/home/z/anaconda3/envs/agent_detection/bin/python3.7 /home/z/zky/Agent-Attention-master/downstream/detection/tools/debug_train.py
/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/utils/setup_env.py:39: UserWarning: Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
f'Setting OMP_NUM_THREADS environment variable for each process '
/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/utils/setup_env.py:49: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
f'Setting MKL_NUM_THREADS environment variable for each process '
2023-12-31 21:14:42,606 - mmdet - INFO - Environment info:
sys.platform: linux
Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0]
CUDA available: True
GPU 0,1,2,3: NVIDIA TITAN X (Pascal)
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 10.1, V10.1.24
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
PyTorch: 1.12.1
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201402
- Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.3
- 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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
- CuDNN 8.3.2 (built against CUDA 11.5)
- Magma 2.5.2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.13.1
OpenCV: 4.6.0
MMCV: 1.6.1
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.3
MMDetection: 2.25.2+daeda61
2023-12-31 21:14:43,557 - mmdet - INFO - Distributed training: False
2023-12-31 21:14:44,477 - mmdet - INFO - Config:
model = dict(
type='RetinaNet',
backbone=dict(
type='AgentPVT',
img_size=224,
patch_size=4,
in_chans=3,
num_classes=6,
embed_dims=[64, 128, 320, 512],
num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4],
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.2,
depths=[3, 4, 18, 3],
sr_ratios=[8, 4, 2, 1],
agent_sr_ratios='1111',
num_stages=4,
agent_num=[9, 16, 49, 49],
downstream_agent_shapes=[(12, 12), (16, 16), (28, 28), (28, 28)],
kernel_size=3,
attn_type='AAAA',
scale=-0.5,
init_cfg=dict(type='Pretrained', checkpoint=None)),
neck=dict(
type='FPN',
in_channels=[64, 128, 320, 512],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5),
bbox_head=dict(
type='RetinaHead',
num_classes=6,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
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='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0,
ignore_iof_thr=-1),
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=0.5),
max_per_img=100))
dataset_type = 'CocoDataset'
data_root = '/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/'
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, 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(
train_dataloader=dict(
samples_per_gpu=2, workers_per_gpu=10, pin_memory=True),
train=dict(
type='CocoDataset',
ann_file=
'/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/annotations/instances_train2017.json',
img_prefix=
'/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/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=
'/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/annotations/instances_val2017.json',
img_prefix=
'/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/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=[123.675, 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=
'/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/annotations/instances_val2017.json',
img_prefix=
'/home/z/zky/Cosistentteacher/ConsistentTeacher-main/data/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=[123.675, 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='AdamW', lr=0.0001, 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)
pretrained = None
lr = 0.0001
work_dir = './work_dirs/agent_pvt_m_rtn_1x_12-16-28-28'
auto_resume = False
gpu_ids = [0]
2023-12-31 21:14:44,477 - mmdet - INFO - Set random seed to 2100000934, deterministic: False
Traceback (most recent call last):
File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 69, in build_from_cfg
return obj_cls(**args)
File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/backbones/agent_pvt.py", line 300, in init
for j in range(depths[i])])
File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/backbones/agent_pvt.py", line 300, in
for j in range(depths[i])])
File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/backbones/agent_pvt.py", line 217, in init
agent_num=agent_num, downstream_agent_shape=downstream_agent_shape, kernel_size=kernel_size, scale=scale)
File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/backbones/agent_pvt.py", line 129, in init
print('Agent Attention sr{} v{} n{} k{} scale{} reso{}'.format(sr_ratio, agent_num, kernel_size, scale, window_size))
IndexError: tuple index out of range
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 69, in build_from_cfg
return obj_cls(**args)
File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/detectors/retinanet.py", line 19, in init
test_cfg, pretrained, init_cfg)
File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/detectors/single_stage.py", line 32, in init
self.backbone = build_backbone(backbone)
File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/builder.py", line 20, in build_backbone
return BACKBONES.build(cfg)
File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 237, in build
return self.build_func(*args, **kwargs, registry=self)
File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/cnn/builder.py", line 27, in build_model_from_cfg
return build_from_cfg(cfg, registry, default_args)
File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 72, in build_from_cfg
raise type(e)(f'{obj_cls.name}: {e}')
IndexError: AgentPVT: tuple index out of range
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/z/zky/Agent-Attention-master/downstream/detection/tools/debug_train.py", line 244, in
main()
File "/home/z/zky/Agent-Attention-master/downstream/detection/tools/debug_train.py", line 215, in main
test_cfg=cfg.get('test_cfg'))
File "/home/z/zky/Agent-Attention-master/downstream/detection/mmdet/models/builder.py", line 59, in build_detector
cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 237, in build
return self.build_func(*args, **kwargs, registry=self)
File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/cnn/builder.py", line 27, in build_model_from_cfg
return build_from_cfg(cfg, registry, default_args)
File "/home/z/anaconda3/envs/agent_detection/lib/python3.7/site-packages/mmcv/utils/registry.py", line 72, in build_from_cfg
raise type(e)(f'{obj_cls.name}: {e}')
IndexError: RetinaNet: AgentPVT: tuple index out of range
Process finished with exit code 1