{"env_info": "sys.platform: linux\nPython: 3.7.16 (default, Jan 17 2023, 22:20:44) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/lustre/share/cuda-11.3\nNVCC: Cuda compilation tools, release 11.3, V11.3.109\nGCC: gcc (GCC) 7.3.0\nPyTorch: 1.11.0+cu113\nPyTorch compiling details: PyTorch built with:\n - GCC 7.3\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 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_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\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+cu113\nOpenCV: 4.7.0\nMMCV: 1.5.0\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\nMMSegmentation: 0.27.0+216ec8a", "seed": 1026896581, "exp_name": "upernet_internimage_l_1024_160k_cityscapes.py", "mmseg_version": "0.27.0+216ec8a", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n type='EncoderDecoder',\n pretrained='open-mmlab://resnet50_v1c',\n backbone=dict(\n type='InternImage',\n core_op='DCNv3',\n channels=160,\n depths=[5, 5, 22, 5],\n groups=[10, 20, 40, 80],\n mlp_ratio=4.0,\n drop_path_rate=0.4,\n norm_layer='LN',\n layer_scale=1.0,\n offset_scale=2.0,\n post_norm=True,\n with_cp=False,\n out_indices=(0, 1, 2, 3),\n init_cfg=dict(\n type='Pretrained',\n checkpoint=\n 'https://github.com/OpenGVLab/InternImage/releases/download/cls_model/internimage_l_22k_192to384.pth'\n ),\n pretrained='open-mmlab://resnet50_v1c'),\n decode_head=dict(\n type='UPerHead',\n in_channels=[160, 320, 640, 1280],\n in_index=[0, 1, 2, 3],\n pool_scales=(1, 2, 3, 6),\n channels=512,\n dropout_ratio=0.1,\n num_classes=150,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n align_corners=False,\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n auxiliary_head=dict(\n type='FCNHead',\n in_channels=640,\n in_index=2,\n channels=256,\n num_convs=1,\n concat_input=False,\n dropout_ratio=0.1,\n num_classes=150,\n norm_cfg=dict(type='SyncBN', requires_grad=True),\n align_corners=False,\n loss_decode=dict(\n type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),\n train_cfg=dict(),\n test_cfg=dict(mode='whole'))\ndataset_type = 'CityscapesDataset'\ndata_root = 'data/cityscapes/'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ncrop_size = (512, 1024)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations'),\n dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),\n dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 1024), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 1024),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n]\ndata = dict(\n samples_per_gpu=2,\n workers_per_gpu=2,\n train=dict(\n type='CityscapesDataset',\n data_root='data/cityscapes/',\n img_dir='leftImg8bit/train',\n ann_dir='gtFine/train',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(type='LoadAnnotations'),\n dict(\n type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),\n dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75),\n dict(type='RandomFlip', prob=0.5),\n dict(type='PhotoMetricDistortion'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='Pad', size=(512, 1024), pad_val=0, seg_pad_val=255),\n dict(type='DefaultFormatBundle'),\n dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n ]),\n val=dict(\n type='CityscapesDataset',\n data_root='data/cityscapes/',\n img_dir='leftImg8bit/val',\n ann_dir='gtFine/val',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 1024),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]),\n test=dict(\n type='CityscapesDataset',\n data_root='data/cityscapes/',\n img_dir='leftImg8bit/val',\n ann_dir='gtFine/val',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=(2048, 1024),\n flip=False,\n transforms=[\n dict(type='Resize', keep_ratio=True),\n dict(type='RandomFlip'),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ])\n ]))\nlog_config = dict(\n interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ncudnn_benchmark = True\noptimizer = dict(\n type='AdamW',\n lr=2e-05,\n betas=(0.9, 0.999),\n weight_decay=0.05,\n constructor='CustomLayerDecayOptimizerConstructor',\n paramwise_cfg=dict(\n num_layers=37,\n layer_decay_rate=0.94,\n depths=[5, 5, 22, 5],\n offset_lr_scale=1.0))\noptimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))\nlr_config = dict(\n policy='poly',\n warmup='linear',\n warmup_iters=1500,\n warmup_ratio=1e-06,\n power=1.0,\n min_lr=0.0,\n by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=160000)\ncheckpoint_config = dict(by_epoch=False, interval=1000, max_keep_ckpts=1)\nevaluation = dict(\n interval=16000, metric='mIoU', pre_eval=True, save_best='mIoU')\npretrained = 'https://github.com/OpenGVLab/InternImage/releases/download/cls_model/internimage_l_22k_192to384.pth'\nwork_dir = './work_dirs/upernet_internimage_l_1024_160k_cityscapes'\ngpu_ids = range(0, 8)\nauto_resume = False\ndevice = 'cuda'\nseed = 1026896581\n", "CLASSES": ["road", "sidewalk", "building", "wall", "fence", "pole", "traffic light", "traffic sign", "vegetation", "terrain", "sky", "person", "rider", "car", "truck", "bus", "train", "motorcycle", "bicycle"], "PALETTE": [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156], [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0], [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60], [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100], [0, 80, 100], [0, 0, 230], [119, 11, 32]], "hook_msgs": {}} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 55338, "data_time": 0.01339, "decode.loss_ce": 4.74845, "decode.acc_seg": 0.63986, "aux.loss_ce": 1.90588, "aux.acc_seg": 0.13485, "loss": 6.65433, "grad_norm": 17.3578, "time": 0.6373} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 55338, "data_time": 0.00657, "decode.loss_ce": 4.74208, "decode.acc_seg": 1.07741, "aux.loss_ce": 1.91801, "aux.acc_seg": 0.13619, "loss": 6.66009, "grad_norm": 17.52879, "time": 0.50628} {"mode": "train", "epoch": 1, "iter": 150, "lr": 0.0, "memory": 55338, "data_time": 0.00611, "decode.loss_ce": 4.63002, "decode.acc_seg": 5.16693, "aux.loss_ce": 1.89527, "aux.acc_seg": 0.20153, "loss": 6.52528, "grad_norm": 20.37913, "time": 0.505} {"mode": "train", "epoch": 2, "iter": 200, "lr": 0.0, "memory": 55338, "data_time": 0.05107, "decode.loss_ce": 4.52738, "decode.acc_seg": 19.02315, "aux.loss_ce": 1.89347, "aux.acc_seg": 0.40726, "loss": 6.42084, "grad_norm": 22.14601, "time": 0.54994} {"mode": "train", "epoch": 2, "iter": 250, "lr": 0.0, "memory": 55338, "data_time": 0.00616, "decode.loss_ce": 4.31096, "decode.acc_seg": 42.06025, "aux.loss_ce": 1.87578, "aux.acc_seg": 1.05081, "loss": 6.18674, "grad_norm": 19.62315, "time": 0.50696} {"mode": "train", "epoch": 2, "iter": 300, "lr": 0.0, "memory": 55338, "data_time": 0.00616, "decode.loss_ce": 4.0016, "decode.acc_seg": 62.22276, "aux.loss_ce": 1.85764, "aux.acc_seg": 3.35917, "loss": 5.85923, "grad_norm": 17.31599, "time": 0.5034} {"mode": "train", "epoch": 2, "iter": 350, "lr": 0.0, "memory": 55338, "data_time": 0.00612, "decode.loss_ce": 3.65429, "decode.acc_seg": 68.71676, "aux.loss_ce": 1.83534, "aux.acc_seg": 13.82099, "loss": 5.48963, "grad_norm": 17.941, "time": 0.50153} {"mode": "train", "epoch": 3, "iter": 400, "lr": 0.0, "memory": 55338, "data_time": 0.05162, "decode.loss_ce": 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0.00734, "decode.loss_ce": 1.65078, "decode.acc_seg": 82.59592, "aux.loss_ce": 1.26795, "aux.acc_seg": 79.78332, "loss": 2.91873, "grad_norm": 9.35517, "time": 0.49787} {"mode": "train", "epoch": 5, "iter": 850, "lr": 0.0, "memory": 55338, "data_time": 0.00659, "decode.loss_ce": 1.50359, "decode.acc_seg": 83.54654, "aux.loss_ce": 1.21375, "aux.acc_seg": 80.78345, "loss": 2.71734, "grad_norm": 10.67444, "time": 0.4949} {"mode": "train", "epoch": 5, "iter": 900, "lr": 0.0, "memory": 55338, "data_time": 0.00645, "decode.loss_ce": 1.36613, "decode.acc_seg": 84.41193, "aux.loss_ce": 1.1697, "aux.acc_seg": 81.55049, "loss": 2.53583, "grad_norm": 11.05113, "time": 0.49474} {"mode": "train", "epoch": 6, "iter": 950, "lr": 0.0, "memory": 55338, "data_time": 0.05182, "decode.loss_ce": 1.25777, "decode.acc_seg": 84.83526, "aux.loss_ce": 1.11186, "aux.acc_seg": 82.50468, "loss": 2.36963, "grad_norm": 11.19585, "time": 0.5435} {"mode": "train", "epoch": 6, "iter": 1000, "lr": 0.0, "memory": 55338, 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