Tool/software:
Error reading previous trained file
Matplotlib created a temporary cache directory at /tmp/matplotlib-sd7fs966 because the default path (/.config/matplotlib) is not a writable directory; it is highly recommended to set the MPLCONFIGDIR environment variable to a writable directory, in particular to speed up the import of Matplotlib and to better support multiprocessing.
/opt/edgeai-mmdetection/mmdet/utils/setup_env.py:32: 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.
warnings.warn(
/opt/edgeai-mmdetection/mmdet/utils/setup_env.py:42: 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.
warnings.warn(
fatal: not a git repository (or any of the parent directories): .git
2025-03-21 07:01:06,793 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.10.14 (main, Jun 25 2024, 21:25:28) [GCC 11.4.0]
CUDA available: False
GCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
PyTorch: 2.0.1+cpu
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, USE_CUDA=0, USE_CUDNN=OFF, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.15.2+cpu
OpenCV: 4.10.0
MMCV: 1.4.8
MMCV Compiler: GCC 11.4
MMCV CUDA Compiler: not available
MMDetection: 2.22.0+
------------------------------------------------------------
2025-03-21 07:01:07,391 - mmdet - INFO - Distributed training: False
2025-03-21 07:01:08,119 - mmdet - INFO - Config:
dataset_type = 'CocoDataset'
data_root = '/opt/EdgeAIRoot/18293833/projects/08f16d60/dataset'
data = dict(
samples_per_gpu=8,
workers_per_gpu=2,
train=dict(
type='MultiImageMixDataset',
dataset=dict(
type='ModelMakerDataset',
ann_file=
'/opt/EdgeAIRoot/18293833/projects/08f16d60/dataset/annotations/instances_train.json',
img_prefix=
'/opt/EdgeAIRoot/18293833/projects/08f16d60/dataset/train',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True)
],
filter_empty_gt=False,
classes=['bad', 'good', 'reverse']),
pipeline=[
dict(type='Mosaic', img_scale=(416, 416), pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.5, 1.5),
border=(-208, -208)),
dict(
type='MixUp',
img_scale=(416, 416),
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(416, 416), 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'])
]),
val=dict(
type='ModelMakerDataset',
ann_file=
'/opt/EdgeAIRoot/18293833/projects/08f16d60/dataset/annotations/instances_val.json',
img_prefix='/opt/EdgeAIRoot/18293833/projects/08f16d60/dataset/val',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(416, 416),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
],
classes=['bad', 'good', 'reverse']),
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=(416, 416),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]),
persistent_workers=True)
evaluation = dict(interval=1, metric='bbox')
cudnn_benchmark = True
resize_with_scale_factor = True
max_epochs = 300
num_last_epochs = 15
interval = 10
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = './EdgeAIRoot/downloads/pretrained/yolox_nano_lite/yolox_nano_lite_416x416_20220214_checkpoint.pth'
resume_from = None
workflow = [('train', 1)]
print_model_complexity = True
optimizer = dict(
type='SGD',
lr=0.001,
momentum=0.9,
weight_decay=0.0005,
nesterov=True,
paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=1,
warmup_iters=1,
num_last_epochs=5,
min_lr_ratio=0.05)
runner = dict(type='EpochBasedRunner', max_epochs=15)
custom_hooks = [
dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),
dict(type='SyncNormHook', num_last_epochs=15, interval=10, priority=48),
dict(
type='ExpMomentumEMAHook',
resume_from=None,
momentum=0.0001,
priority=49)
]
checkpoint_config = dict(interval=10)
log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
img_scale = (416, 416)
input_size = (416, 416)
samples_per_gpu = 16
num_classes_dict = dict(
CocoDataset=80, VOCDataset=20, CityscapesDataset=8, WIDERFaceDataset=1)
dataset_root_dict = dict(
CocoDataset='data/coco/',
VOCDataset='data/VOCdevkit/',
CityscapesDataset='data/cityscapes/',
WIDERFaceDataset='data/WIDERFace/')
num_classes = 80
img_norm_cfg = dict(mean=[0.0, 0.0, 0.0], std=[1.0, 1.0, 1.0], to_rgb=False)
convert_to_lite_model = dict(group_size_dw=None)
quantize = False
initial_learning_rate = 0.01
model = dict(
type='YOLOX',
input_size=(416, 416),
random_size_range=(10, 20),
random_size_interval=10,
backbone=dict(
type='CSPDarknet',
deepen_factor=0.33,
widen_factor=0.25,
use_depthwise=False),
neck=dict(
type='YOLOXPAFPN',
in_channels=[64, 128, 256],
out_channels=64,
num_csp_blocks=1,
use_depthwise=False),
bbox_head=dict(
type='YOLOXHead',
num_classes=3,
in_channels=64,
feat_channels=64,
use_depthwise=False),
train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
train_pipeline = [
dict(type='Mosaic', img_scale=(416, 416), pad_val=114.0),
dict(
type='RandomAffine',
scaling_ratio_range=(0.5, 1.5),
border=(-208, -208)),
dict(
type='MixUp',
img_scale=(416, 416),
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(type='YOLOXHSVRandomAug'),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Resize', img_scale=(416, 416), 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'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(416, 416),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
pad_to_square=True,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
])
]
work_dir = '/opt/EdgeAIRoot/18293833/projects/08f16d60/run/20250321-070054/yolox_nano_lite/training'
total_epochs = 15
export_model = True
auto_resume = False
gpu_ids = [0]
2025-03-21 07:01:08,119 - mmdet - INFO - Set random seed to 1323013171, deterministic: False
2025-03-21 07:01:08,175 - mmdet - INFO - initialize CSPDarknet with init_cfg {'type': 'Kaiming', 'layer': 'Conv2d', 'a': 2.23606797749979, 'distribution': 'uniform', 'mode': 'fan_in', 'nonlinearity': 'leaky_relu'}
2025-03-21 07:01:08,185 - mmdet - INFO - initialize YOLOXPAFPN with init_cfg {'type': 'Kaiming', 'layer': 'Conv2d', 'a': 2.23606797749979, 'distribution': 'uniform', 'mode': 'fan_in', 'nonlinearity': 'leaky_relu'}
2025-03-21 07:01:08,192 - mmdet - INFO - initialize YOLOXHead with init_cfg {'type': 'Kaiming', 'layer': 'Conv2d', 'a': 2.23606797749979, 'distribution': 'uniform', 'mode': 'fan_in', 'nonlinearity': 'leaky_relu'}
2025-03-21 07:01:08,398 - mmdet - INFO - =========================================================================================================
Layer (type:depth-idx) Output Shape Param #
=========================================================================================================
YOLOX [1, 3, 52, 52] --
├─CSPDarknet: 1-1 [1, 64, 52, 52] --
│ └─FocusLite: 2-1 [1, 16, 208, 208] --
│ │ └─ConvModule: 3-1 [1, 12, 208, 208] --
│ │ │ └─Conv2d: 4-1 [1, 12, 208, 208] 324
│ │ │ └─BatchNorm2d: 4-2 [1, 12, 208, 208] 24
│ │ └─ConvModule: 3-2 [1, 16, 208, 208] --
│ │ │ └─Conv2d: 4-3 [1, 16, 208, 208] 1,728
│ │ │ └─BatchNorm2d: 4-4 [1, 16, 208, 208] 32
│ │ │ └─ReLU: 4-5 [1, 16, 208, 208] --
│ └─Sequential: 2-2 [1, 32, 104, 104] --
│ │ └─ConvModule: 3-3 [1, 32, 104, 104] --
│ │ │ └─Conv2d: 4-6 [1, 32, 104, 104] 4,608
│ │ │ └─BatchNorm2d: 4-7 [1, 32, 104, 104] 64
│ │ │ └─ReLU: 4-8 [1, 32, 104, 104] --
│ │ └─CSPLayer: 3-4 [1, 32, 104, 104] --
│ │ │ └─ConvModule: 4-9 [1, 16, 104, 104] --
│ │ │ │ └─Conv2d: 5-1 [1, 16, 104, 104] 512
│ │ │ │ └─BatchNorm2d: 5-2 [1, 16, 104, 104] 32
│ │ │ │ └─ReLU: 5-3 [1, 16, 104, 104] --
│ │ │ └─ConvModule: 4-10 [1, 16, 104, 104] --
│ │ │ │ └─Conv2d: 5-4 [1, 16, 104, 104] 512
│ │ │ │ └─BatchNorm2d: 5-5 [1, 16, 104, 104] 32
│ │ │ │ └─ReLU: 5-6 [1, 16, 104, 104] --
│ │ │ └─Sequential: 4-11 [1, 16, 104, 104] --
│ │ │ │ └─DarknetBottleneck: 5-7 [1, 16, 104, 104] --
│ │ │ │ │ └─ConvModule: 6-1 [1, 16, 104, 104] --
│ │ │ │ │ │ └─Conv2d: 7-1 [1, 16, 104, 104] 256
│ │ │ │ │ │ └─BatchNorm2d: 7-2 [1, 16, 104, 104] 32
│ │ │ │ │ │ └─ReLU: 7-3 [1, 16, 104, 104] --
│ │ │ │ │ └─ConvModule: 6-2 [1, 16, 104, 104] --
│ │ │ │ │ │ └─Conv2d: 7-4 [1, 16, 104, 104] 2,304
│ │ │ │ │ │ └─BatchNorm2d: 7-5 [1, 16, 104, 104] 32
│ │ │ │ │ │ └─ReLU: 7-6 [1, 16, 104, 104] --
│ │ │ └─ConvModule: 4-12 [1, 32, 104, 104] --
│ │ │ │ └─Conv2d: 5-8 [1, 32, 104, 104] 1,024
│ │ │ │ └─BatchNorm2d: 5-9 [1, 32, 104, 104] 64
│ │ │ │ └─ReLU: 5-10 [1, 32, 104, 104] --
│ └─Sequential: 2-3 [1, 64, 52, 52] --
│ │ └─ConvModule: 3-5 [1, 64, 52, 52] --
│ │ │ └─Conv2d: 4-13 [1, 64, 52, 52] 18,432
│ │ │ └─BatchNorm2d: 4-14 [1, 64, 52, 52] 128
│ │ │ └─ReLU: 4-15 [1, 64, 52, 52] --
│ │ └─CSPLayer: 3-6 [1, 64, 52, 52] --
│ │ │ └─ConvModule: 4-16 [1, 32, 52, 52] --
│ │ │ │ └─Conv2d: 5-11 [1, 32, 52, 52] 2,048
│ │ │ │ └─BatchNorm2d: 5-12 [1, 32, 52, 52] 64
│ │ │ │ └─ReLU: 5-13 [1, 32, 52, 52] --
│ │ │ └─ConvModule: 4-17 [1, 32, 52, 52] --
│ │ │ │ └─Conv2d: 5-14 [1, 32, 52, 52] 2,048
│ │ │ │ └─BatchNorm2d: 5-15 [1, 32, 52, 52] 64
│ │ │ │ └─ReLU: 5-16 [1, 32, 52, 52] --
│ │ │ └─Sequential: 4-18 [1, 32, 52, 52] --
│ │ │ │ └─DarknetBottleneck: 5-17 [1, 32, 52, 52] --
│ │ │ │ │ └─ConvModule: 6-3 [1, 32, 52, 52] --
│ │ │ │ │ │ └─Conv2d: 7-7 [1, 32, 52, 52] 1,024
│ │ │ │ │ │ └─BatchNorm2d: 7-8 [1, 32, 52, 52] 64
│ │ │ │ │ │ └─ReLU: 7-9 [1, 32, 52, 52] --
│ │ │ │ │ └─ConvModule: 6-4 [1, 32, 52, 52] --
│ │ │ │ │ │ └─Conv2d: 7-10 [1, 32, 52, 52] 9,216
│ │ │ │ │ │ └─BatchNorm2d: 7-11 [1, 32, 52, 52] 64
│ │ │ │ │ │ └─ReLU: 7-12 [1, 32, 52, 52] --
│ │ │ │ └─DarknetBottleneck: 5-18 [1, 32, 52, 52] --
│ │ │ │ │ └─ConvModule: 6-5 [1, 32, 52, 52] --
│ │ │ │ │ │ └─Conv2d: 7-13 [1, 32, 52, 52] 1,024
│ │ │ │ │ │ └─BatchNorm2d: 7-14 [1, 32, 52, 52] 64
│ │ │ │ │ │ └─ReLU: 7-15 [1, 32, 52, 52] --
│ │ │ │ │ └─ConvModule: 6-6 [1, 32, 52, 52] --
│ │ │ │ │ │ └─Conv2d: 7-16 [1, 32, 52, 52] 9,216
│ │ │ │ │ │ └─BatchNorm2d: 7-17 [1, 32, 52, 52] 64
│ │ │ │ │ │ └─ReLU: 7-18 [1, 32, 52, 52] --
│ │ │ │ └─DarknetBottleneck: 5-19 [1, 32, 52, 52] --
│ │ │ │ │ └─ConvModule: 6-7 [1, 32, 52, 52] --
│ │ │ │ │ │ └─Conv2d: 7-19 [1, 32, 52, 52] 1,024
│ │ │ │ │ │ └─BatchNorm2d: 7-20 [1, 32, 52, 52] 64
│ │ │ │ │ │ └─ReLU: 7-21 [1, 32, 52, 52] --
│ │ │ │ │ └─ConvModule: 6-8 [1, 32, 52, 52] --
│ │ │ │ │ │ └─Conv2d: 7-22 [1, 32, 52, 52] 9,216
│ │ │ │ │ │ └─BatchNorm2d: 7-23 [1, 32, 52, 52] 64
│ │ │ │ │ │ └─ReLU: 7-24 [1, 32, 52, 52] --
│ │ │ └─ConvModule: 4-19 [1, 64, 52, 52] --
│ │ │ │ └─Conv2d: 5-20 [1, 64, 52, 52] 4,096
│ │ │ │ └─BatchNorm2d: 5-21 [1, 64, 52, 52] 128
│ │ │ │ └─ReLU: 5-22 [1, 64, 52, 52] --
│ └─Sequential: 2-4 [1, 128, 26, 26] --
│ │ └─ConvModule: 3-7 [1, 128, 26, 26] --
│ │ │ └─Conv2d: 4-20 [1, 128, 26, 26] 73,728
│ │ │ └─BatchNorm2d: 4-21 [1, 128, 26, 26] 256
│ │ │ └─ReLU: 4-22 [1, 128, 26, 26] --
│ │ └─CSPLayer: 3-8 [1, 128, 26, 26] --
│ │ │ └─ConvModule: 4-23 [1, 64, 26, 26] --
│ │ │ │ └─Conv2d: 5-23 [1, 64, 26, 26] 8,192
│ │ │ │ └─BatchNorm2d: 5-24 [1, 64, 26, 26] 128
│ │ │ │ └─ReLU: 5-25 [1, 64, 26, 26] --
│ │ │ └─ConvModule: 4-24 [1, 64, 26, 26] --
│ │ │ │ └─Conv2d: 5-26 [1, 64, 26, 26] 8,192
│ │ │ │ └─BatchNorm2d: 5-27 [1, 64, 26, 26] 128
│ │ │ │ └─ReLU: 5-28 [1, 64, 26, 26] --
│ │ │ └─Sequential: 4-25 [1, 64, 26, 26] --
│ │ │ │ └─DarknetBottleneck: 5-29 [1, 64, 26, 26] --
│ │ │ │ │ └─ConvModule: 6-9 [1, 64, 26, 26] --
│ │ │ │ │ │ └─Conv2d: 7-25 [1, 64, 26, 26] 4,096
│ │ │ │ │ │ └─BatchNorm2d: 7-26 [1, 64, 26, 26] 128
│ │ │ │ │ │ └─ReLU: 7-27 [1, 64, 26, 26] --
│ │ │ │ │ └─ConvModule: 6-10 [1, 64, 26, 26] --
│ │ │ │ │ │ └─Conv2d: 7-28 [1, 64, 26, 26] 36,864
│ │ │ │ │ │ └─BatchNorm2d: 7-29 [1, 64, 26, 26] 128
│ │ │ │ │ │ └─ReLU: 7-30 [1, 64, 26, 26] --
│ │ │ │ └─DarknetBottleneck: 5-30 [1, 64, 26, 26] --
│ │ │ │ │ └─ConvModule: 6-11 [1, 64, 26, 26] --
│ │ │ │ │ │ └─Conv2d: 7-31 [1, 64, 26, 26] 4,096
│ │ │ │ │ │ └─BatchNorm2d: 7-32 [1, 64, 26, 26] 128
│ │ │ │ │ │ └─ReLU: 7-33 [1, 64, 26, 26] --
│ │ │ │ │ └─ConvModule: 6-12 [1, 64, 26, 26] --
│ │ │ │ │ │ └─Conv2d: 7-34 [1, 64, 26, 26] 36,864
│ │ │ │ │ │ └─BatchNorm2d: 7-35 [1, 64, 26, 26] 128
│ │ │ │ │ │ └─ReLU: 7-36 [1, 64, 26, 26] --
│ │ │ │ └─DarknetBottleneck: 5-31 [1, 64, 26, 26] --
│ │ │ │ │ └─ConvModule: 6-13 [1, 64, 26, 26] --
│ │ │ │ │ │ └─Conv2d: 7-37 [1, 64, 26, 26] 4,096
│ │ │ │ │ │ └─BatchNorm2d: 7-38 [1, 64, 26, 26] 128
│ │ │ │ │ │ └─ReLU: 7-39 [1, 64, 26, 26] --
│ │ │ │ │ └─ConvModule: 6-14 [1, 64, 26, 26] --
│ │ │ │ │ │ └─Conv2d: 7-40 [1, 64, 26, 26] 36,864
│ │ │ │ │ │ └─BatchNorm2d: 7-41 [1, 64, 26, 26] 128
│ │ │ │ │ │ └─ReLU: 7-42 [1, 64, 26, 26] --
│ │ │ └─ConvModule: 4-26 [1, 128, 26, 26] --
│ │ │ │ └─Conv2d: 5-32 [1, 128, 26, 26] 16,384
│ │ │ │ └─BatchNorm2d: 5-33 [1, 128, 26, 26] 256
│ │ │ │ └─ReLU: 5-34 [1, 128, 26, 26] --
│ └─Sequential: 2-5 [1, 256, 13, 13] --
│ │ └─ConvModule: 3-9 [1, 256, 13, 13] --
│ │ │ └─Conv2d: 4-27 [1, 256, 13, 13] 294,912
│ │ │ └─BatchNorm2d: 4-28 [1, 256, 13, 13] 512
│ │ │ └─ReLU: 4-29 [1, 256, 13, 13] --
│ │ └─SPPBottleneck: 3-10 [1, 256, 13, 13] --
│ │ │ └─ConvModule: 4-30 [1, 128, 13, 13] --
│ │ │ │ └─Conv2d: 5-35 [1, 128, 13, 13] 32,768
│ │ │ │ └─BatchNorm2d: 5-36 [1, 128, 13, 13] 256
│ │ │ │ └─ReLU: 5-37 [1, 128, 13, 13] --
│ │ │ └─ModuleList: 4-31 -- --
│ │ │ │ └─SequentialMaxPool2d: 5-38 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-15 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-16 [1, 128, 13, 13] --
│ │ │ │ └─SequentialMaxPool2d: 5-39 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-17 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-18 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-19 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-20 [1, 128, 13, 13] --
│ │ │ │ └─SequentialMaxPool2d: 5-40 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-21 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-22 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-23 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-24 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-25 [1, 128, 13, 13] --
│ │ │ │ │ └─MaxPool2d: 6-26 [1, 128, 13, 13] --
│ │ │ └─ConvModule: 4-32 [1, 256, 13, 13] --
│ │ │ │ └─Conv2d: 5-41 [1, 256, 13, 13] 131,072
│ │ │ │ └─BatchNorm2d: 5-42 [1, 256, 13, 13] 512
│ │ │ │ └─ReLU: 5-43 [1, 256, 13, 13] --
│ │ └─CSPLayer: 3-11 [1, 256, 13, 13] --
│ │ │ └─ConvModule: 4-33 [1, 128, 13, 13] --
│ │ │ │ └─Conv2d: 5-44 [1, 128, 13, 13] 32,768
│ │ │ │ └─BatchNorm2d: 5-45 [1, 128, 13, 13] 256
│ │ │ │ └─ReLU: 5-46 [1, 128, 13, 13] --
│ │ │ └─ConvModule: 4-34 [1, 128, 13, 13] --
│ │ │ │ └─Conv2d: 5-47 [1, 128, 13, 13] 32,768
│ │ │ │ └─BatchNorm2d: 5-48 [1, 128, 13, 13] 256
│ │ │ │ └─ReLU: 5-49 [1, 128, 13, 13] --
│ │ │ └─Sequential: 4-35 [1, 128, 13, 13] --
│ │ │ │ └─DarknetBottleneck: 5-50 [1, 128, 13, 13] --
│ │ │ │ │ └─ConvModule: 6-27 [1, 128, 13, 13] --
│ │ │ │ │ │ └─Conv2d: 7-43 [1, 128, 13, 13] 16,384
│ │ │ │ │ │ └─BatchNorm2d: 7-44 [1, 128, 13, 13] 256
│ │ │ │ │ │ └─ReLU: 7-45 [1, 128, 13, 13] --
│ │ │ │ │ └─ConvModule: 6-28 [1, 128, 13, 13] --
│ │ │ │ │ │ └─Conv2d: 7-46 [1, 128, 13, 13] 147,456
│ │ │ │ │ │ └─BatchNorm2d: 7-47 [1, 128, 13, 13] 256
│ │ │ │ │ │ └─ReLU: 7-48 [1, 128, 13, 13] --
│ │ │ └─ConvModule: 4-36 [1, 256, 13, 13] --
│ │ │ │ └─Conv2d: 5-51 [1, 256, 13, 13] 65,536
│ │ │ │ └─BatchNorm2d: 5-52 [1, 256, 13, 13] 512
│ │ │ │ └─ReLU: 5-53 [1, 256, 13, 13] --
├─YOLOXPAFPN: 1-2 [1, 64, 52, 52] --
│ └─ModuleList: 2-9 -- (recursive)
│ │ └─ConvModule: 3-12 [1, 128, 13, 13] --
│ │ │ └─Conv2d: 4-37 [1, 128, 13, 13] 32,768
│ │ │ └─BatchNorm2d: 4-38 [1, 128, 13, 13] 256
│ │ │ └─ReLU: 4-39 [1, 128, 13, 13] --
│ └─Upsample: 2-7 [1, 128, 26, 26] --
│ └─ModuleList: 2-11 -- (recursive)
│ │ └─CSPLayer: 3-13 [1, 128, 26, 26] --
│ │ │ └─ConvModule: 4-40 [1, 64, 26, 26] --
│ │ │ │ └─Conv2d: 5-54 [1, 64, 26, 26] 16,384
│ │ │ │ └─BatchNorm2d: 5-55 [1, 64, 26, 26] 128
│ │ │ │ └─ReLU: 5-56 [1, 64, 26, 26] --
│ │ │ └─ConvModule: 4-41 [1, 64, 26, 26] --
│ │ │ │ └─Conv2d: 5-57 [1, 64, 26, 26] 16,384
│ │ │ │ └─BatchNorm2d: 5-58 [1, 64, 26, 26] 128
│ │ │ │ └─ReLU: 5-59 [1, 64, 26, 26] --
│ │ │ └─Sequential: 4-42 [1, 64, 26, 26] --
│ │ │ │ └─DarknetBottleneck: 5-60 [1, 64, 26, 26] --
│ │ │ │ │ └─ConvModule: 6-29 [1, 64, 26, 26] --
│ │ │ │ │ │ └─Conv2d: 7-49 [1, 64, 26, 26] 4,096
│ │ │ │ │ │ └─BatchNorm2d: 7-50 [1, 64, 26, 26] 128
│ │ │ │ │ │ └─ReLU: 7-51 [1, 64, 26, 26] --
│ │ │ │ │ └─ConvModule: 6-30 [1, 64, 26, 26] --
│ │ │ │ │ │ └─Conv2d: 7-52 [1, 64, 26, 26] 36,864
│ │ │ │ │ │ └─BatchNorm2d: 7-53 [1, 64, 26, 26] 128
│ │ │ │ │ │ └─ReLU: 7-54 [1, 64, 26, 26] --
│ │ │ └─ConvModule: 4-43 [1, 128, 26, 26] --
│ │ │ │ └─Conv2d: 5-61 [1, 128, 26, 26] 16,384
│ │ │ │ └─BatchNorm2d: 5-62 [1, 128, 26, 26] 256
│ │ │ │ └─ReLU: 5-63 [1, 128, 26, 26] --
│ └─ModuleList: 2-9 -- (recursive)
│ │ └─ConvModule: 3-14 [1, 64, 26, 26] --
│ │ │ └─Conv2d: 4-44 [1, 64, 26, 26] 8,192
│ │ │ └─BatchNorm2d: 4-45 [1, 64, 26, 26] 128
│ │ │ └─ReLU: 4-46 [1, 64, 26, 26] --
│ └─Upsample: 2-10 [1, 64, 52, 52] --
│ └─ModuleList: 2-11 -- (recursive)
│ │ └─CSPLayer: 3-15 [1, 64, 52, 52] --
│ │ │ └─ConvModule: 4-47 [1, 32, 52, 52] --
│ │ │ │ └─Conv2d: 5-64 [1, 32, 52, 52] 4,096
│ │ │ │ └─BatchNorm2d: 5-65 [1, 32, 52, 52] 64
│ │ │ │ └─ReLU: 5-66 [1, 32, 52, 52] --
│ │ │ └─ConvModule: 4-48 [1, 32, 52, 52] --
│ │ │ │ └─Conv2d: 5-67 [1, 32, 52, 52] 4,096
│ │ │ │ └─BatchNorm2d: 5-68 [1, 32, 52, 52] 64
│ │ │ │ └─ReLU: 5-69 [1, 32, 52, 52] --
│ │ │ └─Sequential: 4-49 [1, 32, 52, 52] --
│ │ │ │ └─DarknetBottleneck: 5-70 [1, 32, 52, 52] --
│ │ │ │ │ └─ConvModule: 6-31 [1, 32, 52, 52] --
│ │ │ │ │ │ └─Conv2d: 7-55 [1, 32, 52, 52] 1,024
│ │ │ │ │ │ └─BatchNorm2d: 7-56 [1, 32, 52, 52] 64
│ │ │ │ │ │ └─ReLU: 7-57 [1, 32, 52, 52] --
│ │ │ │ │ └─ConvModule: 6-32 [1, 32, 52, 52] --
│ │ │ │ │ │ └─Conv2d: 7-58 [1, 32, 52, 52] 9,216
│ │ │ │ │ │ └─BatchNorm2d: 7-59 [1, 32, 52, 52] 64
│ │ │ │ │ │ └─ReLU: 7-60 [1, 32, 52, 52] --
│ │ │ └─ConvModule: 4-50 [1, 64, 52, 52] --
│ │ │ │ └─Conv2d: 5-71 [1, 64, 52, 52] 4,096
│ │ │ │ └─BatchNorm2d: 5-72 [1, 64, 52, 52] 128
│ │ │ │ └─ReLU: 5-73 [1, 64, 52, 52] --
│ └─ModuleList: 2-14 -- (recursive)
│ │ └─ConvModule: 3-16 [1, 64, 26, 26] --
│ │ │ └─Conv2d: 4-51 [1, 64, 26, 26] 36,864
│ │ │ └─BatchNorm2d: 4-52 [1, 64, 26, 26] 128
│ │ │ └─ReLU: 4-53 [1, 64, 26, 26] --
│ └─ModuleList: 2-15 -- (recursive)
│ │ └─CSPLayer: 3-17 [1, 128, 26, 26] --
│ │ │ └─ConvModule: 4-54 [1, 64, 26, 26] --
│ │ │ │ └─Conv2d: 5-74 [1, 64, 26, 26] 8,192
│ │ │ │ └─BatchNorm2d: 5-75 [1, 64, 26, 26] 128
│ │ │ │ └─ReLU: 5-76 [1, 64, 26, 26] --
│ │ │ └─ConvModule: 4-55 [1, 64, 26, 26] --
│ │ │ │ └─Conv2d: 5-77 [1, 64, 26, 26] 8,192
│ │ │ │ └─BatchNorm2d: 5-78 [1, 64, 26, 26] 128
│ │ │ │ └─ReLU: 5-79 [1, 64, 26, 26] --
│ │ │ └─Sequential: 4-56 [1, 64, 26, 26] --
│ │ │ │ └─DarknetBottleneck: 5-80 [1, 64, 26, 26] --
│ │ │ │ │ └─ConvModule: 6-33 [1, 64, 26, 26] --
│ │ │ │ │ │ └─Conv2d: 7-61 [1, 64, 26, 26] 4,096
│ │ │ │ │ │ └─BatchNorm2d: 7-62 [1, 64, 26, 26] 128
│ │ │ │ │ │ └─ReLU: 7-63 [1, 64, 26, 26] --
│ │ │ │ │ └─ConvModule: 6-34 [1, 64, 26, 26] --
│ │ │ │ │ │ └─Conv2d: 7-64 [1, 64, 26, 26] 36,864
│ │ │ │ │ │ └─BatchNorm2d: 7-65 [1, 64, 26, 26] 128
│ │ │ │ │ │ └─ReLU: 7-66 [1, 64, 26, 26] --
│ │ │ └─ConvModule: 4-57 [1, 128, 26, 26] --
│ │ │ │ └─Conv2d: 5-81 [1, 128, 26, 26] 16,384
│ │ │ │ └─BatchNorm2d: 5-82 [1, 128, 26, 26] 256
│ │ │ │ └─ReLU: 5-83 [1, 128, 26, 26] --
│ └─ModuleList: 2-14 -- (recursive)
│ │ └─ConvModule: 3-18 [1, 128, 13, 13] --
│ │ │ └─Conv2d: 4-58 [1, 128, 13, 13] 147,456
│ │ │ └─BatchNorm2d: 4-59 [1, 128, 13, 13] 256
│ │ │ └─ReLU: 4-60 [1, 128, 13, 13] --
│ └─ModuleList: 2-15 -- (recursive)
│ │ └─CSPLayer: 3-19 [1, 256, 13, 13] --
│ │ │ └─ConvModule: 4-61 [1, 128, 13, 13] --
│ │ │ │ └─Conv2d: 5-84 [1, 128, 13, 13] 32,768
│ │ │ │ └─BatchNorm2d: 5-85 [1, 128, 13, 13] 256
│ │ │ │ └─ReLU: 5-86 [1, 128, 13, 13] --
│ │ │ └─ConvModule: 4-62 [1, 128, 13, 13] --
│ │ │ │ └─Conv2d: 5-87 [1, 128, 13, 13] 32,768
│ │ │ │ └─BatchNorm2d: 5-88 [1, 128, 13, 13] 256
│ │ │ │ └─ReLU: 5-89 [1, 128, 13, 13] --
│ │ │ └─Sequential: 4-63 [1, 128, 13, 13] --
│ │ │ │ └─DarknetBottleneck: 5-90 [1, 128, 13, 13] --
│ │ │ │ │ └─ConvModule: 6-35 [1, 128, 13, 13] --
│ │ │ │ │ │ └─Conv2d: 7-67 [1, 128, 13, 13] 16,384
│ │ │ │ │ │ └─BatchNorm2d: 7-68 [1, 128, 13, 13] 256
│ │ │ │ │ │ └─ReLU: 7-69 [1, 128, 13, 13] --
│ │ │ │ │ └─ConvModule: 6-36 [1, 128, 13, 13] --
│ │ │ │ │ │ └─Conv2d: 7-70 [1, 128, 13, 13] 147,456
│ │ │ │ │ │ └─BatchNorm2d: 7-71 [1, 128, 13, 13] 256
│ │ │ │ │ │ └─ReLU: 7-72 [1, 128, 13, 13] --
│ │ │ └─ConvModule: 4-64 [1, 256, 13, 13] --
│ │ │ │ └─Conv2d: 5-91 [1, 256, 13, 13] 65,536
│ │ │ │ └─BatchNorm2d: 5-92 [1, 256, 13, 13] 512
│ │ │ │ └─ReLU: 5-93 [1, 256, 13, 13] --
│ └─ModuleList: 2-16 -- --
│ │ └─ConvModule: 3-20 [1, 64, 52, 52] --
│ │ │ └─Conv2d: 4-65 [1, 64, 52, 52] 4,096
│ │ │ └─BatchNorm2d: 4-66 [1, 64, 52, 52] 128
│ │ │ └─ReLU: 4-67 [1, 64, 52, 52] --
│ │ └─ConvModule: 3-21 [1, 64, 26, 26] --
│ │ │ └─Conv2d: 4-68 [1, 64, 26, 26] 8,192
│ │ │ └─BatchNorm2d: 4-69 [1, 64, 26, 26] 128
│ │ │ └─ReLU: 4-70 [1, 64, 26, 26] --
│ │ └─ConvModule: 3-22 [1, 64, 13, 13] --
│ │ │ └─Conv2d: 4-71 [1, 64, 13, 13] 16,384
│ │ │ └─BatchNorm2d: 4-72 [1, 64, 13, 13] 128
│ │ │ └─ReLU: 4-73 [1, 64, 13, 13] --
├─YOLOXHead: 1-3 [1, 3, 52, 52] --
│ └─ModuleList: 2-27 -- (recursive)
│ │ └─Sequential: 3-23 [1, 64, 52, 52] --
│ │ │ └─ConvModule: 4-74 [1, 64, 52, 52] --
│ │ │ │ └─Conv2d: 5-94 [1, 64, 52, 52] 36,864
│ │ │ │ └─BatchNorm2d: 5-95 [1, 64, 52, 52] 128
│ │ │ │ └─ReLU: 5-96 [1, 64, 52, 52] --
│ │ │ └─ConvModule: 4-75 [1, 64, 52, 52] --
│ │ │ │ └─Conv2d: 5-97 [1, 64, 52, 52] 36,864
│ │ │ │ └─BatchNorm2d: 5-98 [1, 64, 52, 52] 128
│ │ │ │ └─ReLU: 5-99 [1, 64, 52, 52] --
│ └─ModuleList: 2-28 -- (recursive)
│ │ └─Sequential: 3-24 [1, 64, 52, 52] --
│ │ │ └─ConvModule: 4-76 [1, 64, 52, 52] --
│ │ │ │ └─Conv2d: 5-100 [1, 64, 52, 52] 36,864
│ │ │ │ └─BatchNorm2d: 5-101 [1, 64, 52, 52] 128
│ │ │ │ └─ReLU: 5-102 [1, 64, 52, 52] --
│ │ │ └─ConvModule: 4-77 [1, 64, 52, 52] --
│ │ │ │ └─Conv2d: 5-103 [1, 64, 52, 52] 36,864
│ │ │ │ └─BatchNorm2d: 5-104 [1, 64, 52, 52] 128
│ │ │ │ └─ReLU: 5-105 [1, 64, 52, 52] --
│ └─ModuleList: 2-29 -- (recursive)
│ │ └─Conv2d: 3-25 [1, 3, 52, 52] 195
│ └─ModuleList: 2-30 -- (recursive)
│ │ └─Conv2d: 3-26 [1, 4, 52, 52] 260
│ └─ModuleList: 2-31 -- (recursive)
│ │ └─Conv2d: 3-27 [1, 1, 52, 52] 65
│ └─ModuleList: 2-27 -- (recursive)
│ │ └─Sequential: 3-28 [1, 64, 26, 26] --
│ │ │ └─ConvModule: 4-78 [1, 64, 26, 26] --
│ │ │ │ └─Conv2d: 5-106 [1, 64, 26, 26] 36,864
│ │ │ │ └─BatchNorm2d: 5-107 [1, 64, 26, 26] 128
│ │ │ │ └─ReLU: 5-108 [1, 64, 26, 26] --
│ │ │ └─ConvModule: 4-79 [1, 64, 26, 26] --
│ │ │ │ └─Conv2d: 5-109 [1, 64, 26, 26] 36,864
│ │ │ │ └─BatchNorm2d: 5-110 [1, 64, 26, 26] 128
│ │ │ │ └─ReLU: 5-111 [1, 64, 26, 26] --
│ └─ModuleList: 2-28 -- (recursive)
│ │ └─Sequential: 3-29 [1, 64, 26, 26] --
│ │ │ └─ConvModule: 4-80 [1, 64, 26, 26] --
│ │ │ │ └─Conv2d: 5-112 [1, 64, 26, 26] 36,864
│ │ │ │ └─BatchNorm2d: 5-113 [1, 64, 26, 26] 128
│ │ │ │ └─ReLU: 5-114 [1, 64, 26, 26] --
│ │ │ └─ConvModule: 4-81 [1, 64, 26, 26] --
│ │ │ │ └─Conv2d: 5-115 [1, 64, 26, 26] 36,864
│ │ │ │ └─BatchNorm2d: 5-116 [1, 64, 26, 26] 128
│ │ │ │ └─ReLU: 5-117 [1, 64, 26, 26] --
│ └─ModuleList: 2-29 -- (recursive)
│ │ └─Conv2d: 3-30 [1, 3, 26, 26] 195
│ └─ModuleList: 2-30 -- (recursive)
│ │ └─Conv2d: 3-31 [1, 4, 26, 26] 260
│ └─ModuleList: 2-31 -- (recursive)
│ │ └─Conv2d: 3-32 [1, 1, 26, 26] 65
│ └─ModuleList: 2-27 -- (recursive)
│ │ └─Sequential: 3-33 [1, 64, 13, 13] --
│ │ │ └─ConvModule: 4-82 [1, 64, 13, 13] --
│ │ │ │ └─Conv2d: 5-118 [1, 64, 13, 13] 36,864
│ │ │ │ └─BatchNorm2d: 5-119 [1, 64, 13, 13] 128
│ │ │ │ └─ReLU: 5-120 [1, 64, 13, 13] --
│ │ │ └─ConvModule: 4-83 [1, 64, 13, 13] --
│ │ │ │ └─Conv2d: 5-121 [1, 64, 13, 13] 36,864
│ │ │ │ └─BatchNorm2d: 5-122 [1, 64, 13, 13] 128
│ │ │ │ └─ReLU: 5-123 [1, 64, 13, 13] --
│ └─ModuleList: 2-28 -- (recursive)
│ │ └─Sequential: 3-34 [1, 64, 13, 13] --
│ │ │ └─ConvModule: 4-84 [1, 64, 13, 13] --
│ │ │ │ └─Conv2d: 5-124 [1, 64, 13, 13] 36,864
│ │ │ │ └─BatchNorm2d: 5-125 [1, 64, 13, 13] 128
│ │ │ │ └─ReLU: 5-126 [1, 64, 13, 13] --
│ │ │ └─ConvModule: 4-85 [1, 64, 13, 13] --
│ │ │ │ └─Conv2d: 5-127 [1, 64, 13, 13] 36,864
│ │ │ │ └─BatchNorm2d: 5-128 [1, 64, 13, 13] 128
│ │ │ │ └─ReLU: 5-129 [1, 64, 13, 13] --
│ └─ModuleList: 2-29 -- (recursive)
│ │ └─Conv2d: 3-35 [1, 3, 13, 13] 195
│ └─ModuleList: 2-30 -- (recursive)
│ │ └─Conv2d: 3-36 [1, 4, 13, 13] 260
│ └─ModuleList: 2-31 -- (recursive)
│ │ └─Conv2d: 3-37 [1, 1, 13, 13] 65
=========================================================================================================
Total params: 2,242,388
Trainable params: 2,242,388
Non-trainable params: 0
Total mult-adds (G): 1.45
=========================================================================================================
Input size (MB): 2.08
Forward/backward pass size (MB): 109.43
Params size (MB): 8.97
Estimated Total Size (MB): 120.47
=========================================================================================================
2025-03-21 07:01:08,399 - mmdet - INFO -
Traceback (most recent call last):
File "/opt/.pyenv/versions/py310/lib/python3.10/site-packages/mmcv/utils/registry.py", line 52, in build_from_cfg
return obj_cls(**args)
File "/opt/edgeai-mmdetection/mmdet/datasets/modelmaker.py", line 27, in __init__
super().__init__(*args, **kwargs)
File "/opt/edgeai-mmdetection/mmdet/datasets/custom.py", line 95, in __init__
self.data_infos = self.load_annotations(local_path)
File "/opt/edgeai-mmdetection/mmdet/datasets/coco.py", line 87, in load_annotations
assert len(set(total_ann_ids)) == len(
AssertionError: Annotation ids in '/opt/EdgeAIRoot/18293833/projects/08f16d60/dataset/annotations/instances_train.json' are not unique!
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/opt/run.py", line 52, in main
run_modelmaker.main(config)
File "/opt/edgeai-modelmaker/scripts/run_modelmaker.py", line 76, in main
model_runner.run()
File "/opt/edgeai-modelmaker/edgeai_modelmaker/ai_modules/vision/runner.py", line 166, in run
self.model_training.run()
File "/opt/edgeai-modelmaker/edgeai_modelmaker/ai_modules/vision/training/edgeai_mmdetection/detection.py", line 417, in run
train_module.main(args)
File "/opt/edgeai-mmdetection/tools/train.py", line 227, in main
datasets = [build_dataset(cfg.data.train)]
File "/opt/edgeai-mmdetection/mmdet/datasets/builder.py", line 75, in build_dataset
cp_cfg['dataset'] = build_dataset(cp_cfg['dataset'])
File "/opt/edgeai-mmdetection/mmdet/datasets/builder.py", line 81, in build_dataset
dataset = build_from_cfg(cfg, DATASETS, default_args)
File "/opt/.pyenv/versions/py310/lib/python3.10/site-packages/mmcv/utils/registry.py", line 55, in build_from_cfg
raise type(e)(f'{obj_cls.__name__}: {e}')
AssertionError: ModelMakerDataset: Annotation ids in '/opt/EdgeAIRoot/18293833/projects/08f16d60/dataset/annotations/instances_train.json' are not unique!
Training failed during Image train with Edge AI studio Model composer, Error reading previous trained file.
Model selection: AM62A
Model: Yolox_nano_lite
