AttributeError: 'ConfigDict' object has no attribute 'dataset'

[James606240]

Thanks for your error report and we appreciate it a lot.

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2. Did you make any modifications on the code or config? Did you understand what you have modified?
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[James606240]

I'm training the custom dataset, but sometimes the error like that:
File "C:\Users\James.conda\envs\mmcv_2\lib\site-packages\mmcv\utils\config.py", line 53, in getattr
raise ex
AttributeError: 'ConfigDict' object has no attribute 'dataset'

and this is my cfg.py code：

from mmcv import Config
from mmdet.apis import set_random_seed
from dataset import XMLCustomDataset

cfg = Config.fromfile('mmdetection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py')



print(f"Default Config:\n{cfg.pretty_text}")

# Modify dataset type and path.
cfg.dataset_type = 'XMLCustomDataset'
cfg.data_root = 'input/data_root/'

cfg.data.test.type = 'XMLCustomDataset'
cfg.data.test.data_root = 'input/data_root/'
cfg.data.test.ann_file = 'dataset/ImageSets/Main/val.txt'
cfg.data.test.img_prefix = 'dataset/'

cfg.data.train.dataset.type = 'XMLCustomDataset'
cfg.data.train.dataset.data_root = 'input/data_root/'
cfg.data.train.dataset.ann_file = 'dataset/ImageSets/Main/train.txt'
cfg.data.train.dataset.img_prefix = 'dataset/'

cfg.data.val.type = 'XMLCustomDataset'
cfg.data.val.data_root = 'input/data_root/'
cfg.data.val.ann_file = 'dataset/ImageSets/Main/val.txt'
cfg.data.val.img_prefix = 'dataset/'

cfg.data.train.pipeline = [
    dict(type='Mosaic', img_scale=(640, 640), pad_val=114.0),
    dict(
        type='RandomAffine',
        scaling_ratio_range=(0.1, 2),
        border=(-320, -320)),
    dict(
        type='MixUp',
        img_scale=(640, 640),
        ratio_range=(0.8, 1.6),
        pad_val=114.0),
    dict(type='YOLOXHSVRandomAug'),
    dict(type='RandomFlip', flip_ratio=0.0),
    dict(type='Resize', img_scale=(640, 640), keep_ratio=True),
    dict(
        type='Pad',
        pad_to_square=True,
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(
        type='FilterAnnotations',
        min_gt_bbox_wh=(1, 1),
        keep_empty=False),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
    ]

# Batch size (samples per GPU).
cfg.data.samples_per_gpu = 4

# Modify number of classes as per the model head.
cfg.model.bbox_head.num_classes = 6
# Comment/Uncomment this to training from scratch/fine-tune according to the

# model checkpoint path.
cfg.load_from = 'checkpoints/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco_20201005_113243-42607475.pth'

# The original learning rate (LR) is set for 8-GPU training.
# We divide it by 8 since we only use one GPU.
cfg.optimizer.lr = 0.0008 / 8
cfg.lr_config.warmup = None
cfg.log_config.interval = 5

# The output directory for training. As per the model name.
cfg.work_dir = 'outputs/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco'

# Evaluation Metric.
cfg.evaluation.metric = 'mAP'
cfg.evaluation.save_best = 'mAP'
# Evaluation times.
cfg.evaluation.interval = 1
# Checkpoint storage interval.
cfg.checkpoint_config.interval = 15

# Set random seed for reproducible results.
cfg.seed = 0
set_random_seed(0, deterministic=False)
cfg.gpu_ids = range(1)
cfg.device = 'cuda'
cfg.runner.max_epochs = 5

# We can also use tensorboard to log the training process
cfg.log_config.hooks = [
    dict(type='TextLoggerHook'),
    dict(type='TensorboardLoggerHook')]

# We can initialize the logger for training and have a look
# at the final config used for training
print('#'*50)
print(f'Config:\n{cfg.pretty_text}')

[jbwang1997]

Hello, could you provide more information, like the full error report and arguments of XMLCustomDataset?

[James606240]

Hello, could you provide more information, like the full error report and arguments of XMLCustomDataset?

C:\Users\James.conda\envs\mmcv_2\python.exe "D:/2021.9.1 北科大在職碩/AI 研究/Pycharm/@2_All_AI(PT)/train.py"
Default Config:
model = dict(
type='CascadeRCNN',
backbone=dict(
type='ResNeSt',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest101'),
stem_channels=128,
radix=2,
reduction_factor=4,
avg_down_stride=True),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
roi_head=dict(
type='CascadeRoIHead',
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=dict(type='SyncBN', requires_grad=True),
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=dict(type='SyncBN', requires_grad=True),
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=dict(type='SyncBN', requires_grad=True),
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
]),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=[
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)
]),
test_cfg=dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=False,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_train2017.json',
img_prefix='data/coco/train2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=False,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]),
val=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
type='CocoDataset',
ann_file='data/coco/annotations/instances_val2017.json',
img_prefix='data/coco/val2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Normalize',
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
norm_cfg = dict(type='SyncBN', requires_grad=True)

Traceback (most recent call last):
File "D:\2021.9.1 北科大在職碩\AI 研究\Pycharm@2_All_AI(PT)\train.py", line 4, in
from cfg import cfg
File "D:\2021.9.1 北科大在職碩\AI 研究\Pycharm@2_All_AI(PT)\cfg.py", line 28, in
cfg.data.train.dataset.type = 'XMLCustomDataset'
File "C:\Users\James.conda\envs\mmcv_2\lib\site-packages\mmcv\utils\config.py", line 53, in getattr
raise ex
AttributeError: 'ConfigDict' object has no attribute 'dataset'

进程已结束,退出代码1

[James606240]

and it is my test：

Model can't train：

1. mmdetection/configs/retinanet/retinanet_r101_fpn_1x_coco
2. mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco
3. mmdetection/configs/openimages/retinanet_r50_fpn_32x2_1x_openimages
4. mmdetection/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco

Model can train：

1. mmdetection/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco
2. mmdetection/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco
3. mmdetection/configs/ssd/ssd512_coco

I'am test from this web: https://debuggercafe.com/custom-dataset-training-using-mmdetection/

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