log_ade20k_80k_upernet_r50_baseline_40.40_miou.json

{"env_info": "sys.platform: linux\nPython: 3.6.12 |Anaconda, Inc.| (default, Sep  8 2020, 23:10:56) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: TITAN Xp\nCUDA_HOME: /usr/local/cuda:/usr/local/cuda:\nGCC: gcc (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0\nPyTorch: 1.7.1\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)\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_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37\n  - CuDNN 7.6.5\n  - Magma 2.5.2\n  - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -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, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=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: 0.8.2\nOpenCV: 4.5.1\nMMCV: 1.4.1\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 10.1\nMMSegmentation: 0.20.2+83dd7bd", "seed": 1851193539, "exp_name": "upernet_r50_512x512_80k_ade20k_adamw.py", "mmseg_version": "0.20.2+83dd7bd", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n    type='EncoderDecoder',\n    pretrained=\n    '/data/pytorch_classification/logs/imagenet/imagenet_e300_resnet50/model_best.pth.tar',\n    backbone=dict(\n        type='ResNet',\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        pretrained=\n        '/data/pytorch_classification/logs/imagenet/imagenet_e300_resnet50/model_best.pth.tar'\n    ),\n    decode_head=dict(\n        type='UPerHead',\n        in_channels=[256, 512, 1024, 2048],\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=1024,\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 = 's3://ylf-dataset/seg/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='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=4,\n    train=dict(\n        type='ADE20KDataset',\n        data_root='s3://ylf-dataset/seg/ADEChallengeData2016/',\n        img_dir='train_nori.json',\n        ann_dir='',\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='s3://ylf-dataset/seg/ADEChallengeData2016/',\n        img_dir='val_nori.json',\n        ann_dir='',\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='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='s3://ylf-dataset/seg/ADEChallengeData2016/',\n        img_dir='val_nori.json',\n        ann_dir='',\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='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(type='AdamW', lr=0.0001, weight_decay=0.0001)\noptimizer_config = dict()\nlr_config = dict(policy='poly', power=0.9, min_lr=0.0, by_epoch=False)\nrunner = dict(type='IterBasedRunner', max_iters=80000)\ncheckpoint_config = dict(by_epoch=False, interval=8000)\nevaluation = dict(interval=8000, metric='mIoU', pre_eval=True)\nwork_dir = './work_dirs/upernet_r50_512x512_80k_ade20k_adamw'\ngpu_ids = range(0, 8)\nseed = 1851193539\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, 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