{"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: GeForce GTX 1080 Ti\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: 1.3.0\nMMGen: 0.1.0+16304a0\nMMCV Compiler: GCC 5.4\nMMCV CUDA Compiler: 9.0", "config": "dataset_type = 'UnconditionalImageDataset'\ntrain_pipeline = [\n dict(\n type='LoadImageFromFile',\n key='real_img',\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 dict(type='Resize', keys=['real_img'], scale=(64, 64)),\n dict(\n type='Normalize',\n keys=['real_img'],\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=['real_img']),\n dict(type='Collect', keys=['real_img'], meta_keys=['real_img_path'])\n]\ndata = dict(\n samples_per_gpu=128,\n workers_per_gpu=4,\n train=dict(\n type='UnconditionalImageDataset',\n imgs_root='data/lsun/bedroom_train',\n pipeline=[\n dict(\n type='LoadImageFromFile',\n key='real_img',\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 dict(type='Resize', keys=['real_img'], scale=(64, 64)),\n dict(\n type='Normalize',\n keys=['real_img'],\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=['real_img']),\n dict(\n type='Collect', keys=['real_img'], meta_keys=['real_img_path'])\n ]),\n val=dict(\n type='UnconditionalImageDataset',\n imgs_root='data/lsun/bedroom_train',\n pipeline=[\n dict(\n type='LoadImageFromFile',\n key='real_img',\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 dict(type='Resize', keys=['real_img'], scale=(64, 64)),\n dict(\n type='Normalize',\n keys=['real_img'],\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=['real_img']),\n dict(\n type='Collect', keys=['real_img'], meta_keys=['real_img_path'])\n ]))\ncheckpoint_config = dict(interval=5000, by_epoch=False, max_keep_ckpts=20)\nlog_config = dict(\n interval=100,\n hooks=[\n dict(type='TextLoggerHook'),\n dict(\n type='PaviLoggerHook',\n init_kwargs=dict(project='ggan-ls-archi-64'))\n ])\ncustom_hooks = [\n dict(\n type='VisualizeUnconditionalSamples',\n output_dir='training_samples',\n interval=3000)\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\nmodel = dict(\n type='StaticUnconditionalGAN',\n generator=dict(type='LSGANGenerator', output_scale=64),\n discriminator=dict(type='LSGANDiscriminator', input_scale=64),\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=(0.5, 0.99)),\n discriminator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)))\nlr_config = None\nevaluation = dict(\n type='GenerativeEvalHook',\n interval=5000,\n metrics=dict(\n type='FID',\n num_images=50000,\n inception_pkl='work_dirs/inception_pkl/lsun-bedroom-128-50k-rgb.pkl',\n bgr2rgb=True),\n sample_kwargs=dict(sample_model='orig'))\ntotal_iters = 160000\nuse_ddp_wrapper = True\nmetrics = dict(\n ms_ssim10k=dict(type='MS_SSIM', num_images=10000),\n swd16k=dict(type='SWD', num_images=16384, image_shape=(3, 64, 64)))\nwork_dir = './work_dirs/experiments/ggan_lsun-bedroom_lsgan_archi_64_b128x1'\ngpu_ids = range(0, 1)\n", "seed": 2021, "exp_name": "ggan_lsun-bedroom_lsgan_archi_64_b128x1.py"} {"mode": "train", "epoch": 1, "iter": 100, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00687, "loss_disc_fake_g": 2.99655, "loss": 0.18757, "loss_disc_fake": 0.07599, "loss_disc_real": 0.11158, "time": 0.18886} {"mode": "train", "epoch": 1, "iter": 200, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.0023, "loss_disc_fake_g": 3.54905, "loss": 0.39013, "loss_disc_fake": 0.1509, "loss_disc_real": 0.23923, "time": 0.17099} {"mode": "train", "epoch": 1, "iter": 300, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00232, "loss_disc_fake_g": 2.26637, "loss": 0.39743, "loss_disc_fake": 0.15134, "loss_disc_real": 0.24609, "time": 0.17192} {"mode": "train", "epoch": 1, "iter": 400, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00256, "loss_disc_fake_g": 2.56034, "loss": 0.17594, "loss_disc_fake": 0.06975, "loss_disc_real": 0.10619, "time": 0.17311} {"mode": "train", "epoch": 1, "iter": 500, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00231, "loss_disc_fake_g": 2.70039, "loss": 0.20398, "loss_disc_fake": 0.07471, "loss_disc_real": 0.12927, "time": 0.17257} {"mode": "train", "epoch": 1, "iter": 600, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00226, "loss_disc_fake_g": 2.51407, "loss": 0.08047, "loss_disc_fake": 0.02347, "loss_disc_real": 0.05701, "time": 0.17303} {"mode": "train", "epoch": 1, "iter": 700, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00228, "loss_disc_fake_g": 2.87129, "loss": 0.2118, "loss_disc_fake": 0.08653, "loss_disc_real": 0.12528, "time": 0.17289} {"mode": "train", "epoch": 1, "iter": 800, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00229, "loss_disc_fake_g": 2.53845, "loss": 0.29113, "loss_disc_fake": 0.12453, "loss_disc_real": 0.16661, "time": 0.17328} {"mode": "train", "epoch": 1, "iter": 900, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00229, "loss_disc_fake_g": 2.23499, "loss": 0.27946, "loss_disc_fake": 0.12008, "loss_disc_real": 0.15938, "time": 0.17421} {"mode": "train", "epoch": 1, "iter": 1000, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00223, "loss_disc_fake_g": 2.42953, "loss": 0.16863, "loss_disc_fake": 0.07397, "loss_disc_real": 0.09466, "time": 0.1742} {"mode": "train", "epoch": 1, "iter": 1100, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00219, "loss_disc_fake_g": 2.47293, "loss": 0.23349, "loss_disc_fake": 0.10735, "loss_disc_real": 0.12613, "time": 0.17411} {"mode": "train", "epoch": 1, "iter": 1200, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00232, "loss_disc_fake_g": 2.35592, "loss": 0.21224, "loss_disc_fake": 0.0966, "loss_disc_real": 0.11564, "time": 0.17412} {"mode": "train", "epoch": 1, "iter": 1300, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00225, "loss_disc_fake_g": 2.18561, "loss": 0.20691, "loss_disc_fake": 0.09298, "loss_disc_real": 0.11393, "time": 0.17403} {"mode": "train", "epoch": 1, "iter": 1400, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00299, "loss_disc_fake_g": 2.90096, "loss": 0.22652, "loss_disc_fake": 0.10575, "loss_disc_real": 0.12077, "time": 0.17501} {"mode": "train", "epoch": 1, "iter": 1500, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00233, "loss_disc_fake_g": 2.48051, "loss": 0.16521, "loss_disc_fake": 0.07513, "loss_disc_real": 0.09008, "time": 0.17411} {"mode": "train", "epoch": 1, "iter": 1600, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.03884, "loss_disc_fake_g": 2.85221, "loss": 0.23607, "loss_disc_fake": 0.10444, "loss_disc_real": 0.13163, "time": 0.21057} {"mode": "train", "epoch": 1, "iter": 1700, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.02299, "loss_disc_fake_g": 2.50115, "loss": 0.26477, "loss_disc_fake": 0.12156, "loss_disc_real": 0.14321, "time": 0.195} {"mode": "train", "epoch": 1, "iter": 1800, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.03444, "loss_disc_fake_g": 2.39595, "loss": 0.22693, "loss_disc_fake": 0.10738, "loss_disc_real": 0.11955, "time": 0.2062} {"mode": "train", "epoch": 1, "iter": 1900, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.01202, "loss_disc_fake_g": 2.43497, "loss": 0.27504, "loss_disc_fake": 0.11583, "loss_disc_real": 0.15921, "time": 0.18385} {"mode": "train", "epoch": 1, "iter": 2000, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00243, "loss_disc_fake_g": 2.05537, "loss": 0.43631, "loss_disc_fake": 0.20669, "loss_disc_real": 0.22963, "time": 0.17437} {"mode": "train", "epoch": 1, "iter": 2100, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.01067, "loss_disc_fake_g": 2.00819, "loss": 0.49064, "loss_disc_fake": 0.22511, "loss_disc_real": 0.26553, "time": 0.18255} {"mode": "train", "epoch": 1, "iter": 2200, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00238, "loss_disc_fake_g": 2.26719, "loss": 0.33088, "loss_disc_fake": 0.14947, "loss_disc_real": 0.18141, "time": 0.17469} {"mode": "train", "epoch": 1, "iter": 2300, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.01124, "loss_disc_fake_g": 2.0834, "loss": 0.41472, "loss_disc_fake": 0.19019, "loss_disc_real": 0.22453, "time": 0.18316} {"mode": "train", "epoch": 1, "iter": 2400, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00235, "loss_disc_fake_g": 1.98135, "loss": 0.44744, "loss_disc_fake": 0.20632, "loss_disc_real": 0.24112, "time": 0.17444} {"mode": "train", "epoch": 1, "iter": 2500, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.01913, "loss_disc_fake_g": 2.08972, "loss": 0.41926, "loss_disc_fake": 0.19607, "loss_disc_real": 0.22319, "time": 0.19118} {"mode": "train", "epoch": 1, "iter": 2600, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.0023, "loss_disc_fake_g": 2.49763, "loss": 0.31273, "loss_disc_fake": 0.15182, "loss_disc_real": 0.16091, "time": 0.17417} {"mode": "train", "epoch": 1, "iter": 2700, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.01308, "loss_disc_fake_g": 2.3896, "loss": 0.26746, "loss_disc_fake": 0.1264, "loss_disc_real": 0.14105, "time": 0.18512} {"mode": "train", "epoch": 1, "iter": 2800, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00418, "loss_disc_fake_g": 2.1588, "loss": 0.33541, "loss_disc_fake": 0.15675, "loss_disc_real": 0.17865, "time": 0.17608} {"mode": "train", "epoch": 1, "iter": 2900, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.02769, "loss_disc_fake_g": 2.19001, "loss": 0.32007, "loss_disc_fake": 0.15011, "loss_disc_real": 0.16996, "time": 0.20008} {"mode": "train", "epoch": 1, "iter": 3000, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.016, "loss_disc_fake_g": 2.23277, "loss": 0.29345, "loss_disc_fake": 0.13504, "loss_disc_real": 0.15841, "time": 0.18858} {"mode": "train", "epoch": 1, "iter": 3100, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00358, "loss_disc_fake_g": 2.38473, "loss": 0.265, "loss_disc_fake": 0.12373, "loss_disc_real": 0.14128, "time": 0.17564} {"mode": "train", "epoch": 1, "iter": 3200, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.02137, "loss_disc_fake_g": 2.45039, "loss": 0.2628, "loss_disc_fake": 0.1191, "loss_disc_real": 0.1437, "time": 0.1933} {"mode": "train", "epoch": 1, "iter": 3300, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00225, "loss_disc_fake_g": 2.32504, "loss": 0.34537, "loss_disc_fake": 0.17066, "loss_disc_real": 0.17472, "time": 0.17425} {"mode": "train", "epoch": 1, "iter": 3400, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.01089, "loss_disc_fake_g": 2.32913, "loss": 0.24103, "loss_disc_fake": 0.11194, "loss_disc_real": 0.12909, "time": 0.18283} {"mode": "train", "epoch": 1, "iter": 3500, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00218, "loss_disc_fake_g": 2.58342, "loss": 0.17816, "loss_disc_fake": 0.08272, "loss_disc_real": 0.09544, "time": 0.17397} {"mode": "train", "epoch": 1, "iter": 3600, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00212, "loss_disc_fake_g": 2.34936, "loss": 0.29303, "loss_disc_fake": 0.14049, "loss_disc_real": 0.15254, "time": 0.17408} {"mode": "train", "epoch": 1, "iter": 3700, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00811, "loss_disc_fake_g": 2.28745, "loss": 0.37288, "loss_disc_fake": 0.17274, "loss_disc_real": 0.20014, "time": 0.18025} {"mode": "train", "epoch": 1, "iter": 3800, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.0042, "loss_disc_fake_g": 2.11887, "loss": 0.41983, "loss_disc_fake": 0.20438, "loss_disc_real": 0.21545, "time": 0.17614} {"mode": "train", "epoch": 1, "iter": 3900, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00211, "loss_disc_fake_g": 2.02476, "loss": 0.35813, "loss_disc_fake": 0.17236, "loss_disc_real": 0.18576, "time": 0.17409} {"mode": "train", "epoch": 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"epoch": 1, "iter": 4400, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00206, "loss_disc_fake_g": 2.48152, "loss": 0.28935, "loss_disc_fake": 0.13243, "loss_disc_real": 0.15692, "time": 0.1739} {"mode": "train", "epoch": 1, "iter": 4500, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00207, "loss_disc_fake_g": 2.0958, "loss": 0.37136, "loss_disc_fake": 0.16857, "loss_disc_real": 0.2028, "time": 0.17403} {"mode": "train", "epoch": 1, "iter": 4600, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00228, "loss_disc_fake_g": 2.26081, "loss": 0.34783, "loss_disc_fake": 0.16739, "loss_disc_real": 0.18043, "time": 0.17418} {"mode": "train", "epoch": 1, "iter": 4700, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 2142, "data_time": 0.00205, "loss_disc_fake_g": 2.07475, "loss": 0.34656, "loss_disc_fake": 0.16389, "loss_disc_real": 0.18268, "time": 0.17411} {"mode": 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