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20230205_103537.log.json
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20230205_103537.log.json
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{"env_info": "sys.platform: linux\nPython: 3.8.16 (default, Jan 17 2023, 23:13:24) [GCC 11.2.0]\nCUDA available: True\nGPU 0: NVIDIA GeForce RTX 3090\nCUDA_HOME: /data/apps/cuda/11.3\nNVCC: Cuda compilation tools, release 11.3, V11.3.58\nGCC: gcc (GCC) 7.3.0\nPyTorch: 1.13.1\nPyTorch compiling details: PyTorch built with:\n - GCC 9.3\n - C++ Version: 201402\n - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\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 11.6\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_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37\n - CuDNN 8.3.2 (built against CUDA 11.5)\n - Magma 2.6.1\n - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.6, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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 -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, 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: 0.14.1\nOpenCV: 4.7.0\nMMCV: 1.7.1\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.6\nMMClassification: 0.25.0+3d4f80d", "seed": 148153025, "mmcls_version": "0.25.0", "config": "model = dict(\n type='ImageClassifier',\n backbone=dict(\n type='SVT',\n arch='base',\n in_channels=3,\n out_indices=(3, ),\n qkv_bias=True,\n norm_cfg=dict(type='LN'),\n norm_after_stage=[False, False, False, True],\n drop_rate=0.0,\n attn_drop_rate=0.0,\n drop_path_rate=0.3),\n neck=dict(type='GlobalAveragePooling'),\n head=dict(\n type='LinearClsHead',\n num_classes=5,\n in_channels=768,\n loss=dict(\n type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),\n cal_acc=False,\n topk=(1, )),\n init_cfg=[\n dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.0),\n dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)\n ],\n train_cfg=dict(augments=[\n dict(type='BatchMixup', alpha=0.8, num_classes=5, prob=0.5),\n dict(type='BatchCutMix', alpha=1.0, num_classes=5, prob=0.5)\n ]))\nrand_increasing_policies = [\n dict(type='AutoContrast'),\n dict(type='Equalize'),\n dict(type='Invert'),\n dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),\n dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)),\n dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)),\n dict(\n type='SolarizeAdd',\n magnitude_key='magnitude',\n magnitude_range=(0, 110)),\n dict(\n type='ColorTransform',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.9)),\n dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)),\n dict(\n type='Brightness', magnitude_key='magnitude',\n magnitude_range=(0, 0.9)),\n dict(\n type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)),\n dict(\n type='Shear',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.3),\n direction='horizontal'),\n dict(\n type='Shear',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.3),\n direction='vertical'),\n dict(\n type='Translate',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.45),\n direction='horizontal'),\n dict(\n type='Translate',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.45),\n direction='vertical')\n]\ndataset_type = 'ImageNet'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='RandomResizedCrop',\n size=224,\n backend='pillow',\n interpolation='bicubic'),\n dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n dict(\n type='RandAugment',\n policies=[\n dict(type='AutoContrast'),\n dict(type='Equalize'),\n dict(type='Invert'),\n dict(\n type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),\n dict(\n type='Posterize', magnitude_key='bits',\n magnitude_range=(4, 0)),\n dict(\n type='Solarize', magnitude_key='thr',\n magnitude_range=(256, 0)),\n dict(\n type='SolarizeAdd',\n magnitude_key='magnitude',\n magnitude_range=(0, 110)),\n dict(\n type='ColorTransform',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.9)),\n dict(\n type='Contrast',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.9)),\n dict(\n type='Brightness',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.9)),\n dict(\n type='Sharpness',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.9)),\n dict(\n type='Shear',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.3),\n direction='horizontal'),\n dict(\n type='Shear',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.3),\n direction='vertical'),\n dict(\n type='Translate',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.45),\n direction='horizontal'),\n dict(\n type='Translate',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.45),\n direction='vertical')\n ],\n num_policies=2,\n total_level=10,\n magnitude_level=9,\n magnitude_std=0.5,\n hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),\n dict(\n type='RandomErasing',\n erase_prob=0.25,\n mode='rand',\n min_area_ratio=0.02,\n max_area_ratio=0.3333333333333333,\n fill_color=[103.53, 116.28, 123.675],\n fill_std=[57.375, 57.12, 58.395]),\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='ToTensor', keys=['gt_label']),\n dict(type='Collect', keys=['img', 'gt_label'])\n]\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='Resize',\n size=(256, -1),\n backend='pillow',\n interpolation='bicubic'),\n dict(type='CenterCrop', crop_size=224),\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]\ndata = dict(\n samples_per_gpu=64,\n workers_per_gpu=2,\n train=dict(\n type='ImageNet',\n data_prefix='data/train',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='RandomResizedCrop',\n size=224,\n backend='pillow',\n interpolation='bicubic'),\n dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n dict(\n type='RandAugment',\n policies=[\n dict(type='AutoContrast'),\n dict(type='Equalize'),\n dict(type='Invert'),\n dict(\n type='Rotate',\n magnitude_key='angle',\n magnitude_range=(0, 30)),\n dict(\n type='Posterize',\n magnitude_key='bits',\n magnitude_range=(4, 0)),\n dict(\n type='Solarize',\n magnitude_key='thr',\n magnitude_range=(256, 0)),\n dict(\n type='SolarizeAdd',\n magnitude_key='magnitude',\n magnitude_range=(0, 110)),\n dict(\n type='ColorTransform',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.9)),\n dict(\n type='Contrast',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.9)),\n dict(\n type='Brightness',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.9)),\n dict(\n type='Sharpness',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.9)),\n dict(\n type='Shear',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.3),\n direction='horizontal'),\n dict(\n type='Shear',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.3),\n direction='vertical'),\n dict(\n type='Translate',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.45),\n direction='horizontal'),\n dict(\n type='Translate',\n magnitude_key='magnitude',\n magnitude_range=(0, 0.45),\n direction='vertical')\n ],\n num_policies=2,\n total_level=10,\n magnitude_level=9,\n magnitude_std=0.5,\n hparams=dict(pad_val=[104, 116, 124],\n interpolation='bicubic')),\n dict(\n type='RandomErasing',\n erase_prob=0.25,\n mode='rand',\n min_area_ratio=0.02,\n max_area_ratio=0.3333333333333333,\n fill_color=[103.53, 116.28, 123.675],\n fill_std=[57.375, 57.12, 58.395]),\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='ToTensor', keys=['gt_label']),\n dict(type='Collect', keys=['img', 'gt_label'])\n ],\n ann_file='data/train.txt',\n classes='data/classes.txt'),\n val=dict(\n type='ImageNet',\n data_prefix='data/val',\n ann_file='data/val.txt',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='Resize',\n size=(256, -1),\n backend='pillow',\n interpolation='bicubic'),\n dict(type='CenterCrop', crop_size=224),\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 classes='data/classes.txt'),\n test=dict(\n type='ImageNet',\n data_prefix='data/val',\n ann_file='data/val.txt',\n pipeline=[\n dict(type='LoadImageFromFile'),\n dict(\n type='Resize',\n size=(256, -1),\n backend='pillow',\n interpolation='bicubic'),\n dict(type='CenterCrop', crop_size=224),\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 classes='data/classes.txt'))\nevaluation = dict(\n interval=1, metric='accuracy', metric_options=dict(topk=(1, )))\nparamwise_cfg = dict(\n norm_decay_mult=0.0,\n bias_decay_mult=0.0,\n custom_keys=dict({\n '.absolute_pos_embed': dict(decay_mult=0.0),\n '.relative_position_bias_table': dict(decay_mult=0.0)\n }),\n _delete=True)\noptimizer = dict(\n type='AdamW',\n lr=6.25e-05,\n weight_decay=0.05,\n eps=1e-08,\n betas=(0.9, 0.999),\n paramwise_cfg=dict(\n norm_decay_mult=0.0,\n bias_decay_mult=0.0,\n custom_keys=dict({\n '.absolute_pos_embed': dict(decay_mult=0.0),\n '.relative_position_bias_table': dict(decay_mult=0.0)\n }),\n _delete=True))\noptimizer_config = dict(grad_clip=dict(max_norm=5.0))\nlr_config = dict(\n policy='CosineAnnealing',\n by_epoch=True,\n min_lr_ratio=0.001,\n warmup='linear',\n warmup_ratio=0.0001,\n warmup_iters=5,\n warmup_by_epoch=True)\nrunner = dict(type='EpochBasedRunner', max_epochs=100)\ncheckpoint_config = dict(interval=1)\nlog_config = dict(\n interval=100,\n hooks=[dict(type='TextLoggerHook'),\n dict(type='TensorboardLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = 'pretrained/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth'\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = 'work/work1_twins_1xb64_flower5_top1'\ngpu_ids = [0]\ndevice = 'cuda'\nseed = 148153025\n", "CLASSES": ["daisy", "dandelion", "rose", "sunflower", "tulip"]}
{"mode": "val", "epoch": 1, "iter": 9, "lr": 1e-05, "accuracy_top-1": 60.31469}
{"mode": "val", "epoch": 2, "iter": 9, "lr": 2e-05, "accuracy_top-1": 91.78322}
{"mode": "val", "epoch": 3, "iter": 9, "lr": 4e-05, "accuracy_top-1": 96.32867}
{"mode": "val", "epoch": 4, "iter": 9, "lr": 5e-05, "accuracy_top-1": 95.62937}
{"mode": "val", "epoch": 5, "iter": 9, "lr": 6e-05, "accuracy_top-1": 96.67832}
{"mode": "val", "epoch": 6, "iter": 9, "lr": 6e-05, "accuracy_top-1": 96.85315}
{"mode": "val", "epoch": 7, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.2028}
{"mode": "val", "epoch": 8, "iter": 9, "lr": 6e-05, "accuracy_top-1": 96.85315}
{"mode": "val", "epoch": 9, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.37762}
{"mode": "val", "epoch": 10, "iter": 9, "lr": 6e-05, "accuracy_top-1": 96.85315}
{"mode": "val", "epoch": 11, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.72727}
{"mode": "val", "epoch": 12, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.72727}
{"mode": "val", "epoch": 13, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.9021}
{"mode": "val", "epoch": 14, "iter": 9, "lr": 6e-05, "accuracy_top-1": 98.25175}
{"mode": "val", "epoch": 15, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.2028}
{"mode": "val", "epoch": 16, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.9021}
{"mode": "val", "epoch": 17, "iter": 9, "lr": 6e-05, "accuracy_top-1": 98.25175}
{"mode": "val", "epoch": 18, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.72727}
{"mode": "val", "epoch": 19, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.9021}
{"mode": "val", "epoch": 20, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.9021}
{"mode": "val", "epoch": 21, "iter": 9, "lr": 6e-05, "accuracy_top-1": 98.42657}
{"mode": "val", "epoch": 22, "iter": 9, "lr": 6e-05, "accuracy_top-1": 97.9021}