{"env_info": "sys.platform: linux\nPython: 3.7.6 (default, Jan 8 2020, 19:59:22) [GCC 7.3.0]\nCUDA available: True\nCUDA_HOME: /mnt/lustre/share/polaris/dep/cuda-9.0-cudnn7.6.5\nNVCC: Cuda compilation tools, release 9.0, V9.0.176\nGPU 0,1,2,3,4,5,6,7: TITAN Xp\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: 1.3.2\nMMGen: 0.2.0+4b1db61\nMMCV Compiler: GCC 5.4\nMMCV CUDA Compiler: 9.0", "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 init_cfg=dict(type='BigGAN'),\n norm_eps=1e-05,\n sn_eps=1e-08,\n auto_sync_bn=False,\n with_embedding_spectral_norm=False),\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 init_cfg=dict(type='BigGAN'),\n sn_eps=1e-08),\n gan_loss=dict(type='GANLoss', gan_type='hinge'))\ntrain_cfg = dict(disc_steps=1, batch_accumulation_steps=1, use_ema=True)\ntest_cfg = None\noptimizer = dict(\n generator=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-08),\n discriminator=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-08))\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='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='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=32,\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='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='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=2)\nlog_config = dict(\n interval=100,\n hooks=[\n dict(type='TextLoggerHook'),\n dict(\n type='PaviLoggerHook',\n init_kwargs=dict(project='sagan-imagenet-mmgen'))\n ])\ncustom_hooks = [\n dict(\n type='VisualizeUnconditionalSamples',\n output_dir='training_samples',\n interval=20000),\n dict(\n type='ExponentialMovingAverageHook',\n module_keys='generator_ema',\n interval=1,\n start_iter=2000,\n interp_cfg=dict(momentum=0.9999),\n priority='VERY_HIGH'),\n dict(\n type='PetrelUploadHook',\n ceph_path=\n 's3://mmgen-dev/sagan/imagenet_BigGAN_ema_bz8x32x1_woSNcBN_noaug/')\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\ninit_cfg = dict(type='BigGAN')\ndisc_step = 1\nn_accu = 1\nn_disc = 1\nlr_config = None\ninception_pkl = './work_dirs/inception_pkl/imagenet.pkl'\nevaluation = dict(\n type='GenerativeEvalHook',\n interval=dict(milestones=[800000], interval=[5000, 2000]),\n metrics=[\n dict(\n type='FID',\n num_images=50000,\n inception_pkl='./work_dirs/inception_pkl/imagenet.pkl',\n bgr2rgb=True),\n dict(type='IS', num_images=50000)\n ],\n best_metric=['fid', 'is'],\n sample_kwargs=dict(sample_model='ema'))\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 IS50k=dict(type='IS', num_images=50000))\nwork_dir = 'work_dirs/sagan-imagenet1k-BigGAN-ema-bz32x8x1-woSNcBN'\ngpu_ids = range(0, 8)\n", "seed": 2021, "exp_name": "sagan_imagenet1k_128_BigGAN_32x8x1_noaug.py", "hook_msgs": {}} {"mode": "train", "epoch": 1, "iter": 100, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.0081, "loss_disc_fake_g": 0.35668, "loss": 2.03898, "loss_disc_fake": 0.97272, "loss_disc_real": 1.06626, "time": 0.83406} {"mode": "train", "epoch": 1, "iter": 200, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00471, "loss_disc_fake_g": 0.33891, "loss": 1.98942, "loss_disc_fake": 0.93315, "loss_disc_real": 1.05627, "time": 0.78795} {"mode": "train", "epoch": 1, "iter": 300, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00457, "loss_disc_fake_g": 0.66299, "loss": 1.88258, "loss_disc_fake": 0.73425, "loss_disc_real": 1.14833, "time": 0.7897} {"mode": "train", "epoch": 1, "iter": 400, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00452, "loss_disc_fake_g": 0.6331, "loss": 1.89092, "loss_disc_fake": 0.54775, "loss_disc_real": 1.34318, "time": 0.78883} {"mode": "train", "epoch": 1, "iter": 500, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00458, "loss_disc_fake_g": 0.10284, "loss": 1.99692, "loss_disc_fake": 0.92442, "loss_disc_real": 1.07251, "time": 0.79171} {"mode": "train", "epoch": 1, "iter": 600, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00468, "loss_disc_fake_g": -0.02889, "loss": 1.98539, "loss_disc_fake": 1.11652, "loss_disc_real": 0.86887, "time": 0.79315} {"mode": "train", "epoch": 1, "iter": 700, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00442, "loss_disc_fake_g": 0.38762, "loss": 1.96743, "loss_disc_fake": 0.69303, "loss_disc_real": 1.2744, "time": 0.79005} {"mode": "train", "epoch": 1, "iter": 800, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00503, "loss_disc_fake_g": 0.32445, "loss": 1.91396, "loss_disc_fake": 0.83942, "loss_disc_real": 1.07454, "time": 0.78939} {"mode": "train", "epoch": 1, "iter": 900, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00478, "loss_disc_fake_g": 0.45871, "loss": 1.95625, "loss_disc_fake": 0.66344, "loss_disc_real": 1.2928, "time": 0.78847} {"mode": "train", "epoch": 1, "iter": 1000, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00446, "loss_disc_fake_g": 0.32197, "loss": 1.94934, "loss_disc_fake": 0.85597, "loss_disc_real": 1.09337, "time": 0.78803} {"mode": "train", "epoch": 1, "iter": 1100, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00464, "loss_disc_fake_g": 0.34677, "loss": 1.93701, "loss_disc_fake": 0.76508, "loss_disc_real": 1.17193, "time": 0.79466} {"mode": "train", "epoch": 1, "iter": 1200, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.0047, "loss_disc_fake_g": 0.34172, "loss": 1.95905, "loss_disc_fake": 0.78422, "loss_disc_real": 1.17483, "time": 0.79542} {"mode": "train", "epoch": 1, "iter": 1300, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.0045, "loss_disc_fake_g": 0.26814, "loss": 1.97396, "loss_disc_fake": 0.83211, "loss_disc_real": 1.14185, "time": 0.79131} {"mode": "train", "epoch": 1, "iter": 1400, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00455, "loss_disc_fake_g": 0.25713, "loss": 1.97894, "loss_disc_fake": 0.8577, "loss_disc_real": 1.12124, "time": 0.78982} {"mode": "train", "epoch": 1, "iter": 1500, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00462, "loss_disc_fake_g": 0.20283, "loss": 1.97478, "loss_disc_fake": 0.93768, "loss_disc_real": 1.0371, "time": 0.79012} {"mode": "train", "epoch": 1, "iter": 1600, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.01305, "loss_disc_fake_g": 0.16449, "loss": 1.96811, "loss_disc_fake": 0.95366, "loss_disc_real": 1.01446, "time": 0.79742} {"mode": "train", "epoch": 1, "iter": 1700, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00454, "loss_disc_fake_g": 0.16668, "loss": 1.97915, "loss_disc_fake": 0.9373, "loss_disc_real": 1.04185, "time": 0.78833} {"mode": "train", "epoch": 1, "iter": 1800, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00446, "loss_disc_fake_g": 0.1616, "loss": 1.9784, "loss_disc_fake": 0.96033, "loss_disc_real": 1.01807, "time": 0.78889} {"mode": "train", "epoch": 1, "iter": 1900, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00463, "loss_disc_fake_g": 0.25915, "loss": 1.97514, "loss_disc_fake": 0.80025, "loss_disc_real": 1.17489, "time": 0.78993} {"mode": "train", "epoch": 1, "iter": 2000, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00445, "loss_disc_fake_g": 0.11241, "loss": 1.97099, "loss_disc_fake": 0.96254, "loss_disc_real": 1.00845, "time": 0.80823} {"mode": "train", "epoch": 1, "iter": 2100, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00452, "loss_disc_fake_g": 0.18517, "loss": 1.96868, "loss_disc_fake": 0.92692, "loss_disc_real": 1.04176, "time": 0.93271} {"mode": "train", "epoch": 1, "iter": 2200, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00423, "loss_disc_fake_g": 0.22892, "loss": 1.97065, "loss_disc_fake": 0.87946, "loss_disc_real": 1.09118, "time": 0.8024} {"mode": "train", "epoch": 1, "iter": 2300, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00415, "loss_disc_fake_g": 0.26282, "loss": 1.96222, "loss_disc_fake": 0.88227, "loss_disc_real": 1.07995, "time": 0.80492} {"mode": "train", "epoch": 1, "iter": 2400, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00417, "loss_disc_fake_g": 0.2365, "loss": 1.9574, "loss_disc_fake": 0.88642, "loss_disc_real": 1.07098, "time": 0.80199} {"mode": "train", "epoch": 1, "iter": 2500, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00449, "loss_disc_fake_g": 0.2076, "loss": 1.95293, "loss_disc_fake": 0.92874, "loss_disc_real": 1.0242, "time": 0.82875} {"mode": "train", "epoch": 1, "iter": 2600, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00429, "loss_disc_fake_g": 0.17634, "loss": 1.94119, "loss_disc_fake": 0.94448, "loss_disc_real": 0.99671, "time": 0.80445} {"mode": "train", "epoch": 1, "iter": 2700, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00439, "loss_disc_fake_g": 0.18276, "loss": 1.9282, "loss_disc_fake": 0.93873, "loss_disc_real": 0.98948, "time": 0.84194} {"mode": "train", "epoch": 1, "iter": 2800, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00423, "loss_disc_fake_g": 0.25775, "loss": 1.92506, "loss_disc_fake": 0.90522, "loss_disc_real": 1.01984, "time": 0.80145} {"mode": "train", "epoch": 1, "iter": 2900, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00422, "loss_disc_fake_g": 0.26693, "loss": 1.92638, "loss_disc_fake": 0.87406, "loss_disc_real": 1.05232, "time": 0.8021} {"mode": "train", "epoch": 1, "iter": 3000, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00415, "loss_disc_fake_g": 0.21099, "loss": 1.92183, "loss_disc_fake": 0.95209, "loss_disc_real": 0.96973, "time": 0.8295} {"mode": "train", "epoch": 1, "iter": 3100, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00408, "loss_disc_fake_g": 0.25691, "loss": 1.91751, "loss_disc_fake": 0.90713, "loss_disc_real": 1.01039, "time": 0.79971} {"mode": "train", "epoch": 1, "iter": 3200, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00446, "loss_disc_fake_g": 0.21223, "loss": 1.90875, "loss_disc_fake": 0.93693, "loss_disc_real": 0.97182, "time": 0.80782} {"mode": "train", "epoch": 1, "iter": 3300, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00429, "loss_disc_fake_g": 0.25167, "loss": 1.92027, "loss_disc_fake": 0.93836, "loss_disc_real": 0.98191, "time": 0.81178} {"mode": "train", "epoch": 1, "iter": 3400, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00426, "loss_disc_fake_g": 0.15458, "loss": 1.9265, "loss_disc_fake": 0.96487, "loss_disc_real": 0.96163, "time": 0.81261} {"mode": "train", "epoch": 1, "iter": 3500, "lr": {"generator": 0.0001, "discriminator": 0.0004}, "memory": 10550, "data_time": 0.00423, "loss_disc_fake_g": 0.20388, "loss": 1.90116, "loss_disc_fake": 0.94088, "loss_disc_real": 0.96028, 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