{"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: 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: 1.3.0\nMMGen: 0.1.0+a046963\nMMCV Compiler: GCC 5.4\nMMCV CUDA Compiler: 9.0", "config": "model = dict(\n type='StaticUnconditionalGAN',\n generator=dict(\n type='DCGANGenerator', output_scale=128, base_channels=1024),\n discriminator=dict(\n type='DCGANDiscriminator',\n input_scale=128,\n output_scale=4,\n out_channels=1),\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)))\ndataset_type = 'UnconditionalImageDataset'\ntrain_pipeline = [\n dict(type='LoadImageFromFile', key='real_img', io_backend='disk'),\n dict(type='Resize', keys=['real_img'], scale=(128, 128)),\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=64,\n workers_per_gpu=4,\n train=dict(\n type='UnconditionalImageDataset',\n imgs_root='./data/celeba/cropped_images_aligned_png/',\n pipeline=[\n dict(type='LoadImageFromFile', key='real_img', io_backend='disk'),\n dict(type='Resize', keys=['real_img'], scale=(128, 128)),\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/celeba/cropped_images_aligned_png/',\n pipeline=[\n dict(type='LoadImageFromFile', key='real_img', io_backend='disk'),\n dict(type='Resize', keys=['real_img'], scale=(128, 128)),\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=3000, by_epoch=False, max_keep_ckpts=20)\nlog_config = dict(\n interval=100,\n hooks=[\n dict(type='TextLoggerHook'),\n dict(\n type='PaviLoggerHook', init_kwargs=dict(project='ggan-celeba-128'))\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\nlr_config = None\nevaluation = dict(\n type='GenerativeEvalHook',\n interval=3000,\n metrics=dict(\n type='FID',\n num_images=50000,\n inception_pkl='work_dirs/fid_pkl/celeba-crop-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, 128, 128)))\nwork_dir = './work_dirs/experiments/ggan_celeba-cropped_128_b64x1'\ngpu_ids = range(0, 1)\n", "seed": 2021, "exp_name": "ggan_celeba-cropped_128_b64x1_300k.py"} {"mode": "train", "epoch": 1, "iter": 100, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.01587, "loss_disc_fake_g": 28.50096, "loss": 0.35249, "loss_disc_fake": 0.17797, "loss_disc_real": 0.17452, "time": 0.45463} {"mode": "train", "epoch": 1, "iter": 200, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00355, "loss_disc_fake_g": 61.61061, "loss": 0.24042, "loss_disc_fake": 0.11751, "loss_disc_real": 0.1229, "time": 0.42443} {"mode": "train", "epoch": 1, "iter": 300, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00666, "loss_disc_fake_g": 51.14216, "loss": 0.28373, "loss_disc_fake": 0.06744, "loss_disc_real": 0.21629, "time": 0.42773} {"mode": "train", "epoch": 1, "iter": 400, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00351, "loss_disc_fake_g": 21.33525, "loss": 1.08659, "loss_disc_fake": 0.35377, "loss_disc_real": 0.73283, "time": 0.42946} {"mode": "train", "epoch": 1, "iter": 500, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.03915, "loss_disc_fake_g": 14.22026, "loss": 0.88715, "loss_disc_fake": 0.27286, "loss_disc_real": 0.61429, "time": 0.46594} {"mode": "train", "epoch": 1, "iter": 600, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.01339, "loss_disc_fake_g": 11.64206, "loss": 0.66462, "loss_disc_fake": 0.24399, "loss_disc_real": 0.42063, "time": 0.44052} {"mode": "train", "epoch": 1, "iter": 700, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00359, "loss_disc_fake_g": 11.10525, "loss": 0.5933, "loss_disc_fake": 0.23663, "loss_disc_real": 0.35668, "time": 0.43139} {"mode": "train", "epoch": 1, "iter": 800, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00336, "loss_disc_fake_g": 9.47498, "loss": 0.72523, "loss_disc_fake": 0.30349, "loss_disc_real": 0.42173, "time": 0.42996} {"mode": "train", "epoch": 1, "iter": 900, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.0113, "loss_disc_fake_g": 9.85828, "loss": 0.66499, "loss_disc_fake": 0.24954, "loss_disc_real": 0.41546, "time": 0.43824} {"mode": "train", "epoch": 1, "iter": 1000, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00324, "loss_disc_fake_g": 9.44101, "loss": 0.74282, "loss_disc_fake": 0.31926, "loss_disc_real": 0.42356, "time": 0.43018} {"mode": "train", "epoch": 1, "iter": 1100, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.0033, "loss_disc_fake_g": 8.82404, "loss": 0.62975, "loss_disc_fake": 0.25551, "loss_disc_real": 0.37424, "time": 0.43084} {"mode": "train", "epoch": 1, "iter": 1200, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00699, "loss_disc_fake_g": 8.84083, "loss": 0.59301, "loss_disc_fake": 0.25366, "loss_disc_real": 0.33936, "time": 0.43514} {"mode": "train", "epoch": 1, "iter": 1300, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00337, "loss_disc_fake_g": 8.69823, "loss": 0.58151, "loss_disc_fake": 0.26441, "loss_disc_real": 0.3171, "time": 0.43078} {"mode": "train", "epoch": 1, "iter": 1400, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.05174, "loss_disc_fake_g": 8.47055, "loss": 0.53404, "loss_disc_fake": 0.22787, "loss_disc_real": 0.30617, "time": 0.47864} {"mode": "train", "epoch": 1, "iter": 1500, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.0036, "loss_disc_fake_g": 8.15129, "loss": 0.46932, "loss_disc_fake": 0.19469, "loss_disc_real": 0.27463, "time": 0.43057} {"mode": "train", "epoch": 1, "iter": 1600, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.0034, "loss_disc_fake_g": 8.30923, "loss": 0.51034, "loss_disc_fake": 0.2074, "loss_disc_real": 0.30294, "time": 0.4302} {"mode": "train", "epoch": 1, "iter": 1700, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00693, "loss_disc_fake_g": 7.72229, "loss": 0.45758, "loss_disc_fake": 0.17458, "loss_disc_real": 0.283, "time": 0.43355} {"mode": "train", "epoch": 1, "iter": 1800, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00317, "loss_disc_fake_g": 7.75656, "loss": 0.422, "loss_disc_fake": 0.17367, "loss_disc_real": 0.24833, "time": 0.42903} {"mode": "train", "epoch": 1, "iter": 1900, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00322, "loss_disc_fake_g": 7.79847, "loss": 0.45011, "loss_disc_fake": 0.17627, "loss_disc_real": 0.27384, "time": 0.43095} {"mode": "train", "epoch": 1, "iter": 2000, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.0031, "loss_disc_fake_g": 7.70108, "loss": 0.54936, "loss_disc_fake": 0.24419, "loss_disc_real": 0.30517, "time": 0.42915} {"mode": "train", "epoch": 1, "iter": 2100, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00584, "loss_disc_fake_g": 7.31259, "loss": 0.48514, "loss_disc_fake": 0.2214, "loss_disc_real": 0.26374, "time": 0.43299} {"mode": "train", "epoch": 1, "iter": 2200, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00323, "loss_disc_fake_g": 7.43686, "loss": 0.44598, "loss_disc_fake": 0.1953, "loss_disc_real": 0.25068, "time": 0.42935} {"mode": "train", "epoch": 1, "iter": 2300, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.0031, "loss_disc_fake_g": 7.48867, "loss": 0.34941, "loss_disc_fake": 0.14195, "loss_disc_real": 0.20746, "time": 0.42884} {"mode": "train", "epoch": 1, "iter": 2400, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00312, "loss_disc_fake_g": 7.64284, "loss": 0.4024, "loss_disc_fake": 0.15737, "loss_disc_real": 0.24503, "time": 0.42858} {"mode": "train", "epoch": 1, "iter": 2500, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00315, "loss_disc_fake_g": 7.69922, "loss": 0.31762, "loss_disc_fake": 0.13315, "loss_disc_real": 0.18447, "time": 0.42825} {"mode": "train", "epoch": 1, "iter": 2600, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.02465, "loss_disc_fake_g": 7.2371, "loss": 0.27088, "loss_disc_fake": 0.12206, "loss_disc_real": 0.14882, "time": 0.45065} {"mode": "train", "epoch": 1, "iter": 2700, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00307, "loss_disc_fake_g": 7.50053, "loss": 0.35489, "loss_disc_fake": 0.14473, "loss_disc_real": 0.21016, "time": 0.42912} {"mode": "train", "epoch": 1, "iter": 2800, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00329, "loss_disc_fake_g": 7.80381, "loss": 0.34049, "loss_disc_fake": 0.15473, "loss_disc_real": 0.18576, "time": 0.42813} {"mode": "train", "epoch": 1, "iter": 2900, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00309, "loss_disc_fake_g": 8.39639, "loss": 0.38029, "loss_disc_fake": 0.17608, "loss_disc_real": 0.20421, "time": 0.42919} {"mode": "val", "epoch": 1, "iter": 3000, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "fid": 103.28541, "fid_mean": 51.18037414550781, "fid_cov": 52.10503} {"mode": "train", "epoch": 1, "iter": 3100, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 9.36244, "loss_disc_fake_g": 7.13745, "loss": 0.37767, "loss_disc_fake": 0.16399, "loss_disc_real": 0.21369, "time": 9.78394} {"mode": "train", "epoch": 2, "iter": 3200, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.13368, "loss_disc_fake_g": 7.39032, "loss": 0.36108, "loss_disc_fake": 0.15909, "loss_disc_real": 0.20199, "time": 0.55798} {"mode": "train", "epoch": 2, "iter": 3300, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00345, "loss_disc_fake_g": 6.95845, "loss": 0.28515, "loss_disc_fake": 0.12363, "loss_disc_real": 0.16152, "time": 0.4282} {"mode": "train", "epoch": 2, "iter": 3400, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00328, "loss_disc_fake_g": 6.79741, "loss": 0.29686, "loss_disc_fake": 0.12919, "loss_disc_real": 0.16767, "time": 0.42808} {"mode": "train", "epoch": 2, "iter": 3500, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00357, "loss_disc_fake_g": 7.36054, "loss": 0.41721, "loss_disc_fake": 0.17776, "loss_disc_real": 0.23945, "time": 0.42951} {"mode": "train", "epoch": 2, "iter": 3600, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00481, "loss_disc_fake_g": 6.3464, "loss": 0.29415, "loss_disc_fake": 0.12199, "loss_disc_real": 0.17216, "time": 0.43138} {"mode": "train", "epoch": 2, "iter": 3700, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00334, "loss_disc_fake_g": 6.51455, "loss": 0.25624, "loss_disc_fake": 0.10474, "loss_disc_real": 0.1515, "time": 0.42981} {"mode": "train", "epoch": 2, "iter": 3800, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00326, "loss_disc_fake_g": 7.41166, "loss": 0.31287, "loss_disc_fake": 0.13899, "loss_disc_real": 0.17389, "time": 0.42932} {"mode": "train", "epoch": 2, "iter": 3900, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.0033, "loss_disc_fake_g": 7.18989, "loss": 0.31876, "loss_disc_fake": 0.13763, "loss_disc_real": 0.18112, "time": 0.42921} {"mode": "train", "epoch": 2, "iter": 4000, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00343, "loss_disc_fake_g": 6.63145, "loss": 0.31188, "loss_disc_fake": 0.11254, "loss_disc_real": 0.19933, "time": 0.42936} {"mode": "train", "epoch": 2, "iter": 4100, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00326, "loss_disc_fake_g": 6.99901, "loss": 0.24427, "loss_disc_fake": 0.11468, "loss_disc_real": 0.1296, "time": 0.42944} {"mode": "train", "epoch": 2, "iter": 4200, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00335, "loss_disc_fake_g": 6.90366, "loss": 0.32914, "loss_disc_fake": 0.1254, "loss_disc_real": 0.20373, "time": 0.43004} {"mode": "train", "epoch": 2, "iter": 4300, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00332, "loss_disc_fake_g": 6.38071, "loss": 0.33286, "loss_disc_fake": 0.15319, "loss_disc_real": 0.17968, "time": 0.42974} {"mode": "train", "epoch": 2, "iter": 4400, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00329, "loss_disc_fake_g": 6.50039, "loss": 0.41884, "loss_disc_fake": 0.17714, "loss_disc_real": 0.24171, "time": 0.4295} {"mode": "train", "epoch": 2, "iter": 4500, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00335, "loss_disc_fake_g": 6.16798, "loss": 0.35875, "loss_disc_fake": 0.17319, "loss_disc_real": 0.18555, "time": 0.4292} {"mode": "train", "epoch": 2, "iter": 4600, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00324, "loss_disc_fake_g": 6.28643, "loss": 0.26533, "loss_disc_fake": 0.11524, "loss_disc_real": 0.15009, "time": 0.42966} {"mode": "train", "epoch": 2, "iter": 4700, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00336, "loss_disc_fake_g": 6.0552, "loss": 0.32909, "loss_disc_fake": 0.15745, "loss_disc_real": 0.17164, "time": 0.43031} {"mode": "train", "epoch": 2, "iter": 4800, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00702, "loss_disc_fake_g": 5.94153, "loss": 0.27504, "loss_disc_fake": 0.12293, "loss_disc_real": 0.15211, "time": 0.43333} {"mode": "train", "epoch": 2, "iter": 4900, "lr": {"generator": 0.0001, "discriminator": 0.0001}, "memory": 3994, "data_time": 0.00314, "loss_disc_fake_g": 6.44939, "loss": 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