{"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(type='DCGANGenerator', output_scale=64, base_channels=1024),\n discriminator=dict(\n type='DCGANDiscriminator',\n input_scale=64,\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=0.001, betas=(0.5, 0.99)),\n discriminator=dict(type='Adam', lr=0.001, betas=(0.5, 0.99)))\ndataset_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/celeba/cropped_images_aligned_png/',\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/celeba/cropped_images_aligned_png/',\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=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-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\nlr_config = None\nevaluation = dict(\n type='GenerativeEvalHook',\n interval=10000,\n metrics=dict(\n type='FID',\n num_images=50000,\n inception_pkl='work_dirs/fid_pkl/celeba-crop-64-50k-rgb.pkl',\n bgr2rgb=True),\n sample_kwargs=dict(sample_model='orig'))\ntotal_iters = 100000\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_celeba-cropped_64_b128x1_300k/'\ngpu_ids = range(0, 1)\n", "seed": 2021, "exp_name": "ggan_celeba-cropped_64_b128x1_300k.py"} {"mode": "train", "epoch": 1, "iter": 100, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.45062, "loss_disc_fake_g": 8.71008, "loss": 2.54433, "loss_disc_fake": 1.29796, "loss_disc_real": 1.24637, "time": 0.70479} {"mode": "train", "epoch": 1, "iter": 200, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.76077, "loss_disc_fake_g": 2.68678, "loss": 1.55891, "loss_disc_fake": 0.80337, "loss_disc_real": 0.75553, "time": 0.98308} {"mode": "train", "epoch": 1, "iter": 300, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.74806, "loss_disc_fake_g": 3.46656, "loss": 1.14049, "loss_disc_fake": 0.49032, "loss_disc_real": 0.65017, "time": 0.96913} {"mode": "train", "epoch": 1, "iter": 400, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.71543, "loss_disc_fake_g": 3.54418, "loss": 0.85065, "loss_disc_fake": 0.40293, "loss_disc_real": 0.44771, "time": 0.93622} {"mode": "train", "epoch": 1, "iter": 500, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.78821, "loss_disc_fake_g": 3.65812, "loss": 0.76587, "loss_disc_fake": 0.36327, "loss_disc_real": 0.4026, "time": 1.0094} {"mode": "train", "epoch": 1, "iter": 600, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.73177, "loss_disc_fake_g": 3.02249, "loss": 1.0636, "loss_disc_fake": 0.46752, "loss_disc_real": 0.59609, "time": 0.95344} {"mode": "train", "epoch": 1, "iter": 700, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.68797, "loss_disc_fake_g": 2.66805, "loss": 1.07063, "loss_disc_fake": 0.54063, "loss_disc_real": 0.53, "time": 0.90951} {"mode": "train", "epoch": 1, "iter": 800, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.72944, "loss_disc_fake_g": 2.31408, "loss": 1.22298, "loss_disc_fake": 0.56313, "loss_disc_real": 0.65985, "time": 0.95186} {"mode": "train", "epoch": 1, "iter": 900, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.64015, "loss_disc_fake_g": 2.60502, "loss": 1.13839, "loss_disc_fake": 0.60488, "loss_disc_real": 0.53351, "time": 0.86307} {"mode": "train", "epoch": 1, "iter": 1000, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.64843, "loss_disc_fake_g": 2.31793, "loss": 1.35727, "loss_disc_fake": 0.66331, "loss_disc_real": 0.69395, "time": 0.89597} {"mode": "train", "epoch": 1, "iter": 1100, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.57705, "loss_disc_fake_g": 2.35342, "loss": 1.24077, "loss_disc_fake": 0.5873, "loss_disc_real": 0.65348, "time": 0.80591} {"mode": "train", "epoch": 1, "iter": 1200, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.58013, "loss_disc_fake_g": 2.32031, "loss": 1.20215, "loss_disc_fake": 0.58023, "loss_disc_real": 0.62191, "time": 0.81465} {"mode": "train", "epoch": 1, "iter": 1300, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.65648, "loss_disc_fake_g": 2.47617, "loss": 1.15156, "loss_disc_fake": 0.55676, "loss_disc_real": 0.5948, "time": 0.87606} {"mode": "train", "epoch": 1, "iter": 1400, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.70362, "loss_disc_fake_g": 2.36626, "loss": 1.00956, "loss_disc_fake": 0.52684, "loss_disc_real": 0.48272, "time": 0.92311} {"mode": "train", "epoch": 1, "iter": 1500, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.68957, "loss_disc_fake_g": 2.37851, "loss": 0.9662, "loss_disc_fake": 0.48606, "loss_disc_real": 0.48014, "time": 0.94395} {"mode": "train", "epoch": 2, "iter": 1600, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 1.59098, "loss_disc_fake_g": 2.73921, "loss": 0.94337, "loss_disc_fake": 0.43646, "loss_disc_real": 0.50691, "time": 1.81435} {"mode": "train", "epoch": 2, "iter": 1700, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00246, "loss_disc_fake_g": 2.78786, "loss": 0.79601, "loss_disc_fake": 0.38393, "loss_disc_real": 0.41207, "time": 0.21762} {"mode": "train", "epoch": 2, "iter": 1800, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00239, "loss_disc_fake_g": 3.11171, "loss": 0.71324, "loss_disc_fake": 0.3626, "loss_disc_real": 0.35064, "time": 0.22042} {"mode": "train", "epoch": 2, "iter": 1900, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00223, "loss_disc_fake_g": 2.99658, "loss": 0.66851, "loss_disc_fake": 0.30397, "loss_disc_real": 0.36454, "time": 0.22095} {"mode": "train", "epoch": 2, "iter": 2000, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00227, "loss_disc_fake_g": 2.86746, "loss": 0.72677, "loss_disc_fake": 0.35546, "loss_disc_real": 0.37131, "time": 0.22209} {"mode": "train", "epoch": 2, "iter": 2100, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00229, "loss_disc_fake_g": 3.07504, "loss": 0.70883, "loss_disc_fake": 0.33387, "loss_disc_real": 0.37496, "time": 0.2212} {"mode": "train", "epoch": 2, "iter": 2200, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00234, "loss_disc_fake_g": 2.4897, "loss": 0.82144, "loss_disc_fake": 0.41544, "loss_disc_real": 0.40599, "time": 0.222} {"mode": "train", "epoch": 2, "iter": 2300, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00238, "loss_disc_fake_g": 2.56497, "loss": 0.8058, "loss_disc_fake": 0.39116, "loss_disc_real": 0.41464, "time": 0.22267} {"mode": "train", "epoch": 2, "iter": 2400, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00227, "loss_disc_fake_g": 2.49586, "loss": 0.80231, "loss_disc_fake": 0.42044, "loss_disc_real": 0.38188, "time": 0.22255} {"mode": "train", "epoch": 2, "iter": 2500, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00214, "loss_disc_fake_g": 2.49849, "loss": 0.81077, "loss_disc_fake": 0.379, "loss_disc_real": 0.43177, "time": 0.22218} {"mode": "train", "epoch": 2, "iter": 2600, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.0021, "loss_disc_fake_g": 2.38968, "loss": 0.7679, "loss_disc_fake": 0.36964, "loss_disc_real": 0.39826, "time": 0.22208} {"mode": "train", "epoch": 2, "iter": 2700, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00211, "loss_disc_fake_g": 2.5248, "loss": 0.76321, "loss_disc_fake": 0.36494, "loss_disc_real": 0.39827, "time": 0.22232} {"mode": "train", "epoch": 2, "iter": 2800, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.0021, "loss_disc_fake_g": 2.63469, "loss": 0.86227, "loss_disc_fake": 0.40315, "loss_disc_real": 0.45912, "time": 0.22189} {"mode": "train", "epoch": 2, "iter": 2900, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00215, "loss_disc_fake_g": 2.13877, "loss": 0.763, "loss_disc_fake": 0.36803, "loss_disc_real": 0.39497, "time": 0.22226} {"mode": "train", "epoch": 2, "iter": 3000, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00211, "loss_disc_fake_g": 2.25194, "loss": 0.82023, "loss_disc_fake": 0.40018, "loss_disc_real": 0.42005, "time": 0.2433} {"mode": "train", "epoch": 2, "iter": 3100, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.01127, "loss_disc_fake_g": 2.51539, "loss": 0.71917, "loss_disc_fake": 0.35295, "loss_disc_real": 0.36622, "time": 0.23108} {"mode": "train", "epoch": 3, "iter": 3200, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.1095, "loss_disc_fake_g": 2.42021, "loss": 0.73708, "loss_disc_fake": 0.35316, "loss_disc_real": 0.38392, "time": 0.32953} {"mode": "train", "epoch": 3, "iter": 3300, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00223, "loss_disc_fake_g": 3.3636, "loss": 0.54854, "loss_disc_fake": 0.25328, "loss_disc_real": 0.29526, "time": 0.22141} {"mode": "train", "epoch": 3, "iter": 3400, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00217, "loss_disc_fake_g": 2.96499, "loss": 0.56256, "loss_disc_fake": 0.25474, "loss_disc_real": 0.30782, "time": 0.22172} {"mode": "train", "epoch": 3, "iter": 3500, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00228, "loss_disc_fake_g": 2.59009, "loss": 0.70102, "loss_disc_fake": 0.34137, "loss_disc_real": 0.35965, "time": 0.22232} {"mode": "train", "epoch": 3, "iter": 3600, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00208, "loss_disc_fake_g": 2.26423, "loss": 0.74974, "loss_disc_fake": 0.3744, "loss_disc_real": 0.37534, "time": 0.22199} {"mode": "train", "epoch": 3, "iter": 3700, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00214, "loss_disc_fake_g": 2.28358, "loss": 0.79628, "loss_disc_fake": 0.40702, "loss_disc_real": 0.38926, "time": 0.2224} {"mode": "train", "epoch": 3, "iter": 3800, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00211, "loss_disc_fake_g": 2.23981, "loss": 0.72422, "loss_disc_fake": 0.35675, "loss_disc_real": 0.36747, "time": 0.22254} {"mode": "train", "epoch": 3, "iter": 3900, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00206, "loss_disc_fake_g": 2.35589, "loss": 0.73213, "loss_disc_fake": 0.36005, "loss_disc_real": 0.37208, "time": 0.22226} {"mode": "train", "epoch": 3, "iter": 4000, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00206, "loss_disc_fake_g": 2.08419, "loss": 0.76234, "loss_disc_fake": 0.3766, "loss_disc_real": 0.38574, "time": 0.22233} {"mode": "train", "epoch": 3, "iter": 4100, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00214, "loss_disc_fake_g": 2.36332, "loss": 0.76105, "loss_disc_fake": 0.3785, "loss_disc_real": 0.38255, "time": 0.22264} {"mode": "train", "epoch": 3, "iter": 4200, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.0021, "loss_disc_fake_g": 2.31673, "loss": 0.73607, "loss_disc_fake": 0.36371, "loss_disc_real": 0.37236, "time": 0.22258} {"mode": "train", "epoch": 3, "iter": 4300, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00217, "loss_disc_fake_g": 2.21193, "loss": 0.76421, "loss_disc_fake": 0.37544, "loss_disc_real": 0.38877, "time": 0.22266} {"mode": "train", "epoch": 3, "iter": 4400, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00215, "loss_disc_fake_g": 2.25143, "loss": 0.81037, "loss_disc_fake": 0.38969, "loss_disc_real": 0.42068, "time": 0.22268} {"mode": "train", "epoch": 3, "iter": 4500, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00208, "loss_disc_fake_g": 2.20236, "loss": 0.68191, "loss_disc_fake": 0.33372, "loss_disc_real": 0.34819, "time": 0.2226} {"mode": "train", "epoch": 3, "iter": 4600, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00221, "loss_disc_fake_g": 2.20565, "loss": 0.79459, "loss_disc_fake": 0.38698, "loss_disc_real": 0.40761, "time": 0.22299} {"mode": "train", "epoch": 3, "iter": 4700, "lr": {"generator": 0.001, "discriminator": 0.001}, "memory": 3437, "data_time": 0.00216, "loss_disc_fake_g": 2.28967, "loss": 0.69436, "loss_disc_fake": 0.33381, "loss_disc_real": 0.36056, "time": 0.22248} {"mode": "train", "epoch": 4, "iter": 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