{"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": 2108890933, "exp_name": "upernet_internimage_b_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=112,\n        depths=[4, 4, 21, 4],\n        groups=[7, 14, 28, 56],\n        mlp_ratio=4.0,\n        drop_path_rate=0.4,\n        norm_layer='LN',\n        layer_scale=1.0,\n        offset_scale=1.0,\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_b_1k_224.pth'\n        ),\n        pretrained='open-mmlab://resnet50_v1c'),\n    decode_head=dict(\n        type='UPerHead',\n        in_channels=[112, 224, 448, 896],\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=448,\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=6e-05,\n    betas=(0.9, 0.999),\n    weight_decay=0.05,\n    constructor='CustomLayerDecayOptimizerConstructor',\n    paramwise_cfg=dict(\n        num_layers=30, layer_decay_rate=1.0, depths=[4, 4, 18, 4]))\noptimizer_config = dict()\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_b_1k_224.pth'\nwork_dir = './work_dirs/upernet_internimage_b_1024_160k_cityscapes'\ngpu_ids = range(0, 8)\nauto_resume = False\ndevice = 'cuda'\nseed = 2108890933\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": {}}
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{"mode": "train", "epoch": 3, "iter": 500, "lr": 2e-05, "memory": 49155, "data_time": 0.00582, "decode.loss_ce": 1.60093, "decode.acc_seg": 83.59621, "aux.loss_ce": 1.24976, "aux.acc_seg": 80.90138, "loss": 2.85069, "time": 0.38684}
{"mode": "train", "epoch": 3, "iter": 550, "lr": 2e-05, "memory": 49155, "data_time": 0.00566, "decode.loss_ce": 1.39353, "decode.acc_seg": 84.04112, "aux.loss_ce": 1.15805, "aux.acc_seg": 81.58568, "loss": 2.55159, "time": 0.38841}
{"mode": "train", "epoch": 4, "iter": 600, "lr": 2e-05, "memory": 49155, "data_time": 0.05147, "decode.loss_ce": 1.2009, "decode.acc_seg": 84.67221, "aux.loss_ce": 1.07156, "aux.acc_seg": 82.38227, "loss": 2.27246, "time": 0.43636}
{"mode": "train", "epoch": 4, "iter": 650, "lr": 3e-05, "memory": 49155, "data_time": 0.0059, "decode.loss_ce": 1.05295, "decode.acc_seg": 85.24849, "aux.loss_ce": 0.97155, "aux.acc_seg": 83.40648, "loss": 2.0245, "time": 0.38915}
{"mode": "train", "epoch": 4, "iter": 700, "lr": 3e-05, "memory": 49155, "data_time": 0.00607, "decode.loss_ce": 0.93191, "decode.acc_seg": 86.26409, "aux.loss_ce": 0.90543, "aux.acc_seg": 84.0084, "loss": 1.83733, "time": 0.38672}
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{"mode": "train", "epoch": 10, "iter": 1700, "lr": 6e-05, "memory": 49155, "data_time": 0.05145, "decode.loss_ce": 0.25768, "decode.acc_seg": 92.15464, "aux.loss_ce": 0.16116, "aux.acc_seg": 90.32464, "loss": 0.41884, "time": 0.4368}
{"mode": "train", "epoch": 10, "iter": 1750, "lr": 6e-05, "memory": 49155, "data_time": 0.00599, "decode.loss_ce": 0.25269, "decode.acc_seg": 92.24124, "aux.loss_ce": 0.15565, "aux.acc_seg": 90.4282, "loss": 0.40834, "time": 0.38817}
{"mode": "train", "epoch": 10, "iter": 1800, "lr": 6e-05, "memory": 49155, "data_time": 0.00596, "decode.loss_ce": 0.26058, "decode.acc_seg": 91.89556, "aux.loss_ce": 0.15337, "aux.acc_seg": 90.09925, "loss": 0.41395, "time": 0.38727}
{"mode": "train", "epoch": 10, "iter": 1850, "lr": 6e-05, "memory": 49155, "data_time": 0.00624, "decode.loss_ce": 0.25583, "decode.acc_seg": 92.09341, "aux.loss_ce": 0.14924, "aux.acc_seg": 90.55437, "loss": 0.40507, "time": 0.38671}
{"mode": "train", "epoch": 11, "iter": 1900, "lr": 6e-05, "memory": 49155, "data_time": 0.05179, "decode.loss_ce": 0.26863, "decode.acc_seg": 91.73093, "aux.loss_ce": 0.14864, "aux.acc_seg": 90.35408, "loss": 0.41727, "time": 0.4356}
{"mode": "train", "epoch": 11, "iter": 1950, "lr": 6e-05, "memory": 49155, "data_time": 0.00651, "decode.loss_ce": 0.26056, "decode.acc_seg": 91.82283, "aux.loss_ce": 0.14647, "aux.acc_seg": 90.2366, "loss": 0.40703, "time": 0.38617}
{"mode": "train", "epoch": 11, "iter": 2000, "lr": 6e-05, "memory": 49155, "data_time": 0.00653, "decode.loss_ce": 0.23686, "decode.acc_seg": 92.43068, "aux.loss_ce": 0.1368, "aux.acc_seg": 90.79125, "loss": 0.37366, "time": 0.46322}
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