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configs/_base_/datasets/ade20k_instance.py 0 → 100644
View file @ a8562a56
# dataset settings
dataset_type = 'ADE20KInstanceDataset'
data_root = 'data/ADEChallengeData2016/'
# 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/ADEChallengeData2016/'
# 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
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(2560, 640), 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'))
]
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='ade20k_instance_val.json',
data_prefix=dict(img='images/validation'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'ade20k_instance_val.json',
metric=['bbox', 'segm'],
format_only=False,
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/ade20k_panoptic.py 0 → 100644
View file @ a8562a56
# dataset settings
dataset_type = 'ADE20KPanopticDataset'
data_root = 'data/ADEChallengeData2016/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(2560, 640), 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'))
]
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='ade20k_panoptic_val.json',
data_prefix=dict(img='images/validation/', seg='ade20k_panoptic_val/'),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoPanopticMetric',
ann_file=data_root + 'ade20k_panoptic_val.json',
seg_prefix=data_root + 'ade20k_panoptic_val/',
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/ade20k_semantic.py 0 → 100644
View file @ a8562a56
dataset_type = 'ADE20KSegDataset'
data_root = 'data/ADEChallengeData2016/'
# 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/ADEChallengeData2016/'
# 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
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(2048, 512), keep_ratio=True),
dict(
type='LoadAnnotations',
with_bbox=False,
with_mask=False,
with_seg=True,
reduce_zero_label=True),
dict(
type='PackDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape'))
]
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='images/validation',
seg_map_path='annotations/validation'),
pipeline=test_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='SemSegMetric', iou_metrics=['mIoU'])
test_evaluator = val_evaluator
configs/_base_/datasets/cityscapes_detection.py 0 → 100644
View file @ a8562a56
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
# 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/segmentation/cityscapes/'
# 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/segmentation/',
# 'data/': 's3://openmmlab/datasets/segmentation/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=[(2048, 800), (2048, 1024)],
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=(2048, 1024), 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=1,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='RepeatDataset',
times=8,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instancesonly_filtered_gtFine_train.json',
data_prefix=dict(img='leftImg8bit/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/instancesonly_filtered_gtFine_val.json',
data_prefix=dict(img='leftImg8bit/val/'),
test_mode=True,
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='CocoMetric',
ann_file=data_root + 'annotations/instancesonly_filtered_gtFine_val.json',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
configs/_base_/datasets/cityscapes_instance.py 0 → 100644
View file @ a8562a56
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
# 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/segmentation/cityscapes/'
# 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/segmentation/',
# 'data/': 's3://openmmlab/datasets/segmentation/'
# }))
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomResize',
scale=[(2048, 800), (2048, 1024)],
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=(2048, 1024), 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=1,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='RepeatDataset',
times=8,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/instancesonly_filtered_gtFine_train.json',
data_prefix=dict(img='leftImg8bit/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/instancesonly_filtered_gtFine_val.json',
data_prefix=dict(img='leftImg8bit/val/'),
test_mode=True,
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=test_pipeline,
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = [
dict(
type='CocoMetric',
ann_file=data_root +
'annotations/instancesonly_filtered_gtFine_val.json',
metric=['bbox', 'segm'],
backend_args=backend_args),
dict(
type='CityScapesMetric',
seg_prefix=data_root + 'gtFine/val',
outfile_prefix='./work_dirs/cityscapes_metric/instance',
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='annotations/instancesonly_filtered_gtFine_test.json',
# data_prefix=dict(img='leftImg8bit/test/'),
# test_mode=True,
# filter_cfg=dict(filter_empty_gt=True, min_size=32),
# pipeline=test_pipeline))
# test_evaluator = dict(
# type='CityScapesMetric',
# format_only=True,
# outfile_prefix='./work_dirs/cityscapes_metric/test')
configs/_base_/datasets/coco_caption.py 0 → 100644
View file @ a8562a56
# data settings
dataset_type = 'CocoCaptionDataset'
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
test_pipeline = [
dict(
type='LoadImageFromFile',
imdecode_backend='pillow',
backend_args=backend_args),
dict(
type='Resize',
scale=(224, 224),
interpolation='bicubic',
backend='pillow'),
dict(type='PackInputs', meta_keys=['image_id']),
]
# ann_file download from
# train dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json # noqa
# val dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json # noqa
# test dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json # noqa
# val evaluator: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json # noqa
# test evaluator: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json # noqa
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/coco_karpathy_val.json',
pipeline=test_pipeline,
))
val_evaluator = dict(
type='COCOCaptionMetric',
ann_file=data_root + 'annotations/coco_karpathy_val_gt.json',
)
# # If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/coco_detection.py 0 → 100644
View file @ a8562a56
# 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),
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/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',
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',
# format_only=True,
# ann_file=data_root + 'annotations/image_info_test-dev2017.json',
# outfile_prefix='./work_dirs/coco_detection/test')
configs/_base_/datasets/coco_instance.py 0 → 100644
View file @ a8562a56
# 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),
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 @ a8562a56
# 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 @ a8562a56
# 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 @ a8562a56
# 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 @ a8562a56
# 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 @ a8562a56
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 @ a8562a56
# 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 @ a8562a56
# 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 @ a8562a56
# 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 @ a8562a56
# 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 @ a8562a56
# 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 @ a8562a56
# 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 @ a8562a56
# 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
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