=unsup_pipeline,
backend_args=backend_args)
train_dataloader = dict(
batch_size=batch_size,
num_workers=num_workers,
persistent_workers=True,
sampler=dict(
type='GroupMultiSourceSampler',
batch_size=batch_size,
source_ratio=[1, 4]),
dataset=dict(
type='ConcatDataset', datasets=[labeled_dataset, unlabeled_dataset]))
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='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/instances_val2017.json',
metric='bbox',
format_only=False,
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/v3det.py 0 → 100644
View file @ b12850fe
# dataset settings
dataset_type = 'V3DetDataset'
data_root = 'data/V3Det/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
# If you don't have a gt annotation, delete the pipeline
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='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=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/v3det_2023_v1_train.json',
data_prefix=dict(img=''),
filter_cfg=dict(filter_empty_gt=True, min_size=4),
pipeline=train_pipeline,
backend_args=backend_args)))
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='annotations/v3det_2023_v1_val.json',
data_prefix=dict(img=''),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/v3det_2023_v1_val.json',
metric='bbox',
format_only=False,
backend_args=backend_args,
use_mp_eval=True,
proposal_nums=[300])
test_evaluator = val_evaluator
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