AssertionError: train_cfg specified in both outer field and model field

[HtAgoni]

For the configuration file, I only changed the data path and the number of categories.Run the following code:
python tools/train.py configs/consistent-teacher/consistent_teacher_r50_fpn_coco_180k_10p.py --work-dir ./work-dir --gpus 1

Then,

/root/anaconda3/envs/cont2/lib/python3.6/site-packages/mmdet/models/builder.py:53: UserWarning: train_cfg and test_cfg is deprecated, please specify them in model
'please specify them in model', UserWarning)
Traceback (most recent call last):
File "tools/train.py", line 200, in
main()
File "tools/train.py", line 171, in main
cfg.model, train_cfg=cfg.get("train_cfg"), test_cfg=cfg.get("test_cfg")
File "/root/anaconda3/envs/cont2/lib/python3.6/site-packages/mmdet/models/builder.py", line 55, in build_detector
'train_cfg specified in both outer field and model field '
AssertionError: train_cfg specified in both outer field and model field

[HtAgoni]

mmdet_base = "../../../mmdetection/configs/base"
base = [
f"{mmdet_base}/datasets/coco_detection.py",
f"{mmdet_base}/schedules/schedule_1x.py",
f"{mmdet_base}/default_runtime.py",
]

model = dict(
type='RetinaNet',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5),
bbox_head=dict(
type='FAM3DHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_type='anchor_based',
anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
octave_base_scale=8,
scales_per_octave=1,
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[.0, .0, .0, .0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
activated=True, # use probability instead of logit as input
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
train_cfg=dict(
assigner=dict(type='DynamicSoftLabelAssigner', topk=13, iou_factor=2.0),
alpha=1,
beta=6,
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))

img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

train_pipeline = [
dict(type="LoadImageFromFile"),
dict(type="LoadAnnotations", with_bbox=True),
dict(
type="Sequential",
transforms=[
dict(
type="RandResize",
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode="range",
keep_ratio=True,
),
dict(type="RandFlip", flip_ratio=0.5),
dict(
type="OneOf",
transforms=[
dict(type=k)
for k in [
"Identity",
"AutoContrast",
"RandEqualize",
"RandSolarize",
"RandColor",
"RandContrast",
"RandBrightness",
"RandSharpness",
"RandPosterize",
]
],
),
],
record=True,
),
dict(type="Pad", size_divisor=32),
dict(type="Normalize", **img_norm_cfg),
dict(type="ExtraAttrs", tag="sup"),
dict(type="DefaultFormatBundle"),
dict(
type="Collect",
keys=["img", "gt_bboxes", "gt_labels"],
meta_keys=(
"filename",
"ori_shape",
"img_shape",
"img_norm_cfg",
"pad_shape",
"scale_factor",
"tag",
),
),
]

strong_pipeline = [
dict(
type="Sequential",
transforms=[
dict(
type="RandResize",
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode="range",
keep_ratio=True,
),
dict(type="RandFlip", flip_ratio=0.5),
dict(
type="ShuffledSequential",
transforms=[
dict(
type="OneOf",
transforms=[
dict(type=k)
for k in [
"Identity",
"AutoContrast",
"RandEqualize",
"RandSolarize",
"RandColor",
"RandContrast",
"RandBrightness",
"RandSharpness",
"RandPosterize",
]
],
),
dict(
type="OneOf",
transforms=[
dict(type="RandTranslate", x=(-0.1, 0.1)),
dict(type="RandTranslate", y=(-0.1, 0.1)),
dict(type="RandRotate", angle=(-30, 30)),
[
dict(type="RandShear", x=(-30, 30)),
dict(type="RandShear", y=(-30, 30)),
],
],
),
],
),
dict(
type="RandErase",
n_iterations=(1, 5),
size=[0, 0.2],
squared=True,
),
],
record=True,
),
dict(type="Pad", size_divisor=32),
dict(type="Normalize", **img_norm_cfg),
dict(type="ExtraAttrs", tag="unsup_student"),
dict(type="DefaultFormatBundle"),
dict(
type="Collect",
keys=["img", "gt_bboxes", "gt_labels"],
meta_keys=(
"filename",
"ori_shape",
"img_shape",
"img_norm_cfg",
"pad_shape",
"scale_factor",
"tag",
"transform_matrix",
),
),
]
weak_pipeline = [
dict(
type="Sequential",
transforms=[
dict(
type="RandResize",
img_scale=[(1333, 400), (1333, 1200)],
multiscale_mode="range",
keep_ratio=True,
),
dict(type="RandFlip", flip_ratio=0.5),
],
record=True,
),
dict(type="Pad", size_divisor=32),
dict(type="Normalize", **img_norm_cfg),
dict(type="ExtraAttrs", tag="unsup_teacher"),
dict(type="DefaultFormatBundle"),
dict(
type="Collect",
keys=["img", "gt_bboxes", "gt_labels"],
meta_keys=(
"filename",
"ori_shape",
"img_shape",
"img_norm_cfg",
"pad_shape",
"scale_factor",
"tag",
"transform_matrix",
),
),
]
unsup_pipeline = [
dict(type="LoadImageFromFile"),
# dict(type="LoadAnnotations", with_bbox=True),
# generate fake labels for data format compatibility
dict(type="PseudoSamples", with_bbox=True),
dict(
type="MultiBranch", unsup_teacher=strong_pipeline, unsup_student=weak_pipeline
),
]

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", **img_norm_cfg),
dict(type="Pad", size_divisor=32),
dict(type="ImageToTensor", keys=["img"]),
dict(type="Collect", keys=["img"]),
],
),
]

fold = 1
percent = 10
data = dict(
samples_per_gpu=5,
workers_per_gpu=5,
train=dict(
delete=True,
type="SemiDataset",
sup=dict(
type="CocoDataset",
ann_file="data/coco_semi/semi_supervised/instances_train2017.${fold}@${percent}.json",
img_prefix="data/coco/train2017/",
pipeline=train_pipeline,
),
unsup=dict(
type="CocoDataset",
ann_file="data/coco_semi/semi_supervised/instances_train2017.${fold}@${percent}-unlabeled.json",
img_prefix="data/coco/train2017/",
pipeline=unsup_pipeline,
filter_empty_gt=False,
),
),
val=dict(
img_prefix="data/coco/val2017/",
ann_file='data/coco/annotations/instances_val2017.json',
pipeline=test_pipeline
),
test=dict(
pipeline=test_pipeline,
img_prefix="data/coco/val2017/",
ann_file='data/coco/annotations/instances_val2017.json'
),
sampler=dict(
train=dict(
type="SemiBalanceSampler",
sample_ratio=[1, 4],
by_prob=False,
# at_least_one=True,
epoch_length=7330,
)
),
)

semi_wrapper = dict(
type="ConsistentTeacher",
model="${model}",
train_cfg=dict(
num_scores=100,
dynamic_ratio=1.0,
warmup_step=10000,
min_pseduo_box_size=0,
unsup_weight=2.0,
),
test_cfg=dict(inference_on="teacher"),
)

custom_hooks = [
dict(type="NumClassCheckHook"),
dict(type="WeightSummary"),
dict(type='SetIterInfoHook'),
dict(type="MeanTeacher", momentum=0.9995, interval=1, warm_up=0),
]
evaluation = dict(type="SubModulesDistEvalHook", interval=4000)
optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(
delete=True, grad_clip=dict(max_norm=20, norm_type=2))
lr_config = dict(step=[180000, 180000])
runner = dict(delete=True, type="IterBasedRunner", max_iters=180000)
checkpoint_config = dict(by_epoch=False, interval=4000, max_keep_ckpts=2)

log_config = dict(
interval=50,
hooks=[
dict(type="TextLoggerHook", by_epoch=False),
dict(
type="WandbLoggerHook",
init_kwargs=dict(
project="consistent-teacher",
name="${cfg_name}",
config=dict(
fold="${fold}",
percent="${percent}",
work_dirs="${work_dir}",
total_step="${runner.max_iters}",
),
),
by_epoch=False,
)

],

)
fp16 = None

用的自带的配置文件

[MrDec]

请问您解决了吗

[vs6112129]

please make sure ur mmdet version when u git clone and it will be solved

[rankiih]

please make sure ur mmdet version when u git clone and it will be solved

你好，请问有具体的解决方案吗，我现在也遇到这个问题

[rankiih]

please make sure ur mmdet version when u git clone and it will be solved

Hello, do you have any specific solutions? I am encountering this problem now as well.