{"env_info": "sys.platform: linux\nPython: 3.8.19 (default, Mar 20 2024, 19:58:24) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA GeForce RTX 4090 D\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 12.1, V12.1.105\nGCC: gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nPyTorch: 2.1.2\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\n  - C++ Version: 201703\n  - Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4)\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: AVX512\n  - CUDA Runtime 12.1\n  - NVCC architecture flags: -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_90,code=sm_90\n  - CuDNN 8.9.2\n  - Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=12.1, CUDNN_VERSION=8.9.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, 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, USE_ROCM=OFF, \n\nTorchVision: 0.16.2\nOpenCV: 4.10.0\nMMCV: 1.7.2\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 12.1\nMMSegmentation: 0.30.0+", "seed": null, "exp_name": "fpn_recnext_m3_ade20k_40k.py", "mmseg_version": "0.30.0+", "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='recnext_m3',\n        depth=50,\n        num_stages=4,\n        out_indices=(0, 1, 2, 3),\n        dilations=(1, 1, 1, 1),\n        strides=(1, 2, 2, 2),\n        norm_cfg=dict(type='SyncBN', requires_grad=True),\n        norm_eval=False,\n        style='pytorch',\n        contract_dilation=True,\n        init_cfg=dict(\n            type='Pretrained',\n            checkpoint='pretrain/recnext_m3_distill_300e.pth'),\n        pretrained='open-mmlab://resnet50_v1c'),\n    neck=dict(\n        type='FPN',\n        in_channels=[64, 128, 256, 512],\n        out_channels=256,\n        num_outs=4),\n    decode_head=dict(\n        type='FPNHead',\n        in_channels=[256, 256, 256, 256],\n        in_index=[0, 1, 2, 3],\n        feature_strides=[4, 8, 16, 32],\n        channels=128,\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    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='AlignResize', keep_ratio=True, 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=4,\n    workers_per_gpu=4,\n    train=dict(\n        type='RepeatDataset',\n        times=50,\n        dataset=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(\n                    type='Resize',\n                    img_scale=(2048, 512),\n                    ratio_range=(0.5, 2.0)),\n                dict(\n                    type='RandomCrop',\n                    crop_size=(512, 512),\n                    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='AlignResize', keep_ratio=True, 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='AlignResize', keep_ratio=True, 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\ngpu_multiples = 1\noptimizer = dict(type='AdamW', lr=0.0001, weight_decay=0.05)\noptimizer_config = dict()\nlr_config = dict(policy='poly', power=0.9, min_lr=1e-06, by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=80000)\ncheckpoint_config = dict(by_epoch=False, interval=8000)\nevaluation = dict(interval=8000, metric='mIoU')\nwork_dir = './work_dirs/fpn_recnext_m3_ade20k_40k'\ngpu_ids = range(0, 8)\ndevice = 'cuda'\nseed = None\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, 230], [80, 50, 50], [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255], [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7], [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82], [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3], [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255], [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220], [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224], [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255], [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7], [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153], [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255], [140, 140, 140], [250, 10, 15], [20, 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{"mode": "train", "epoch": 1, "iter": 1600, "lr": 0.0001, "memory": 4602, "data_time": 0.01074, "decode.loss_ce": 1.01239, "decode.acc_seg": 67.26516, "loss": 1.01239, "time": 0.13933}
{"mode": "train", "epoch": 1, "iter": 1650, "lr": 0.0001, "memory": 4602, "data_time": 0.01103, "decode.loss_ce": 1.02173, "decode.acc_seg": 66.90125, "loss": 1.02173, "time": 0.13092}
{"mode": "train", "epoch": 1, "iter": 1700, "lr": 0.0001, "memory": 4602, "data_time": 0.01185, "decode.loss_ce": 0.99847, "decode.acc_seg": 67.43488, "loss": 0.99847, "time": 0.13369}
{"mode": "train", "epoch": 1, "iter": 1750, "lr": 0.0001, "memory": 4602, "data_time": 0.01112, "decode.loss_ce": 0.99268, "decode.acc_seg": 67.35126, "loss": 0.99268, "time": 0.13338}
{"mode": "train", "epoch": 1, "iter": 1800, "lr": 0.0001, "memory": 4602, "data_time": 0.01156, "decode.loss_ce": 0.96634, "decode.acc_seg": 68.826, "loss": 0.96634, "time": 0.1332}
{"mode": "train", "epoch": 1, "iter": 1850, "lr": 0.0001, "memory": 4602, "data_time": 0.01182, "decode.loss_ce": 0.97461, "decode.acc_seg": 68.2092, "loss": 0.97461, "time": 0.13181}
{"mode": "train", "epoch": 1, "iter": 1900, "lr": 0.0001, "memory": 4602, "data_time": 0.01119, "decode.loss_ce": 0.99061, "decode.acc_seg": 67.89974, "loss": 0.99061, "time": 0.13418}
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