{"mmaction_version": "0.15.01b39778", "config": "model = dict(\n type='Recognizer2D',\n backbone=dict(\n type='ResNetTSM',\n pretrained='torchvision://resnet50',\n depth=50,\n norm_eval=False,\n shift_div=8,\n num_segments=16),\n cls_head=dict(\n type='TSMHead',\n num_classes=400,\n in_channels=2048,\n spatial_type='avg',\n consensus=dict(type='AvgConsensus', dim=1),\n dropout_ratio=0.5,\n init_std=0.001,\n is_shift=True,\n num_segments=16),\n train_cfg=None,\n test_cfg=dict(average_clips='prob'))\ncheckpoint_config = dict(interval=1)\nlog_config = dict(interval=20, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ndataset_type = 'RawframeDataset'\ndata_root = 'data/kinetics400/rawframes_train'\ndata_root_val = 'data/kinetics400/rawframes_val'\nann_file_train = 'data/kinetics400/kinetics_train_list_hd320.txt'\nann_file_val = 'data/kinetics400/kinetics_val_list_hd320.txt'\nann_file_test = 'data/kinetics400/kinetics_val_list_hd320.txt'\nimg_norm_cfg = dict(\n mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_bgr=False)\nmc_cfg = dict(\n server_list_cfg='/mnt/lustre/share/memcached_client/server_list.conf',\n client_cfg='/mnt/lustre/share/memcached_client/client.conf',\n sys_path='/mnt/lustre/share/pymc/py3')\ntrain_pipeline = [\n dict(type='SampleFrames', clip_len=1, frame_interval=1, num_clips=16),\n dict(\n type='RawFrameDecode',\n io_backend='memcached',\n server_list_cfg='/mnt/lustre/share/memcached_client/server_list.conf',\n client_cfg='/mnt/lustre/share/memcached_client/client.conf',\n sys_path='/mnt/lustre/share/pymc/py3'),\n dict(\n type='MultiScaleCrop',\n input_size=224,\n scales=(1, 0.875, 0.75, 0.66),\n random_crop=False,\n max_wh_scale_gap=1,\n num_fixed_crops=13),\n dict(type='Resize', scale=(224, 224), keep_ratio=False),\n dict(type='Flip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_bgr=False),\n dict(type='FormatShape', input_format='NCHW'),\n dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),\n dict(type='ToTensor', keys=['imgs', 'label'])\n]\nval_pipeline = [\n dict(\n type='SampleFrames',\n clip_len=1,\n frame_interval=1,\n num_clips=16,\n test_mode=True),\n dict(\n type='RawFrameDecode',\n io_backend='memcached',\n server_list_cfg='/mnt/lustre/share/memcached_client/server_list.conf',\n client_cfg='/mnt/lustre/share/memcached_client/client.conf',\n sys_path='/mnt/lustre/share/pymc/py3'),\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_bgr=False),\n dict(type='FormatShape', input_format='NCHW'),\n dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),\n dict(type='ToTensor', keys=['imgs'])\n]\ntest_pipeline = [\n dict(\n type='SampleFrames',\n clip_len=1,\n frame_interval=1,\n num_clips=16,\n test_mode=True),\n dict(\n type='RawFrameDecode',\n io_backend='memcached',\n server_list_cfg='/mnt/lustre/share/memcached_client/server_list.conf',\n client_cfg='/mnt/lustre/share/memcached_client/client.conf',\n sys_path='/mnt/lustre/share/pymc/py3'),\n dict(type='Resize', scale=(-1, 256)),\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_bgr=False),\n dict(type='FormatShape', input_format='NCHW'),\n dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),\n dict(type='ToTensor', keys=['imgs'])\n]\ndata = dict(\n videos_per_gpu=4,\n workers_per_gpu=4,\n val_dataloader=dict(videos_per_gpu=6),\n test_dataloader=dict(videos_per_gpu=6),\n train=dict(\n type='RawframeDataset',\n ann_file='data/kinetics400/kinetics_train_list_hd320.txt',\n data_prefix='data/kinetics400/rawframes_train',\n pipeline=[\n dict(\n type='SampleFrames',\n clip_len=1,\n frame_interval=1,\n num_clips=16),\n dict(\n type='RawFrameDecode',\n io_backend='memcached',\n server_list_cfg=\n '/mnt/lustre/share/memcached_client/server_list.conf',\n client_cfg='/mnt/lustre/share/memcached_client/client.conf',\n sys_path='/mnt/lustre/share/pymc/py3'),\n dict(\n type='MultiScaleCrop',\n input_size=224,\n scales=(1, 0.875, 0.75, 0.66),\n random_crop=False,\n max_wh_scale_gap=1,\n num_fixed_crops=13),\n dict(type='Resize', scale=(224, 224), keep_ratio=False),\n dict(type='Flip', flip_ratio=0.5),\n dict(\n type='Normalize',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n to_bgr=False),\n dict(type='FormatShape', input_format='NCHW'),\n dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),\n dict(type='ToTensor', keys=['imgs', 'label'])\n ]),\n val=dict(\n type='RawframeDataset',\n ann_file='data/kinetics400/kinetics_val_list_hd320.txt',\n data_prefix='data/kinetics400/rawframes_val',\n pipeline=[\n dict(\n type='SampleFrames',\n clip_len=1,\n frame_interval=1,\n num_clips=16,\n test_mode=True),\n dict(\n type='RawFrameDecode',\n io_backend='memcached',\n server_list_cfg=\n '/mnt/lustre/share/memcached_client/server_list.conf',\n client_cfg='/mnt/lustre/share/memcached_client/client.conf',\n sys_path='/mnt/lustre/share/pymc/py3'),\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_bgr=False),\n dict(type='FormatShape', input_format='NCHW'),\n dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),\n dict(type='ToTensor', keys=['imgs'])\n ]),\n test=dict(\n type='RawframeDataset',\n ann_file='data/kinetics400/kinetics_val_list_hd320.txt',\n data_prefix='data/kinetics400/rawframes_val',\n pipeline=[\n dict(\n type='SampleFrames',\n clip_len=1,\n frame_interval=1,\n num_clips=16,\n test_mode=True),\n dict(\n type='RawFrameDecode',\n io_backend='memcached',\n server_list_cfg=\n '/mnt/lustre/share/memcached_client/server_list.conf',\n client_cfg='/mnt/lustre/share/memcached_client/client.conf',\n sys_path='/mnt/lustre/share/pymc/py3'),\n dict(type='Resize', scale=(-1, 256)),\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_bgr=False),\n dict(type='FormatShape', input_format='NCHW'),\n dict(type='Collect', keys=['imgs', 'label'], meta_keys=[]),\n dict(type='ToTensor', keys=['imgs'])\n ]))\nevaluation = dict(\n interval=1,\n metrics=['top_k_accuracy', 'mean_class_accuracy'],\n start=40,\n gpu_collect=True)\noptimizer = dict(\n type='SGD',\n constructor='TSMOptimizerConstructor',\n paramwise_cfg=dict(fc_lr5=True),\n lr=0.01,\n momentum=0.9,\n weight_decay=0.0001)\noptimizer_config = dict(grad_clip=dict(max_norm=20, norm_type=2))\nlr_config = dict(policy='step', step=[20, 40])\ntotal_epochs = 50\nwork_dir = './work_dirs/tsm_r50_uniform_1x1x16_50e_kinetics400_rgb/'\ngpu_ids = range(0, 16)\nomnisource = False\nmodule_hooks = []\nseed = None\n", "epoch": 27, "iter": 101439, "env_info": "sys.platform: linux\nPython: 3.7.6 (default, Jan 8 2020, 19:59:22) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: GeForce GTX 1080 Ti\nCUDA_HOME: /mnt/lustre/share/polaris/dep/cuda-9.0-cudnn7.6.5\nNVCC: Cuda compilation tools, release 9.0, V9.0.176\nGCC: gcc (GCC) 5.4.0\nPyTorch: 1.5.0\nPyTorch compiling details: PyTorch built with:\n - GCC 5.4\n - C++ Version: 201402\n - Intel(R) Math Kernel Library Version 2020.0.1 Product Build 20200208 for Intel(R) 64 architecture applications\n - Intel(R) MKL-DNN v0.21.1 (Git Hash 912ce228837d1ce28e1a61806118835de03f5751)\n - OpenMP 201307 (a.k.a. OpenMP 4.0)\n - NNPACK is enabled\n - CPU capability usage: AVX2\n - CUDA Runtime 9.0\n - NVCC architecture flags: -gencode;arch=compute_52,code=sm_52;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_70,code=compute_70\n - CuDNN 7.6.5\n - Magma 2.5.0\n - Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_INTERNAL_THREADPOOL_IMPL -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-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=ON, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF, \n\nTorchVision: 0.6.0\nOpenCV: 4.2.0\nMMCV: 1.3.6\nMMCV Compiler: GCC 5.4\nMMCV CUDA Compiler: 9.0\nMMAction2: 0.15.0+1b39778", "seed": null, "config_name": "tsm_r50_uniform_1x1x16_50e_kinetics400_rgb.py", "work_dir": "tsm_r50_uniform_1x1x16_50e_kinetics400_rgb", "hook_msgs": {"last_ckpt": "/mnt/lustre/linjintao/try/mmaction2_dev/work_dirs/tsm_r50_uniform_1x1x16_50e_kinetics400_rgb/epoch_26.pth"}, "mmcv_version": "1.3.6", "time": "Mon Jun 21 07:44:48 2021"} {"mode": "train", "epoch": 28, "iter": 20, "lr": 0.001, "memory": 7083, "data_time": 3.33272, "top1_acc": 0.73203, "top5_acc": 0.89531, "loss_cls": 1.09725, "loss": 1.09725, "grad_norm": 5.58562, "time": 4.23184} {"mode": "train", "epoch": 28, "iter": 40, "lr": 0.001, "memory": 7083, "data_time": 0.00516, "top1_acc": 0.73672, "top5_acc": 0.89688, "loss_cls": 1.08392, "loss": 1.08392, "grad_norm": 5.6789, "time": 0.54995} {"mode": "train", "epoch": 28, "iter": 60, "lr": 0.001, "memory": 7083, "data_time": 0.00195, "top1_acc": 0.73516, "top5_acc": 0.88906, "loss_cls": 1.11904, "loss": 1.11904, "grad_norm": 5.62875, "time": 0.54899} {"mode": "train", "epoch": 28, "iter": 80, "lr": 0.001, "memory": 7083, "data_time": 0.00172, "top1_acc": 0.73203, "top5_acc": 0.89688, "loss_cls": 1.06771, "loss": 1.06771, "grad_norm": 5.68606, "time": 0.54796} {"mode": "train", "epoch": 28, "iter": 100, "lr": 0.001, "memory": 7083, "data_time": 0.00211, "top1_acc": 0.75, "top5_acc": 0.91875, "loss_cls": 1.00083, "loss": 1.00083, "grad_norm": 5.46142, "time": 0.55129} {"mode": "train", "epoch": 28, "iter": 120, "lr": 0.001, "memory": 7083, "data_time": 0.00164, "top1_acc": 0.74766, "top5_acc": 0.91094, "loss_cls": 0.98457, "loss": 0.98457, "grad_norm": 5.51378, "time": 0.5502} {"mode": "train", "epoch": 28, "iter": 140, "lr": 0.001, "memory": 7083, "data_time": 0.00204, "top1_acc": 0.72734, "top5_acc": 0.89609, "loss_cls": 1.11936, "loss": 1.11936, "grad_norm": 5.55131, "time": 0.55238} {"mode": "train", "epoch": 28, "iter": 160, "lr": 0.001, "memory": 7083, "data_time": 0.00173, "top1_acc": 0.73828, "top5_acc": 0.91172, "loss_cls": 1.01983, "loss": 1.01983, "grad_norm": 5.37995, "time": 0.55094} {"mode": "train", "epoch": 28, "iter": 180, "lr": 0.001, "memory": 7083, "data_time": 0.00197, "top1_acc": 0.69844, "top5_acc": 0.89062, "loss_cls": 1.15534, "loss": 1.15534, "grad_norm": 5.87966, "time": 0.55305} {"mode": "train", "epoch": 28, "iter": 200, "lr": 0.001, "memory": 7083, "data_time": 0.00156, "top1_acc": 0.71172, "top5_acc": 0.90938, "loss_cls": 1.06113, "loss": 1.06113, "grad_norm": 5.70228, "time": 0.55364} {"mode": "train", "epoch": 28, "iter": 220, "lr": 0.001, "memory": 7083, "data_time": 0.00224, "top1_acc": 0.725, "top5_acc": 0.88906, "loss_cls": 1.1104, "loss": 1.1104, "grad_norm": 5.67648, "time": 0.56075} {"mode": "train", "epoch": 28, "iter": 240, "lr": 0.001, "memory": 7083, "data_time": 0.00159, "top1_acc": 0.74766, "top5_acc": 0.90938, "loss_cls": 0.99614, "loss": 0.99614, "grad_norm": 5.29901, "time": 0.5552} {"mode": "train", "epoch": 28, "iter": 260, "lr": 0.001, "memory": 7083, "data_time": 0.00147, "top1_acc": 0.73984, "top5_acc": 0.90234, "loss_cls": 1.03307, "loss": 1.03307, "grad_norm": 5.42145, "time": 0.55612} {"mode": "train", "epoch": 28, "iter": 280, "lr": 0.001, "memory": 7083, "data_time": 0.0018, "top1_acc": 0.73672, "top5_acc": 0.90391, "loss_cls": 1.04338, "loss": 1.04338, "grad_norm": 5.4655, "time": 0.56036} {"mode": "train", "epoch": 28, "iter": 300, "lr": 0.001, "memory": 7083, "data_time": 0.00189, "top1_acc": 0.74766, "top5_acc": 0.90547, "loss_cls": 1.02232, "loss": 1.02232, "grad_norm": 5.41607, "time": 0.56044} {"mode": "train", "epoch": 28, "iter": 320, "lr": 0.001, "memory": 7083, "data_time": 0.00163, "top1_acc": 0.72969, "top5_acc": 0.90703, "loss_cls": 1.07313, "loss": 1.07313, "grad_norm": 5.64002, "time": 0.55828} {"mode": "train", "epoch": 28, "iter": 340, "lr": 0.001, "memory": 7083, "data_time": 0.00112, "top1_acc": 0.72969, "top5_acc": 0.91016, "loss_cls": 1.02964, "loss": 1.02964, "grad_norm": 5.3323, "time": 0.56085} {"mode": "train", "epoch": 28, "iter": 360, "lr": 0.001, "memory": 7083, "data_time": 0.00159, "top1_acc": 0.71797, "top5_acc": 0.88906, "loss_cls": 1.1216, "loss": 1.1216, "grad_norm": 5.69251, "time": 0.56046} {"mode": "train", "epoch": 28, "iter": 380, "lr": 0.001, "memory": 7083, "data_time": 0.0017, "top1_acc": 0.73047, "top5_acc": 0.89766, "loss_cls": 1.0781, "loss": 1.0781, "grad_norm": 5.51671, "time": 0.56709} {"mode": "train", "epoch": 28, "iter": 400, "lr": 0.001, "memory": 7083, "data_time": 0.00164, "top1_acc": 0.74219, "top5_acc": 0.90781, "loss_cls": 1.04663, "loss": 1.04663, "grad_norm": 5.45218, "time": 0.55969} {"mode": "train", "epoch": 28, "iter": 420, "lr": 0.001, "memory": 7083, "data_time": 0.00184, "top1_acc": 0.71094, "top5_acc": 0.90391, "loss_cls": 1.08168, "loss": 1.08168, "grad_norm": 5.80574, "time": 0.56067} {"mode": "train", "epoch": 28, "iter": 440, "lr": 0.001, "memory": 7083, "data_time": 0.00164, "top1_acc": 0.73906, "top5_acc": 0.89766, "loss_cls": 1.06616, "loss": 1.06616, "grad_norm": 5.35426, "time": 0.56152} {"mode": "train", "epoch": 28, "iter": 460, "lr": 0.001, "memory": 7083, "data_time": 0.00187, "top1_acc": 0.72734, "top5_acc": 0.89766, "loss_cls": 1.04327, "loss": 1.04327, "grad_norm": 5.47474, "time": 0.56209} {"mode": "train", "epoch": 28, "iter": 480, "lr": 0.001, "memory": 7083, "data_time": 0.00177, "top1_acc": 0.72031, "top5_acc": 0.91953, "loss_cls": 1.04761, "loss": 1.04761, "grad_norm": 5.49892, "time": 0.56064} {"mode": "train", "epoch": 28, "iter": 500, "lr": 0.001, "memory": 7083, "data_time": 0.00191, "top1_acc": 0.73359, "top5_acc": 0.90703, "loss_cls": 1.03815, "loss": 1.03815, "grad_norm": 5.41399, "time": 0.56295} {"mode": "train", "epoch": 28, "iter": 520, "lr": 0.001, "memory": 7083, "data_time": 0.00155, "top1_acc": 0.74922, "top5_acc": 0.90625, "loss_cls": 1.0413, "loss": 1.0413, "grad_norm": 5.69964, "time": 0.70617} {"mode": "train", "epoch": 28, "iter": 540, "lr": 0.001, "memory": 7083, "data_time": 0.00537, "top1_acc": 0.72656, "top5_acc": 0.89375, "loss_cls": 1.0881, "loss": 1.0881, "grad_norm": 5.58497, "time": 0.56349} {"mode": "train", "epoch": 28, "iter": 560, "lr": 0.001, "memory": 7083, "data_time": 0.00145, "top1_acc": 0.73125, "top5_acc": 0.90391, "loss_cls": 1.04273, "loss": 1.04273, "grad_norm": 5.55556, "time": 0.55679} {"mode": "train", "epoch": 28, "iter": 580, "lr": 0.001, "memory": 7083, "data_time": 0.00164, "top1_acc": 0.70469, "top5_acc": 0.89375, "loss_cls": 1.13914, "loss": 1.13914, "grad_norm": 5.829, "time": 0.56098} {"mode": "train", "epoch": 28, "iter": 600, "lr": 0.001, "memory": 7083, "data_time": 0.00148, "top1_acc": 0.74531, "top5_acc": 0.89297, "loss_cls": 1.02371, "loss": 1.02371, "grad_norm": 5.36888, "time": 0.56023} {"mode": "train", "epoch": 28, "iter": 620, "lr": 0.001, "memory": 7083, "data_time": 0.00168, "top1_acc": 0.74062, "top5_acc": 0.91406, "loss_cls": 1.00817, "loss": 1.00817, "grad_norm": 5.6222, "time": 0.56021} {"mode": "train", "epoch": 28, "iter": 640, "lr": 0.001, "memory": 7083, "data_time": 0.00155, "top1_acc": 0.74844, "top5_acc": 0.92109, "loss_cls": 0.97839, "loss": 0.97839, "grad_norm": 5.45972, "time": 0.56394} {"mode": "train", "epoch": 28, "iter": 660, "lr": 0.001, "memory": 7083, "data_time": 0.00201, "top1_acc": 0.73047, "top5_acc": 0.9, "loss_cls": 1.08609, "loss": 1.08609, "grad_norm": 5.68618, "time": 0.5605} {"mode": "train", "epoch": 28, "iter": 680, "lr": 0.001, "memory": 7083, "data_time": 0.00158, "top1_acc": 0.73906, "top5_acc": 0.90781, "loss_cls": 1.03494, 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