upernet_vitae_nc_base_rvsa_v3_kvdiff_wsz7_512x512_160k_potsdam_rgb_dpr10_lr6e5_lrd90_ps16_class5_ignore5.log.json

{"env_info": "sys.platform: linux\nPython: 3.8.5 (default, Sep  4 2020, 07:30:14) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3: A100-SXM4-40GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Build cuda_11.1.TC455_06.29190527_0\nGCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\nPyTorch: 1.9.0+cu111\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 11.1\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 8.0.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, 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=1.9.0, 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: 0.10.0+cu111\nOpenCV: 4.4.0\nMMCV: 1.4.1\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1\nMMSegmentation: 0.21.0+", "seed": 179095428, "exp_name": "upernet_vitae_nc_base_rvsa_v3_kvdiff_wsz7_512x512_160k_potsdam_rgb_dpr10_lr6e5_lrd90_ps16_class5_ignore5.py", "mmseg_version": "0.21.0+", "config": "norm_cfg = dict(type='SyncBN', requires_grad=True)\nmodel = dict(\n    type='EncoderDecoder',\n    pretrained=\n    '../mae-main/output/mae_vitae_base_pretrn/millionAID_224/1600_0.75_0.00015_0.05_2048/checkpoint-1599-transform-no-average.pth',\n    backbone=dict(\n        type='ViTAE_NC_Win_RVSA_V3_KVDIFF_WSZ7',\n        img_size=512,\n        embed_dim=768,\n        depth=12,\n        num_heads=12,\n        mlp_ratio=4,\n        qkv_bias=True,\n        qk_scale=None,\n        drop_rate=0.0,\n        attn_drop_rate=0.0,\n        drop_path_rate=0.1,\n        use_abs_pos_emb=True,\n        patch_size=16,\n        out_indices=[3, 5, 7, 11],\n        pretrained=\n        '../mae-main/output/mae_vitae_base_pretrn/millionAID_224/1600_0.75_0.00015_0.05_2048/checkpoint-1599-transform-no-average.pth'\n    ),\n    decode_head=dict(\n        type='UPerHead',\n        in_channels=[768, 768, 768, 768],\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=5,\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        ignore_index=5),\n    auxiliary_head=dict(\n        type='FCNHead',\n        in_channels=768,\n        in_index=2,\n        channels=256,\n        num_convs=1,\n        concat_input=False,\n        dropout_ratio=0.1,\n        num_classes=5,\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        ignore_index=5),\n    train_cfg=dict(),\n    test_cfg=dict(mode='slide', stride=(384, 384), crop_size=(512, 512)))\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 = 'work_dirs/upernet_vitae_nc_base_rvsa_v3_kvdiff_wsz7_512x512_160k_potsdam_rgb_dpr10_lr6e5_lrd90_ps16_class5_ignore5/latest.pth'\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='LayerDecayOptimizerConstructor',\n    paramwise_cfg=dict(num_layers=12, layer_decay_rate=0.9))\noptimizer_config = dict(grad_clip=None)\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', 'mFscore'], pre_eval=True)\ndataset_type = 'PotsdamDataset'\ndata_root = '/workspace/CV/users/wangdi153/Dataset/potsdam_rgb_dataset/'\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=False),\n    dict(type='Resize', img_scale=(512, 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=5),\n    dict(type='DefaultFormatBundle'),\n    dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n]\nval_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=(512, 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='Pad', size_divisor=512),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(\n        type='MultiScaleFlipAug',\n        img_scale=None,\n        img_ratios=[1.0],\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='PotsdamDataset',\n        data_root='/workspace/CV/users/wangdi153/Dataset/potsdam_rgb_dataset/',\n        img_dir='img_dir/train',\n        ann_dir='ann_dir/train',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='LoadAnnotations', reduce_zero_label=False),\n            dict(type='Resize', img_scale=(512, 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=5),\n            dict(type='DefaultFormatBundle'),\n            dict(type='Collect', keys=['img', 'gt_semantic_seg'])\n        ]),\n    val=dict(\n        type='PotsdamDataset',\n        data_root='/workspace/CV/users/wangdi153/Dataset/potsdam_rgb_dataset/',\n        img_dir='img_dir/val',\n        ann_dir='ann_dir/val',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=(512, 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='Pad', size_divisor=512),\n                    dict(type='ImageToTensor', keys=['img']),\n                    dict(type='Collect', keys=['img'])\n                ])\n        ]),\n    test=dict(\n        type='PotsdamDataset',\n        data_root='/workspace/CV/users/wangdi153/Dataset/potsdam_rgb_dataset/',\n        img_dir='img_dir/val_all',\n        ann_dir='ann_dir/val_all',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(\n                type='MultiScaleFlipAug',\n                img_scale=None,\n                img_ratios=[1.0],\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        ]))\nfind_unused_parameters = True\nwork_dir = './work_dirs/upernet_vitae_nc_base_rvsa_v3_kvdiff_wsz7_512x512_160k_potsdam_rgb_dpr10_lr6e5_lrd90_ps16_class5_ignore5'\ngpu_ids = range(0, 4)\nauto_resume = False\nseed = 179095428\n", "CLASSES": ["impervious_surface", "building", "low_vegetation", "tree", "car", "clutter"], "PALETTE": [[255, 255, 255], [0, 0, 255], [0, 255, 255], [0, 255, 0], [255, 255, 0], [255, 0, 0]]}
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