data is no problem ,I use other config file it runs . but use this config file it dosen't work ,the problem is CocoDataset` in mmdet/datasets/coco.py: 'CocoDataset' object has no attribute 'coco

[niceworks]

train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=273, val_interval=7)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=2000),
dict(type='MultiStepLR', by_epoch=True, milestones=[218, 246], gamma=0.1)
]
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005),
clip_grad=dict(max_norm=35, norm_type=2))
auto_scale_lr = dict(enable=False, base_batch_size=64)
default_scope = 'mmdet'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=7),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'))
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='DetLocalVisualizer',
vis_backends=[dict(type='LocalVisBackend')],
name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[0, 0, 0],
std=[255.0, 255.0, 255.0],
bgr_to_rgb=True,
pad_size_divisor=32)
model = dict(
type='YOLOV3',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[0, 0, 0],
std=[255.0, 255.0, 255.0],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='Darknet',
depth=53,
out_indices=(3, 4, 5),
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://darknet53')),
neck=dict(
type='YOLOV3Neck',
num_scales=3,
in_channels=[1024, 512, 256],
out_channels=[512, 256, 128]),
bbox_head=dict(
type='YOLOV3Head',
num_classes=80,
in_channels=[512, 256, 128],
out_channels=[1024, 512, 256],
anchor_generator=dict(
type='YOLOAnchorGenerator',
base_sizes=[[(116, 90), (156, 198), (373, 326)],
[(30, 61), (62, 45), (59, 119)],
[(10, 13), (16, 30), (33, 23)]],
strides=[32, 16, 8]),
bbox_coder=dict(type='YOLOBBoxCoder'),
featmap_strides=[32, 16, 8],
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0,
reduction='sum'),
loss_conf=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0,
reduction='sum'),
loss_xy=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=2.0,
reduction='sum'),
loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')),
train_cfg=dict(
assigner=dict(
type='GridAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0)),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
conf_thr=0.005,
nms=dict(type='nms', iou_threshold=0.45),
max_per_img=100))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Expand', mean=[0, 0, 0], to_rgb=True, ratio_range=(1, 2)),
dict(
type='MinIoURandomCrop',
min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
min_crop_size=0.3),
dict(type='Resize', scale=(320, 320), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackDetInputs')
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(320, 320), 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=8,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='CocoDataset',
data_root='data/coco/',
ann_file='annotations/instances_train2017.json',
data_prefix=dict(img='train2017/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Expand', mean=[0, 0, 0], to_rgb=True,
ratio_range=(1, 2)),
dict(
type='MinIoURandomCrop',
min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
min_crop_size=0.3),
dict(type='Resize', scale=(320, 320), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='PackDetInputs')
],
backend_args=None))
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
data_root='data/coco/',
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(320, 320), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
backend_args=None))
test_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
data_root='data/coco/',
ann_file='annotations/instances_val2017.json',
data_prefix=dict(img='val2017/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(320, 320), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor'))
],
backend_args=None))
val_evaluator = dict(
type='CocoMetric',
ann_file='data/coco/annotations/instances_val2017.json',
metric='bbox',
backend_args=None)
test_evaluator = dict(
type='CocoMetric',
ann_file='data/coco/annotations/instances_val2017.json',
metric='bbox',
backend_args=None)
input_size = (320, 320)
launcher = 'none'
work_dir = './work_dirs\yolov3_d53_8xb8-320-273e_coco'

[niceworks]