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config.py
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config.py
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
#----------------------------------------------------------------------------
# Convenience class that behaves exactly like dict(), but allows accessing
# the keys and values using the attribute syntax, i.e., "mydict.key = value".
class EasyDict(dict):
def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)
def __getattr__(self, name): return self[name]
def __setattr__(self, name, value): self[name] = value
def __delattr__(self, name): del self[name]
#----------------------------------------------------------------------------
# Paths.
data_dir = 'datasets'
result_dir = 'results'
#----------------------------------------------------------------------------
# TensorFlow options.
tf_config = EasyDict() # TensorFlow session config, set by tfutil.init_tf().
env = EasyDict() # Environment variables, set by the main program in train.py.
tf_config['graph_options.place_pruned_graph'] = True # False (default) = Check that all ops are available on the designated device. True = Skip the check for ops that are not used.
#tf_config['gpu_options.allow_growth'] = False # False (default) = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed.
#env.CUDA_VISIBLE_DEVICES = '0' # Unspecified (default) = Use all available GPUs. List of ints = CUDA device numbers to use.
env.TF_CPP_MIN_LOG_LEVEL = '1' # 0 (default) = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info.
#----------------------------------------------------------------------------
# Official training configs, targeted mainly for CelebA-HQ.
# To run, comment/uncomment the lines as appropriate and launch train.py.
desc = 'pgan' # Description string included in result subdir name.
random_seed = 1000 # Global random seed.
dataset = EasyDict() # Options for dataset.load_dataset().
train = EasyDict(func='train.train_progressive_gan')#, resume_run_id=58, resume_kimg=3246) # Options for main training func.
G = EasyDict(func='networks.G_paper') # Options for generator network.
D = EasyDict(func='networks.D_paper') # Options for discriminator network.
E = EasyDict(func='networks.E_clusterGAN')
G_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for generator optimizer.
D_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for discriminator optimizer.
E_opt = EasyDict(beta1=0.0, beta2=0.99, epsilon=1e-8) # Options for discriminator optimizer.
G_loss = EasyDict(func='loss.G_wgan_acgan') # Options for generator loss.
D_loss = EasyDict(func='loss.D_wgangp_acgan') # Options for discriminator loss.
E_loss = EasyDict(func='loss.E_wgangp_acgan') # Options for encoder loss.
sched = EasyDict() # Options for train.TrainingSchedule.
#grid = EasyDict(size='1080p', layout='random') # Options for train.setup_snapshot_image_grid().
grid = EasyDict(size='1080p', layout='row_per_cluster')
G_anomaly = EasyDict(func='networks.G_anomaly') # Options for generator network.
D_anomaly_test = EasyDict(func='networks.D_anomaly')
D_anomaly_Gout = EasyDict(func='networks.D_anomaly')
# Dataset (choose one).
#desc += '-celebahq'; dataset = EasyDict(tfrecord_dir='celebahq'); train.mirror_augment = True
#desc += '-celeba'; dataset = EasyDict(tfrecord_dir='celeba'); train.mirror_augment = True
#desc += '-cifar10_ab1_0.02'; dataset = EasyDict(tfrecord_dir='cifar10_ab1_0.02'); #train.mirror_augment = True
#desc += '-lsun_bedroom_bw_0.0'; dataset = EasyDict(tfrecord_dir='lsun_bedroom_bw_0.0');
#desc += '-cifar100'; dataset = EasyDict(tfrecord_dir='cifar100')
#desc += '-svhn'; dataset = EasyDict(tfrecord_dir='svhn')
desc += '-mnist_2_0.0'; dataset = EasyDict(tfrecord_dir='mnist_2_0.0')
#desc += '-cellvideos_64_64'; dataset = EasyDict(tfrecord_dir='cellvideos_64_64')
#desc += '-cellvideos_junk_64_64_8bit_rot'; dataset = EasyDict(tfrecord_dir='cellvideos_junk_64_64_8bit_rot');
#desc += '-cellvideos_64_64_rot_8bit_0.0005'; dataset = EasyDict(tfrecord_dir='cellvideos_64_64_rot_8bit_0.0005')
#desc += '-mnistrgb'; dataset = EasyDict(tfrecord_dir='mnistrgb')
#desc += '-syn1024rgb'; dataset = EasyDict(class_name='dataset.SyntheticDataset', resolution=1024, num_channels=3)
#desc += '-lsun-airplane'; dataset = EasyDict(tfrecord_dir='lsun-airplane-100k'); train.mirror_augment = True
#desc += '-lsun-bedroom'; dataset = EasyDict(tfrecord_dir='lsun-bedroom-100k'); train.mirror_augment = True
#desc += '-lsun-bicycle'; dataset = EasyDict(tfrecord_dir='lsun-bicycle-100k'); train.mirror_augment = True
#desc += '-lsun-bird'; dataset = EasyDict(tfrecord_dir='lsun-bird-100k'); train.mirror_augment = True
#desc += '-lsun-boat'; dataset = EasyDict(tfrecord_dir='lsun-boat-100k'); train.mirror_augment = True
#desc += '-lsun-bottle'; dataset = EasyDict(tfrecord_dir='lsun-bottle-100k'); train.mirror_augment = True
#desc += '-lsun-bridge'; dataset = EasyDict(tfrecord_dir='lsun-bridge-100k'); train.mirror_augment = True
#desc += '-lsun-bus'; dataset = EasyDict(tfrecord_dir='lsun-bus-100k'); train.mirror_augment = True
#desc += '-lsun-car'; dataset = EasyDict(tfrecord_dir='lsun-car-100k'); train.mirror_augment = True
#desc += '-lsun-cat'; dataset = EasyDict(tfrecord_dir='lsun-cat-100k'); train.mirror_augment = True
#desc += '-lsun-chair'; dataset = EasyDict(tfrecord_dir='lsun-chair-100k'); train.mirror_augment = True
#desc += '-lsun-churchoutdoor'; dataset = EasyDict(tfrecord_dir='lsun-churchoutdoor-100k'); train.mirror_augment = True
#desc += '-lsun-classroom'; dataset = EasyDict(tfrecord_dir='lsun-classroom-100k'); train.mirror_augment = True
#desc += '-lsun-conferenceroom'; dataset = EasyDict(tfrecord_dir='lsun-conferenceroom-100k'); train.mirror_augment = True
#desc += '-lsun-cow'; dataset = EasyDict(tfrecord_dir='lsun-cow-100k'); train.mirror_augment = True
#desc += '-lsun-diningroom'; dataset = EasyDict(tfrecord_dir='lsun-diningroom-100k'); train.mirror_augment = True
#desc += '-lsun-diningtable'; dataset = EasyDict(tfrecord_dir='lsun-diningtable-100k'); train.mirror_augment = True
#desc += '-lsun-dog'; dataset = EasyDict(tfrecord_dir='lsun-dog-100k'); train.mirror_augment = True
#desc += '-lsun-horse'; dataset = EasyDict(tfrecord_dir='lsun-horse-100k'); train.mirror_augment = True
#desc += '-lsun-kitchen'; dataset = EasyDict(tfrecord_dir='lsun-kitchen-100k'); train.mirror_augment = True
#desc += '-lsun-livingroom'; dataset = EasyDict(tfrecord_dir='lsun-livingroom-100k'); train.mirror_augment = True
#desc += '-lsun-motorbike'; dataset = EasyDict(tfrecord_dir='lsun-motorbike-100k'); train.mirror_augment = True
#desc += '-lsun-person'; dataset = EasyDict(tfrecord_dir='lsun-person-100k'); train.mirror_augment = True
#desc += '-lsun-pottedplant'; dataset = EasyDict(tfrecord_dir='lsun-pottedplant-100k'); train.mirror_augment = True
#desc += '-lsun-restaurant'; dataset = EasyDict(tfrecord_dir='lsun-restaurant-100k'); train.mirror_augment = True
#desc += '-lsun-sheep'; dataset = EasyDict(tfrecord_dir='lsun-sheep-100k'); train.mirror_augment = True
#desc += '-lsun-sofa'; dataset = EasyDict(tfrecord_dir='lsun-sofa-100k'); train.mirror_augment = True
#desc += '-lsun-tower'; dataset = EasyDict(tfrecord_dir='lsun-tower-100k'); train.mirror_augment = True
#desc += '-lsun-train'; dataset = EasyDict(tfrecord_dir='lsun-train-100k'); train.mirror_augment = True
#desc += '-lsun-tvmonitor'; dataset = EasyDict(tfrecord_dir='lsun-tvmonitor-100k'); train.mirror_augment = True
# Conditioning & snapshot options.
#desc += '-cond'; dataset.max_label_size = 'full' # conditioned on full label
#desc += '-cond1'; dataset.max_label_size = 1 # conditioned on first component of the label
#desc += '-g4k'; grid.size = '4k'
#desc += '-grpc'; grid.layout = 'row_per_class'
# Config presets (choose one).
#desc += '-preset-v1-1gpu'; num_gpus = 1; D.mbstd_group_size = 16; sched.minibatch_base = 16; sched.minibatch_dict = {256: 14, 512: 6, 1024: 3}; sched.lod_training_kimg = 800; sched.lod_transition_kimg = 800; train.total_kimg = 19000
desc += '-preset-v2-1gpu'; num_gpus = 1; sched.minibatch_base = 4; sched.minibatch_dict = {4: 128, 8: 128, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8, 512: 4}; sched.G_lrate_dict = {1024: 0.0015}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
#desc += '-preset-v2-2gpus'; num_gpus = 2; sched.minibatch_base = 8; sched.minibatch_dict = {4: 256, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16, 256: 8}; sched.G_lrate_dict = {512: 0.0015, 1024: 0.002}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
#desc += '-preset-v2-4gpus'; num_gpus = 4; sched.minibatch_base = 16; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16}; sched.G_lrate_dict = {256: 0.0015, 512: 0.002, 1024: 0.003}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
#desc += '-preset-v2-8gpus'; num_gpus = 8; sched.minibatch_base = 32; sched.minibatch_dict = {4: 512, 8: 256, 16: 128, 32: 64, 64: 32}; sched.G_lrate_dict = {128: 0.0015, 256: 0.002, 512: 0.003, 1024: 0.003}; sched.D_lrate_dict = EasyDict(sched.G_lrate_dict); train.total_kimg = 12000
# Numerical precision (choose one).
desc += '-fp32'; sched.max_minibatch_per_gpu = {256: 16, 512: 8, 1024: 4}
#desc += '-fp16'; G.dtype = 'float16'; D.dtype = 'float16'; G.pixelnorm_epsilon=1e-4; G_opt.use_loss_scaling = True; D_opt.use_loss_scaling = True; sched.max_minibatch_per_gpu = {512: 16, 1024: 8}
# Disable individual features.
#desc += '-nogrowing'; sched.lod_initial_resolution = 1024; sched.lod_training_kimg = 0; sched.lod_transition_kimg = 0; train.total_kimg = 10000
#desc += '-nopixelnorm'; G.use_pixelnorm = False
#desc += '-nowscale'; G.use_wscale = False; D.use_wscale = False
#desc += '-noleakyrelu'; G.use_leakyrelu = False
#desc += '-nosmoothing'; train.G_smoothing = 0.0
#desc += '-norepeat'; train.minibatch_repeats = 1
#desc += '-noreset'; train.reset_opt_for_new_lod = False
# Special modes.
#desc += '-BENCHMARK'; sched.lod_initial_resolution = 4; sched.lod_training_kimg = 3; sched.lod_transition_kimg = 3; train.total_kimg = (8*2+1)*3; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1000; train.network_snapshot_ticks = 1000
#desc += '-BENCHMARK0'; sched.lod_initial_resolution = 1024; train.total_kimg = 10; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1000; train.network_snapshot_ticks = 1000
#desc += '-VERBOSE'; sched.tick_kimg_base = 1; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 1; train.network_snapshot_ticks = 100
#desc += '-GRAPH'; train.save_tf_graph = True
#desc += '-HIST'; train.save_weight_histograms = True
#----------------------------------------------------------------------------
# Utility scripts.
# To run, uncomment the appropriate line and launch train.py.
#train = EasyDict(func='util_scripts.generate_fake_images', run_id=23, num_pngs=1000); num_gpus = 1; desc = 'fake-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_fake_images', run_id=23, grid_size=[15,8], num_pngs=10, image_shrink=4); num_gpus = 1; desc = 'fake-grids-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_interpolation_video', run_id=1, grid_size=[1,1], duration_sec=60.0, smoothing_sec=1.0); num_gpus = 1; desc = 'interpolation-video-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_training_video', run_id=3, duration_sec=60.0); num_gpus = 1; desc = 'training-video-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-swd-16k.txt', metrics=['swd'], num_images=16384, real_passes=2); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-fid-10k.txt', metrics=['fid'], num_images=10000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-fid-50k.txt', metrics=['fid'], num_images=50000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-is-50k.txt', metrics=['is'], num_images=50000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#train = EasyDict(func='util_scripts.evaluate_metrics', run_id=23, log='metric-msssim-20k.txt', metrics=['msssim'], num_images=20000, real_passes=1); num_gpus = 1; desc = train.log.split('.')[0] + '-' + str(train.run_id)
#Anomaly detection example
#train = EasyDict(func='util_scripts.anomaly_detection_encoder', run_id=0, log='anomaly_detection_cv_32_mnist', test_data_folder="datasets/mnist_2_0.0/validation/anomaly/"); num_gpus = 1; desc = 'anomaly-detection-' + str(train.run_id)
#----------------------------------------------------------------------------