{"env_info": "sys.platform: linux\nPython: 3.8.13 (default, Mar 28 2022, 11:38:47) [GCC 7.5.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: Tesla V100-SXM2-32GB\nCUDA_HOME: /mnt/lustre/share/cuda-11.1\nNVCC: Cuda compilation tools, release 11.1, V11.1.74\nGCC: gcc (GCC) 5.4.0\nPyTorch: 1.9.1+cu102\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 v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n - OpenMP 201511 (a.k.a. OpenMP 4.5)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 10.2\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\n - CuDNN 7.6.5\n - Magma 2.5.2\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, 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 -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-variable -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=1.9.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.10.1+cu102\nOpenCV: 4.5.5\nMMCV: 1.5.1\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 10.2\nMMSegmentation: 0.27.0+e4a5885", "seed": 0, "exp_name": "segformer_mit-b5_8x1_1024x1024_160k_cityscapes.py", "mmseg_version": "0.27.0+e4a5885", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n type='EncoderDecoder',\n pretrained=None,\n backbone=dict(\n type='MixVisionTransformer',\n in_channels=3,\n embed_dims=64,\n num_stages=4,\n num_layers=[3, 6, 40, 3],\n num_heads=[1, 2, 5, 8],\n patch_sizes=[7, 3, 3, 3],\n sr_ratios=[8, 4, 2, 1],\n out_indices=(0, 1, 2, 3),\n mlp_ratio=4,\n qkv_bias=True,\n drop_rate=0.0,\n attn_drop_rate=0.0,\n drop_path_rate=0.1,\n init_cfg=dict(\n type='Pretrained',\n checkpoint=\n 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth'\n )),\n decode_head=dict(\n type='SegformerHead',\n in_channels=[64, 128, 320, 512],\n in_index=[0, 1, 2, 3],\n channels=256,\n dropout_ratio=0.1,\n num_classes=19,\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 train_cfg=dict(),\n test_cfg=dict(mode='slide', crop_size=(1024, 1024), stride=(768, 768)))\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 = (1024, 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=(1024, 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=(1024, 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=1,\n workers_per_gpu=1,\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(\n type='RandomCrop', crop_size=(1024, 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=(1024, 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=0.01,\n paramwise_cfg=dict(\n custom_keys=dict(\n pos_block=dict(decay_mult=0.0),\n norm=dict(decay_mult=0.0),\n head=dict(lr_mult=10.0))))\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=16000)\nevaluation = dict(interval=16000, metric='mIoU', pre_eval=True)\ncheckpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pth'\nwork_dir = './work_dirs/segformer_mit-b5_8x1_1024x1024_160k_cityscapes'\ngpu_ids = range(0, 8)\nauto_resume = False\ndevice = 'cuda'\nseed = 0\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]]} {"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 18332, "data_time": 0.08415, "decode.loss_ce": 2.47834, "decode.acc_seg": 8.01009, "loss": 2.47834, "time": 0.99713} {"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 18332, "data_time": 0.03487, "decode.loss_ce": 2.33387, "decode.acc_seg": 44.6416, "loss": 2.33387, "time": 0.87939} {"mode": "train", "epoch": 1, "iter": 150, "lr": 1e-05, "memory": 18332, "data_time": 0.02779, "decode.loss_ce": 1.90665, "decode.acc_seg": 67.69894, "loss": 1.90665, "time": 0.77788} {"mode": "train", "epoch": 1, "iter": 200, "lr": 1e-05, "memory": 18332, "data_time": 0.01145, "decode.loss_ce": 1.39508, "decode.acc_seg": 76.27914, "loss": 1.39508, "time": 0.74627} {"mode": "train", "epoch": 1, "iter": 250, "lr": 1e-05, "memory": 18332, "data_time": 0.02975, "decode.loss_ce": 1.00211, "decode.acc_seg": 80.17881, "loss": 1.00211, "time": 0.8809} {"mode": "train", "epoch": 1, "iter": 300, "lr": 1e-05, "memory": 18332, "data_time": 0.01328, "decode.loss_ce": 0.72987, "decode.acc_seg": 83.08439, "loss": 0.72987, "time": 0.93648} {"mode": "train", "epoch": 1, "iter": 350, "lr": 1e-05, "memory": 18332, "data_time": 0.01411, "decode.loss_ce": 0.60847, "decode.acc_seg": 84.21186, "loss": 0.60847, "time": 0.74599} {"mode": "train", "epoch": 2, "iter": 400, "lr": 2e-05, "memory": 18332, "data_time": 0.0558, "decode.loss_ce": 0.48999, "decode.acc_seg": 86.57692, "loss": 0.48999, "time": 0.82847} {"mode": "train", "epoch": 2, "iter": 450, "lr": 2e-05, "memory": 18332, "data_time": 0.04813, "decode.loss_ce": 0.48063, "decode.acc_seg": 85.97926, "loss": 0.48063, "time": 0.89371} {"mode": "train", "epoch": 2, "iter": 500, "lr": 2e-05, "memory": 18332, "data_time": 0.0197, "decode.loss_ce": 0.43483, "decode.acc_seg": 87.47453, "loss": 0.43483, "time": 0.84155} {"mode": "train", "epoch": 2, "iter": 550, "lr": 2e-05, "memory": 18332, "data_time": 0.01097, "decode.loss_ce": 0.40277, "decode.acc_seg": 87.76474, "loss": 0.40277, "time": 0.77394} {"mode": "train", "epoch": 2, "iter": 600, "lr": 2e-05, "memory": 18332, "data_time": 0.01888, "decode.loss_ce": 0.35918, "decode.acc_seg": 88.44074, "loss": 0.35918, "time": 0.88466} {"mode": "train", "epoch": 2, "iter": 650, "lr": 3e-05, "memory": 18332, "data_time": 0.00991, "decode.loss_ce": 0.32062, "decode.acc_seg": 89.69971, "loss": 0.32062, "time": 0.7421} {"mode": "train", "epoch": 2, "iter": 700, "lr": 3e-05, "memory": 18332, "data_time": 0.00956, "decode.loss_ce": 0.34857, "decode.acc_seg": 88.68816, "loss": 0.34857, "time": 0.80984} {"mode": "train", "epoch": 3, "iter": 750, "lr": 3e-05, "memory": 18332, "data_time": 0.06376, "decode.loss_ce": 0.30571, "decode.acc_seg": 89.76074, "loss": 0.30571, "time": 0.98346} {"mode": "train", "epoch": 3, "iter": 800, "lr": 3e-05, "memory": 18332, "data_time": 0.01145, "decode.loss_ce": 0.28185, "decode.acc_seg": 90.31395, "loss": 0.28185, "time": 0.83413} {"mode": "train", "epoch": 3, "iter": 850, "lr": 3e-05, "memory": 18332, "data_time": 0.00941, "decode.loss_ce": 0.26447, "decode.acc_seg": 90.92778, "loss": 0.26447, "time": 0.74511} {"mode": "train", "epoch": 3, "iter": 900, "lr": 4e-05, "memory": 18332, "data_time": 0.00955, "decode.loss_ce": 0.24846, "decode.acc_seg": 91.14956, "loss": 0.24846, "time": 0.78448} {"mode": "train", "epoch": 3, "iter": 950, "lr": 4e-05, "memory": 18332, "data_time": 0.00937, "decode.loss_ce": 0.23676, "decode.acc_seg": 91.62238, "loss": 0.23676, "time": 0.76102} {"mode": "train", "epoch": 3, "iter": 1000, "lr": 4e-05, "memory": 18332, "data_time": 0.00924, "decode.loss_ce": 0.25415, "decode.acc_seg": 91.121, "loss": 0.25415, "time": 0.74497} {"mode": "train", "epoch": 3, "iter": 1050, "lr": 4e-05, "memory": 18332, "data_time": 0.0165, "decode.loss_ce": 0.22673, "decode.acc_seg": 91.68884, "loss": 0.22673, "time": 0.99445} {"mode": "train", "epoch": 3, "iter": 1100, "lr": 4e-05, "memory": 18332, "data_time": 0.02396, "decode.loss_ce": 0.2141, "decode.acc_seg": 92.28085, "loss": 0.2141, "time": 0.91842} {"mode": "train", "epoch": 4, "iter": 1150, "lr": 5e-05, "memory": 18332, "data_time": 0.05743, "decode.loss_ce": 0.2314, "decode.acc_seg": 91.71555, "loss": 0.2314, "time": 0.79285} {"mode": "train", "epoch": 4, "iter": 1200, "lr": 5e-05, "memory": 18332, "data_time": 0.0123, "decode.loss_ce": 0.20133, "decode.acc_seg": 92.68402, "loss": 0.20133, "time": 0.80183} {"mode": "train", "epoch": 4, "iter": 1250, "lr": 5e-05, "memory": 18332, "data_time": 0.01583, "decode.loss_ce": 0.18746, "decode.acc_seg": 92.93253, "loss": 0.18746, "time": 0.91343} {"mode": "train", "epoch": 4, "iter": 1300, "lr": 5e-05, "memory": 18332, "data_time": 0.00924, "decode.loss_ce": 0.21074, "decode.acc_seg": 92.09253, "loss": 0.21074, "time": 0.74342} {"mode": "train", "epoch": 4, "iter": 1350, "lr": 5e-05, "memory": 18332, "data_time": 0.0089, "decode.loss_ce": 0.18745, "decode.acc_seg": 93.02854, "loss": 0.18745, "time": 0.75677} {"mode": "train", "epoch": 4, "iter": 1400, "lr": 6e-05, "memory": 18332, "data_time": 0.015, "decode.loss_ce": 0.19658, "decode.acc_seg": 92.73201, "loss": 0.19658, "time": 0.86906} {"mode": "train", "epoch": 4, "iter": 1450, "lr": 6e-05, "memory": 18332, "data_time": 0.00942, "decode.loss_ce": 0.18372, "decode.acc_seg": 92.94684, "loss": 0.18372, "time": 0.74936} {"mode": "train", "epoch": 5, "iter": 1500, "lr": 6e-05, "memory": 18332, "data_time": 0.05549, "decode.loss_ce": 0.17172, "decode.acc_seg": 93.49228, "loss": 0.17172, "time": 0.88809} {"mode": "train", "epoch": 5, "iter": 1550, "lr": 6e-05, "memory": 18332, "data_time": 0.012, "decode.loss_ce": 0.17456, "decode.acc_seg": 93.45903, "loss": 0.17456, "time": 0.78476} {"mode": "train", "epoch": 5, "iter": 1600, "lr": 6e-05, "memory": 18332, "data_time": 0.00944, "decode.loss_ce": 0.16216, "decode.acc_seg": 93.50152, "loss": 0.16216, "time": 0.74292} {"mode": "train", "epoch": 5, "iter": 1650, "lr": 6e-05, "memory": 18332, "data_time": 0.00954, "decode.loss_ce": 0.16248, "decode.acc_seg": 93.99559, "loss": 0.16248, "time": 0.75967} {"mode": "train", "epoch": 5, "iter": 1700, "lr": 6e-05, "memory": 18332, "data_time": 0.0422, "decode.loss_ce": 0.1614, "decode.acc_seg": 93.80706, "loss": 0.1614, "time": 0.84985} {"mode": "train", "epoch": 5, "iter": 1750, "lr": 6e-05, "memory": 18332, "data_time": 0.00969, "decode.loss_ce": 0.16122, "decode.acc_seg": 93.79263, "loss": 0.16122, "time": 0.74635} {"mode": "train", "epoch": 5, "iter": 1800, "lr": 6e-05, "memory": 18332, "data_time": 0.01324, "decode.loss_ce": 0.19015, "decode.acc_seg": 92.79893, "loss": 0.19015, "time": 0.91505} {"mode": "train", "epoch": 5, "iter": 1850, "lr": 6e-05, "memory": 18332, "data_time": 0.01897, "decode.loss_ce": 0.17661, "decode.acc_seg": 93.16997, "loss": 0.17661, "time": 0.85236} {"mode": "train", "epoch": 6, "iter": 1900, "lr": 6e-05, "memory": 18332, "data_time": 0.05462, "decode.loss_ce": 0.16399, "decode.acc_seg": 93.72103, "loss": 0.16399, "time": 0.81545} {"mode": "train", "epoch": 6, "iter": 1950, "lr": 6e-05, "memory": 18332, "data_time": 0.00931, "decode.loss_ce": 0.1524, "decode.acc_seg": 94.04008, "loss": 0.1524, "time": 0.75274} {"mode": "train", "epoch": 6, "iter": 2000, "lr": 6e-05, "memory": 18332, "data_time": 0.01981, "decode.loss_ce": 0.14979, "decode.acc_seg": 94.1811, "loss": 0.14979, "time": 0.89822} {"mode": "train", "epoch": 6, "iter": 2050, "lr": 6e-05, "memory": 18332, "data_time": 0.01826, "decode.loss_ce": 0.15566, "decode.acc_seg": 93.95843, "loss": 0.15566, "time": 0.84001} {"mode": "train", "epoch": 6, "iter": 2100, "lr": 6e-05, "memory": 18332, "data_time": 0.01208, "decode.loss_ce": 0.18939, "decode.acc_seg": 92.99363, "loss": 0.18939, "time": 0.83814} {"mode": "train", "epoch": 6, "iter": 2150, "lr": 6e-05, "memory": 18332, "data_time": 0.01027, "decode.loss_ce": 0.15581, "decode.acc_seg": 94.01711, "loss": 0.15581, "time": 0.86201} {"mode": "train", "epoch": 6, "iter": 2200, "lr": 6e-05, "memory": 18332, "data_time": 0.02301, "decode.loss_ce": 0.14728, "decode.acc_seg": 94.22863, "loss": 0.14728, "time": 0.87276} {"mode": "train", "epoch": 7, "iter": 2250, "lr": 6e-05, "memory": 18332, "data_time": 0.05609, "decode.loss_ce": 0.14807, "decode.acc_seg": 94.32467, "loss": 0.14807, "time": 0.78962} {"mode": "train", "epoch": 7, "iter": 2300, "lr": 6e-05, "memory": 18332, "data_time": 0.00938, "decode.loss_ce": 0.14248, "decode.acc_seg": 94.57115, "loss": 0.14248, "time": 0.76894} {"mode": "train", "epoch": 7, "iter": 2350, "lr": 6e-05, "memory": 18332, 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