{"env_info": "sys.platform: linux\nPython: 3.8.10 (default, May 19 2021, 18:05:58) [GCC 7.3.0]\nCUDA available: True\nCUDA_HOME: /mnt/lustre/share/cuda-11.1\nNVCC: Build cuda_11.1.TC455_06.29069683_0\nGPU 0,1,2,3: Tesla V100-SXM2-32GB\nGCC: gcc (GCC) 5.3.0\nPyTorch: 1.8.1\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) oneAPI Math Kernel Library Version 2021.2-Product Build 20210312 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v1.7.0 (Git Hash 7aed236906b1f7a05c0917e5257a1af05e9ff683)\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_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 8.0.5\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 -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=1.8.1, 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, \n\nTorchVision: 0.9.1\nOpenCV: 4.5.2\nMMCV: 1.3.1\nMMGen: 0.2.0+4599ce9\nMMCV Compiler: GCC 7.3\nMMCV CUDA Compiler: 11.1", "config": "model = dict(\n    type='BasiccGAN',\n    generator=dict(\n        type='SAGANGenerator',\n        output_scale=128,\n        base_channels=64,\n        attention_cfg=dict(type='SelfAttentionBlock'),\n        attention_after_nth_block=4,\n        with_spectral_norm=True,\n        num_classes=1000),\n    discriminator=dict(\n        type='ProjDiscriminator',\n        input_scale=128,\n        base_channels=64,\n        attention_cfg=dict(type='SelfAttentionBlock'),\n        attention_after_nth_block=1,\n        with_spectral_norm=True,\n        num_classes=1000),\n    gan_loss=dict(type='GANLoss', gan_type='hinge'))\ntrain_cfg = dict(disc_steps=1)\ntest_cfg = None\noptimizer = dict(\n    generator=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999)),\n    discriminator=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999)))\ndataset_type = 'mmcls.ImageNet'\nimg_norm_cfg = dict(\n    mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=False)\nmemcache_load = dict(\n    type='LoadImageFromFile',\n    file_client_args=dict(\n        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'))\ntrain_pipeline = [\n    dict(\n        type='LoadImageFromFile',\n        file_client_args=dict(\n            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='RandomCropLongEdge', keys=['img']),\n    dict(type='Resize', size=(128, 128), backend='pillow'),\n    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n    dict(\n        type='Normalize',\n        mean=[127.5, 127.5, 127.5],\n        std=[127.5, 127.5, 127.5],\n        to_rgb=False),\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(\n        type='LoadImageFromFile',\n        file_client_args=dict(\n            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='CenterCropLongEdge', keys=['img']),\n    dict(type='Resize', size=(128, 128), backend='pillow'),\n    dict(\n        type='Normalize',\n        mean=[127.5, 127.5, 127.5],\n        std=[127.5, 127.5, 127.5],\n        to_rgb=False),\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='mmcls.ImageNet',\n        data_prefix='data/imagenet/train',\n        pipeline=[\n            dict(\n                type='LoadImageFromFile',\n                file_client_args=dict(\n                    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='RandomCropLongEdge', keys=['img']),\n            dict(type='Resize', size=(128, 128), backend='pillow'),\n            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n            dict(\n                type='Normalize',\n                mean=[127.5, 127.5, 127.5],\n                std=[127.5, 127.5, 127.5],\n                to_rgb=False),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='ToTensor', keys=['gt_label']),\n            dict(type='Collect', keys=['img', 'gt_label'])\n        ]),\n    val=dict(\n        type='mmcls.ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(\n                type='LoadImageFromFile',\n                file_client_args=dict(\n                    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='CenterCropLongEdge', keys=['img']),\n            dict(type='Resize', size=(128, 128), backend='pillow'),\n            dict(\n                type='Normalize',\n                mean=[127.5, 127.5, 127.5],\n                std=[127.5, 127.5, 127.5],\n                to_rgb=False),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]),\n    test=dict(\n        type='mmcls.ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(\n                type='LoadImageFromFile',\n                file_client_args=dict(\n                    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='CenterCropLongEdge', keys=['img']),\n            dict(type='Resize', size=(128, 128), backend='pillow'),\n            dict(\n                type='Normalize',\n                mean=[127.5, 127.5, 127.5],\n                std=[127.5, 127.5, 127.5],\n                to_rgb=False),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]))\ncheckpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=20)\nlog_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])\ncustom_hooks = [\n    dict(\n        type='VisualizeUnconditionalSamples',\n        output_dir='training_samples',\n        interval=1000),\n    dict(\n        type='PetrelUploadHook',\n        ceph_path='s3://mmgen-dev/sagan/imagenet_woReLUinplace/')\n]\nrunner = dict(\n    type='DynamicIterBasedRunner',\n    is_dynamic_ddp=False,\n    pass_training_status=True)\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 10000)]\nfind_unused_parameters = False\ncudnn_benchmark = True\nlr_config = None\ninception_pkl = './work_dirs/inception_pkl/imagenet.pkl'\nevaluation = dict(\n    type='GenerativeEvalHook',\n    interval=10000,\n    metrics=[\n        dict(\n            type='FID',\n            num_images=50000,\n            inception_pkl='./work_dirs/inception_pkl/imagenet.pkl',\n            bgr2rgb=True,\n            inception_args=dict(type='StyleGAN')),\n        dict(type='IS', num_images=50000)\n    ],\n    best_metric=['fid', 'is'],\n    sample_kwargs=dict(sample_model='orig'))\nn_disc = 1\ntotal_iters = 1000000\nuse_ddp_wrapper = True\nmetrics = dict(\n    fid50k=dict(\n        type='FID',\n        num_images=50000,\n        inception_pkl='./work_dirs/inception_pkl/imagenet.pkl',\n        inception_args=dict(type='StyleGAN')),\n    IS50k=dict(type='IS', num_images=50000))\nwork_dir = 'work_dirs/sagan_imagenet_woReLUinplace'\ngpu_ids = range(0, 4)\n", "seed": 2021, "exp_name": "sagan_imagenet1k_128_woReLUinplace_train.py", "hook_msgs": {}}
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{"mode": "train","epoch": 1,"iter": 4000,"lr": {"generator": 0.0001,"discriminator": 0.0004},"memory": 16926,"data_time": 0.00587,"loss_disc_fake_g": 0.38497,"loss": 1.68661,"loss_disc_fake": 0.85794,"loss_disc_real": 0.82867,"time": 0.8997}
{"mode": "train","epoch": 1,"iter": 4100,"lr": {"generator": 0.0001,"discriminator": 0.0004},"memory": 16926,"data_time": 0.00987,"loss_disc_fake_g": 0.42726,"loss": 1.67113,"loss_disc_fake": 0.83247,"loss_disc_real": 0.83865,"time": 0.91303}
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