{"env_info": "sys.platform: linux\nPython: 3.9.0 (default, Nov 15 2020, 14:28:56) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1: NVIDIA A100-SXM4-80GB\nCUDA_HOME: /mnt/petrelfs/share/cuda-11.1\nNVCC: Build cuda_11.1.TC455_06.29069683_0\nGCC: gcc (GCC) 5.4.0\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: 4.6.0\nMMCV: 1.4.8\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.3\nMMSegmentation: 0.27.0+", "seed": 648644275, "exp_name": "upernet_dcnv3_small_512_160k_ade20k_lwlr_098.py", "mmseg_version": "0.27.0+", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n    type='EncoderDecoder',\n    pretrained=None,\n    backbone=dict(\n        type='DCNv3_5_Post',\n        deform_points=9,\n        embed_dim=80,\n        depths=[4, 4, 21, 4],\n        num_heads=[5, 10, 20, 40],\n        deform_padding=True,\n        dilation_rates=[1],\n        kernel_size=3,\n        mlp_ratio=4.0,\n        drop_path_rate=0.3,\n        op_types=['D', 'D', 'D', 'D'],\n        out_indices=(0, 1, 2, 3),\n        dcnv3_core_type='MSDeformAttnGrid1_softmax',\n        layer_scale=1.0,\n        cpe_norm_layer='LN',\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint=\n            '/mnt/petrelfs/wangwenhai/workspace/DCNv3-dev/classification/work_dirs_shared/dcnv3_5_post_s_v3_ls_2n/ckpt_epoch_299.pth'\n        )),\n    decode_head=dict(\n        type='UPerHead',\n        in_channels=[80, 160, 320, 640],\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=320,\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 = 'ADE20KDataset'\ndata_root = 'data/ade/ADEChallengeData2016'\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, 512)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='LoadAnnotations', reduce_zero_label=True),\n    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n    dict(type='RandomCrop', crop_size=(512, 512), 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, 512), 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, 512),\n        flip=False,\n        transforms=[\n            dict(type='Resize', keep_ratio=True),\n            dict(type='ResizeToMultiple', size_divisor=32),\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=8,\n    workers_per_gpu=4,\n    train=dict(\n        type='ADE20KDataset',\n        data_root='data/ade/ADEChallengeData2016',\n        img_dir='images/training',\n        ann_dir='annotations/training',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', reduce_zero_label=True),\n            dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),\n            dict(type='RandomCrop', crop_size=(512, 512), 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, 512), 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='ADE20KDataset',\n        data_root='data/ade/ADEChallengeData2016',\n        img_dir='images/validation',\n        ann_dir='annotations/validation',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(2048, 512),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='ResizeToMultiple', size_divisor=32),\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='ADE20KDataset',\n        data_root='data/ade/ADEChallengeData2016',\n        img_dir='images/validation',\n        ann_dir='annotations/validation',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(2048, 512),\n                flip=False,\n                transforms=[\n                    dict(type='Resize', keep_ratio=True),\n                    dict(type='ResizeToMultiple', size_divisor=32),\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='InternLayerDecayOptimizerConstructor',\n    paramwise_cfg=dict(\n        num_layers=33, layer_decay_rate=0.98, depths=[4, 4, 21, 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 = '/mnt/petrelfs/wangwenhai/workspace/DCNv3-dev/classification/work_dirs_shared/dcnv3_5_post_s_v3_ls_2n/ckpt_epoch_299.pth'\nwork_dir = './work_dirs/upernet_dcnv3_small_512_160k_ade20k_lwlr_098'\ngpu_ids = range(0, 2)\nauto_resume = True\ndevice = 'cuda'\nseed = 648644275\n", "CLASSES": ["wall", "building", "sky", "floor", "tree", "ceiling", "road", "bed ", "windowpane", "grass", "cabinet", "sidewalk", "person", "earth", "door", "table", "mountain", "plant", "curtain", "chair", "car", "water", "painting", "sofa", "shelf", "house", "sea", "mirror", "rug", "field", "armchair", "seat", "fence", "desk", "rock", "wardrobe", "lamp", "bathtub", "railing", "cushion", "base", "box", "column", "signboard", "chest of drawers", "counter", "sand", "sink", "skyscraper", "fireplace", "refrigerator", "grandstand", "path", "stairs", "runway", "case", "pool table", "pillow", "screen door", "stairway", "river", "bridge", "bookcase", "blind", "coffee table", "toilet", "flower", "book", "hill", "bench", "countertop", "stove", "palm", "kitchen island", "computer", "swivel chair", "boat", "bar", "arcade machine", "hovel", "bus", "towel", "light", "truck", "tower", "chandelier", "awning", "streetlight", "booth", "television receiver", "airplane", "dirt track", "apparel", "pole", "land", "bannister", "escalator", "ottoman", "bottle", "buffet", "poster", "stage", "van", "ship", "fountain", "conveyer belt", "canopy", "washer", "plaything", "swimming pool", "stool", "barrel", "basket", "waterfall", "tent", "bag", "minibike", "cradle", "oven", "ball", "food", "step", "tank", "trade name", "microwave", "pot", "animal", "bicycle", "lake", "dishwasher", "screen", "blanket", "sculpture", "hood", "sconce", "vase", "traffic light", "tray", "ashcan", "fan", "pier", "crt screen", "plate", "monitor", "bulletin board", "shower", "radiator", "glass", "clock", "flag"], "PALETTE": [[120, 120, 120], [180, 120, 120], [6, 230, 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{"mode": "train", "epoch": 1, "iter": 1050, "lr": 2e-05, "memory": 20713, "data_time": 0.02109, "decode.loss_ce": 1.43437, "decode.acc_seg": 62.52637, "aux.loss_ce": 0.80998, "aux.acc_seg": 55.6326, "loss": 2.24435, "time": 0.79961}
{"mode": "train", "epoch": 1, "iter": 1100, "lr": 2e-05, "memory": 20713, "data_time": 0.02238, "decode.loss_ce": 1.33935, "decode.acc_seg": 64.04785, "aux.loss_ce": 0.75951, "aux.acc_seg": 56.49627, "loss": 2.09885, "time": 0.80924}
{"mode": "train", "epoch": 1, "iter": 1150, "lr": 2e-05, "memory": 20713, "data_time": 0.02181, "decode.loss_ce": 1.39863, "decode.acc_seg": 62.32265, "aux.loss_ce": 0.76911, "aux.acc_seg": 54.95933, "loss": 2.16774, "time": 0.82168}
{"mode": "train", "epoch": 1, "iter": 1200, "lr": 2e-05, "memory": 20713, "data_time": 0.02032, "decode.loss_ce": 1.32297, "decode.acc_seg": 63.96469, "aux.loss_ce": 0.73263, "aux.acc_seg": 56.4738, "loss": 2.0556, "time": 0.79824}
{"mode": "train", "epoch": 1, "iter": 1250, "lr": 2e-05, "memory": 20713, "data_time": 0.02024, "decode.loss_ce": 1.23639, "decode.acc_seg": 65.46184, "aux.loss_ce": 0.68996, "aux.acc_seg": 58.59916, "loss": 1.92635, "time": 0.7894}
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