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TypeError: loss() missing 1 required positional argument: 'img_metas' #1

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LeonHardt427 opened this issue Aug 18, 2021 · 7 comments
Open

TypeError: loss() missing 1 required positional argument: 'img_metas' #1

LeonHardt427 opened this issue Aug 18, 2021 · 7 comments

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@LeonHardt427
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I meet an Error when running Centernet2 using mmdetection. Customed dataset was used by coco format. Error message is showed as follows：

Traceback (most recent call last):
File "tools/train.py", line 188, in
main()
File "tools/train.py", line 184, in main
meta=meta)
File "/ProjectRoot/mmdetection/mmdet/apis/train.py", line 170, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/usr/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
epoch_runner(data_loaders[i], **kwargs)
File "/usr/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
File "/usr/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 30, in run_iter
**kwargs)
File "/usr/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 67, in train_step
return self.module.train_step(*inputs[0], **kwargs[0])
File "/ProjectRoot/mmdetection/mmdet/models/detectors/base.py", line 237, in train_step
losses = self(**data)
File "/usr/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 98, in new_func
return old_func(*args, **kwargs)
File "/ProjectRoot/mmdetection/mmdet/models/detectors/base.py", line 171, in forward
return self.forward_train(img, img_metas, **kwargs)
File "/ProjectRoot/mmdetection/mmdet/models/detectors/two_stage.py", line 140, in forward_train
proposal_cfg=proposal_cfg)
File "/ProjectRoot/mmdetection/mmdet/models/dense_heads/base_dense_head.py", line 54, in forward_train
losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
TypeError: loss() missing 1 required positional argument: 'img_metas'

------log is：
2021-08-18 10:59:05,420 - mmdet - INFO - Environment info:

sys.platform: linux
Python: 3.7.5rc1 (default, Aug 5 2021, 15:04:37) [GCC 8.5.0]
CUDA available: True
GPU 0: GeForce RTX 2080 Ti
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (GCC) 8.5.0
PyTorch: 1.8.1+cu102
PyTorch compiling details: PyTorch built with:

  • GCC 7.3
  • C++ Version: 201402
  • Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  • Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)
  • OpenMP 201511 (a.k.a. OpenMP 4.5)
  • NNPACK is enabled
  • CPU capability usage: AVX2
  • CUDA Runtime 10.2
  • NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70
  • CuDNN 7.6.5
  • Magma 2.5.2
  • Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.8.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.9.1+cu102
OpenCV: 4.5.3
MMCV: 1.3.11
MMCV Compiler: GCC 8.5
MMCV CUDA Compiler: 10.2
MMDetection: 2.15.1+682f03d

2021-08-18 10:59:06,215 - mmdet - INFO - Distributed training: False
2021-08-18 10:59:07,115 - mmdet - INFO - Config:
model = dict(
type='CenterNet2',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(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=[512, 1024, 2048],
out_channels=256,
num_outs=5,
add_extra_convs='on_output',
relu_before_extra_convs=True),
rpn_head=dict(
type='CustomCenterNetHead',
num_classes=1,
norm='BN',
in_channel=256,
num_features=5,
num_cls_convs=4,
num_box_convs=4,
num_share_convs=0,
use_deformable=False,
only_proposal=True,
fpn_strides=[8, 16, 32, 64, 128],
loss_center_heatmap=dict(
type='CustomGaussianFocalLoss',
alpha=0.25,
ignore_high_fp=0.85,
loss_weight=0.5),
loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
roi_head=dict(
type='CustomCascadeRoIHead',
num_stages=3,
stage_loss_weights=[1, 1, 1],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[8, 16, 32, 64, 128]),
bbox_head=[
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1,
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='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1,
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='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1,
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(),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.9),
min_bbox_size=0),
rcnn=[
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),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.8,
neg_iou_thr=0.8,
min_pos_iou=0.8,
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=0.9),
min_bbox_size=0),
rcnn=dict(
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.7),
max_per_img=100)))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
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='Resize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 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_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 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=[123.675, 116.28, 103.53],
std=[58.395, 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=4,
workers_per_gpu=1,
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),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 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_test2017.json',
img_prefix='data/coco/test2017/',
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=[123.675, 116.28, 103.53],
std=[58.395, 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_test2017.json',
img_prefix='data/coco/test2017/',
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=[123.675, 116.28, 103.53],
std=[58.395, 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=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.0001,
step=[10, 15])
runner = dict(type='EpochBasedRunner', max_epochs=20)
checkpoint_config = dict(interval=1)
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1), ('val', 1)]
find_unused_parameters = True
work_dir = './work_dirs/centernet2_cascade_res50_fpn_1x_coco'
gpu_ids = range(0, 1)

2021-08-18 10:59:11,768 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}
2021-08-18 10:59:19,291 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2021-08-18 10:59:20,857 - mmdet - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'layer': 'Linear', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
2021-08-18 10:59:21,948 - mmdet - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'layer': 'Linear', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
2021-08-18 10:59:22,985 - mmdet - INFO - initialize Shared2FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'layer': 'Linear', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
Name of parameter - Initialization information

backbone.conv1.weight - torch.Size([64, 3, 7, 7]):
PretrainedInit: load from torchvision://resnet50

backbone.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.bn2.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.bn2.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.bn3.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.bn3.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.downsample.1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.0.downsample.1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.1.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.1.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.1.bn2.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.1.bn2.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.1.bn3.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.1.bn3.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.2.bn1.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.2.bn1.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.2.bn2.weight - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.2.bn2.bias - torch.Size([64]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.2.bn3.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer1.2.bn3.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.bn3.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.bn3.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.downsample.1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.0.downsample.1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.1.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.1.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.1.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.1.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.1.bn3.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.1.bn3.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.2.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.2.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.2.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.2.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.2.bn3.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.2.bn3.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.3.bn1.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.3.bn1.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.3.bn2.weight - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.3.bn2.bias - torch.Size([128]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.3.bn3.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer2.3.bn3.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.downsample.1.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.0.downsample.1.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.1.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.1.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.1.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.1.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.1.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.1.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.2.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.2.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.2.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.2.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.2.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.2.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.3.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.3.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.3.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.3.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.3.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.3.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.4.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.4.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.4.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.4.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.4.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.4.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.5.bn1.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.5.bn1.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.5.bn2.weight - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.5.bn2.bias - torch.Size([256]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.5.bn3.weight - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer3.5.bn3.bias - torch.Size([1024]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.bn1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.bn1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.bn2.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.bn2.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.bn3.weight - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.bn3.bias - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.downsample.1.weight - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.0.downsample.1.bias - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.1.bn1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.1.bn1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.1.bn2.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.1.bn2.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.1.bn3.weight - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.1.bn3.bias - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.2.bn1.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.2.bn1.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.2.bn2.weight - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.2.bn2.bias - torch.Size([512]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.2.bn3.weight - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50

backbone.layer4.2.bn3.bias - torch.Size([2048]):
PretrainedInit: load from torchvision://resnet50

neck.lateral_convs.0.conv.weight - torch.Size([256, 512, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0

neck.lateral_convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of CenterNet2

neck.lateral_convs.1.conv.weight - torch.Size([256, 1024, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0

neck.lateral_convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of CenterNet2

neck.lateral_convs.2.conv.weight - torch.Size([256, 2048, 1, 1]):
XavierInit: gain=1, distribution=uniform, bias=0

neck.lateral_convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of CenterNet2

neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

neck.fpn_convs.0.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of CenterNet2

neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

neck.fpn_convs.1.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of CenterNet2

neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

neck.fpn_convs.2.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of CenterNet2

neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

neck.fpn_convs.3.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of CenterNet2

neck.fpn_convs.4.conv.weight - torch.Size([256, 256, 3, 3]):
XavierInit: gain=1, distribution=uniform, bias=0

neck.fpn_convs.4.conv.bias - torch.Size([256]):
The value is the same before and after calling init_weights of CenterNet2

rpn_head.cls_tower.0.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.cls_tower.0.bias - torch.Size([256]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.cls_tower.2.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.cls_tower.2.bias - torch.Size([256]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.cls_tower.4.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.cls_tower.4.bias - torch.Size([256]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.cls_tower.6.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.cls_tower.6.bias - torch.Size([256]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.bbox_tower.0.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.bbox_tower.0.bias - torch.Size([256]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.bbox_tower.2.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.bbox_tower.2.bias - torch.Size([256]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.bbox_tower.4.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.bbox_tower.4.bias - torch.Size([256]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.bbox_tower.6.weight - torch.Size([256, 256, 3, 3]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.bbox_tower.6.bias - torch.Size([256]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.scales.0.scale - torch.Size([]):
The value is the same before and after calling init_weights of CenterNet2

rpn_head.scales.1.scale - torch.Size([]):
The value is the same before and after calling init_weights of CenterNet2

rpn_head.scales.2.scale - torch.Size([]):
The value is the same before and after calling init_weights of CenterNet2

rpn_head.scales.3.scale - torch.Size([]):
The value is the same before and after calling init_weights of CenterNet2

rpn_head.scales.4.scale - torch.Size([]):
The value is the same before and after calling init_weights of CenterNet2

rpn_head.agn_hm.weight - torch.Size([1, 256, 3, 3]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.agn_hm.bias - torch.Size([1]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.bbox_pred.weight - torch.Size([4, 256, 3, 3]):
Initialized by user-defined init_weights in CustomCenterNetHead

rpn_head.bbox_pred.bias - torch.Size([4]):
Initialized by user-defined init_weights in CustomCenterNetHead

roi_head.bbox_head.0.fc_cls.weight - torch.Size([2, 1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.0.fc_cls.bias - torch.Size([2]):
NormalInit: mean=0, std=0.01, bias=0

roi_head.bbox_head.0.fc_reg.weight - torch.Size([4, 1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.0.fc_reg.bias - torch.Size([4]):
NormalInit: mean=0, std=0.001, bias=0

roi_head.bbox_head.0.shared_fcs.0.weight - torch.Size([1024, 12544]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.0.shared_fcs.0.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.0.shared_fcs.1.weight - torch.Size([1024, 1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.0.shared_fcs.1.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.1.fc_cls.weight - torch.Size([2, 1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.1.fc_cls.bias - torch.Size([2]):
NormalInit: mean=0, std=0.01, bias=0

roi_head.bbox_head.1.fc_reg.weight - torch.Size([4, 1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.1.fc_reg.bias - torch.Size([4]):
NormalInit: mean=0, std=0.001, bias=0

roi_head.bbox_head.1.shared_fcs.0.weight - torch.Size([1024, 12544]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.1.shared_fcs.0.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.1.shared_fcs.1.weight - torch.Size([1024, 1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.1.shared_fcs.1.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.2.fc_cls.weight - torch.Size([2, 1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.2.fc_cls.bias - torch.Size([2]):
NormalInit: mean=0, std=0.01, bias=0

roi_head.bbox_head.2.fc_reg.weight - torch.Size([4, 1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.2.fc_reg.bias - torch.Size([4]):
NormalInit: mean=0, std=0.001, bias=0

roi_head.bbox_head.2.shared_fcs.0.weight - torch.Size([1024, 12544]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.2.shared_fcs.0.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.2.shared_fcs.1.weight - torch.Size([1024, 1024]):
XavierInit: gain=1, distribution=normal, bias=0

roi_head.bbox_head.2.shared_fcs.1.bias - torch.Size([1024]):
XavierInit: gain=1, distribution=normal, bias=0
2021-08-18 10:59:31,962 - mmdet - INFO - Start running, host: wangzhan.wang@ide-container-online-5597-job-0-1629164823-0, work_dir: /ProjectRoot/mmdetection/work_dirs/centernet2_cascade_res50_fpn_1x_coco
2021-08-18 10:59:31,962 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) CheckpointHook
(NORMAL ) EvalHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

before_train_epoch:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) EvalHook
(NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

before_train_iter:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) EvalHook
(LOW ) IterTimerHook

after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(NORMAL ) EvalHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

after_train_epoch:
(NORMAL ) CheckpointHook
(NORMAL ) EvalHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

before_val_epoch:
(NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

before_val_iter:
(LOW ) IterTimerHook

after_val_iter:
(LOW ) IterTimerHook

after_val_epoch:
(VERY_LOW ) TextLoggerHook
(VERY_LOW ) TensorboardLoggerHook

after_run:
(VERY_LOW ) TensorboardLoggerHook

2021-08-18 10:59:31,964 - mmdet - INFO - workflow: [('train', 1), ('val', 1)], max: 20 epochs
Traceback (most recent call last):
File "tools/train.py", line 188, in
main()
File "tools/train.py", line 184, in main
meta=meta)
File "/ProjectRoot/mmdetection/mmdet/apis/train.py", line 170, in train_detector
runner.run(data_loaders, cfg.workflow)
File "/usr/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
epoch_runner(data_loaders[i], **kwargs)
File "/usr/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
File "/usr/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 30, in run_iter
**kwargs)
File "/usr/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 67, in train_step
return self.module.train_step(*inputs[0], **kwargs[0])
File "/ProjectRoot/mmdetection/mmdet/models/detectors/base.py", line 237, in train_step
losses = self(**data)
File "/usr/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, **kwargs)
File "/usr/lib/python3.7/site-packages/mmcv/runner/fp16_utils.py", line 98, in new_func
return old_func(*args, **kwargs)
File "/ProjectRoot/mmdetection/mmdet/models/detectors/base.py", line 171, in forward
return self.forward_train(img, img_metas, **kwargs)
File "/ProjectRoot/mmdetection/mmdet/models/detectors/two_stage.py", line 140, in forward_train
proposal_cfg=proposal_cfg)
File "/ProjectRoot/mmdetection/mmdet/models/dense_heads/base_dense_head.py", line 54, in forward_train
losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
TypeError: loss() missing 1 required positional argument: 'img_metas'
@tianyuluan
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halo, have you solved this problem?

@SherlockHua1995
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Hi, I met the same error in training process.Have you solved the problem? @LeonHardt427 @tianyuluan @MMz000 @Jacky-gsq
Any advice would be appreciated!

@SherlockHua1995
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I have solved the problem(TypeError: loss() missing 1 required positional argument: 'img_metas').
And in training, after 400 steps , the training process stoped,with batched_nms() raised runtimerror:
boxes.max()
operation doesnot have an identity.

@jianminglv20
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Hello, may I ask how do you solve this problem and where do you need to modify the code?@SherlockHua1995

@dnth
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dnth commented Jan 26, 2022

I face this issue in mmdet==2.20.0. Anyone found a solution yet?

@OscJD
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OscJD commented Mar 15, 2022

I have the same error
(TypeError: loss() missing 1 required positional argument: 'img_metas')

@k-takasan
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I faced the same error, but I have solved it.
I incorporated my own dataset into the following repository and ran the training:
https://github.com/Jacky-gsq/Centernet2-mmdet

The env. :
Python: 3.7
PyTorch: 1.7.1 + TorchVision: 0.8.2
MMCV: 1.3.8
MMDetection: 2.13.0

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@dnth @LeonHardt427 @OscJD @SherlockHua1995 @tianyuluan @jianminglv20 @k-takasan