基于MMdetection3.10
困扰了大半天的问题，终于解决了。
方法1：定位到configs\_base_\datasets\coco_detection.py
将里面的路径全部换为自己的路径，最重要的是将以下注释取消掉，特别注意以下两个参数
改好的文件示例
# dataset settings
dataset_type = 'CocoDataset'
# data_root = 'data/coco/'
data_root = "E:/******************/COCO2017/" # 自己的根路径# 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 = Nonetrain_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 pipelinedict(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='train/'),ann_file="train/annotations/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='annotations/instances_val2017.json',# data_prefix=dict(img='val2017/'),ann_file = data_root+"test/annotations/test.json", data_prefix=dict(img='test/images/'),test_mode=True,pipeline=test_pipeline,backend_args=backend_args))
test_dataloader = val_dataloaderval_evaluator = dict(type='CocoMetric',# ann_file=data_root + 'annotations/instances_val2017.json',ann_file = data_root+"test/annotations/test.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+"test/annotations/test.json",outfile_prefix='./work_dirs/coco_detection/test')
方法2，直接在配置文件中重写test_evaluator 中的 format_only=True,
outfile_prefix='./work_dirs/coco_detection/test'两个参数
最终：
