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upernet_vitae_nc_base_rvsa_v3_kvdiff_wsz7_512x512_160k_loveda_dpr10_lr6e5_lrd90_ps16.log.json
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upernet_vitae_nc_base_rvsa_v3_kvdiff_wsz7_512x512_160k_loveda_dpr10_lr6e5_lrd90_ps16.log.json
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{"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": 2136719857, "exp_name": "upernet_vitae_nc_base_rvsa_v3_kvdiff_wsz7_512x512_160k_loveda_dpr10_lr6e5_lrd90_ps16.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=7,\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=255),\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=7,\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=255),\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 = 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='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 = 'LoveDADataset'\ndata_root = '/workspace/CV/users/wangdi153/Dataset/loveda_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=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=None,\n img_ratios=[1.0],\n flip=True,\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='LoveDADataset',\n data_root='/workspace/CV/users/wangdi153/Dataset/loveda_dataset',\n img_dir='trainval/images',\n ann_dir='trainval/labels',\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 test=dict(\n type='LoveDADataset',\n data_root='/workspace/CV/users/wangdi153/Dataset/loveda_dataset',\n img_dir='test/images',\n ann_dir='test/labels',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='MultiScaleFlipAug',\n img_scale=None,\n img_ratios=[1.0],\n flip=True,\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_loveda_dpr10_lr6e5_lrd90_ps16'\ngpu_ids = range(0, 4)\nauto_resume = False\nseed = 2136719857\n", "CLASSES": ["background", "building", "road", "water", "barren", "forest", "agricultural"], "PALETTE": [[255, 255, 255], [255, 0, 0], [255, 255, 0], [0, 0, 255], [159, 129, 183], [0, 255, 0], [255, 195, 128]]}
{"mode": "train", "epoch": 1, "iter": 50, "lr": 0.0, "memory": 17560, "data_time": 0.00895, "decode.loss_ce": 1.61486, "decode.acc_seg": 13.34155, "aux.loss_ce": 0.63789, "aux.acc_seg": 17.69479, "loss": 2.25275, "time": 0.44156}
{"mode": "train", "epoch": 1, "iter": 100, "lr": 0.0, "memory": 17560, "data_time": 0.00365, "decode.loss_ce": 1.58571, "decode.acc_seg": 24.83086, "aux.loss_ce": 0.64778, "aux.acc_seg": 20.67616, "loss": 2.23349, "time": 0.31811}
{"mode": "train", "epoch": 1, "iter": 150, "lr": 0.0, "memory": 17560, "data_time": 0.00381, "decode.loss_ce": 1.43692, "decode.acc_seg": 32.99287, "aux.loss_ce": 0.61918, "aux.acc_seg": 25.50351, "loss": 2.05609, "time": 0.31806}
{"mode": "train", "epoch": 1, "iter": 200, "lr": 0.0, "memory": 17560, "data_time": 0.00388, "decode.loss_ce": 1.35842, "decode.acc_seg": 39.20751, "aux.loss_ce": 0.62716, "aux.acc_seg": 31.1848, "loss": 1.98558, "time": 0.31575}
{"mode": "train", "epoch": 1, "iter": 250, "lr": 0.0, "memory": 17560, "data_time": 0.00372, "decode.loss_ce": 1.25911, "decode.acc_seg": 41.82982, "aux.loss_ce": 0.60511, "aux.acc_seg": 36.18913, "loss": 1.86422, "time": 0.31541}
{"mode": "train", "epoch": 1, "iter": 300, "lr": 0.0, "memory": 17560, "data_time": 0.00414, "decode.loss_ce": 1.17401, "decode.acc_seg": 46.03567, "aux.loss_ce": 0.59637, "aux.acc_seg": 39.92276, "loss": 1.77038, "time": 0.31726}
{"mode": "train", "epoch": 1, "iter": 350, "lr": 0.0, "memory": 17560, "data_time": 0.004, "decode.loss_ce": 1.06643, "decode.acc_seg": 45.04785, "aux.loss_ce": 0.55083, "aux.acc_seg": 40.25927, "loss": 1.61727, "time": 0.3133}
{"mode": "train", "epoch": 1, "iter": 400, "lr": 0.0, "memory": 17560, "data_time": 0.004, "decode.loss_ce": 1.049, "decode.acc_seg": 48.89485, "aux.loss_ce": 0.54383, "aux.acc_seg": 43.88519, "loss": 1.59283, "time": 0.31851}
{"mode": "train", "epoch": 1, "iter": 450, "lr": 0.0, "memory": 17560, "data_time": 0.00425, "decode.loss_ce": 0.98995, "decode.acc_seg": 48.24521, "aux.loss_ce": 0.51095, "aux.acc_seg": 43.38298, "loss": 1.5009, "time": 0.31125}
{"mode": "train", "epoch": 1, "iter": 500, "lr": 1e-05, "memory": 17560, "data_time": 0.00449, "decode.loss_ce": 0.94541, "decode.acc_seg": 52.1349, "aux.loss_ce": 0.49531, "aux.acc_seg": 46.83077, "loss": 1.44072, "time": 0.31346}
{"mode": "train", "epoch": 2, "iter": 550, "lr": 1e-05, "memory": 17560, "data_time": 0.05009, "decode.loss_ce": 0.89142, "decode.acc_seg": 50.4562, "aux.loss_ce": 0.46073, "aux.acc_seg": 44.71208, "loss": 1.35216, "time": 0.35799}
{"mode": "train", "epoch": 2, "iter": 600, "lr": 1e-05, "memory": 17560, "data_time": 0.00434, "decode.loss_ce": 0.86056, "decode.acc_seg": 51.99174, "aux.loss_ce": 0.44707, "aux.acc_seg": 47.29676, "loss": 1.30763, "time": 0.31518}
{"mode": "train", "epoch": 2, "iter": 650, "lr": 1e-05, "memory": 17560, "data_time": 0.00391, "decode.loss_ce": 0.84203, "decode.acc_seg": 53.574, "aux.loss_ce": 0.43086, "aux.acc_seg": 49.43103, "loss": 1.27289, "time": 0.31737}
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