{"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: 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+bc231ec\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=32,\n base_channels=256,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=2,\n with_spectral_norm=True,\n num_classes=10,\n act_cfg=dict(type='ReLU', inplace=True)),\n discriminator=dict(\n type='ProjDiscriminator',\n input_scale=32,\n base_channels=128,\n attention_cfg=dict(type='SelfAttentionBlock'),\n attention_after_nth_block=1,\n with_spectral_norm=True,\n num_classes=10,\n act_cfg=dict(type='ReLU', inplace=True)),\n gan_loss=dict(type='GANLoss', gan_type='hinge'))\ntrain_cfg = dict(disc_steps=5)\ntest_cfg = None\noptimizer = dict(\n generator=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)),\n discriminator=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)))\ndataset_type = 'mmcls.CIFAR10'\nimg_norm_cfg = dict(\n mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True)\ntrain_pipeline = [\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=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(\n type='Normalize',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\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=4,\n train=dict(\n type='RepeatDataset',\n times=1000,\n dataset=dict(\n type='mmcls.CIFAR10',\n data_prefix='data/cifar10',\n pipeline=[\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=True),\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.CIFAR10',\n data_prefix='data/cifar10',\n pipeline=[\n dict(\n type='Normalize',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n to_rgb=True),\n dict(type='ImageToTensor', keys=['img']),\n dict(type='Collect', keys=['img'])\n ]),\n test=dict(\n type='mmcls.CIFAR10',\n data_prefix='data/cifar10',\n pipeline=[\n dict(\n type='Normalize',\n mean=[127.5, 127.5, 127.5],\n std=[127.5, 127.5, 127.5],\n to_rgb=True),\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/cifar10_wReLUinplace/')\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/cifar10.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/cifar10.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 = 5\ntotal_iters = 500000\nuse_ddp_wrapper = True\nmetrics = dict(\n fid50k=dict(\n type='FID',\n num_images=50000,\n inception_pkl='./work_dirs/inception_pkl/cifar10.pkl',\n inception_args=dict(type='StyleGAN')),\n IS50k=dict(type='IS', num_images=50000))\nwork_dir = 'work_dirs/sagan_cifar10_wReLUinplace'\ngpu_ids = range(0, 1)\n", "seed": 2021, "exp_name": "sagan_cifar10_32_wReLUinplace_train.py", "hook_msgs": {}} {"mode": "train", "epoch": 1, "iter": 100, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00168, "loss_disc_fake": 0.40991, "loss_disc_real": 0.52269, "loss": 0.9326, "time": 0.08059, "loss_disc_fake_g": 2.79163} {"mode": "train", "epoch": 1, "iter": 200, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00191, "loss_disc_fake": 0.18083, "loss_disc_real": 0.35613, "loss": 0.53696, "time": 0.04967, "loss_disc_fake_g": 2.3575} {"mode": "train", "epoch": 1, "iter": 300, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00137, "loss_disc_fake": 0.32115, "loss_disc_real": 0.4456, "loss": 0.76676, "time": 0.05387, "loss_disc_fake_g": 2.02715} {"mode": "train", "epoch": 1, "iter": 400, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00137, "loss_disc_fake": 0.32619, "loss_disc_real": 0.45797, "loss": 0.78416, "time": 0.04935, "loss_disc_fake_g": 1.90669} {"mode": "train", "epoch": 1, "iter": 500, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00135, "loss_disc_fake": 0.20886, "loss_disc_real": 0.33134, "loss": 0.5402, "time": 0.04901, "loss_disc_fake_g": 1.89645} {"mode": "train", "epoch": 1, "iter": 600, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00133, "loss_disc_fake": 0.28399, "loss_disc_real": 0.441, "loss": 0.725, "time": 0.04898, "loss_disc_fake_g": 1.63415} {"mode": "train", "epoch": 1, "iter": 700, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.0013, "loss_disc_fake": 0.35265, "loss_disc_real": 0.54284, "loss": 0.89549, "time": 0.0488, "loss_disc_fake_g": 1.53321} {"mode": "train", "epoch": 1, "iter": 800, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00136, "loss_disc_fake": 0.41011, "loss_disc_real": 0.50497, "loss": 0.91508, "time": 0.04917, "loss_disc_fake_g": 1.46488} {"mode": "train", "epoch": 1, "iter": 900, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00132, "loss_disc_fake": 0.44307, "loss_disc_real": 0.5737, "loss": 1.01677, "time": 0.04882, "loss_disc_fake_g": 1.29473} {"mode": "train", "epoch": 1, "iter": 1000, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00135, "loss_disc_fake": 0.45171, "loss_disc_real": 0.5595, "loss": 1.01121, "time": 0.06588, "loss_disc_fake_g": 1.10861} {"mode": "train", "epoch": 1, "iter": 1100, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00309, "loss_disc_fake": 0.47211, "loss_disc_real": 0.60804, "loss": 1.08015, "time": 0.05076, "loss_disc_fake_g": 1.03976} {"mode": "train", "epoch": 1, "iter": 1200, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00134, "loss_disc_fake": 0.50279, "loss_disc_real": 0.6365, "loss": 1.13929, "time": 0.04931, "loss_disc_fake_g": 0.94085} {"mode": "train", "epoch": 1, "iter": 1300, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00133, "loss_disc_fake": 0.4865, "loss_disc_real": 0.59957, "loss": 1.08607, "time": 0.04907, "loss_disc_fake_g": 0.92298} {"mode": "train", "epoch": 1, "iter": 1400, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.0016, "loss_disc_fake": 0.49712, "loss_disc_real": 0.60772, "loss": 1.10483, "time": 0.0493, "loss_disc_fake_g": 0.99373} {"mode": "train", "epoch": 1, "iter": 1500, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00139, "loss_disc_fake": 0.47329, "loss_disc_real": 0.55105, "loss": 1.02434, "time": 0.04904, "loss_disc_fake_g": 1.0008} {"mode": "train", "epoch": 1, "iter": 1600, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00132, "loss_disc_fake": 0.49969, "loss_disc_real": 0.57344, "loss": 1.07313, "time": 0.04897, "loss_disc_fake_g": 0.9868} {"mode": "train", "epoch": 1, "iter": 1700, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00132, "loss_disc_fake": 0.46467, "loss_disc_real": 0.59413, "loss": 1.05881, "time": 0.04897, "loss_disc_fake_g": 1.0177} {"mode": "train", "epoch": 1, "iter": 1800, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.0015, "loss_disc_fake": 0.4957, "loss_disc_real": 0.60799, "loss": 1.10369, "time": 0.0494, "loss_disc_fake_g": 1.03844} {"mode": "train", "epoch": 1, "iter": 1900, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00132, "loss_disc_fake": 0.51195, "loss_disc_real": 0.61406, "loss": 1.12601, "time": 0.04924, "loss_disc_fake_g": 0.97714} {"mode": "train", "epoch": 1, "iter": 2000, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00204, "loss_disc_fake": 0.48262, "loss_disc_real": 0.57613, "loss": 1.05876, "time": 0.07901, "loss_disc_fake_g": 0.94532} {"mode": "train", "epoch": 1, "iter": 2100, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00409, "loss_disc_fake": 0.52744, "loss_disc_real": 0.63042, "loss": 1.15785, "time": 0.05242, "loss_disc_fake_g": 0.93661} {"mode": "train", "epoch": 1, "iter": 2200, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00138, "loss_disc_fake": 0.52523, "loss_disc_real": 0.61695, "loss": 1.14218, "time": 0.04952, "loss_disc_fake_g": 0.84452} {"mode": "train", "epoch": 1, "iter": 2300, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00134, "loss_disc_fake": 0.54443, "loss_disc_real": 0.58964, "loss": 1.13407, "time": 0.04931, "loss_disc_fake_g": 0.77142} {"mode": "train", "epoch": 1, "iter": 2400, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00161, "loss_disc_fake": 0.53067, "loss_disc_real": 0.60946, "loss": 1.14013, "time": 0.04968, "loss_disc_fake_g": 0.78701} {"mode": "train", "epoch": 1, "iter": 2500, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.0013, "loss_disc_fake": 0.52041, "loss_disc_real": 0.60318, "loss": 1.12359, "time": 0.04881, "loss_disc_fake_g": 0.78475} {"mode": "train", "epoch": 1, "iter": 2600, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00132, "loss_disc_fake": 0.50283, "loss_disc_real": 0.61392, "loss": 1.11675, "time": 0.04896, "loss_disc_fake_g": 0.75777} {"mode": "train", "epoch": 1, "iter": 2700, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00133, "loss_disc_fake": 0.51721, "loss_disc_real": 0.59652, "loss": 1.11373, "time": 0.04909, "loss_disc_fake_g": 0.85237} {"mode": "train", "epoch": 1, "iter": 2800, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00138, "loss_disc_fake": 0.51163, "loss_disc_real": 0.58956, "loss": 1.10119, "time": 0.05004, "loss_disc_fake_g": 0.88939} {"mode": "train", "epoch": 1, "iter": 2900, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00133, "loss_disc_fake": 0.50217, "loss_disc_real": 0.5825, "loss": 1.08466, "time": 0.04892, "loss_disc_fake_g": 0.92659} {"mode": "train", "epoch": 1, "iter": 3000, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00135, "loss_disc_fake": 0.47402, "loss_disc_real": 0.58683, "loss": 1.06085, "time": 0.04926, "loss_disc_fake_g": 0.95203} {"mode": "train", "epoch": 1, "iter": 3100, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00156, "loss_disc_fake": 0.49803, "loss_disc_real": 0.56901, "loss": 1.06704, "time": 0.0495, "loss_disc_fake_g": 0.96501} {"mode": "train", "epoch": 1, "iter": 3200, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00131, "loss_disc_fake": 0.50332, "loss_disc_real": 0.56906, "loss": 1.07238, "time": 0.04907, "loss_disc_fake_g": 0.92279} {"mode": "train", "epoch": 1, "iter": 3300, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00133, "loss_disc_fake": 0.51838, "loss_disc_real": 0.57676, "loss": 1.09515, "time": 0.04898, "loss_disc_fake_g": 0.92557} {"mode": "train", "epoch": 1, "iter": 3400, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00131, "loss_disc_fake": 0.51857, "loss_disc_real": 0.59879, "loss": 1.11736, "time": 0.04896, "loss_disc_fake_g": 0.85664} {"mode": "train", "epoch": 1, "iter": 3500, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00129, "loss_disc_fake": 0.51227, "loss_disc_real": 0.59551, "loss": 1.10779, "time": 0.04884, "loss_disc_fake_g": 0.87785} {"mode": "train", "epoch": 1, "iter": 3600, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00131, "loss_disc_fake": 0.49538, "loss_disc_real": 0.56178, "loss": 1.05716, "time": 0.0488, "loss_disc_fake_g": 0.89532} {"mode": "train", "epoch": 1, "iter": 3700, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00132, "loss_disc_fake": 0.491, "loss_disc_real": 0.59927, "loss": 1.09028, "time": 0.04892, "loss_disc_fake_g": 0.88985} {"mode": "train", "epoch": 1, "iter": 3800, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00131, "loss_disc_fake": 0.48244, "loss_disc_real": 0.60006, "loss": 1.08251, "time": 0.04924, "loss_disc_fake_g": 0.90264} {"mode": "train", "epoch": 1, "iter": 3900, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.0013, "loss_disc_fake": 0.48988, "loss_disc_real": 0.58651, "loss": 1.07639, "time": 0.04888, "loss_disc_fake_g": 1.00893} {"mode": "train", "epoch": 1, "iter": 4000, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00155, "loss_disc_fake": 0.46988, "loss_disc_real": 0.55332, "loss": 1.0232, "time": 0.04916, "loss_disc_fake_g": 0.99023} {"mode": "train", "epoch": 1, "iter": 4100, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00148, "loss_disc_fake": 0.46467, "loss_disc_real": 0.55297, "loss": 1.01764, "time": 0.04936, "loss_disc_fake_g": 0.99608} {"mode": "train", "epoch": 1, "iter": 4200, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00132, "loss_disc_fake": 0.45305, "loss_disc_real": 0.53747, "loss": 0.99052, "time": 0.0493, "loss_disc_fake_g": 1.03035} {"mode": "train", "epoch": 1, "iter": 4300, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00129, "loss_disc_fake": 0.47593, "loss_disc_real": 0.5645, "loss": 1.04044, "time": 0.04906, "loss_disc_fake_g": 1.06297} {"mode": "train", "epoch": 1, "iter": 4400, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3535, "data_time": 0.00131, "loss_disc_fake": 0.48168, "loss_disc_real": 0.55903, "loss": 1.04072, "time": 0.0488, "loss_disc_fake_g": 1.03215} {"mode": 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