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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 = './'
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'] = False # 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') # 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.
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.
G_loss = EasyDict(func='loss.G_wgan_acgan') # Options for generator loss.
D_loss = EasyDict(func='loss.D_wgangp_acgan') # Options for discriminator loss.
sched = EasyDict() # Options for train.TrainingSchedule.
grid = EasyDict(size='1080p', layout='random') # Options for train.setup_snapshot_image_grid().
# desc += '-mein3d_texture_uv_tf_512'; dataset = EasyDict(tfrecord_dir='mein3d_texture_uv_tf_512');
# desc += '-mein3d_shape_uv_tf_512_bary'; dataset = EasyDict(tfrecord_dir='mein3d_shape_uv_tf_512_bary',dynamic_range=[-1,1],dtype = 'float32');
desc += '-3dmd_all_newuv_crop_tf'; dataset = EasyDict(tfrecord_dir='3dmd_all_newuv_crop_tf',dynamic_range=[-1,1],dtype = 'float32');
G.lod_sep = 7
D.lod_sep = 7
dataset.max_label_size = 'full'
grid.layout = 'row_per_class'
grid.size = '4k'
# Continue
#train.resume_run_id = 30
#train.resume_kimg = 12000
#train.resume_time = 7*24*60*60 + 5*60*60 + 0*60
# 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, 512: 4}; 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 = 16000
#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 = 100; sched.tick_kimg_dict = {}; train.image_snapshot_ticks = 2; train.network_snapshot_ticks = 2
#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.fit_real_images', run_id=0, png_prefix='', num_pngs=1000); num_gpus = 1; desc = 'real-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_fake_images_glob', run_id=0, num_pngs=1000); num_gpus = 1; desc = 'fake-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_fake_images', run_id=0, png_prefix='', num_pngs=100000); 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=0, grid_size=[1,1], duration_sec=600.0, smoothing_sec=1.0); num_gpus = 1; desc = 'interpolation-video-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_interpolation_images', run_id=30, grid_size=[1,1], duration_sec=60.0, smoothing_sec=1.0); num_gpus = 1; desc = 'interpolation-images-' + str(train.run_id)
#train = EasyDict(func='util_scripts.generate_interpolation_video_bydim', run_id=0, grid_size=[1,1], duration_sec=10.0, mp4_fps=30, smoothing_sec=1.0,dim=3); num_gpus = 1; desc = 'interpolation-video-' + str(train.run_id) + '_dim'+str(train.dim)
#train = EasyDict(func='util_scripts.generate_training_video', run_id=0, duration_sec=20.0); 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)