def run_pickle(submit_config, metric_args, network_pkl, dataset_args, mirror_augment):
ctx = dnnlib.RunContext(submit_config)
tflib.init_tf()
print('Evaluating %s metric on network_pkl "%s"...' % (metric_args.name, network_pkl))
metric = dnnlib.util.call_func_by_name(**metric_args)
print()
metric.run(network_pkl, dataset_args=dataset_args, mirror_augment=mirror_augment, num_gpus=submit_config.num_gpus)
print()
ctx.close()
def run_snapshot(submit_config, metric_args, run_id, snapshot):
ctx = dnnlib.RunContext(submit_config)
tflib.init_tf()
print('Evaluating %s metric on run_id %s, snapshot %s...' % (metric_args.name, run_id, snapshot))
run_dir = misc.locate_run_dir(run_id)
network_pkl = misc.locate_network_pkl(run_dir, snapshot)
metric = dnnlib.util.call_func_by_name(**metric_args)
print()
metric.run(network_pkl, run_dir=run_dir, num_gpus=submit_config.num_gpus)
print()
ctx.close()
def test_d(submit_config,
resume_run_id,
dataset_args,
tf_config={},
resume_snapshot=None):
ctx = dnnlib.RunContext(submit_config, train)
tflib.init_tf(tf_config)
network_pkl = misc.locate_network_pkl(resume_run_id, resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
G, D, Gs = misc.load_pkl(network_pkl)
latents_1 = tf.placeholder(tf.float32)
labels_1 = None
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**dataset_args)
w_1 = Gs.components.mapping.get_output_for(latents_1,
labels_1,
is_validation=True)
fake_image_1_op = Gs.components.synthesis.get_output_for(
w_1, is_validation=True, randomize_noise=False)
reals, labels = training_set.get_minibatch_tf()
lod_in = tf.placeholder(tf.float32, name='lod_in', shape=[])
reals = process_reals(reals, lod_in, False, training_set.dynamic_range,
[-1, 1])
d_pred_real = D.get_output_for(reals, labels_1)
d_pred_fake = D.get_output_for(fake_image_1_op, labels_1)
training_set.configure(1, 0)
for i in range(15):
latents_1_val = np.random.randn(1, *G.input_shape[1:])
# d_pred, fake_image_1 = tflib.run([d_pred_op, fake_image_1_op], feed_dict={latents_1: latents_1_val, lod_in: 0})
d_pred_real_, d_pred_fake_, real_image = tflib.run(
[d_pred_real, d_pred_fake, reals],
feed_dict={
latents_1: latents_1_val,
lod_in: 0
})
print(d_pred_real_, d_pred_fake_)
misc.save_mri_image(real_image,
os.path.join(submit_config.run_dir,
'real_{}.nii.gz'.format(i)),
drange=[-1, 1])
def run_all_snapshots(submit_config, metric_args, run_id):
ctx = dnnlib.RunContext(submit_config)
tflib.init_tf()
print('Evaluating %s metric on all snapshots of run_id %s...' % (metric_args.name, run_id))
run_dir = misc.locate_run_dir(run_id)
network_pkls = misc.list_network_pkls(run_dir)
metric = dnnlib.util.call_func_by_name(**metric_args)
print()
for idx, network_pkl in enumerate(network_pkls):
ctx.update('', idx, len(network_pkls))
metric.run(network_pkl, run_dir=run_dir, num_gpus=submit_config.num_gpus)
print()
ctx.close()
def validate(submit_config: dnnlib.SubmitConfig, noise: dict, dataset: dict, network_snapshot: str):
noise_augmenter = dnnlib.util.call_func_by_name(**noise)
validation_set = ValidationSet(submit_config)
validation_set.load(**dataset)
ctx = dnnlib.RunContext(submit_config, config)
tfutil.init_tf(config.tf_config)
with tf.device("/gpu:0"):
net = util.load_snapshot(network_snapshot)
validation_set.evaluate(net, 0, noise_augmenter.add_validation_noise_np)
ctx.close()
def mixing(submit_config, resume_run_id, tf_config = {}, resume_snapshot=None):
ctx = dnnlib.RunContext(submit_config, train)
tflib.init_tf(tf_config)
network_pkl = misc.locate_network_pkl(resume_run_id, resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
G, D, Gs = misc.load_pkl(network_pkl)
latents_1_val = np.random.randn(1,*G.input_shape[1:])
latents_2_val = np.random.randn(1,*G.input_shape[1:])
# latents_2_val = latents_1_val
latents_1 = tf.placeholder(tf.float32)
labels_1 = tf.constant([[0,0,0,0,1,0]])
latents_2 = tf.placeholder(tf.float32)
labels_2 = tf.constant([[0,0,0,0,1,0]])
w_1 = Gs.components.mapping.get_output_for(latents_1, labels_1, is_validation=True)
w_2 = Gs.components.mapping.get_output_for(latents_2, labels_2, is_validation=True)
# w_1_val = tflib.run(w_1)
# w_2_val = tflib.run(w_2)
fake_image_1_op = Gs.components.synthesis.get_output_for(w_1, is_validation=True, randomize_noise=False)
fake_image_2_op = Gs.components.synthesis.get_output_for(w_2, is_validation=True, randomize_noise=False)
fake_image_1 = tflib.run(fake_image_1_op, feed_dict={latents_1: latents_1_val, latents_2: latents_2_val})
fake_image_2 = tflib.run(fake_image_2_op, feed_dict={latents_1: latents_1_val, latents_2: latents_2_val})
misc.save_image(fake_image_1[0], os.path.join(submit_config.run_dir,'fake_image_1.png'), drange=[-1,1])
misc.save_image(fake_image_2[0], os.path.join(submit_config.run_dir,'fake_image_2.png'), drange=[-1,1])
for i in range(15):
w_mix = tf.concat([w_1[:,:i],w_2[:,i:]], axis=1)
fake_mix_op = Gs.components.synthesis.get_output_for(w_mix, is_validation=True, randomize_noise=False)
fake_mix_image = tflib.run(fake_mix_op, feed_dict={latents_1: latents_1_val, latents_2: latents_2_val})
misc.save_image(fake_mix_image[0], os.path.join(submit_config.run_dir,'fake_mix_12_{}.png'.format(i)), drange=[-1,1])
for i in range(15):
w_mix = tf.concat([w_2[:,:i],w_1[:,i:]], axis=1)
fake_mix_op = Gs.components.synthesis.get_output_for(w_mix, is_validation=True, randomize_noise=False)
fake_mix_image = tflib.run(fake_mix_op, feed_dict={latents_1: latents_1_val, latents_2: latents_2_val})
misc.save_image(fake_mix_image[0], os.path.join(submit_config.run_dir,'fake_mix_21_{}.png'.format(i)), drange=[-1,1])
def validate(submit_config: dnnlib.SubmitConfig, noise: dict, dataset: dict,
network_snapshot: str):
noise_augmenter = dnnlib.util.call_func_by_name(**noise)
validation_set = ValidationSet(submit_config)
validation_set.load(**dataset)
# Create a run context (hides low level details, exposes simple API to manage the run)
ctx = dnnlib.RunContext(submit_config, config)
# Initialize TensorFlow graph and session using good default settings
tfutil.init_tf(config.tf_config)
# Construct the network using the Network helper class and a function defined in config.net_config
with tf.device("/gpu:0"):
net = load_snapshot(network_snapshot)
validation_set.evaluate(net, 0,
noise_augmenter.add_validation_noise_np)
ctx.close()
def train(submit_config: dnnlib.SubmitConfig, iteration_count: int,
eval_interval: int, minibatch_size: int, learning_rate: float,
ramp_down_perc: float, noise: dict, validation_config: dict,
train_tfrecords: str, noise2noise: bool):
noise_augmenter = dnnlib.util.call_func_by_name(**noise)
validation_set = ValidationSet(submit_config)
validation_set.load(**validation_config)
# Create a run context (hides low level details, exposes simple API to manage the run)
# noinspection PyTypeChecker
ctx = dnnlib.RunContext(submit_config, config)
# Initialize TensorFlow graph and session using good default settings
tfutil.init_tf(config.tf_config)
dataset_iter = create_dataset(train_tfrecords, minibatch_size,
noise_augmenter.add_train_noise_tf)
# Construct the network using the Network helper class and a function defined in config.net_config
with tf.device("/gpu:0"):
net = tflib.Network(**config.net_config)
# Optionally print layer information
net.print_layers()
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'), tf.device("/cpu:0"):
lrate_in = tf.placeholder(tf.float32, name='lrate_in', shape=[])
noisy_input, noisy_target, clean_target = dataset_iter.get_next()
noisy_input_split = tf.split(noisy_input, submit_config.num_gpus)
noisy_target_split = tf.split(noisy_target, submit_config.num_gpus)
clean_target_split = tf.split(clean_target, submit_config.num_gpus)
# Define the loss function using the Optimizer helper class, this will take care of multi GPU
opt = tflib.Optimizer(learning_rate=lrate_in, **config.optimizer_config)
for gpu in range(submit_config.num_gpus):
with tf.device("/gpu:%d" % gpu):
net_gpu = net if gpu == 0 else net.clone()
denoised = net_gpu.get_output_for(noisy_input_split[gpu])
if noise2noise:
meansq_error = tf.reduce_mean(
tf.square(noisy_target_split[gpu] - denoised))
else:
meansq_error = tf.reduce_mean(
tf.square(clean_target_split[gpu] - denoised))
# Create an autosummary that will average over all GPUs
with tf.control_dependencies([autosummary("Loss", meansq_error)]):
opt.register_gradients(meansq_error, net_gpu.trainables)
train_step = opt.apply_updates()
# Create a log file for Tensorboard
summary_log = tf.summary.FileWriter(submit_config.run_dir)
summary_log.add_graph(tf.get_default_graph())
print('Training...')
time_maintenance = ctx.get_time_since_last_update()
ctx.update(loss='run %d' % submit_config.run_id,
cur_epoch=0,
max_epoch=iteration_count)
# ***********************************
# The actual training loop
for i in range(iteration_count):
# Whether to stop the training or not should be asked from the context
if ctx.should_stop():
break
# Dump training status
if i % eval_interval == 0:
time_train = ctx.get_time_since_last_update()
time_total = ctx.get_time_since_start()
# Evaluate 'x' to draw a batch of inputs
[source_mb, target_mb] = tfutil.run([noisy_input, clean_target])
denoised = net.run(source_mb)
save_image(submit_config, denoised[0],
"img_{0}_y_pred.png".format(i))
save_image(submit_config, target_mb[0], "img_{0}_y.png".format(i))
save_image(submit_config, source_mb[0],
"img_{0}_x_aug.png".format(i))
validation_set.evaluate(net, i,
noise_augmenter.add_validation_noise_np)
print(
'iter %-10d time %-12s eta %-12s sec/eval %-7.1f sec/iter %-7.2f maintenance %-6.1f'
% (autosummary('Timing/iter', i),
dnnlib.util.format_time(
autosummary('Timing/total_sec', time_total)),
dnnlib.util.format_time(
autosummary('Timing/total_sec',
(time_train / eval_interval) *
(iteration_count - i))),
autosummary('Timing/sec_per_eval', time_train),
autosummary('Timing/sec_per_iter',
time_train / eval_interval),
autosummary('Timing/maintenance_sec', time_maintenance)))
dnnlib.tflib.autosummary.save_summaries(summary_log, i)
ctx.update(loss='run %d' % submit_config.run_id,
cur_epoch=i,
max_epoch=iteration_count)
time_maintenance = ctx.get_last_update_interval() - time_train
# Training epoch
lrate = compute_ramped_down_lrate(i, iteration_count, ramp_down_perc,
learning_rate)
tfutil.run([train_step], {lrate_in: lrate})
# End of training
print("Elapsed time: {0}".format(
util.format_time(ctx.get_time_since_start())))
save_snapshot(submit_config, net, 'final')
# Summary log and context should be closed at the end
summary_log.close()
ctx.close()
total_kimg = 15000, # Total length of the training, measured in thousands of real images.
mirror_augment = False, # Enable mirror augment?
drange_net = [-1,1], # Dynamic range used when feeding image data to the networks.
image_snapshot_ticks = 1, # How often to export image snapshots?
network_snapshot_ticks = 1, # How often to export network snapshots?
save_tf_graph = False, # Include full TensorFlow computation graph in the tfevents file?
save_weight_histograms = False, # Include weight histograms in the tfevents file?
resume_run_id = None, # Run ID or network pkl to resume training from, None = start from scratch.
#resume_run_id = 'results/00011-sgan-custom_datasets-1gpu/network-snapshot-005645.pkl',
resume_snapshot = None, # Snapshot index to resume training from, None = autodetect.
resume_kimg = 0.0 # Assumed training progress at the beginning. Affects reporting and training schedule.
#resume_kimg = 5645,
resume_time = 0.0): # Assumed wallclock time at the beginning. Affects reporting.
# Initialize dnnlib and TensorFlow.
ctx = dnnlib.RunContext(submit_config, train)
tflib.init_tf(tf_config)
# Load training set.
training_set = dataset.load_dataset(data_dir=config.data_dir, verbose=True, **dataset_args)
# Construct networks.
with tf.device('/gpu:0'):
if resume_run_id is not None:
network_pkl = misc.locate_network_pkl(resume_run_id, resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
G, D, Gs = misc.load_pkl(network_pkl)
else:
print('Constructing networks...')
G = tflib.Network('G', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **G_args)
D = tflib.Network('D', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **D_args)
def joint_train(
submit_config,
opt,
metric_arg_list,
sched_args = {}, # 训练计划设置。
grid_args = {}, # setup_snapshot_image_grid()相关设置。
dataset_args = {}, # 数据集设置。
total_kimg = 15000, # 训练的总长度，以成千上万个真实图像为统计。
drange_net = [-1,1], # 将图像数据馈送到网络时使用的动态范围。
image_snapshot_ticks = 1, # 多久导出一次图像快照？
network_snapshot_ticks = 10, # 多久导出一次网络模型存储？
D_repeats = 1, # G每迭代一次训练判别器多少次。
minibatch_repeats = 4, # 调整训练参数前要运行的minibatch的数量。
mirror_augment = False, # 启用镜像增强？
reset_opt_for_new_lod = True, # 引入新层时是否重置优化器内部状态（例如Adam时刻）？
save_tf_graph = False, # 在tfevents文件中包含完整的TensorFlow计算图吗？
save_weight_histograms = False, # 在tfevents文件中包括权重直方图？
resume_run_id = None, # 运行已有ID或载入已有网络pkl以从中恢复训练，None = 从头开始。
resume_snapshot = None, # 要从哪恢复训练的快照的索引，None = 自动检测。
resume_kimg = 0.0, # 在训练开始时给定当前训练进度。影响报告和训练计划。
resume_time = 0.0, # 在训练开始时给定统计时间。影响报告。
*args,
**kwargs
):
output_dir = opt.output_dir
graph_kwargs = util.set_graph_kwargs(opt)
graph_util = importlib.import_module('graphs.' + opt.model + '.graph_util')
constants = importlib.import_module('graphs.' + opt.model + '.constants')
model = graphs.find_model_using_name(opt.model, opt.transform)
g = model(submit_config=submit_config, dataset_args=dataset_args, **graph_kwargs, **kwargs)
g.initialize_graph()
# create training samples
#num_samples = opt.num_samples
# if opt.model == 'biggan' and opt.biggan.category is not None:
# graph_inputs = graph_util.graph_input(g, num_samples, seed=0, category=opt.biggan.category)
# else:
# graph_inputs = graph_util.graph_input(g, num_samples, seed=0)
w_snapshot_ticks = opt.model_save_freq
ctx = dnnlib.RunContext(submit_config, train)
training_set = dataset.load_dataset(data_dir=config.data_dir, verbose=True, **dataset_args)
with tf.device('/gpu:0'):
try:
peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
except tf.errors.NotFoundError:
peak_gpu_mem_op = tf.constant(0)
# 设置快照图像网格
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(g.G, training_set, **grid_args)
sched = training_loop.training_schedule(cur_nimg=total_kimg*1000, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
grid_fakes = g.Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
# 建立运行目录
print('Setting up run dir...')
misc.save_image_grid(grid_reals, os.path.join(submit_config.run_dir, 'reals.png'), drange=training_set.dynamic_range, grid_size=grid_size)
misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % resume_kimg), drange=drange_net, grid_size=grid_size)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
g.G.setup_weight_histograms(); g.D.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
# 训练
print('Training...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
loss_values = []
while cur_nimg < total_kimg * 1000:
if ctx.should_stop(): break
# 选择训练参数并配置训练操作。
sched = training_loop.training_schedule(cur_nimg=cur_nimg, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
training_set.configure(sched.minibatch // submit_config.num_gpus, sched.lod)
if reset_opt_for_new_lod:
if np.floor(sched.lod) != np.floor(prev_lod) or np.ceil(sched.lod) != np.ceil(prev_lod):
g.G_opt.reset_optimizer_state(); # D_opt.reset_optimizer_state()
prev_lod = sched.lod
# 进行训练。
for _mb_repeat in range(minibatch_repeats):
alpha_for_graph, alpha_for_target = g.get_train_alpha(constants.BATCH_SIZE)
if not isinstance(alpha_for_graph, list):
alpha_for_graph = [alpha_for_graph]
alpha_for_target = [alpha_for_target]
for ag, at in zip(alpha_for_graph, alpha_for_target):
feed_dict_out = graph_util.graph_input(g, constants.BATCH_SIZE, seed=0)
out_zs = g.sess.run(g.outputs_orig, feed_dict_out)
target_fn, mask_out = g.get_target_np(out_zs, at)
feed_dict = feed_dict_out
feed_dict[g.alpha] = ag
feed_dict[g.target] = target_fn
feed_dict[g.mask] = mask_out
feed_dict[g.lod_in] = sched.lod
feed_dict[g.lrate_in] = sched.D_lrate
feed_dict[g.minibatch_in] = sched.minibatch
curr_loss, _, Gs_op, G_op = g.sess.run([g.joint_loss, g.train_step, g.Gs_update_op, g.G_train_op], feed_dict=feed_dict)
loss_values.append(curr_loss)
cur_nimg += sched.minibatch
#tflib.run([g.Gs_update_op], {lod_in: sched.lod, lrate_in: sched.D_lrate, minibatch_in: sched.minibatch})
#tflib.run([g.G_train_op], {lod_in: sched.lod, lrate_in: sched.G_lrate, minibatch_in: sched.minibatch})
# 每个tick执行一次维护任务。
done = (cur_nimg >= total_kimg * 1000)
if cur_nimg >= tick_start_nimg + sched.tick_kimg * 1000 or done:
cur_tick += 1
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = ctx.get_time_since_last_update()
total_time = ctx.get_time_since_start() + resume_time
# 报告进度。
print('tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f gpumem %-4.1f' % (
autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch),
dnnlib.util.format_time(autosummary('Timing/total_sec', total_time)),
autosummary('Timing/sec_per_tick', tick_time),
autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
autosummary('Timing/maintenance_sec', maintenance_time),
autosummary('Resources/peak_gpu_mem_gb', peak_gpu_mem_op.eval() / 2**30)))
autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
# 保存快照。
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = g.Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % (cur_nimg // 1000)), drange=drange_net, grid_size=grid_size)
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(submit_config.run_dir, 'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
misc.save_pkl((g.G, g.D, g.Gs), pkl)
metrics.run(pkl, run_dir=submit_config.run_dir, num_gpus=submit_config.num_gpus, tf_config=tf_config)
if cur_tick % w_snapshot_ticks == 0 or done:
g.saver.save(g.sess, './{}/model_{}.ckpt'.format(
output_dir, (cur_nimg // 1000)),
write_meta_graph=False, write_state=False)
# 更新摘要和RunContext。
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update('%.2f' % sched.lod, cur_epoch=cur_nimg // 1000, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# 保存最终结果。
misc.save_pkl((g.G, g.D, g.Gs), os.path.join(submit_config.run_dir, 'network-final.pkl'))
summary_log.close()
ctx.close()
loss_values = np.array(loss_values)
np.save('./{}/loss_values.npy'.format(output_dir), loss_values)
f, ax = plt.subplots(figsize=(10, 4))
ax.plot(loss_values)
f.savefig('./{}/loss_values.png'.format(output_dir))
def training_loop(
submit_config,
G_args={}, # Options for generator network.
D_args={}, # Options for discriminator network.
G_opt_args={}, # Options for generator optimizer.
D_opt_args={}, # Options for discriminator optimizer.
G_loss_args={}, # Options for generator loss.
D_loss_args={}, # Options for discriminator loss.
dataset_args={}, # Options for dataset.load_dataset().
sched_args={}, # Options for train.TrainingSchedule.
grid_args={}, # Options for train.setup_snapshot_image_grid().
metric_arg_list=[], # Options for MetricGroup.
tf_config={}, # Options for tflib.init_tf().
G_smoothing_kimg=10.0, # Half-life of the running average of generator weights.
D_repeats=1, # How many times the discriminator is trained per G iteration.
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
total_kimg=15000, # Total length of the training, measured in thousands of real images.
mirror_augment=False, # Enable mirror augment?
drange_net=[
-1, 1
], # Dynamic range used when feeding image data to the networks.
image_snapshot_ticks=1, # How often to export image snapshots?
network_snapshot_ticks=10, # How often to export network snapshots?
save_tf_graph=False, # Include full TensorFlow computation graph in the tfevents file?
save_weight_histograms=False, # Include weight histograms in the tfevents file?
resume_run_id=None, # Run ID or network pkl to resume training from, None = start from scratch.
resume_snapshot=None, # Snapshot index to resume training from, None = autodetect.
resume_kimg=0.0, # Assumed training progress at the beginning. Affects reporting and training schedule.
resume_time=0.0,
): # Assumed wallclock time at the beginning. Affects reporting.
# Initialize dnnlib and TensorFlow.
ctx = dnnlib.RunContext(submit_config, train)
tflib.init_tf(tf_config)
# Load training set.
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**dataset_args)
# Construct networks.
with tf.device("/gpu:0"):
if resume_run_id is not None:
network_pkl = misc.locate_network_pkl(resume_run_id,
resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
G, D, Gs = misc.load_pkl(network_pkl)
else:
print("Constructing networks...")
G = tflib.Network(
name="G",
num_inputs=2, # one for latents and one for labels
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**G_args)
D = tflib.Network(
name="D",
num_inputs=int(np.log2(training_set.shape[1])) - 1 +
1, # +1 for labels :)
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**D_args)
Gs = G.clone("Gs")
G.print_layers()
D.print_layers()
print("Building TensorFlow graph...")
with tf.name_scope("Inputs"), tf.device("/cpu:0"):
lrate_in = tf.placeholder(tf.float32, name="lrate_in", shape=[])
minibatch_in = tf.placeholder(tf.int32, name="minibatch_in", shape=[])
minibatch_split = minibatch_in // submit_config.num_gpus
Gs_beta = (0.5**tf.div(tf.cast(minibatch_in,
tf.float32), G_smoothing_kimg *
1000.0) if G_smoothing_kimg > 0.0 else 0.0)
G_opt = tflib.Optimizer(name="TrainG",
learning_rate=lrate_in,
**G_opt_args)
D_opt = tflib.Optimizer(name="TrainD",
learning_rate=lrate_in,
**D_opt_args)
for gpu in range(submit_config.num_gpus):
with tf.name_scope("GPU%d" % gpu), tf.device("/gpu:%d" % gpu):
G_gpu = G if gpu == 0 else G.clone(G.name + "_shadow")
D_gpu = D if gpu == 0 else D.clone(D.name + "_shadow")
reals, labels = training_set.get_minibatch_tf()
reals = process_reals(
reals,
mirror_augment,
training_set.dynamic_range,
drange_net,
depth=training_set.resolution_log2 - 1,
)
with tf.name_scope("G_loss"):
G_loss = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
**G_loss_args)
with tf.name_scope("D_loss"):
D_loss = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
reals=reals,
labels=labels,
**D_loss_args)
G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
with tf.device("/gpu:0"):
try:
peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
except tf.errors.NotFoundError:
peak_gpu_mem_op = tf.constant(0)
# Choose training parameters and configure training ops.
sched = training_schedule(cur_nimg=total_kimg * 1000,
training_set=training_set,
**sched_args)
print("Setting up snapshot image grid...")
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(
G, training_set, **grid_args)
grid_fakes = Gs.run(
grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_size // submit_config.num_gpus,
)
print("Setting up run dir...")
fake_multi_scale_dirs = [
os.path.join(submit_config.run_dir,
str(2**res) + "x" + str(2**res))
for res in range(2, 2 + len(grid_fakes))
]
misc.save_image_grid(
grid_reals,
os.path.join(submit_config.run_dir, "reals.png"),
drange=training_set.dynamic_range,
grid_size=grid_size,
)
misc.save_image_grids(
grid_fakes,
[
os.path.join(fake_multi_scale_dir, "fakes%06d.png" % resume_kimg)
for fake_multi_scale_dir in fake_multi_scale_dirs
],
drange=drange_net,
grid_size=grid_size,
)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms()
D.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
print("Training...\n")
ctx.update("", cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
# configure the training_set to a proper minibatch size
training_set.configure(sched.minibatch_size // submit_config.num_gpus)
while cur_nimg < total_kimg * 1000:
if ctx.should_stop():
break
# Run training ops.
for _mb_repeat in range(minibatch_repeats):
for _D_repeat in range(D_repeats):
tflib.run(
[D_train_op, Gs_update_op],
{
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch_size
},
)
cur_nimg += sched.minibatch_size
tflib.run(
[G_train_op],
{
lrate_in: sched.G_lrate,
minibatch_in: sched.minibatch_size
},
)
# Perform maintenance tasks once per tick.
done = cur_nimg >= total_kimg * 1000
if cur_nimg >= tick_start_nimg + sched.tick_kimg * 1000 or done:
cur_tick += 1
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = ctx.get_time_since_last_update()
total_time = ctx.get_time_since_start() + resume_time
# Report progress.
print(
"tick %-5d kimg %-8.1f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f gpumem %-4.1f"
% (
autosummary("Progress/tick", cur_tick),
autosummary("Progress/kimg", cur_nimg / 1000.0),
autosummary("Progress/minibatch", sched.minibatch_size),
dnnlib.util.format_time(
autosummary("Timing/total_sec", total_time)),
autosummary("Timing/sec_per_tick", tick_time),
autosummary("Timing/sec_per_kimg", tick_time / tick_kimg),
autosummary("Timing/maintenance_sec", maintenance_time),
autosummary("Resources/peak_gpu_mem_gb",
peak_gpu_mem_op.eval() / 2**30),
))
autosummary("Timing/total_hours", total_time / (60.0 * 60.0))
autosummary("Timing/total_days", total_time / (24.0 * 60.0 * 60.0))
# Save snapshots.
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(
grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch_size //
submit_config.num_gpus,
)
misc.save_image_grids(
grid_fakes,
[
os.path.join(fake_multi_scale_dir, "fakes%06d.png" %
(cur_nimg // 1000))
for fake_multi_scale_dir in fake_multi_scale_dirs
],
drange=drange_net,
grid_size=grid_size,
)
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(
submit_config.run_dir,
"network-snapshot-%06d.pkl" % (cur_nimg // 1000),
)
misc.save_pkl((G, D, Gs), pkl)
metrics.run(
pkl,
run_dir=submit_config.run_dir,
num_gpus=submit_config.num_gpus,
tf_config=tf_config,
)
# Update summaries and RunContext.
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update(cur_epoch=cur_nimg // 1000, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# Write final results.
misc.save_pkl((G, D, Gs),
os.path.join(submit_config.run_dir, "network-final.pkl"))
summary_log.close()
ctx.close()
def training_loop(
submit_config,
G_args = {}, # 生成网络的设置。
D_args = {}, # 判别网络的设置。
G_opt_args = {}, # 生成网络优化器设置。
D_opt_args = {}, # 判别网络优化器设置。
G_loss_args = {}, # 生成损失设置。
D_loss_args = {}, # 判别损失设置。
dataset_args = {}, # 数据集设置。
sched_args = {}, # 训练计划设置。
grid_args = {}, # setup_snapshot_image_grid()相关设置。
metric_arg_list = [], # 指标方法设置。
tf_config = {}, # tflib.init_tf()相关设置。
G_smoothing_kimg = 10.0, # 生成器权重的运行平均值的半衰期。
D_repeats = 1, # G每迭代一次训练判别器多少次。
minibatch_repeats = 4, # 调整训练参数前要运行的minibatch的数量。
reset_opt_for_new_lod = True, # 引入新层时是否重置优化器内部状态（例如Adam时刻）？
total_kimg = 15000, # 训练的总长度，以成千上万个真实图像为统计。
mirror_augment = False, # 启用镜像增强？
drange_net = [-1,1], # 将图像数据馈送到网络时使用的动态范围。
image_snapshot_ticks = 1, # 多久导出一次图像快照？
network_snapshot_ticks = 10, # 多久导出一次网络模型存储？
save_tf_graph = False, # 在tfevents文件中包含完整的TensorFlow计算图吗？
save_weight_histograms = False, # 在tfevents文件中包括权重直方图？
resume_run_id = None, # 运行已有ID或载入已有网络pkl以从中恢复训练，None = 从头开始。
resume_snapshot = None, # 要从哪恢复训练的快照的索引，None = 自动检测。
resume_kimg = 0.0, # 在训练开始时给定当前训练进度。影响报告和训练计划。
resume_time = 0.0): # 在训练开始时给定统计时间。影响报告。
# 初始化dnnlib和TensorFlow。
ctx = dnnlib.RunContext(submit_config, train)
tflib.init_tf(tf_config)
# 载入训练集。
training_set = dataset.load_dataset(data_dir=config.data_dir, verbose=True, **dataset_args)
# 构建网络。
with tf.device('/gpu:0'):
if resume_run_id is not None:
network_pkl = misc.locate_network_pkl(resume_run_id, resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
G, D, Gs = misc.load_pkl(network_pkl)
else:
print('Constructing networks...')
G = tflib.Network('G', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **G_args)
D = tflib.Network('D', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **D_args)
Gs = G.clone('Gs')
G.print_layers(); D.print_layers()
# 构建计算图与优化器
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'), tf.device('/cpu:0'):
lod_in = tf.placeholder(tf.float32, name='lod_in', shape=[])
# tf.placeholder:可以理解为形参，用于定于过程，具体执行时再赋具体的值。
lrate_in = tf.placeholder(tf.float32, name='lrate_in', shape=[])
minibatch_in = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
minibatch_split = minibatch_in // submit_config.num_gpus
Gs_beta = 0.5 ** tf.div(tf.cast(minibatch_in, tf.float32), G_smoothing_kimg * 1000.0) if G_smoothing_kimg > 0.0 else 0.0
G_opt = tflib.Optimizer(name='TrainG', learning_rate=lrate_in, **G_opt_args)
D_opt = tflib.Optimizer(name='TrainD', learning_rate=lrate_in, **D_opt_args)
for gpu in range(submit_config.num_gpus):
with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % gpu):
G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
D_gpu = D if gpu == 0 else D.clone(D.name + '_shadow')
lod_assign_ops = [tf.assign(G_gpu.find_var('lod'), lod_in), tf.assign(D_gpu.find_var('lod'), lod_in)]
reals, labels = training_set.get_minibatch_tf()
reals = process_reals(reals, lod_in, mirror_augment, training_set.dynamic_range, drange_net)
with tf.name_scope('G_loss'), tf.control_dependencies(lod_assign_ops):
G_loss = dnnlib.util.call_func_by_name(G=G_gpu, D=D_gpu, opt=G_opt, training_set=training_set, minibatch_size=minibatch_split, **G_loss_args)
with tf.name_scope('D_loss'), tf.control_dependencies(lod_assign_ops):
D_loss = dnnlib.util.call_func_by_name(G=G_gpu, D=D_gpu, opt=D_opt, training_set=training_set, minibatch_size=minibatch_split, reals=reals, labels=labels, **D_loss_args)
G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
with tf.device('/gpu:0'):
try:
peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
except tf.errors.NotFoundError:
peak_gpu_mem_op = tf.constant(0)
# 设置快照图像网格
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(G, training_set, **grid_args)
sched = training_schedule(cur_nimg=total_kimg*1000, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
grid_fakes = Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
# 建立运行目录
print('Setting up run dir...')
misc.save_image_grid(grid_reals, os.path.join(submit_config.run_dir, 'reals.png'), drange=training_set.dynamic_range, grid_size=grid_size)
misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % resume_kimg), drange=drange_net, grid_size=grid_size)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms(); D.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
# 训练
print('Training...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
while cur_nimg < total_kimg * 1000:
if ctx.should_stop(): break
# 选择训练参数并配置训练操作。
sched = training_schedule(cur_nimg=cur_nimg, training_set=training_set, num_gpus=submit_config.num_gpus, **sched_args)
training_set.configure(sched.minibatch // submit_config.num_gpus, sched.lod)
if reset_opt_for_new_lod:
if np.floor(sched.lod) != np.floor(prev_lod) or np.ceil(sched.lod) != np.ceil(prev_lod):
G_opt.reset_optimizer_state(); D_opt.reset_optimizer_state()
prev_lod = sched.lod
# 进行训练。
for _mb_repeat in range(minibatch_repeats):
for _D_repeat in range(D_repeats):
tflib.run([D_train_op, Gs_update_op], {lod_in: sched.lod, lrate_in: sched.D_lrate, minibatch_in: sched.minibatch})
cur_nimg += sched.minibatch
tflib.run([G_train_op], {lod_in: sched.lod, lrate_in: sched.G_lrate, minibatch_in: sched.minibatch})
# 每个tick执行一次维护任务。
done = (cur_nimg >= total_kimg * 1000)
if cur_nimg >= tick_start_nimg + sched.tick_kimg * 1000 or done:
cur_tick += 1
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = ctx.get_time_since_last_update()
total_time = ctx.get_time_since_start() + resume_time
# 报告进度。
print('tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f gpumem %-4.1f' % (
autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch),
dnnlib.util.format_time(autosummary('Timing/total_sec', total_time)),
autosummary('Timing/sec_per_tick', tick_time),
autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
autosummary('Timing/maintenance_sec', maintenance_time),
autosummary('Resources/peak_gpu_mem_gb', peak_gpu_mem_op.eval() / 2**30)))
autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
# 保存快照。
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents, grid_labels, is_validation=True, minibatch_size=sched.minibatch//submit_config.num_gpus)
misc.save_image_grid(grid_fakes, os.path.join(submit_config.run_dir, 'fakes%06d.png' % (cur_nimg // 1000)), drange=drange_net, grid_size=grid_size)
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(submit_config.run_dir, 'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
misc.save_pkl((G, D, Gs), pkl)
metrics.run(pkl, run_dir=submit_config.run_dir, num_gpus=submit_config.num_gpus, tf_config=tf_config)
# 更新摘要和RunContext。
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update('%.2f' % sched.lod, cur_epoch=cur_nimg // 1000, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# 保存最终结果。
misc.save_pkl((G, D, Gs), os.path.join(submit_config.run_dir, 'network-final.pkl'))
summary_log.close()
ctx.close()
def training_loop(
submit_config,
G_args={}, # Options for generator network.
D_args={}, # Options for discriminator network.
G_opt_args={}, # Options for generator optimizer.
D_opt_args={}, # Options for discriminator optimizer.
G_loss_args={}, # Options for generator loss.
D_loss_args={}, # Options for discriminator loss.
dataset_args={}, # Options for dataset.load_dataset().
sched_args={}, # Options for train.TrainingSchedule.
grid_args={}, # Options for train.setup_snapshot_image_grid().
metric_arg_list=[], # Options for MetricGroup.
tf_config={}, # Options for tflib.init_tf().
G_smoothing_kimg=10.0, # Half-life of the running average of generator weights.
D_repeats=1, # How many times the discriminator is trained per G iteration.
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
reset_opt_for_new_lod=True, # Reset optimizer internal state (e.g. Adam moments) when new layers are introduced?
total_kimg=15000, # Total length of the training, measured in thousands of real images.
mirror_augment=False, # Enable mirror augment?
drange_net=[
-1, 1
], # Dynamic range used when feeding image data to the networks.
image_snapshot_ticks=10, # How often to export image snapshots?
network_snapshot_ticks=10, # How often to export network snapshots?
save_tf_graph=False, # Include full TensorFlow computation graph in the tfevents file?
save_weight_histograms=False, # Include weight histograms in the tfevents file?
resume_run_id=None, # Run ID or network pkl to resume training from, None = start from scratch.
resume_snapshot=None, # Snapshot index to resume training from, None = autodetect.
resume_kimg=0.0, # Assumed training progress at the beginning. Affects reporting and training schedule.
resume_time=0.0
): # Assumed wallclock time at the beginning. Affects reporting.
# Initialize dnnlib and TensorFlow.
ctx = dnnlib.RunContext(submit_config, train)
# ajay - move init to after graph creation?
tflib.init_tf(tf_config)
# Load training set.
print('ajay - config data dir', config.data_dir)
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
num_hosts=hvd.size(),
index=hvd.rank(),
**dataset_args)
# Construct networks.
print('Constructing networks...')
G = tflib.Network('G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**G_args)
D = tflib.Network('D',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**D_args)
Gs = G.clone('Gs')
# with tf.device('/gpu:0'):
# if resume_run_id is not None:
# network_pkl = misc.locate_network_pkl(resume_run_id, resume_snapshot)
# print('Loading networks from "%s"...' % network_pkl)
# G, D, Gs = misc.load_pkl(network_pkl)
# else:
# print('Constructing networks...')
# G = tflib.Network('G', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **G_args)
# D = tflib.Network('D', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **D_args)
# Gs = G.clone('Gs')
G.print_layers()
D.print_layers()
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'), tf.device('/cpu:0'):
lod_in = tf.placeholder(tf.float32, name='lod_in', shape=[])
lrate_in = tf.placeholder(tf.float32, name='lrate_in', shape=[])
minibatch_in = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
minibatch_split = minibatch_in // submit_config.num_gpus
Gs_beta = 0.5**tf.div(tf.cast(minibatch_in,
tf.float32), G_smoothing_kimg *
1000.0) if G_smoothing_kimg > 0.0 else 0.0
G_opt = tf.train.AdamOptimizer(learning_rate=lrate_in,
beta1=0.0,
beta2=0.99,
epsilon=1e-8)
G_opt = hvd.DistributedOptimizer(G_opt)
D_opt = tf.train.AdamOptimizer(learning_rate=lrate_in,
beta1=0.0,
beta2=0.99,
epsilon=1e-8)
D_opt = hvd.DistributedOptimizer(D_opt)
G_gpu = G
D_gpu = D
lod_assign_ops = [
tf.assign(G_gpu.find_var('lod'), lod_in),
tf.assign(D_gpu.find_var('lod'), lod_in)
]
# ajay - check if unique minibatch is guaranteed i.e sharding is done right!
reals, labels = training_set.get_minibatch_tf()
reals = process_reals(reals, lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
with tf.name_scope('G_loss'), tf.control_dependencies(lod_assign_ops):
G_loss = dnnlib.util.call_func_by_name(G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
**G_loss_args)
with tf.name_scope('D_loss'), tf.control_dependencies(lod_assign_ops):
D_loss = dnnlib.util.call_func_by_name(G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
reals=reals,
labels=labels,
**D_loss_args)
G_grads = G_opt.compute_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
D_grads = D_opt.compute_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
G_train_op = G_opt.apply_gradients(G_grads)
D_train_op = D_opt.apply_gradients(D_grads)
# Horovod
init_op = tf.initialize_all_variables()
bcast_op = hvd.broadcast_global_variables(0)
# ajay
tf.get_default_session().run([init_op])
tflib.run([bcast_op])
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(
G, training_set, **grid_args)
# todo: ajay - note num_gpus need to change to hvd size when going multi-node
sched = training_schedule(cur_nimg=total_kimg * 1000,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
if hvd.rank() == 0:
print('Setting up run dir...')
misc.save_image_grid(grid_reals,
os.path.join(submit_config.run_dir, 'reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
misc.save_image_grid(grid_fakes,
os.path.join(submit_config.run_dir,
'fakes%06d.png' % resume_kimg),
drange=drange_net,
grid_size=grid_size)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms()
D.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
print('Training...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
while cur_nimg < (total_kimg * 1000):
if ctx.should_stop(): break
# Choose training parameters and configure training ops.
sched = training_schedule(cur_nimg=cur_nimg,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
training_set.configure(sched.minibatch // submit_config.num_gpus,
sched.lod)
# todo: ajay - find a way to manually resetoptimizer
if reset_opt_for_new_lod:
if np.floor(sched.lod) != np.floor(prev_lod) or np.ceil(
sched.lod) != np.ceil(prev_lod):
tflib.assert_tf_initialized()
G_opt_reset_op = [var.initializer for var in G_opt.variables()]
D_opt_reset_op = [var.initializer for var in D_opt.variables()]
tflib.run(G_opt_reset_op)
tflib.run(D_opt_reset_op)
# G_opt.reset_optimizer_state(); D_opt.reset_optimizer_state()
prev_lod = sched.lod
# grp_train_op = tf.group(D_train_op, [Gs_update_op])
# Run training ops.
for _mb_repeat in range(minibatch_repeats):
for _D_repeat in range(D_repeats):
tflib.run(
[D_train_op, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch
})
cur_nimg += sched.minibatch #// submit_config.num_gpus
tflib.run(
[G_train_op], {
lod_in: sched.lod,
lrate_in: sched.G_lrate,
minibatch_in: sched.minibatch
})
# Perform maintenance tasks once per tick.
done = (cur_nimg >= (total_kimg * 1000))
if cur_nimg >= tick_start_nimg + sched.tick_kimg * 1000 or done:
cur_tick += 1
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = ctx.get_time_since_last_update()
total_time = ctx.get_time_since_start() + resume_time
# Report progress.
# ajay
#ajay mod
if hvd.rank() == 0:
print(
'tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f '
%
(autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch),
dnnlib.util.format_time(
autosummary('Timing/total_sec', total_time)),
autosummary('Timing/sec_per_tick', tick_time),
autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
autosummary('Timing/maintenance_sec', maintenance_time)))
autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
autosummary('Timing/total_days',
total_time / (24.0 * 60.0 * 60.0))
# print('tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f gpumem %-4.1f' % (
# autosummary('Progress/tick', cur_tick),
# autosummary('Progress/kimg', cur_nimg / 1000.0),
# autosummary('Progress/lod', sched.lod),
# autosummary('Progress/minibatch', sched.minibatch),
# dnnlib.util.format_time(autosummary('Timing/total_sec', total_time)),
# autosummary('Timing/sec_per_tick', tick_time),
# autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
# autosummary('Timing/maintenance_sec', maintenance_time),
# autosummary('Resources/peak_gpu_mem_gb', peak_gpu_mem_op.eval() / 2**30)))
# autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
# autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
# Save snapshots.
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
misc.save_image_grid(grid_fakes,
os.path.join(
submit_config.run_dir,
'fakes%06d.png' % (cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(
submit_config.run_dir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
misc.save_pkl((G, D, Gs), pkl)
# ajay - note modifying to 1 for eval
metrics.run(pkl,
run_dir=submit_config.run_dir,
num_gpus=1,
tf_config=tf_config)
# Update summaries and RunContext.
metrics.update_autosummaries()
if hvd.rank() == 0:
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update('%.2f' % sched.lod,
cur_epoch=cur_nimg // 1000,
max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# Write final results.
if hvd.rank() == 0:
misc.save_pkl((G, D, Gs),
os.path.join(submit_config.run_dir, 'network-final.pkl'))
summary_log.close()
ctx.close()
def training_loop(
submit_config,
HP_args={}, # Options for the Hessian Penalty.
G_args={}, # Options for generator network.
D_args={}, # Options for discriminator network.
G_opt_args={}, # Options for generator optimizer.
D_opt_args={}, # Options for discriminator optimizer.
G_loss_args={}, # Options for generator loss.
D_loss_args={}, # Options for discriminator loss.
dataset_args={}, # Options for dataset.load_dataset().
sched_args={}, # Options for train.TrainingSchedule.
grid_args={}, # Options for train.setup_snapshot_image_grid().
metric_arg_list=[], # Options for MetricGroup.
tf_config={}, # Options for tflib.init_tf().
G_smoothing_kimg=10.0, # Half-life of the running average of generator weights.
D_repeats=1, # How many times the discriminator is trained per G iteration.
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
reset_opt_for_new_lod=True, # Reset optimizer internal state (e.g. Adam moments) when new layers are introduced?
total_kimg=15000, # Total length of the training, measured in thousands of real images.
mirror_augment=False, # Enable mirror augment?
drange_net=[
-1, 1
], # Dynamic range used when feeding image data to the networks.
image_snapshot_ticks=1, # How often to export image snapshots?
interp_snapshot_ticks=20, # How often to generate interpolation visualizations in TensorBoard?
network_snapshot_ticks=5, # How often to export network snapshots?
network_metric_ticks=5, # How often to evaluate network snapshots on specified metrics?
save_tf_graph=False, # Include full TensorFlow computation graph in the tfevents file?
save_weight_histograms=False, # Include weight histograms in the tfevents file?
resume_run_id=None, # Run ID or network pkl to resume training from, None = start from scratch.
resume_snapshot=None, # Snapshot index to resume training from, None = autodetect.
resume_kimg=0.0, # Assumed training progress at the beginning. Affects reporting and training schedule.
resume_time=0.0
): # Assumed wallclock time at the beginning. Affects reporting.
# Initialize dnnlib and TensorFlow.
ctx = dnnlib.RunContext(submit_config, train)
tflib.init_tf(tf_config)
# Load training set.
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**dataset_args)
# Create a copy of dataset_args for running the metrics:
metrics_dataset_args = deepcopy(dataset_args)
metrics_dataset_args.shuffle_mb = 0
print('Saving interp videos every %s ticks' % interp_snapshot_ticks)
print('Saving network snapshot every %s ticks' % network_snapshot_ticks)
# Construct networks.
with tf.device('/gpu:0'):
if resume_run_id is not None:
network_pkl = misc.locate_network_pkl(resume_run_id,
resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
G, D, Gs = misc.load_pkl(network_pkl)
else:
print('Constructing networks...')
G = tflib.Network('G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**G_args)
D = tflib.Network('D',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**D_args)
Gs = G.clone('Gs')
# G.print_layers(); D.print_layers()
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'), tf.device('/cpu:0'):
lod_in = tf.placeholder(tf.float32, name='lod_in', shape=[])
lrate_in = tf.placeholder(tf.float32, name='lrate_in', shape=[])
minibatch_in = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
minibatch_split = minibatch_in // submit_config.num_gpus
Gs_beta = 0.5**tf.div(tf.cast(minibatch_in,
tf.float32), G_smoothing_kimg *
1000.0) if G_smoothing_kimg > 0.0 else 0.0
# The loss weighting of the Hessian Penalty can be dynamic over training, so we need to use a placeholder:
hessian_weight = tf.placeholder(tf.float32,
name='hessian_weight',
shape=[])
G_opt = tflib.Optimizer(name='TrainG',
learning_rate=lrate_in,
**G_opt_args)
D_opt = tflib.Optimizer(name='TrainD',
learning_rate=lrate_in,
**D_opt_args)
reg_ops = [
] # Returning the values of the Hessian Penalty/ InfoGAN losses so they can be registered in TensorBoard
for gpu in range(submit_config.num_gpus):
with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % gpu):
G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
D_gpu = D if gpu == 0 else D.clone(D.name + '_shadow')
lod_assign_ops = [
tf.assign(G_gpu.find_var('lod'), lod_in),
tf.assign(D_gpu.find_var('lod'), lod_in)
]
reals, labels = training_set.get_minibatch_tf()
reals = process_reals(reals, lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
with tf.name_scope('G_loss'), tf.control_dependencies(
lod_assign_ops):
G_loss, G_hessian_penalty = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
hp_lambda=hessian_weight,
HP_args=HP_args,
gpu_ix=gpu,
lod_in=lod_in,
max_lod=training_set.resolution_log2,
**G_loss_args)
if HP_args.hp_lambda > 0:
reg_ops += [G_hessian_penalty]
with tf.name_scope('D_loss'), tf.control_dependencies(
lod_assign_ops):
D_loss, mutual_info = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
reals=reals,
labels=labels,
gpu_ix=gpu,
infogan_nz=D_args.infogan_nz,
**D_loss_args)
# print([name for name in D_gpu.trainables.keys()])
# gps = [weight for name, weight in G_gpu.trainables.items()][0]
# dps = [weight for name, weight in D_gpu.trainables.items() if 'Q_Encoder' in name][0]
# gg = autosummary('Loss/G_info_grad', tf.reduce_sum(tf.gradients(mutual_info, gps)[0]**2))
# dg = autosummary('Loss/D_info_grad', tf.reduce_sum(tf.gradients(mutual_info, dps)[0]**2))
# reg_ops.extend([dg, gg, dps, gps])
if G_args.infogan_lambda > 0 or D_args.infogan_lambda > 0:
reg_ops += [mutual_info]
# Note, even though we are adding mutual_info loss here, the only time the loss is non-zero
# is when infogan_lambda > 0 (in Hessian Penalty experiments, we always set it to zero):
G_opt.register_gradients(
G_loss + G_args.infogan_lambda * mutual_info, G_gpu.trainables)
D_opt.register_gradients(
tf.reduce_mean(D_loss) + D_args.infogan_lambda * mutual_info,
D_gpu.trainables)
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
with tf.device('/gpu:0'):
try:
peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
except tf.errors.NotFoundError:
peak_gpu_mem_op = tf.constant(0)
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(
G, training_set, **grid_args)
sched = training_schedule(cur_nimg=total_kimg * 1000,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
print('Setting up snapshot interpolation...')
nz = G.input_shapes[0][1]
interp_steps = 24 # Number of frames in the visualization
interp_batch_size = 8 # Number of gifs per row of visualization
interp_z = vis_tools.sample_interp_zs(nz, interp_batch_size, interp_steps)
interp_labels = np.zeros(
[interp_steps * interp_batch_size * nz, training_set.label_size],
dtype=training_set.label_dtype)
print('Setting up run dir...')
misc.save_image_grid(grid_reals,
os.path.join(submit_config.run_dir, 'reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
misc.save_image_grid(grid_fakes,
os.path.join(submit_config.run_dir,
'fakes%06d.png' % resume_kimg),
drange=drange_net,
grid_size=grid_size)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
summary_log.add_summary(
build_image_summary(os.path.join(submit_config.run_dir, 'reals.png'),
'samples/real'), 0)
summary_log.add_summary(
build_image_summary(
os.path.join(submit_config.run_dir, 'fakes%06d.png' % resume_kimg),
'samples/Gs'), resume_kimg)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms()
D.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
if interp_snapshot_ticks != -1 and interp_snapshot_ticks < 9999:
print('Generating initial interpolations...')
vis_tools.make_and_save_interpolation_gifs(
Gs,
interp_z,
interp_labels,
minibatch_size=sched.minibatch // submit_config.num_gpus,
interp_steps=interp_steps,
interp_batch_size=interp_batch_size,
cur_kimg=resume_kimg,
summary_log=summary_log)
print('Training...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
num_G_grad_steps = 0
while cur_nimg < total_kimg * 1000:
if ctx.should_stop():
break
# Choose training parameters and configure training ops.
sched = training_schedule(cur_nimg=cur_nimg,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
training_set.configure(sched.minibatch // submit_config.num_gpus,
sched.lod)
if reset_opt_for_new_lod:
if np.floor(sched.lod) != np.floor(prev_lod) or np.ceil(
sched.lod) != np.ceil(prev_lod):
G_opt.reset_optimizer_state()
D_opt.reset_optimizer_state()
prev_lod = sched.lod
# Run training ops.
for _mb_repeat in range(minibatch_repeats):
for _D_repeat in range(D_repeats):
tflib.run(
[D_train_op, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch
})
cur_nimg += sched.minibatch
cur_hessian_weight = get_current_hessian_penalty_loss_weight(
HP_args.hp_lambda, HP_args.hp_start_nimg, cur_nimg,
HP_args.warmup_nimg)
tflib.run(
[G_train_op] + reg_ops, {
lod_in: sched.lod,
lrate_in: sched.G_lrate,
minibatch_in: sched.minibatch,
hessian_weight: cur_hessian_weight
})
num_G_grad_steps += 1
# Perform maintenance tasks once per tick.
done = (cur_nimg >= total_kimg * 1000)
if cur_nimg >= tick_start_nimg + sched.tick_kimg * 1000 or done:
cur_tick += 1
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = ctx.get_time_since_last_update()
total_time = ctx.get_time_since_start() + resume_time
# Report progress.
print(
'tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d hessian_weight %s time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f gpumem %-4.1f'
% (autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch),
autosummary('Progress/hessian_weight', cur_hessian_weight),
dnnlib.util.format_time(
autosummary('Timing/total_sec', total_time)),
autosummary('Timing/sec_per_tick', tick_time),
autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
autosummary('Timing/maintenance_sec', maintenance_time),
autosummary('Resources/peak_gpu_mem_gb',
peak_gpu_mem_op.eval() / 2**30)))
autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
autosummary('Progress/G_grad_steps', num_G_grad_steps)
# Save snapshots and fake image samples (for both Gs and G):
if cur_tick % image_snapshot_ticks == 0 or done:
iter = (cur_nimg // 1000)
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
grid_fakes_inst = G.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
fake_path = os.path.join(submit_config.run_dir,
'fakes%06d.png' % iter)
ifake_path = os.path.join(submit_config.run_dir,
'ifakes%06d.png' % iter)
misc.save_image_grid(grid_fakes,
fake_path,
drange=drange_net,
grid_size=grid_size)
misc.save_image_grid(grid_fakes_inst,
ifake_path,
drange=drange_net,
grid_size=grid_size)
summary_log.add_summary(
build_image_summary(fake_path, 'samples/Gs'), iter)
summary_log.add_summary(
build_image_summary(ifake_path, 'samples/G'), iter)
# Generate/Save Interpolation Visualizations:
if interp_snapshot_ticks != -1 and cur_tick % interp_snapshot_ticks == 0:
vis_tools.make_and_save_interpolation_gifs(
Gs,
interp_z,
interp_labels,
minibatch_size=sched.minibatch // submit_config.num_gpus,
interp_steps=interp_steps,
interp_batch_size=interp_batch_size,
cur_kimg=cur_nimg // 1000,
summary_log=summary_log)
# Save snapshot and run metrics:
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(
submit_config.run_dir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
misc.save_pkl((G, D, Gs), pkl)
if cur_tick % network_metric_ticks == 0 or done or cur_tick == 1:
metrics.run(pkl,
dataset_args=metrics_dataset_args,
mirror_augment=mirror_augment,
num_gpus=submit_config.num_gpus,
tf_config=tf_config)
# Update summaries and RunContext.
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update('%.2f' % sched.lod,
cur_epoch=cur_nimg // 1000,
max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# Write final results.
misc.save_pkl((G, D, Gs),
os.path.join(submit_config.run_dir,
'network-snapshot-%06d.pkl' % total_kimg))
summary_log.close()
ctx.close()
def training_loop(
submit_config,
#---------------------------------------------------------------
# Modified by Deng et al.
noise_dim=32,
weight_args={},
train_stage_args={},
#---------------------------------------------------------------
G_args={}, # Options for generator network.
D_args={}, # Options for discriminator network.
G_opt_args={}, # Options for generator optimizer.
D_opt_args={}, # Options for discriminator optimizer.
G_loss_args={}, # Options for generator loss.
D_loss_args={}, # Options for discriminator loss.
dataset_args={}, # Options for dataset.load_dataset().
sched_args={}, # Options for train.TrainingSchedule.
grid_args={}, # Options for train.setup_snapshot_image_grid().
metric_arg_list=[], # Options for MetricGroup.
tf_config={}, # Options for tflib.init_tf().
G_smoothing_kimg=10.0, # Half-life of the running average of generator weights.
D_repeats=1, # How many times the discriminator is trained per G iteration.
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
reset_opt_for_new_lod=True, # Reset optimizer internal state (e.g. Adam moments) when new layers are introduced?
total_kimg=15000, # Total length of the training, measured in thousands of real images.
mirror_augment=True, # Enable mirror augment?
drange_net=[
-1, 1
], # Dynamic range used when feeding image data to the networks.
image_snapshot_ticks=1, # How often to export image snapshots?
network_snapshot_ticks=10, # How often to export network snapshots?
save_tf_graph=True, # Include full TensorFlow computation graph in the tfevents file?
save_weight_histograms=False, # Include weight histograms in the tfevents file?
resume_run_id=87, # Run ID or network pkl to resume training from, None = start from scratch.
resume_snapshot=2364, # Snapshot index to resume training from, None = autodetect.
resume_kimg=2364, # Assumed training progress at the beginning. Affects reporting and training schedule.
resume_time=0.0,
**_kwargs
): # Assumed wallclock time at the beginning. Affects reporting.
# Initialize dnnlib and TensorFlow.
PI = 3.1415927
ctx = dnnlib.RunContext(submit_config, train)
tflib.init_tf(tf_config)
# Load training set.
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**dataset_args)
# Create 3d face reconstruction block
FaceRender = Face3D()
# Construct networks.
with tf.device('/gpu:0'):
if resume_run_id is not None:
network_pkl = misc.locate_network_pkl(resume_run_id,
resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
G, D, Gs = misc.load_pkl(network_pkl)
else:
print('Constructing networks...')
#---------------------------------------------------------------
# Modified by Deng et al.
G = tflib.Network('G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
latent_size=254 + noise_dim,
**G_args)
#---------------------------------------------------------------
D = tflib.Network('D',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**D_args)
Gs = G.clone('Gs')
G.print_layers()
D.print_layers()
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'), tf.device('/cpu:0'):
lod_in = tf.placeholder(tf.float32, name='lod_in', shape=[])
resolution = tf.placeholder(tf.float32, name='resolution', shape=[])
lrate_in = tf.placeholder(tf.float32, name='lrate_in', shape=[])
minibatch_in = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
minibatch_split = minibatch_in // submit_config.num_gpus
Gs_beta = 0.5**tf.div(tf.cast(minibatch_in,
tf.float32), G_smoothing_kimg *
1000.0) if G_smoothing_kimg > 0.0 else 0.0
G_opt = tflib.Optimizer(name='TrainG',
learning_rate=lrate_in,
**G_opt_args)
D_opt = tflib.Optimizer(name='TrainD',
learning_rate=lrate_in,
**D_opt_args)
for gpu in range(submit_config.num_gpus):
with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % (gpu)):
G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
D_gpu = D if gpu == 0 else D.clone(D.name + '_shadow')
lod_assign_ops = [
tf.assign(G_gpu.find_var('lod'), lod_in),
tf.assign(D_gpu.find_var('lod'), lod_in)
]
reals, labels = training_set.get_minibatch_tf()
reals = process_reals(reals, lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
#---------------------------------------------------------------
# Modified by Deng et al.
G_loss,D_loss = dnnlib.util.call_func_by_name(FaceRender=FaceRender,noise_dim=noise_dim,weight_args=weight_args,\
G_gpu=G_gpu,D_gpu=D_gpu,G_opt=G_opt,D_opt=D_opt,training_set=training_set,G_loss_args=G_loss_args,D_loss_args=D_loss_args,\
lod_assign_ops=lod_assign_ops,reals=reals,labels=labels,minibatch_split=minibatch_split,resolution=resolution,\
drange_net=drange_net,lod_in=lod_in,**train_stage_args)
#---------------------------------------------------------------
G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
with tf.device('/gpu:0'):
try:
peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
except tf.errors.NotFoundError:
peak_gpu_mem_op = tf.constant(0)
#---------------------------------------------------------------
# Modified by Deng et al.
restore_weights_and_initialize(train_stage_args)
print('Setting up snapshot image grid...')
sched = training_schedule(cur_nimg=total_kimg * 1000,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
grid_size, grid_reals, grid_labels = misc.setup_snapshot_image_grid(
G, training_set, **grid_args)
grid_latents = tf.random_normal([np.prod(grid_size), 128 + 32 + 16 + 3])
grid_INPUTcoeff = z_to_lambda_mapping(grid_latents)
grid_INPUTcoeff_w_t = tf.concat(
[grid_INPUTcoeff, tf.zeros([np.prod(grid_size), 3])], axis=1)
with tf.name_scope('FaceRender'):
grid_render_img, _, _, _ = FaceRender.Reconstruction_Block(
grid_INPUTcoeff_w_t, 256, np.prod(grid_size), progressive=False)
grid_render_img = tf.transpose(grid_render_img, perm=[0, 3, 1, 2])
grid_render_img = process_reals(grid_render_img, lod_in, False,
training_set.dynamic_range, drange_net)
grid_INPUTcoeff_, grid_renders = tflib.run(
[grid_INPUTcoeff, grid_render_img], {lod_in: sched.lod})
grid_noise = np.random.randn(np.prod(grid_size), 32)
grid_INPUTcoeff_w_noise = np.concatenate([grid_INPUTcoeff_, grid_noise],
axis=1)
grid_fakes = Gs.run(grid_INPUTcoeff_w_noise,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
grid_fakes = np.concatenate([grid_fakes, grid_renders], axis=3)
misc.save_image_grid(grid_fakes,
os.path.join(submit_config.run_dir,
'fakes%06d.png' % resume_kimg),
drange=drange_net,
grid_size=grid_size)
misc.save_image_grid(grid_reals,
os.path.join(submit_config.run_dir, 'reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
#---------------------------------------------------------------
summary_log = tf.summary.FileWriter(submit_config.run_dir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms()
D.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
print('Training...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
while cur_nimg < total_kimg * 1000:
if ctx.should_stop(): break
# Choose training parameters and configure training ops.
sched = training_schedule(cur_nimg=cur_nimg,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
training_set.configure(sched.minibatch // submit_config.num_gpus,
sched.lod)
if reset_opt_for_new_lod:
if np.floor(sched.lod) != np.floor(prev_lod) or np.ceil(
sched.lod) != np.ceil(prev_lod):
G_opt.reset_optimizer_state()
D_opt.reset_optimizer_state()
prev_lod = sched.lod
# Run training ops.
for _mb_repeat in range(minibatch_repeats):
for _D_repeat in range(D_repeats):
tflib.run(
[D_train_op, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch,
resolution: sched.resolution
})
cur_nimg += sched.minibatch
tflib.run(
[G_train_op], {
lod_in: sched.lod,
lrate_in: sched.G_lrate,
minibatch_in: sched.minibatch,
resolution: sched.resolution
})
# print('iter')
# Perform maintenance tasks once per tick.
done = (cur_nimg >= total_kimg * 1000)
if cur_nimg >= tick_start_nimg + sched.tick_kimg * 1000 or done:
cur_tick += 1
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = ctx.get_time_since_last_update()
total_time = ctx.get_time_since_start() + resume_time
# Report progress.
print(
'tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f gpumem %-4.1f'
% (autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch),
dnnlib.util.format_time(
autosummary('Timing/total_sec', total_time)),
autosummary('Timing/sec_per_tick', tick_time),
autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
autosummary('Timing/maintenance_sec', maintenance_time),
autosummary('Resources/peak_gpu_mem_gb',
peak_gpu_mem_op.eval() / 2**30)))
autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
# Save snapshots.
if cur_tick % image_snapshot_ticks == 0 or done:
#---------------------------------------------------------------
# Modified by Deng et al.
grid_fakes = Gs.run(grid_INPUTcoeff_w_noise,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
grid_fakes = np.concatenate([grid_fakes, grid_renders], axis=3)
misc.save_image_grid(grid_fakes,
os.path.join(
submit_config.run_dir,
'fakes%06d.png' % (cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
#---------------------------------------------------------------
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(
submit_config.run_dir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
misc.save_pkl((G, D, Gs), pkl)
metrics.run(pkl,
run_dir=submit_config.run_dir,
num_gpus=submit_config.num_gpus,
tf_config=tf_config)
# Update summaries and RunContext.
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update('%.2f' % sched.lod,
cur_epoch=cur_nimg // 1000,
max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# Write final results.
misc.save_pkl((G, D, Gs),
os.path.join(submit_config.run_dir, 'network-final.pkl'))
summary_log.close()
ctx.close()
#----------------------------------------------------------------------------
def training_loop(
submit_config,
G_args={}, # Options for generator network.
D_args={}, # Options for discriminator network.
G_opt_args={}, # Options for generator optimizer.
D_opt_args={}, # Options for discriminator optimizer.
G_loss_args={}, # Options for generator loss.
D_loss_args={}, # Options for discriminator loss.
dataset_args={}, # Options for dataset.load_dataset().
sched_args={}, # Options for train.TrainingSchedule.
grid_args={}, # Options for train.setup_snapshot_image_grid().
metric_arg_list=[], # Options for MetricGroup.
tf_config={}, # Options for tflib.init_tf().
G_smoothing_kimg=10.0, # Half-life of the running average of generator weights.
D_repeats=1, # How many times the discriminator is trained per G iteration.
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
reset_opt_for_new_lod=True, # Reset optimizer internal state (e.g. Adam moments) when new layers are introduced?
total_kimg=15000, # Total length of the training, measured in thousands of real images.
mirror_augment=False, # Enable mirror augment?
drange_net=[
-1, 1
], # Dynamic range used when feeding image data to the networks.
image_snapshot_ticks=1, # How often to export image snapshots?
network_snapshot_ticks=10, # How often to export network snapshots?
save_tf_graph=False, # Include full TensorFlow computation graph in the tfevents file?
save_weight_histograms=False, # Include weight histograms in the tfevents file?
resume_run_id=None, # Run ID or network pkl to resume training from, None = start from scratch.
resume_snapshot=None, # Snapshot index to resume training from, None = autodetect.
resume_kimg=10000.0, # Assumed training progress at the beginning. Affects reporting and training schedule.
resume_time=0.0
): # Assumed wallclock time at the beginning. Affects reporting.
# Initialize dnnlib and TensorFlow.
ctx = dnnlib.RunContext(submit_config, train)
tflib.init_tf(tf_config)
# Load training set.
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**dataset_args)
# Construct networks.
with tf.device('/gpu:0'):
if resume_run_id is not None:
network_pkl = misc.locate_network_pkl(resume_run_id,
resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
G, D, Gs = misc.load_pkl(network_pkl)
else:
#print('Constructing networks...')
#G = tflib.Network('G', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **G_args)
#D = tflib.Network('D', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **D_args)
#Gs = G.clone('Gs')
url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ'
with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
G, D, Gs = pickle.load(f)
print('Loading pretrained FFHQ network')
G.print_layers()
D.print_layers()
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'), tf.device('/cpu:0'):
lod_in = tf.placeholder(tf.float32, name='lod_in', shape=[])
lrate_in = tf.placeholder(tf.float32, name='lrate_in', shape=[])
minibatch_in = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
minibatch_split = minibatch_in // submit_config.num_gpus
Gs_beta = 0.5**tf.div(tf.cast(minibatch_in,
tf.float32), G_smoothing_kimg *
1000.0) if G_smoothing_kimg > 0.0 else 0.0
G_opt = tflib.Optimizer(name='TrainG',
learning_rate=lrate_in,
**G_opt_args)
D_opt = tflib.Optimizer(name='TrainD',
learning_rate=lrate_in,
**D_opt_args)
for gpu in range(submit_config.num_gpus):
with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % gpu):
G_gpu = G if gpu == 0 else G.clone(G.name + '_shadow')
D_gpu = D if gpu == 0 else D.clone(D.name + '_shadow')
lod_assign_ops = [
tf.assign(G_gpu.find_var('lod'), lod_in),
tf.assign(D_gpu.find_var('lod'), lod_in)
]
reals, labels = training_set.get_minibatch_tf()
reals = process_reals(reals, lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
with tf.name_scope('G_loss'), tf.control_dependencies(
lod_assign_ops):
G_loss = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
**G_loss_args)
with tf.name_scope('D_loss'), tf.control_dependencies(
lod_assign_ops):
D_loss = dnnlib.util.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
reals=reals,
labels=labels,
**D_loss_args)
G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=Gs_beta)
with tf.device('/gpu:0'):
try:
peak_gpu_mem_op = tf.contrib.memory_stats.MaxBytesInUse()
except tf.errors.NotFoundError:
peak_gpu_mem_op = tf.constant(0)
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = misc.setup_snapshot_image_grid(
G, training_set, **grid_args)
sched = training_schedule(cur_nimg=total_kimg * 1000,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
print('Setting up run dir...')
misc.save_image_grid(grid_reals,
os.path.join(submit_config.run_dir, 'reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
misc.save_image_grid(grid_fakes,
os.path.join(submit_config.run_dir,
'fakes%06d.png' % resume_kimg),
drange=drange_net,
grid_size=grid_size)
cmd = "gsutil cp " + os.path.join(submit_config.run_dir, 'fakes%06d.png' %
resume_kimg) + " gs://stylegan_out"
response = subprocess.run(cmd, shell=True)
summary_log = tf.summary.FileWriter(submit_config.run_dir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms()
D.setup_weight_histograms()
metrics = metric_base.MetricGroup(metric_arg_list)
print('Training...\n')
ctx.update('', cur_epoch=resume_kimg, max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval()
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
prev_lod = -1.0
while cur_nimg < total_kimg * 1000:
if ctx.should_stop(): break
# Choose training parameters and configure training ops.
sched = training_schedule(cur_nimg=cur_nimg,
training_set=training_set,
num_gpus=submit_config.num_gpus,
**sched_args)
training_set.configure(sched.minibatch // submit_config.num_gpus,
sched.lod)
if reset_opt_for_new_lod:
if np.floor(sched.lod) != np.floor(prev_lod) or np.ceil(
sched.lod) != np.ceil(prev_lod):
G_opt.reset_optimizer_state()
D_opt.reset_optimizer_state()
prev_lod = sched.lod
# Run training ops.
for _mb_repeat in range(minibatch_repeats):
for _D_repeat in range(D_repeats):
tflib.run(
[D_train_op, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch
})
cur_nimg += sched.minibatch
tflib.run(
[G_train_op], {
lod_in: sched.lod,
lrate_in: sched.G_lrate,
minibatch_in: sched.minibatch
})
# Perform maintenance tasks once per tick.
done = (cur_nimg >= total_kimg * 1000)
if cur_nimg >= tick_start_nimg + sched.tick_kimg * 1000 or done:
cur_tick += 1
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = ctx.get_time_since_last_update()
total_time = ctx.get_time_since_start() + resume_time
# Report progress.
print(
'tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %-6.1f gpumem %-4.1f'
% (autosummary('Progress/tick', cur_tick),
autosummary('Progress/kimg', cur_nimg / 1000.0),
autosummary('Progress/lod', sched.lod),
autosummary('Progress/minibatch', sched.minibatch),
dnnlib.util.format_time(
autosummary('Timing/total_sec', total_time)),
autosummary('Timing/sec_per_tick', tick_time),
autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
autosummary('Timing/maintenance_sec', maintenance_time),
autosummary('Resources/peak_gpu_mem_gb',
peak_gpu_mem_op.eval() / 2**30)))
autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
# Save snapshots.
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents,
grid_labels,
is_validation=True,
minibatch_size=sched.minibatch //
submit_config.num_gpus)
misc.save_image_grid(grid_fakes,
os.path.join(
submit_config.run_dir,
'fakes%06d.png' % (cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
cmd = "gsutil cp " + os.path.join(
submit_config.run_dir, 'fakes%06d.png' %
(cur_nimg // 1000)) + " gs://stylegan_out"
response = subprocess.run(cmd, shell=True)
if cur_tick % network_snapshot_ticks == 0 or done or cur_tick == 1:
pkl = os.path.join(
submit_config.run_dir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000))
misc.save_pkl((G, D, Gs), pkl)
metrics.run(pkl,
run_dir=submit_config.run_dir,
num_gpus=submit_config.num_gpus,
tf_config=tf_config)
# Update summaries and RunContext.
metrics.update_autosummaries()
tflib.autosummary.save_summaries(summary_log, cur_nimg)
ctx.update('%.2f' % sched.lod,
cur_epoch=cur_nimg // 1000,
max_epoch=total_kimg)
maintenance_time = ctx.get_last_update_interval() - tick_time
# Write final results.
misc.save_pkl((G, D, Gs),
os.path.join(submit_config.run_dir, 'network-final.pkl'))
summary_log.close()
ctx.close()
def train(submit_config: dnnlib.SubmitConfig, iteration_count: int,
eval_interval: int, minibatch_size: int, learning_rate: float,
ramp_down_perc: float, noise: dict, validation_config: dict,
train_tfrecords: str, noise2noise: bool):
noise_augmenter = dnnlib.util.call_func_by_name(**noise)
validation_set = ValidationSet(submit_config)
validation_set.load(**validation_config)
# Create a run context (hides low level details, exposes simple API to manage the run)
ctx = dnnlib.RunContext(submit_config, config)
# Initialize TensorFlow graph and session using good default settings
tfutil.init_tf(config.tf_config)
dataset_iter = create_dataset(train_tfrecords, minibatch_size,
noise_augmenter.add_train_noise_tf)
# Construct the network using the Network helper class and a function defined in config.net_config
with tf.device("/gpu:0"):
net = tflib.Network(**config.net_config)
# Optionally print layer information
net.print_layers()
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'), tf.device("/cpu:0"):
lrate_in = tf.compat.v1.placeholder(tf.float32,
name='lrate_in',
shape=[])
#print("DEBUG train:", "dataset iter got called")
noisy_input, noisy_target, clean_target = dataset_iter.get_next()
noisy_input_split = tf.split(noisy_input, submit_config.num_gpus)
noisy_target_split = tf.split(noisy_target, submit_config.num_gpus)
print(len(noisy_input_split), noisy_input_split)
clean_target_split = tf.split(clean_target, submit_config.num_gpus)
# Split [?, 3, 256, 256] across num_gpus over axis 0 (i.e. the batch)
# Define the loss function using the Optimizer helper class, this will take care of multi GPU
opt = tflib.Optimizer(learning_rate=lrate_in, **config.optimizer_config)
radii = np.arange(128).reshape(128, 1) #image size 256, binning = 3
radial_masks = np.apply_along_axis(radial_mask, 1, radii, 128, 128,
np.arange(0, 256), np.arange(0, 256),
20)
print("RN SHAPE!!!!!!!!!!:", radial_masks.shape)
radial_masks = np.expand_dims(radial_masks, 1) # (128, 1, 256, 256)
#radial_masks = np.squeeze(np.stack((radial_masks,) * 3, -1)) # 43, 3, 256, 256
#radial_masks = radial_masks.transpose([0, 3, 1, 2])
radial_masks = radial_masks.astype(np.complex64)
radial_masks = tf.expand_dims(radial_masks, 1)
rn = tf.compat.v1.placeholder_with_default(radial_masks,
[128, None, 1, 256, 256])
rn_split = tf.split(rn, submit_config.num_gpus, axis=1)
freq_nyq = int(np.floor(int(256) / 2.0))
spatial_freq = radii.astype(np.float32) / freq_nyq
spatial_freq = spatial_freq / max(spatial_freq)
for gpu in range(submit_config.num_gpus):
with tf.device("/gpu:%d" % gpu):
net_gpu = net if gpu == 0 else net.clone()
denoised_1 = net_gpu.get_output_for(noisy_input_split[gpu])
denoised_2 = net_gpu.get_output_for(noisy_target_split[gpu])
print(noisy_input_split[gpu].get_shape(),
rn_split[gpu].get_shape())
if noise2noise:
meansq_error = fourier_ring_correlation(
noisy_target_split[gpu], denoised_1, rn_split[gpu],
spatial_freq) - fourier_ring_correlation(
noisy_target_split[gpu] - denoised_2,
noisy_input_split[gpu] - denoised_1, rn_split[gpu],
spatial_freq)
else:
meansq_error = tf.reduce_mean(
tf.square(clean_target_split[gpu] - denoised))
# Create an autosummary that will average over all GPUs
#tf.summary.histogram(name, var)
with tf.control_dependencies([autosummary("Loss", meansq_error)]):
opt.register_gradients(meansq_error, net_gpu.trainables)
train_step = opt.apply_updates()
# Create a log file for Tensorboard
summary_log = tf.compat.v1.summary.FileWriter(submit_config.run_dir)
summary_log.add_graph(tf.compat.v1.get_default_graph())
print('Training...')
time_maintenance = ctx.get_time_since_last_update()
ctx.update(loss='run %d' % submit_config.run_id,
cur_epoch=0,
max_epoch=iteration_count)
# The actual training loop
for i in range(iteration_count):
# Whether to stop the training or not should be asked from the context
if ctx.should_stop():
break
# Dump training status
if i % eval_interval == 0:
time_train = ctx.get_time_since_last_update()
time_total = ctx.get_time_since_start()
print("DEBUG TRAIN!", noisy_input.dtype, noisy_input[0][0].dtype)
# Evaluate 'x' to draw a batch of inputs
[source_mb, target_mb] = tfutil.run([noisy_input, clean_target])
denoised = net.run(source_mb)
save_image(submit_config, denoised[0],
"img_{0}_y_pred.tif".format(i))
save_image(submit_config, target_mb[0], "img_{0}_y.tif".format(i))
save_image(submit_config, source_mb[0],
"img_{0}_x_aug.tif".format(i))
validation_set.evaluate(net, i,
noise_augmenter.add_validation_noise_np)
print(
'iter %-10d time %-12s sec/eval %-7.1f sec/iter %-7.2f maintenance %-6.1f'
% (autosummary('Timing/iter', i),
dnnlib.util.format_time(
autosummary('Timing/total_sec', time_total)),
autosummary('Timing/sec_per_eval', time_train),
autosummary('Timing/sec_per_iter',
time_train / eval_interval),
autosummary('Timing/maintenance_sec', time_maintenance)))
dnnlib.tflib.autosummary.save_summaries(summary_log, i)
ctx.update(loss='run %d' % submit_config.run_id,
cur_epoch=i,
max_epoch=iteration_count)
time_maintenance = ctx.get_last_update_interval() - time_train
save_snapshot(submit_config, net, str(i))
lrate = compute_ramped_down_lrate(i, iteration_count, ramp_down_perc,
learning_rate)
tfutil.run([train_step], {lrate_in: lrate})
print("Elapsed time: {0}".format(
util.format_time(ctx.get_time_since_start())))
save_snapshot(submit_config, net, 'final')
# Summary log and context should be closed at the end
summary_log.close()
ctx.close()
