20230205_103537.log

2023-02-05 10:35:37,261 - mmcls - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.16 (default, Jan 17 2023, 23:13:24) [GCC 11.2.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 3090
CUDA_HOME: /data/apps/cuda/11.3
NVCC: Cuda compilation tools, release 11.3, V11.3.58
GCC: gcc (GCC) 7.3.0
PyTorch: 1.13.1
PyTorch compiling details: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201402
  - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX2
  - CUDA Runtime 11.6
  - 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
  - CuDNN 8.3.2  (built against CUDA 11.5)
  - Magma 2.6.1
  - 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, 

TorchVision: 0.14.1
OpenCV: 4.7.0
MMCV: 1.7.1
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.6
MMClassification: 0.25.0+3d4f80d
------------------------------------------------------------

2023-02-05 10:35:37,262 - mmcls - INFO - Distributed training: False
2023-02-05 10:35:37,653 - mmcls - INFO - Config:
model = dict(
    type='ImageClassifier',
    backbone=dict(
        type='SVT',
        arch='base',
        in_channels=3,
        out_indices=(3, ),
        qkv_bias=True,
        norm_cfg=dict(type='LN'),
        norm_after_stage=[False, False, False, True],
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.3),
    neck=dict(type='GlobalAveragePooling'),
    head=dict(
        type='LinearClsHead',
        num_classes=5,
        in_channels=768,
        loss=dict(
            type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'),
        cal_acc=False,
        topk=(1, )),
    init_cfg=[
        dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.0),
        dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0)
    ],
    train_cfg=dict(augments=[
        dict(type='BatchMixup', alpha=0.8, num_classes=5, prob=0.5),
        dict(type='BatchCutMix', alpha=1.0, num_classes=5, prob=0.5)
    ]))
rand_increasing_policies = [
    dict(type='AutoContrast'),
    dict(type='Equalize'),
    dict(type='Invert'),
    dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
    dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)),
    dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)),
    dict(
        type='SolarizeAdd',
        magnitude_key='magnitude',
        magnitude_range=(0, 110)),
    dict(
        type='ColorTransform',
        magnitude_key='magnitude',
        magnitude_range=(0, 0.9)),
    dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
    dict(
        type='Brightness', magnitude_key='magnitude',
        magnitude_range=(0, 0.9)),
    dict(
        type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)),
    dict(
        type='Shear',
        magnitude_key='magnitude',
        magnitude_range=(0, 0.3),
        direction='horizontal'),
    dict(
        type='Shear',
        magnitude_key='magnitude',
        magnitude_range=(0, 0.3),
        direction='vertical'),
    dict(
        type='Translate',
        magnitude_key='magnitude',
        magnitude_range=(0, 0.45),
        direction='horizontal'),
    dict(
        type='Translate',
        magnitude_key='magnitude',
        magnitude_range=(0, 0.45),
        direction='vertical')
]
dataset_type = 'ImageNet'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='RandomResizedCrop',
        size=224,
        backend='pillow',
        interpolation='bicubic'),
    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
    dict(
        type='RandAugment',
        policies=[
            dict(type='AutoContrast'),
            dict(type='Equalize'),
            dict(type='Invert'),
            dict(
                type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
            dict(
                type='Posterize', magnitude_key='bits',
                magnitude_range=(4, 0)),
            dict(
                type='Solarize', magnitude_key='thr',
                magnitude_range=(256, 0)),
            dict(
                type='SolarizeAdd',
                magnitude_key='magnitude',
                magnitude_range=(0, 110)),
            dict(
                type='ColorTransform',
                magnitude_key='magnitude',
                magnitude_range=(0, 0.9)),
            dict(
                type='Contrast',
                magnitude_key='magnitude',
                magnitude_range=(0, 0.9)),
            dict(
                type='Brightness',
                magnitude_key='magnitude',
                magnitude_range=(0, 0.9)),
            dict(
                type='Sharpness',
                magnitude_key='magnitude',
                magnitude_range=(0, 0.9)),
            dict(
                type='Shear',
                magnitude_key='magnitude',
                magnitude_range=(0, 0.3),
                direction='horizontal'),
            dict(
                type='Shear',
                magnitude_key='magnitude',
                magnitude_range=(0, 0.3),
                direction='vertical'),
            dict(
                type='Translate',
                magnitude_key='magnitude',
                magnitude_range=(0, 0.45),
                direction='horizontal'),
            dict(
                type='Translate',
                magnitude_key='magnitude',
                magnitude_range=(0, 0.45),
                direction='vertical')
        ],
        num_policies=2,
        total_level=10,
        magnitude_level=9,
        magnitude_std=0.5,
        hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')),
    dict(
        type='RandomErasing',
        erase_prob=0.25,
        mode='rand',
        min_area_ratio=0.02,
        max_area_ratio=0.3333333333333333,
        fill_color=[103.53, 116.28, 123.675],
        fill_std=[57.375, 57.12, 58.395]),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='ToTensor', keys=['gt_label']),
    dict(type='Collect', keys=['img', 'gt_label'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='Resize',
        size=(256, -1),
        backend='pillow',
        interpolation='bicubic'),
    dict(type='CenterCrop', crop_size=224),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='ImageToTensor', keys=['img']),
    dict(type='Collect', keys=['img'])
]
data = dict(
    samples_per_gpu=64,
    workers_per_gpu=2,
    train=dict(
        type='ImageNet',
        data_prefix='data/train',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='RandomResizedCrop',
                size=224,
                backend='pillow',
                interpolation='bicubic'),
            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
            dict(
                type='RandAugment',
                policies=[
                    dict(type='AutoContrast'),
                    dict(type='Equalize'),
                    dict(type='Invert'),
                    dict(
                        type='Rotate',
                        magnitude_key='angle',
                        magnitude_range=(0, 30)),
                    dict(
                        type='Posterize',
                        magnitude_key='bits',
                        magnitude_range=(4, 0)),
                    dict(
                        type='Solarize',
                        magnitude_key='thr',
                        magnitude_range=(256, 0)),
                    dict(
                        type='SolarizeAdd',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 110)),
                    dict(
                        type='ColorTransform',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.9)),
                    dict(
                        type='Contrast',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.9)),
                    dict(
                        type='Brightness',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.9)),
                    dict(
                        type='Sharpness',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.9)),
                    dict(
                        type='Shear',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.3),
                        direction='horizontal'),
                    dict(
                        type='Shear',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.3),
                        direction='vertical'),
                    dict(
                        type='Translate',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.45),
                        direction='horizontal'),
                    dict(
                        type='Translate',
                        magnitude_key='magnitude',
                        magnitude_range=(0, 0.45),
                        direction='vertical')
                ],
                num_policies=2,
                total_level=10,
                magnitude_level=9,
                magnitude_std=0.5,
                hparams=dict(pad_val=[104, 116, 124],
                             interpolation='bicubic')),
            dict(
                type='RandomErasing',
                erase_prob=0.25,
                mode='rand',
                min_area_ratio=0.02,
                max_area_ratio=0.3333333333333333,
                fill_color=[103.53, 116.28, 123.675],
                fill_std=[57.375, 57.12, 58.395]),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='ToTensor', keys=['gt_label']),
            dict(type='Collect', keys=['img', 'gt_label'])
        ],
        ann_file='data/train.txt',
        classes='data/classes.txt'),
    val=dict(
        type='ImageNet',
        data_prefix='data/val',
        ann_file='data/val.txt',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='Resize',
                size=(256, -1),
                backend='pillow',
                interpolation='bicubic'),
            dict(type='CenterCrop', crop_size=224),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ],
        classes='data/classes.txt'),
    test=dict(
        type='ImageNet',
        data_prefix='data/val',
        ann_file='data/val.txt',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='Resize',
                size=(256, -1),
                backend='pillow',
                interpolation='bicubic'),
            dict(type='CenterCrop', crop_size=224),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ],
        classes='data/classes.txt'))
evaluation = dict(
    interval=1, metric='accuracy', metric_options=dict(topk=(1, )))
paramwise_cfg = dict(
    norm_decay_mult=0.0,
    bias_decay_mult=0.0,
    custom_keys=dict({
        '.absolute_pos_embed': dict(decay_mult=0.0),
        '.relative_position_bias_table': dict(decay_mult=0.0)
    }),
    _delete=True)
optimizer = dict(
    type='AdamW',
    lr=6.25e-05,
    weight_decay=0.05,
    eps=1e-08,
    betas=(0.9, 0.999),
    paramwise_cfg=dict(
        norm_decay_mult=0.0,
        bias_decay_mult=0.0,
        custom_keys=dict({
            '.absolute_pos_embed': dict(decay_mult=0.0),
            '.relative_position_bias_table': dict(decay_mult=0.0)
        }),
        _delete=True))
optimizer_config = dict(grad_clip=dict(max_norm=5.0))
lr_config = dict(
    policy='CosineAnnealing',
    by_epoch=True,
    min_lr_ratio=0.001,
    warmup='linear',
    warmup_ratio=0.0001,
    warmup_iters=5,
    warmup_by_epoch=True)
runner = dict(type='EpochBasedRunner', max_epochs=100)
checkpoint_config = dict(interval=1)
log_config = dict(
    interval=100,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'pretrained/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth'
resume_from = None
workflow = [('train', 1)]
work_dir = 'work/work1_twins_1xb64_flower5_top1'
gpu_ids = [0]

2023-02-05 10:35:37,654 - mmcls - INFO - Set random seed to 148153025, deterministic: False
2023-02-05 10:35:40,891 - mmcls - INFO - load checkpoint from local path: pretrained/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth
2023-02-05 10:35:41,276 - mmcls - WARNING - The model and loaded state dict do not match exactly

size mismatch for head.fc.weight: copying a param with shape torch.Size([1000, 768]) from checkpoint, the shape in current model is torch.Size([5, 768]).
size mismatch for head.fc.bias: copying a param with shape torch.Size([1000]) from checkpoint, the shape in current model is torch.Size([5]).
2023-02-05 10:35:41,281 - mmcls - INFO - Start running, host: scz0a6s@g0099, work_dir: /data/run01/scz0a6s/mmclassification/mmclassification/work/work1_twins_1xb64_flower5_top1
2023-02-05 10:35:41,281 - mmcls - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       
(NORMAL      ) CheckpointHook                     
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) TensorboardLoggerHook              
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) TensorboardLoggerHook              
 -------------------- 
before_train_iter:
(VERY_HIGH   ) CosineAnnealingLrUpdaterHook       
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
 -------------------- 
after_train_iter:
(ABOVE_NORMAL) OptimizerHook                      
(NORMAL      ) CheckpointHook                     
(LOW         ) IterTimerHook                      
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) TensorboardLoggerHook              
 -------------------- 
after_train_epoch:
(NORMAL      ) CheckpointHook                     
(LOW         ) EvalHook                           
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) TensorboardLoggerHook              
 -------------------- 
before_val_epoch:
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) TensorboardLoggerHook              
 -------------------- 
before_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_epoch:
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) TensorboardLoggerHook              
 -------------------- 
after_run:
(VERY_LOW    ) TextLoggerHook                     
(VERY_LOW    ) TensorboardLoggerHook              
 -------------------- 
2023-02-05 10:35:41,281 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs
2023-02-05 10:35:41,281 - mmcls - INFO - Checkpoints will be saved to /data/run01/scz0a6s/mmclassification/mmclassification/work/work1_twins_1xb64_flower5_top1 by HardDiskBackend.
2023-02-05 10:36:02,149 - mmcls - INFO - Saving checkpoint at 1 epochs
2023-02-05 10:36:05,370 - mmcls - INFO - Epoch(val) [1][9]	accuracy_top-1: 60.3147
2023-02-05 10:36:21,718 - mmcls - INFO - Saving checkpoint at 2 epochs
2023-02-05 10:36:24,119 - mmcls - INFO - Epoch(val) [2][9]	accuracy_top-1: 91.7832
2023-02-05 10:36:40,568 - mmcls - INFO - Saving checkpoint at 3 epochs
2023-02-05 10:36:42,985 - mmcls - INFO - Epoch(val) [3][9]	accuracy_top-1: 96.3287
2023-02-05 10:36:59,440 - mmcls - INFO - Saving checkpoint at 4 epochs
2023-02-05 10:37:01,936 - mmcls - INFO - Epoch(val) [4][9]	accuracy_top-1: 95.6294
2023-02-05 10:37:18,445 - mmcls - INFO - Saving checkpoint at 5 epochs
2023-02-05 10:37:20,836 - mmcls - INFO - Epoch(val) [5][9]	accuracy_top-1: 96.6783
2023-02-05 10:37:37,348 - mmcls - INFO - Saving checkpoint at 6 epochs
2023-02-05 10:37:39,751 - mmcls - INFO - Epoch(val) [6][9]	accuracy_top-1: 96.8531
2023-02-05 10:37:56,312 - mmcls - INFO - Saving checkpoint at 7 epochs
2023-02-05 10:37:58,749 - mmcls - INFO - Epoch(val) [7][9]	accuracy_top-1: 97.2028
2023-02-05 10:38:15,325 - mmcls - INFO - Saving checkpoint at 8 epochs
2023-02-05 10:38:17,729 - mmcls - INFO - Epoch(val) [8][9]	accuracy_top-1: 96.8531
2023-02-05 10:38:34,319 - mmcls - INFO - Saving checkpoint at 9 epochs
2023-02-05 10:38:36,805 - mmcls - INFO - Epoch(val) [9][9]	accuracy_top-1: 97.3776
2023-02-05 10:38:53,393 - mmcls - INFO - Saving checkpoint at 10 epochs
2023-02-05 10:38:55,801 - mmcls - INFO - Epoch(val) [10][9]	accuracy_top-1: 96.8531
2023-02-05 10:39:12,384 - mmcls - INFO - Saving checkpoint at 11 epochs
2023-02-05 10:39:14,792 - mmcls - INFO - Epoch(val) [11][9]	accuracy_top-1: 97.7273
2023-02-05 10:39:31,362 - mmcls - INFO - Saving checkpoint at 12 epochs
2023-02-05 10:39:33,753 - mmcls - INFO - Epoch(val) [12][9]	accuracy_top-1: 97.7273
2023-02-05 10:39:50,345 - mmcls - INFO - Saving checkpoint at 13 epochs
2023-02-05 10:39:52,733 - mmcls - INFO - Epoch(val) [13][9]	accuracy_top-1: 97.9021
2023-02-05 10:40:09,317 - mmcls - INFO - Saving checkpoint at 14 epochs
2023-02-05 10:40:11,709 - mmcls - INFO - Epoch(val) [14][9]	accuracy_top-1: 98.2517
2023-02-05 10:40:28,259 - mmcls - INFO - Saving checkpoint at 15 epochs
2023-02-05 10:40:30,709 - mmcls - INFO - Epoch(val) [15][9]	accuracy_top-1: 97.2028
2023-02-05 10:40:47,254 - mmcls - INFO - Saving checkpoint at 16 epochs
2023-02-05 10:40:49,767 - mmcls - INFO - Epoch(val) [16][9]	accuracy_top-1: 97.9021
2023-02-05 10:41:06,322 - mmcls - INFO - Saving checkpoint at 17 epochs
2023-02-05 10:41:08,749 - mmcls - INFO - Epoch(val) [17][9]	accuracy_top-1: 98.2517
2023-02-05 10:41:25,315 - mmcls - INFO - Saving checkpoint at 18 epochs
2023-02-05 10:41:27,732 - mmcls - INFO - Epoch(val) [18][9]	accuracy_top-1: 97.7273
2023-02-05 10:41:44,329 - mmcls - INFO - Saving checkpoint at 19 epochs
2023-02-05 10:41:46,729 - mmcls - INFO - Epoch(val) [19][9]	accuracy_top-1: 97.9021
2023-02-05 10:42:03,307 - mmcls - INFO - Saving checkpoint at 20 epochs
2023-02-05 10:42:05,762 - mmcls - INFO - Epoch(val) [20][9]	accuracy_top-1: 97.9021
2023-02-05 10:42:22,324 - mmcls - INFO - Saving checkpoint at 21 epochs
2023-02-05 10:42:24,769 - mmcls - INFO - Epoch(val) [21][9]	accuracy_top-1: 98.4266
2023-02-05 10:42:41,339 - mmcls - INFO - Saving checkpoint at 22 epochs
2023-02-05 10:42:43,778 - mmcls - INFO - Epoch(val) [22][9]	accuracy_top-1: 97.9021