{"env_info": "sys.platform: linux\nPython: 3.8.0 (default, Nov 6 2019, 21:49:08) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1: NVIDIA RTX A6000\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 11.6, V11.6.124\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\nPyTorch: 1.11.0\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.5.2 (Git Hash a9302535553c73243c632ad3c4c80beec3d19a1e)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - LAPACK is enabled (usually provided by MKL)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 11.3\n - 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\n - CuDNN 8.2\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, 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 -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-sign-compare -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 -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, 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, \n\nTorchVision: 0.12.0\nOpenCV: 4.7.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.3\nMMDetection: 2.28.1+c14dd6c", "config": "optimizer = dict(\n type='SGD',\n lr=0.01,\n momentum=0.9,\n weight_decay=0.0005,\n nesterov=True,\n paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0))\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(\n policy='YOLOX',\n warmup='exp',\n by_epoch=False,\n warmup_by_epoch=True,\n warmup_ratio=1,\n warmup_iters=5,\n num_last_epochs=15,\n min_lr_ratio=0.05)\nrunner = dict(type='EpochBasedRunner', max_epochs=300)\ncheckpoint_config = dict(interval=10)\nlog_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n dict(type='YOLOXModeSwitchHook', num_last_epochs=15, priority=48),\n dict(type='SyncNormHook', num_last_epochs=15, interval=10, priority=48),\n dict(\n type='ExpMomentumEMAHook',\n resume_from=None,\n momentum=0.0001,\n priority=49)\n]\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nopencv_num_threads = 0\nmp_start_method = 'fork'\nauto_scale_lr = dict(enable=False, base_batch_size=64)\nimg_scale = (800, 800)\nmodel = dict(\n type='YOLOX',\n input_size=(800, 800),\n random_size_range=(15, 25),\n random_size_interval=10,\n backbone=dict(type='CSPDarknet', deepen_factor=1.33, widen_factor=1.25),\n neck=dict(\n type='YOLOXPAFPN',\n in_channels=[320, 640, 1280],\n out_channels=320,\n num_csp_blocks=4),\n bbox_head=dict(\n type='YOLOXHead', num_classes=2, in_channels=320, feat_channels=320),\n train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),\n test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))\ndata_root = 'data/coco/'\ndataset_type = 'CocoDataset'\ntrain_pipeline = [\n dict(type='Mosaic', img_scale=(800, 800), pad_val=114.0),\n dict(\n type='RandomAffine', scaling_ratio_range=(0.1, 2),\n border=(-400, -400)),\n dict(\n type='MixUp',\n img_scale=(800, 800),\n ratio_range=(0.8, 1.6),\n pad_val=114.0),\n dict(type='YOLOXHSVRandomAug'),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='Resize', img_scale=(800, 800), keep_ratio=True),\n dict(\n type='Pad',\n pad_to_square=True,\n pad_val=dict(img=(114.0, 114.0, 114.0))),\n dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n]\ntrain_dataset = dict(\n type='MultiImageMixDataset',\n dataset=dict(\n type='CocoDataset',\n ann_file='/data/cvprw/AIC23/dataset/coco_AIC23/train/train.json',\n img_prefix='/data/cvprw/AIC23/dataset/train_result/detect_result',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True)\n ],\n filter_empty_gt=False,\n classes=('person', 'none')),\n pipeline=[\n dict(type='Mosaic', img_scale=(800, 800), pad_val=114.0),\n dict(\n type='RandomAffine',\n scaling_ratio_range=(0.1, 2),\n border=(-400, -400)),\n dict(\n type='MixUp',\n img_scale=(800, 800),\n ratio_range=(0.8, 1.6),\n pad_val=114.0),\n dict(type='YOLOXHSVRandomAug'),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='Resize', img_scale=(800, 800), keep_ratio=True),\n dict(\n type='Pad',\n pad_to_square=True,\n pad_val=dict(img=(114.0, 114.0, 114.0))),\n dict(\n type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n ])\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(800, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Pad',\n pad_to_square=True,\n pad_val=dict(img=(114.0, 114.0, 114.0))),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=8,\n workers_per_gpu=20,\n persistent_workers=True,\n train=dict(\n type='MultiImageMixDataset',\n dataset=dict(\n type='CocoDataset',\n ann_file='/data/cvprw/AIC23/dataset/coco_AIC23/train/train.json',\n img_prefix='/data/cvprw/AIC23/dataset/train_result/detect_result',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations', with_bbox=True)\n ],\n filter_empty_gt=False,\n classes=('person', 'none')),\n pipeline=[\n dict(type='Mosaic', img_scale=(800, 800), pad_val=114.0),\n dict(\n type='RandomAffine',\n scaling_ratio_range=(0.1, 2),\n border=(-400, -400)),\n dict(\n type='MixUp',\n img_scale=(800, 800),\n ratio_range=(0.8, 1.6),\n pad_val=114.0),\n dict(type='YOLOXHSVRandomAug'),\n dict(type='RandomFlip', flip_ratio=0.5),\n dict(type='Resize', img_scale=(800, 800), keep_ratio=True),\n dict(\n type='Pad',\n pad_to_square=True,\n pad_val=dict(img=(114.0, 114.0, 114.0))),\n dict(\n type='FilterAnnotations',\n min_gt_bbox_wh=(1, 1),\n keep_empty=False),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n ]),\n val=dict(\n type='CocoDataset',\n ann_file='/data/cvprw/AIC23/dataset/coco_AIC23/val/val.json',\n img_prefix='/data/cvprw/AIC23/dataset/val_result/detect_result',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(800, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Pad',\n pad_to_square=True,\n pad_val=dict(img=(114.0, 114.0, 114.0))),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img'])\n ])\n ],\n classes=('person', 'none')),\n test=dict(\n type='CocoDataset',\n ann_file='/data/cvprw/AIC23/dataset/coco_AIC23/val/val.json',\n img_prefix='/data/cvprw/AIC23/dataset/val_result/detect_result',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(800, 800),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Pad',\n pad_to_square=True,\n pad_val=dict(img=(114.0, 114.0, 114.0))),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img'])\n ])\n ],\n classes=('person', 'none')))\nmax_epochs = 300\nnum_last_epochs = 15\ninterval = 10\nevaluation = dict(\n save_best='auto', interval=10, dynamic_intervals=[(285, 1)], metric='bbox')\nclasses_list = ('person', 'none')\nclass_num = 2\nwork_dir = '/data/cvprw/AIC23/detect/experiments/yolox'\nauto_resume = False\ngpu_ids = range(0, 2)\n", "seed": 237280180, "exp_name": "yolox_x.py", "hook_msgs": {}} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 27301, "data_time": 0.30281, "loss_cls": 0.93277, "loss_bbox": 4.73394, "loss_obj": 16.08001, "loss": 21.74672, "time": 1.11573} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 27311, "data_time": 0.02697, "loss_cls": 0.94376, "loss_bbox": 4.73124, "loss_obj": 14.84887, "loss": 20.52387, "time": 0.64283} {"mode": "train", "epoch": 1, "iter": 150, "lr": 1e-05, "memory": 27311, "data_time": 0.0286, "loss_cls": 0.95839, "loss_bbox": 4.71992, "loss_obj": 13.74546, "loss": 19.42377, "time": 0.61798} {"mode": "train", "epoch": 1, "iter": 200, "lr": 1e-05, "memory": 27311, "data_time": 0.02739, "loss_cls": 1.01821, "loss_bbox": 4.66007, "loss_obj": 12.68184, "loss": 18.36013, "time": 0.63385} {"mode": "train", "epoch": 1, "iter": 250, "lr": 2e-05, "memory": 27311, "data_time": 0.03304, "loss_cls": 1.04318, "loss_bbox": 4.5411, "loss_obj": 9.47101, "loss": 15.05529, "time": 0.55521} {"mode": "train", "epoch": 1, "iter": 300, "lr": 3e-05, "memory": 27311, "data_time": 0.03418, "loss_cls": 1.0325, "loss_bbox": 4.42689, "loss_obj": 8.62393, "loss": 14.08332, "time": 0.55756} {"mode": "train", "epoch": 1, "iter": 350, "lr": 4e-05, "memory": 27311, "data_time": 0.0275, "loss_cls": 0.99729, "loss_bbox": 4.25468, "loss_obj": 7.91582, "loss": 13.1678, "time": 0.61856} {"mode": "train", "epoch": 1, "iter": 400, "lr": 5e-05, "memory": 27311, "data_time": 0.03439, "loss_cls": 0.96112, "loss_bbox": 4.07949, "loss_obj": 6.43095, "loss": 11.47156, "time": 0.57506} {"mode": "train", "epoch": 1, "iter": 450, "lr": 7e-05, "memory": 27311, "data_time": 0.0304, "loss_cls": 0.88352, "loss_bbox": 3.79325, "loss_obj": 5.49329, "loss": 10.17007, "time": 0.63296} {"mode": "train", "epoch": 1, "iter": 500, "lr": 8e-05, "memory": 27311, "data_time": 0.02819, "loss_cls": 0.82585, "loss_bbox": 3.62894, "loss_obj": 4.79606, "loss": 9.25085, "time": 0.62482} {"mode": "train", "epoch": 1, "iter": 550, "lr": 0.0001, "memory": 27311, "data_time": 0.02725, "loss_cls": 0.78021, "loss_bbox": 3.47951, "loss_obj": 4.39215, "loss": 8.65187, "time": 0.59424} {"mode": "train", "epoch": 1, "iter": 600, "lr": 0.00012, "memory": 27311, "data_time": 0.02875, "loss_cls": 0.74833, "loss_bbox": 3.34371, "loss_obj": 4.23194, "loss": 8.32398, "time": 0.66273} {"mode": "train", "epoch": 1, "iter": 650, "lr": 0.00014, "memory": 27311, "data_time": 0.02655, "loss_cls": 0.73187, "loss_bbox": 3.30935, "loss_obj": 3.80589, "loss": 7.84711, "time": 0.61759} {"mode": "train", "epoch": 1, "iter": 700, "lr": 0.00016, "memory": 27311, "data_time": 0.03221, "loss_cls": 0.71517, "loss_bbox": 3.25711, "loss_obj": 3.53084, "loss": 7.50312, "time": 0.60202} {"mode": "train", "epoch": 1, "iter": 750, "lr": 0.00018, "memory": 27311, "data_time": 0.03361, "loss_cls": 0.70701, "loss_bbox": 3.19949, "loss_obj": 3.39378, "loss": 7.30028, "time": 0.59747} {"mode": "train", "epoch": 1, "iter": 800, "lr": 0.00021, "memory": 27311, "data_time": 0.02474, "loss_cls": 0.69405, "loss_bbox": 3.1108, "loss_obj": 3.30322, "loss": 7.10807, "time": 0.65523} {"mode": "train", "epoch": 1, "iter": 850, "lr": 0.00023, "memory": 27311, "data_time": 0.02792, "loss_cls": 0.69192, "loss_bbox": 3.07627, "loss_obj": 3.03549, "loss": 6.80368, "time": 0.61953} {"mode": "train", "epoch": 1, "iter": 900, "lr": 0.00026, "memory": 27312, "data_time": 0.02805, "loss_cls": 0.68344, "loss_bbox": 3.07067, "loss_obj": 2.87236, "loss": 6.62647, "time": 0.60086} {"mode": "train", "epoch": 1, "iter": 950, "lr": 0.00029, "memory": 27312, "data_time": 0.02974, "loss_cls": 0.67974, "loss_bbox": 3.01579, "loss_obj": 2.80941, "loss": 6.50493, "time": 0.65466} {"mode": "train", "epoch": 1, "iter": 1000, "lr": 0.00032, "memory": 27312, "data_time": 0.02918, "loss_cls": 0.67424, "loss_bbox": 3.01715, "loss_obj": 2.6006, "loss": 6.292, "time": 0.58635} {"mode": "train", "epoch": 1, "iter": 1050, "lr": 0.00036, "memory": 27312, "data_time": 0.03321, "loss_cls": 0.66945, "loss_bbox": 3.00753, "loss_obj": 2.47644, "loss": 6.15342, "time": 0.58542} {"mode": "train", "epoch": 1, "iter": 1100, "lr": 0.00039, "memory": 27312, "data_time": 0.02789, "loss_cls": 0.66189, "loss_bbox": 2.96103, "loss_obj": 2.38108, "loss": 6.004, "time": 0.54348} {"mode": "train", "epoch": 2, "iter": 50, "lr": 0.00044, "memory": 27312, "data_time": 0.24727, "loss_cls": 0.65652, "loss_bbox": 2.92048, "loss_obj": 2.37185, "loss": 5.94885, "time": 0.98679} {"mode": "train", "epoch": 2, "iter": 100, "lr": 0.00048, "memory": 27312, "data_time": 0.02986, "loss_cls": 0.64796, "loss_bbox": 2.87453, "loss_obj": 2.1667, "loss": 5.68918, "time": 0.60067} {"mode": "train", "epoch": 2, "iter": 150, "lr": 0.00052, "memory": 27312, "data_time": 0.02859, "loss_cls": 0.64463, "loss_bbox": 2.79198, "loss_obj": 2.10883, "loss": 5.54544, "time": 0.65945} {"mode": "train", "epoch": 2, "iter": 200, "lr": 0.00056, "memory": 27312, "data_time": 0.02666, "loss_cls": 0.63713, "loss_bbox": 2.75475, "loss_obj": 1.95369, "loss": 5.34556, "time": 0.64152} {"mode": "train", "epoch": 2, "iter": 250, "lr": 0.0006, "memory": 27312, "data_time": 0.02595, "loss_cls": 0.63505, "loss_bbox": 2.73832, "loss_obj": 1.90803, "loss": 5.28141, "time": 0.67025} {"mode": "train", "epoch": 2, "iter": 300, "lr": 0.00065, "memory": 27312, "data_time": 0.02675, "loss_cls": 0.62827, "loss_bbox": 2.69677, "loss_obj": 1.81722, "loss": 5.14226, "time": 0.59586} {"mode": "train", "epoch": 2, "iter": 350, "lr": 0.00069, "memory": 27312, "data_time": 0.03073, "loss_cls": 0.62165, "loss_bbox": 2.64655, "loss_obj": 1.7523, "loss": 5.02049, "time": 0.64393} {"mode": "train", "epoch": 2, "iter": 400, "lr": 0.00074, "memory": 27312, "data_time": 0.0272, "loss_cls": 0.62583, "loss_bbox": 2.67637, "loss_obj": 1.72566, "loss": 5.02786, "time": 0.59611} {"mode": "train", "epoch": 2, "iter": 450, "lr": 0.00079, "memory": 27312, "data_time": 0.03907, "loss_cls": 0.62732, "loss_bbox": 2.67664, "loss_obj": 1.68052, "loss": 4.98449, "time": 0.57053} {"mode": "train", "epoch": 2, "iter": 500, "lr": 0.00084, "memory": 27312, "data_time": 0.02634, "loss_cls": 0.61921, "loss_bbox": 2.60737, "loss_obj": 1.71093, "loss": 4.93751, "time": 0.69684} {"mode": "train", "epoch": 2, "iter": 550, "lr": 0.00089, "memory": 27312, "data_time": 0.03086, "loss_cls": 0.61314, "loss_bbox": 2.5808, "loss_obj": 1.53845, "loss": 4.73239, "time": 0.58469} {"mode": "train", "epoch": 2, "iter": 600, "lr": 0.00095, "memory": 27312, "data_time": 0.02612, "loss_cls": 0.60464, "loss_bbox": 2.51217, "loss_obj": 1.42896, "loss": 4.54576, "time": 0.59649} {"mode": "train", "epoch": 2, "iter": 650, "lr": 0.001, "memory": 27312, "data_time": 0.03228, "loss_cls": 0.60283, "loss_bbox": 2.50226, "loss_obj": 1.41354, "loss": 4.51863, "time": 0.58389} {"mode": "train", "epoch": 2, "iter": 700, "lr": 0.00106, "memory": 27312, "data_time": 0.03053, "loss_cls": 0.60365, "loss_bbox": 2.5224, "loss_obj": 1.42866, "loss": 4.55471, "time": 0.61893} {"mode": "train", "epoch": 2, "iter": 750, "lr": 0.00112, "memory": 27312, "data_time": 0.02775, "loss_cls": 0.59782, "loss_bbox": 2.49447, "loss_obj": 1.4388, "loss": 4.53109, "time": 0.5822} {"mode": "train", "epoch": 2, "iter": 800, "lr": 0.00118, "memory": 27312, "data_time": 0.03088, "loss_cls": 0.59692, "loss_bbox": 2.48592, "loss_obj": 1.42204, "loss": 4.50488, "time": 0.60323} {"mode": "train", "epoch": 2, "iter": 850, "lr": 0.00125, "memory": 27312, "data_time": 0.02818, "loss_cls": 0.58791, "loss_bbox": 2.40906, "loss_obj": 1.27402, "loss": 4.27099, "time": 0.58256} {"mode": "train", "epoch": 2, "iter": 900, "lr": 0.00131, "memory": 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