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20240929_151820.log
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2024-09-29 15:18:20,855 - pyskl - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]
CUDA available: True
GPU 0: NVIDIA RTX 2000 Ada Generation
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.8, V11.8.89
GCC: x86_64-linux-gnu-gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
PyTorch: 2.0.1+cu118
PyTorch compiling details: PyTorch built with:
- GCC 9.3
- C++ Version: 201703
- Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- LAPACK is enabled (usually provided by MKL)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 11.8
- 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;-gencode;arch=compute_90,code=sm_90
- CuDNN 8.7
- Magma 2.6.1
- Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -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_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,
TorchVision: 0.15.2+cu118
OpenCV: 4.10.0
MMCV: 1.5.0
MMCV Compiler: GCC 11.4
MMCV CUDA Compiler: 11.8
pyskl: 0.1.0+274a397
------------------------------------------------------------
2024-09-29 15:18:21,100 - pyskl - INFO - Config: model = dict(
type='Recognizer3D',
backbone=dict(
type='ResNet3dSlowOnly',
in_channels=21,
base_channels=32,
num_stages=3,
out_indices=(2, ),
stage_blocks=(4, 6, 3),
conv1_stride=(1, 1),
pool1_stride=(1, 1),
inflate=(0, 1, 1),
spatial_strides=(2, 2, 2),
temporal_strides=(1, 1, 2)),
cls_head=dict(type='I3DHead', in_channels=512, num_classes=6, dropout=0.5),
test_cfg=dict(average_clips='prob'))
dataset_type = 'PoseDataset'
ann_file = '/root/pyskl_thesis/hand_pose_dataset_val.pkl'
hand_kp = [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20
]
train_pipeline = [
dict(type='UniformSampleFrames', clip_len=10),
dict(type='PoseDecode'),
dict(type='PoseCompact', hw_ratio=1.0, allow_imgpad=True),
dict(type='Resize', scale=(-1, 64)),
dict(type='RandomResizedCrop', area_range=(0.56, 1.0)),
dict(type='Resize', scale=(56, 56), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5),
dict(type='GeneratePoseTarget', with_kp=True, with_limb=False),
dict(type='FormatShape', input_format='NCTHW_Heatmap'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
]
val_pipeline = [
dict(type='UniformSampleFrames', clip_len=48, num_clips=1),
dict(type='PoseDecode'),
dict(type='PoseCompact', hw_ratio=1.0, allow_imgpad=True),
dict(type='Resize', scale=(64, 64), keep_ratio=False),
dict(type='GeneratePoseTarget', with_kp=True, with_limb=False),
dict(type='FormatShape', input_format='NCTHW_Heatmap'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]
test_pipeline = [
dict(type='UniformSampleFrames', clip_len=48, num_clips=10),
dict(type='PoseDecode'),
dict(type='PoseCompact', hw_ratio=1.0, allow_imgpad=True),
dict(type='Resize', scale=(64, 64), keep_ratio=False),
dict(
type='GeneratePoseTarget', with_kp=True, with_limb=False,
double=False),
dict(type='FormatShape', input_format='NCTHW_Heatmap'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]
data = dict(
videos_per_gpu=16,
workers_per_gpu=4,
test_dataloader=dict(videos_per_gpu=1),
train=dict(
type='RepeatDataset',
times=10,
dataset=dict(
type='PoseDataset',
ann_file='/root/pyskl_thesis/hand_pose_dataset_val.pkl',
split='train',
pipeline=[
dict(type='UniformSampleFrames', clip_len=10),
dict(type='PoseDecode'),
dict(type='PoseCompact', hw_ratio=1.0, allow_imgpad=True),
dict(type='Resize', scale=(-1, 64)),
dict(type='RandomResizedCrop', area_range=(0.56, 1.0)),
dict(type='Resize', scale=(56, 56), keep_ratio=False),
dict(type='Flip', flip_ratio=0.5),
dict(type='GeneratePoseTarget', with_kp=True, with_limb=False),
dict(type='FormatShape', input_format='NCTHW_Heatmap'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs', 'label'])
])),
val=dict(
type='PoseDataset',
ann_file='/root/pyskl_thesis/hand_pose_dataset_val.pkl',
split='val',
pipeline=[
dict(type='UniformSampleFrames', clip_len=48, num_clips=1),
dict(type='PoseDecode'),
dict(type='PoseCompact', hw_ratio=1.0, allow_imgpad=True),
dict(type='Resize', scale=(64, 64), keep_ratio=False),
dict(type='GeneratePoseTarget', with_kp=True, with_limb=False),
dict(type='FormatShape', input_format='NCTHW_Heatmap'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]),
test=dict(
type='PoseDataset',
ann_file='/root/pyskl_thesis/hand_pose_dataset_val.pkl',
split='val',
pipeline=[
dict(type='UniformSampleFrames', clip_len=48, num_clips=10),
dict(type='PoseDecode'),
dict(type='PoseCompact', hw_ratio=1.0, allow_imgpad=True),
dict(type='Resize', scale=(64, 64), keep_ratio=False),
dict(
type='GeneratePoseTarget',
with_kp=True,
with_limb=False,
double=False),
dict(type='FormatShape', input_format='NCTHW_Heatmap'),
dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),
dict(type='ToTensor', keys=['imgs'])
]))
optimizer = dict(type='SGD', lr=0.4, momentum=0.9, weight_decay=0.0003)
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
lr_config = dict(policy='CosineAnnealing', by_epoch=False, min_lr=0)
total_epochs = 24
checkpoint_config = dict(interval=1)
evaluation = dict(
interval=1, metrics=['top_k_accuracy', 'mean_class_accuracy'], topk=(1, 5))
log_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')])
log_level = 'INFO'
work_dir = './work_dirs/posec3d/test_slow_mp_val'
device = 'cuda'
gpu_ids = [1]
dist_params = dict(backend='nccl')
2024-09-29 15:18:21,101 - pyskl - INFO - Set random seed to 1690257781, deterministic: False
2024-09-29 15:18:21,147 - pyskl - INFO - 48 videos remain after valid thresholding
2024-09-29 15:18:21,376 - pyskl - INFO - Start running, host: root@d6791b9bed33, work_dir: /root/pyskl_thesis/work_dirs/posec3d/test_slow_mp_val
2024-09-29 15:18:21,377 - pyskl - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(NORMAL ) CheckpointHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_epoch:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
--------------------
before_train_iter:
(VERY_HIGH ) CosineAnnealingLrUpdaterHook
(LOW ) IterTimerHook
--------------------
after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
--------------------
after_train_epoch:
(NORMAL ) CheckpointHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_epoch:
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook
--------------------
before_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_iter:
(LOW ) IterTimerHook
--------------------
after_val_epoch:
(VERY_LOW ) TextLoggerHook
--------------------
after_run:
(VERY_LOW ) TextLoggerHook
--------------------
2024-09-29 15:18:21,377 - pyskl - INFO - workflow: [('train', 1)], max: 24 epochs
2024-09-29 15:18:21,377 - pyskl - INFO - Checkpoints will be saved to /root/pyskl_thesis/work_dirs/posec3d/test_slow_mp_val by HardDiskBackend.
2024-09-29 15:18:28,687 - pyskl - INFO - Epoch [1][20/30] lr: 3.993e-01, eta: 0:04:15, time: 0.365, data_time: 0.143, memory: 2132, top1_acc: 0.1844, top5_acc: 0.8250, loss_cls: 1.8159, loss: 1.8159, grad_norm: 0.2315
2024-09-29 15:18:30,686 - pyskl - INFO - Saving checkpoint at 1 epochs
2024-09-29 15:18:36,924 - pyskl - INFO - Epoch [2][20/30] lr: 3.954e-01, eta: 0:03:00, time: 0.309, data_time: 0.110, memory: 2132, top1_acc: 0.1906, top5_acc: 0.8406, loss_cls: 1.8575, loss: 1.8575, grad_norm: 0.2521
2024-09-29 15:18:38,928 - pyskl - INFO - Saving checkpoint at 2 epochs
2024-09-29 15:18:45,171 - pyskl - INFO - Epoch [3][20/30] lr: 3.882e-01, eta: 0:02:37, time: 0.310, data_time: 0.111, memory: 2132, top1_acc: 0.2031, top5_acc: 0.8406, loss_cls: 1.8586, loss: 1.8586, grad_norm: 0.2512
2024-09-29 15:18:47,182 - pyskl - INFO - Saving checkpoint at 3 epochs
2024-09-29 15:18:53,414 - pyskl - INFO - Epoch [4][20/30] lr: 3.778e-01, eta: 0:02:23, time: 0.309, data_time: 0.110, memory: 2132, top1_acc: 0.2062, top5_acc: 0.8406, loss_cls: 1.8568, loss: 1.8568, grad_norm: 0.2505
2024-09-29 15:18:55,436 - pyskl - INFO - Saving checkpoint at 4 epochs
2024-09-29 15:19:01,713 - pyskl - INFO - Epoch [5][20/30] lr: 3.643e-01, eta: 0:02:13, time: 0.312, data_time: 0.112, memory: 2132, top1_acc: 0.2031, top5_acc: 0.8562, loss_cls: 1.8580, loss: 1.8580, grad_norm: 0.2518
2024-09-29 15:19:03,716 - pyskl - INFO - Saving checkpoint at 5 epochs
2024-09-29 15:19:09,945 - pyskl - INFO - Epoch [6][20/30] lr: 3.480e-01, eta: 0:02:03, time: 0.309, data_time: 0.110, memory: 2132, top1_acc: 0.1938, top5_acc: 0.8469, loss_cls: 1.8605, loss: 1.8605, grad_norm: 0.2540
2024-09-29 15:19:11,949 - pyskl - INFO - Saving checkpoint at 6 epochs
2024-09-29 15:19:18,190 - pyskl - INFO - Epoch [7][20/30] lr: 3.292e-01, eta: 0:01:55, time: 0.310, data_time: 0.110, memory: 2132, top1_acc: 0.1875, top5_acc: 0.8531, loss_cls: 1.8638, loss: 1.8638, grad_norm: 0.2567
2024-09-29 15:19:20,196 - pyskl - INFO - Saving checkpoint at 7 epochs
2024-09-29 15:19:26,446 - pyskl - INFO - Epoch [8][20/30] lr: 3.082e-01, eta: 0:01:47, time: 0.310, data_time: 0.111, memory: 2132, top1_acc: 0.1875, top5_acc: 0.8562, loss_cls: 1.8673, loss: 1.8673, grad_norm: 0.2596
2024-09-29 15:19:28,448 - pyskl - INFO - Saving checkpoint at 8 epochs
2024-09-29 15:19:34,705 - pyskl - INFO - Epoch [9][20/30] lr: 2.853e-01, eta: 0:01:40, time: 0.310, data_time: 0.110, memory: 2132, top1_acc: 0.1875, top5_acc: 0.8688, loss_cls: 1.8705, loss: 1.8705, grad_norm: 0.2624
2024-09-29 15:19:36,718 - pyskl - INFO - Saving checkpoint at 9 epochs
2024-09-29 15:19:42,997 - pyskl - INFO - Epoch [10][20/30] lr: 2.610e-01, eta: 0:01:33, time: 0.311, data_time: 0.112, memory: 2132, top1_acc: 0.1625, top5_acc: 0.8625, loss_cls: 1.8721, loss: 1.8721, grad_norm: 0.2642
2024-09-29 15:19:45,003 - pyskl - INFO - Saving checkpoint at 10 epochs
2024-09-29 15:19:51,249 - pyskl - INFO - Epoch [11][20/30] lr: 2.356e-01, eta: 0:01:26, time: 0.310, data_time: 0.110, memory: 2132, top1_acc: 0.1594, top5_acc: 0.8531, loss_cls: 1.8708, loss: 1.8708, grad_norm: 0.2640
2024-09-29 15:19:53,259 - pyskl - INFO - Saving checkpoint at 11 epochs
2024-09-29 15:19:59,530 - pyskl - INFO - Epoch [12][20/30] lr: 2.096e-01, eta: 0:01:19, time: 0.311, data_time: 0.111, memory: 2132, top1_acc: 0.1406, top5_acc: 0.8344, loss_cls: 1.8652, loss: 1.8652, grad_norm: 0.2612
2024-09-29 15:20:01,540 - pyskl - INFO - Saving checkpoint at 12 epochs
2024-09-29 15:20:07,763 - pyskl - INFO - Epoch [13][20/30] lr: 1.834e-01, eta: 0:01:13, time: 0.309, data_time: 0.109, memory: 2132, top1_acc: 0.1375, top5_acc: 0.8219, loss_cls: 1.8550, loss: 1.8550, grad_norm: 0.2558
2024-09-29 15:20:09,786 - pyskl - INFO - Saving checkpoint at 13 epochs
2024-09-29 15:20:16,024 - pyskl - INFO - Epoch [14][20/30] lr: 1.576e-01, eta: 0:01:06, time: 0.309, data_time: 0.110, memory: 2132, top1_acc: 0.1281, top5_acc: 0.8313, loss_cls: 1.8418, loss: 1.8418, grad_norm: 0.2480
2024-09-29 15:20:18,035 - pyskl - INFO - Saving checkpoint at 14 epochs
2024-09-29 15:20:24,305 - pyskl - INFO - Epoch [15][20/30] lr: 1.324e-01, eta: 0:00:59, time: 0.311, data_time: 0.112, memory: 2132, top1_acc: 0.1281, top5_acc: 0.8094, loss_cls: 1.8286, loss: 1.8286, grad_norm: 0.2394
2024-09-29 15:20:26,318 - pyskl - INFO - Saving checkpoint at 15 epochs
2024-09-29 15:20:32,581 - pyskl - INFO - Epoch [16][20/30] lr: 1.084e-01, eta: 0:00:53, time: 0.311, data_time: 0.112, memory: 2132, top1_acc: 0.1375, top5_acc: 0.8094, loss_cls: 1.8178, loss: 1.8178, grad_norm: 0.2321
2024-09-29 15:20:34,596 - pyskl - INFO - Saving checkpoint at 16 epochs
2024-09-29 15:20:40,831 - pyskl - INFO - Epoch [17][20/30] lr: 8.600e-02, eta: 0:00:46, time: 0.309, data_time: 0.110, memory: 2132, top1_acc: 0.1281, top5_acc: 0.7937, loss_cls: 1.8101, loss: 1.8101, grad_norm: 0.2266
2024-09-29 15:20:42,836 - pyskl - INFO - Saving checkpoint at 17 epochs
2024-09-29 15:20:49,090 - pyskl - INFO - Epoch [18][20/30] lr: 6.553e-02, eta: 0:00:40, time: 0.310, data_time: 0.110, memory: 2132, top1_acc: 0.1250, top5_acc: 0.7906, loss_cls: 1.8045, loss: 1.8045, grad_norm: 0.2226
2024-09-29 15:20:51,117 - pyskl - INFO - Saving checkpoint at 18 epochs
2024-09-29 15:20:57,334 - pyskl - INFO - Epoch [19][20/30] lr: 4.735e-02, eta: 0:00:33, time: 0.308, data_time: 0.109, memory: 2132, top1_acc: 0.1250, top5_acc: 0.7906, loss_cls: 1.8004, loss: 1.8004, grad_norm: 0.2196
2024-09-29 15:20:59,361 - pyskl - INFO - Saving checkpoint at 19 epochs
2024-09-29 15:21:05,594 - pyskl - INFO - Epoch [20][20/30] lr: 3.179e-02, eta: 0:00:27, time: 0.309, data_time: 0.110, memory: 2132, top1_acc: 0.1250, top5_acc: 0.7906, loss_cls: 1.7974, loss: 1.7974, grad_norm: 0.2174
2024-09-29 15:21:07,603 - pyskl - INFO - Saving checkpoint at 20 epochs
2024-09-29 15:21:13,873 - pyskl - INFO - Epoch [21][20/30] lr: 1.911e-02, eta: 0:00:21, time: 0.311, data_time: 0.111, memory: 2132, top1_acc: 0.1250, top5_acc: 0.7906, loss_cls: 1.7951, loss: 1.7951, grad_norm: 0.2157
2024-09-29 15:21:15,891 - pyskl - INFO - Saving checkpoint at 21 epochs
2024-09-29 15:21:22,129 - pyskl - INFO - Epoch [22][20/30] lr: 9.521e-03, eta: 0:00:14, time: 0.309, data_time: 0.110, memory: 2132, top1_acc: 0.1187, top5_acc: 0.7812, loss_cls: 1.7936, loss: 1.7936, grad_norm: 0.2145
2024-09-29 15:21:24,143 - pyskl - INFO - Saving checkpoint at 22 epochs
2024-09-29 15:21:30,389 - pyskl - INFO - Epoch [23][20/30] lr: 3.192e-03, eta: 0:00:08, time: 0.310, data_time: 0.111, memory: 2132, top1_acc: 0.1187, top5_acc: 0.7812, loss_cls: 1.7925, loss: 1.7925, grad_norm: 0.2137
2024-09-29 15:21:32,400 - pyskl - INFO - Saving checkpoint at 23 epochs
2024-09-29 15:21:38,624 - pyskl - INFO - Epoch [24][20/30] lr: 2.303e-04, eta: 0:00:02, time: 0.309, data_time: 0.110, memory: 2132, top1_acc: 0.1156, top5_acc: 0.8000, loss_cls: 1.7919, loss: 1.7919, grad_norm: 0.2133
2024-09-29 15:21:40,629 - pyskl - INFO - Saving checkpoint at 24 epochs