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# dataset settings | |
dataset_type = 'Objects365V1Dataset' | |
data_root = 'data/Objects365/Obj365_v1/' | |
# file_client_args = dict( | |
# backend='petrel', | |
# path_mapping=dict({ | |
# './data/': 's3://openmmlab/datasets/detection/', | |
# 'data/': 's3://openmmlab/datasets/detection/' | |
# })) | |
file_client_args = dict(backend='disk') | |
train_pipeline = [ | |
dict(type='LoadImageFromFile', file_client_args=file_client_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', file_client_args=file_client_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)) | |
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)) | |
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) | |
test_evaluator = val_evaluator | |