{"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 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 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_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/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_woReLUinplace'\ngpu_ids = range(0, 1)\n", "seed": 2021, "exp_name": "sagan_cifar10_32_woReLUinplace_train.py", "hook_msgs": {}} {"mode": "train", "epoch": 1, "iter": 100, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00144, "loss_disc_fake": 0.52446, "loss_disc_real": 0.67133, "loss": 1.19579, "time": 0.07213, "loss_disc_fake_g": 3.60087} {"mode": "train", "epoch": 1, "iter": 200, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00197, "loss_disc_fake": 0.1958, "loss_disc_real": 0.34555, "loss": 0.54135, "time": 0.04929, "loss_disc_fake_g": 2.88005} {"mode": "train", "epoch": 1, "iter": 300, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00133, "loss_disc_fake": 0.3623, "loss_disc_real": 0.48276, "loss": 0.84506, "time": 0.04856, "loss_disc_fake_g": 2.4618} {"mode": "train", "epoch": 1, "iter": 400, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00134, "loss_disc_fake": 0.4427, "loss_disc_real": 0.48007, "loss": 0.92276, "time": 0.04863, "loss_disc_fake_g": 2.06607} {"mode": "train", "epoch": 1, "iter": 500, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00164, "loss_disc_fake": 0.34487, "loss_disc_real": 0.44472, "loss": 0.78959, "time": 0.04905, "loss_disc_fake_g": 1.95178} {"mode": "train", "epoch": 1, "iter": 600, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00136, "loss_disc_fake": 0.35784, "loss_disc_real": 0.4674, "loss": 0.82524, "time": 0.04865, "loss_disc_fake_g": 1.53044} {"mode": "train", "epoch": 1, "iter": 700, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00135, "loss_disc_fake": 0.44908, "loss_disc_real": 0.54223, "loss": 0.99131, "time": 0.04894, "loss_disc_fake_g": 1.30512} {"mode": "train", "epoch": 1, "iter": 800, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00135, "loss_disc_fake": 0.4287, "loss_disc_real": 0.54021, "loss": 0.96891, "time": 0.04859, "loss_disc_fake_g": 1.18821} {"mode": "train", "epoch": 1, "iter": 900, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00159, "loss_disc_fake": 0.41807, "loss_disc_real": 0.51477, "loss": 0.93284, "time": 0.04891, "loss_disc_fake_g": 1.21447} {"mode": "train", "epoch": 1, "iter": 1000, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00144, "loss_disc_fake": 0.45917, "loss_disc_real": 0.55924, "loss": 1.01841, "time": 0.04966, "loss_disc_fake_g": 1.14933} {"mode": "train", "epoch": 1, "iter": 1100, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.01197, "loss_disc_fake": 0.50883, "loss_disc_real": 0.59631, "loss": 1.10515, "time": 0.07641, "loss_disc_fake_g": 1.03037} {"mode": "train", "epoch": 1, "iter": 1200, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00138, "loss_disc_fake": 0.50604, "loss_disc_real": 0.60534, "loss": 1.11138, "time": 0.0488, "loss_disc_fake_g": 0.9693} {"mode": "train", "epoch": 1, "iter": 1300, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00136, "loss_disc_fake": 0.54542, "loss_disc_real": 0.64493, "loss": 1.19035, "time": 0.04902, "loss_disc_fake_g": 0.91021} {"mode": "train", "epoch": 1, "iter": 1400, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00134, "loss_disc_fake": 0.54141, "loss_disc_real": 0.63835, "loss": 1.17976, "time": 0.0488, "loss_disc_fake_g": 0.89859} {"mode": "train", "epoch": 1, "iter": 1500, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00132, "loss_disc_fake": 0.49538, "loss_disc_real": 0.58998, "loss": 1.08537, "time": 0.04871, "loss_disc_fake_g": 0.87159} {"mode": "train", "epoch": 1, "iter": 1600, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00133, "loss_disc_fake": 0.52498, "loss_disc_real": 0.63828, "loss": 1.16326, "time": 0.04873, "loss_disc_fake_g": 0.85332} {"mode": "train", "epoch": 1, "iter": 1700, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00141, "loss_disc_fake": 0.51523, "loss_disc_real": 0.61398, "loss": 1.12922, "time": 0.04938, "loss_disc_fake_g": 0.85227} {"mode": "train", "epoch": 1, "iter": 1800, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00134, "loss_disc_fake": 0.49992, "loss_disc_real": 0.59285, "loss": 1.09278, "time": 0.04892, "loss_disc_fake_g": 0.88583} {"mode": "train", "epoch": 1, "iter": 1900, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00131, "loss_disc_fake": 0.4943, "loss_disc_real": 0.59452, "loss": 1.08882, "time": 0.04859, "loss_disc_fake_g": 0.9316} {"mode": "train", "epoch": 1, "iter": 2000, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00205, "loss_disc_fake": 0.51072, "loss_disc_real": 0.5993, "loss": 1.11003, "time": 0.07856, "loss_disc_fake_g": 0.89019} {"mode": "train", "epoch": 1, "iter": 2100, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00442, "loss_disc_fake": 0.48185, "loss_disc_real": 0.58243, "loss": 1.06428, "time": 0.05276, "loss_disc_fake_g": 0.89139} {"mode": "train", "epoch": 1, "iter": 2200, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00151, "loss_disc_fake": 0.48865, "loss_disc_real": 0.56936, "loss": 1.05801, "time": 0.04946, "loss_disc_fake_g": 0.95246} {"mode": "train", "epoch": 1, "iter": 2300, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00145, "loss_disc_fake": 0.50472, "loss_disc_real": 0.61741, "loss": 1.12213, "time": 0.0493, "loss_disc_fake_g": 0.96082} {"mode": "train", "epoch": 1, "iter": 2400, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00147, "loss_disc_fake": 0.51095, "loss_disc_real": 0.58293, "loss": 1.09388, "time": 0.04887, "loss_disc_fake_g": 0.9205} {"mode": "train", "epoch": 1, "iter": 2500, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00132, "loss_disc_fake": 0.5031, "loss_disc_real": 0.58956, "loss": 1.09266, "time": 0.04852, "loss_disc_fake_g": 0.9536} {"mode": "train", "epoch": 1, "iter": 2600, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00135, "loss_disc_fake": 0.48148, "loss_disc_real": 0.59819, "loss": 1.07967, "time": 0.04874, "loss_disc_fake_g": 1.00705} {"mode": "train", "epoch": 1, "iter": 2700, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00132, "loss_disc_fake": 0.48569, "loss_disc_real": 0.55207, "loss": 1.03776, "time": 0.04864, "loss_disc_fake_g": 0.99474} {"mode": "train", "epoch": 1, "iter": 2800, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.0017, "loss_disc_fake": 0.47421, "loss_disc_real": 0.57773, "loss": 1.05194, "time": 0.05046, "loss_disc_fake_g": 1.00402} {"mode": "train", "epoch": 1, "iter": 2900, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00133, "loss_disc_fake": 0.49047, "loss_disc_real": 0.57767, "loss": 1.06814, "time": 0.04855, "loss_disc_fake_g": 0.98997} {"mode": "train", "epoch": 1, "iter": 3000, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00131, "loss_disc_fake": 0.49067, "loss_disc_real": 0.57646, "loss": 1.06713, "time": 0.04862, "loss_disc_fake_g": 0.9876} {"mode": "train", "epoch": 1, "iter": 3100, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00151, "loss_disc_fake": 0.50798, "loss_disc_real": 0.5902, "loss": 1.09818, "time": 0.04944, "loss_disc_fake_g": 0.94409} {"mode": "train", "epoch": 1, "iter": 3200, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00161, "loss_disc_fake": 0.4954, "loss_disc_real": 0.59217, "loss": 1.08757, "time": 0.04938, "loss_disc_fake_g": 0.92162} {"mode": "train", "epoch": 1, "iter": 3300, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00137, "loss_disc_fake": 0.48325, "loss_disc_real": 0.5624, "loss": 1.04565, "time": 0.04962, "loss_disc_fake_g": 0.89677} {"mode": "train", "epoch": 1, "iter": 3400, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00134, "loss_disc_fake": 0.47609, "loss_disc_real": 0.56413, "loss": 1.04022, "time": 0.04946, "loss_disc_fake_g": 0.88289} {"mode": "train", "epoch": 1, "iter": 3500, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00136, "loss_disc_fake": 0.50624, "loss_disc_real": 0.55977, "loss": 1.066, "time": 0.04939, "loss_disc_fake_g": 0.88972} {"mode": "train", "epoch": 1, "iter": 3600, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00161, "loss_disc_fake": 0.49031, "loss_disc_real": 0.56067, "loss": 1.05098, "time": 0.04967, "loss_disc_fake_g": 0.88825} {"mode": "train", "epoch": 1, "iter": 3700, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00137, "loss_disc_fake": 0.46958, "loss_disc_real": 0.56102, "loss": 1.0306, "time": 0.04939, "loss_disc_fake_g": 0.95085} {"mode": "train", "epoch": 1, "iter": 3800, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00136, "loss_disc_fake": 0.47087, "loss_disc_real": 0.57621, "loss": 1.04708, "time": 0.04873, "loss_disc_fake_g": 0.95908} {"mode": "train", "epoch": 1, "iter": 3900, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00133, "loss_disc_fake": 0.47355, "loss_disc_real": 0.54804, "loss": 1.02159, "time": 0.04898, "loss_disc_fake_g": 0.99858} {"mode": "train", "epoch": 1, "iter": 4000, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00136, "loss_disc_fake": 0.49326, "loss_disc_real": 0.56505, "loss": 1.05832, "time": 0.04894, "loss_disc_fake_g": 1.01659} {"mode": "train", "epoch": 1, "iter": 4100, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00161, "loss_disc_fake": 0.45246, "loss_disc_real": 0.53428, "loss": 0.98674, "time": 0.04921, "loss_disc_fake_g": 1.07525} {"mode": "train", "epoch": 1, "iter": 4200, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.0013, "loss_disc_fake": 0.52726, "loss_disc_real": 0.59016, "loss": 1.11742, "time": 0.04882, "loss_disc_fake_g": 0.94767} {"mode": "train", "epoch": 1, "iter": 4300, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00131, "loss_disc_fake": 0.49343, "loss_disc_real": 0.56786, "loss": 1.0613, "time": 0.04912, "loss_disc_fake_g": 0.95621} {"mode": "train", "epoch": 1, "iter": 4400, "lr": {"generator": 0.0002, "discriminator": 0.0002}, "memory": 3620, "data_time": 0.00157, "loss_disc_fake": 0.48965, "loss_disc_real": 0.56298, "loss": 1.05263, "time": 0.0488, "loss_disc_fake_g": 0.94322} {"mode": "train", "epoch": 1, "iter": 4500, "lr": {"generator": 0.0002, "discriminator": 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