def interpolate_latents(run_id,
snapshot,
video_fps=30,
filter_frames=30,
num_frames=60 * 30,
drange_net=[-1, 1],
image_grid_size=None,
zoom=None,
video_bitrate='16M'):
import moviepy.editor # pip install moviepy
# Choose parameters.
net = imgapi_load_net(run_id=run_id, snapshot=snapshot)
w, h = net.G.output_shape[3], net.G.output_shape[2]
if image_grid_size is None and zoom is None: image_grid_size = (1, 1)
if zoom is None: zoom = max(min(1920 / w, 1080 / h), 1)
if image_grid_size is None:
image_grid_size = np.clip(int(np.floor(1920 / (w * zoom))), 1,
16), np.clip(
int(np.floor(1080 / (h * zoom))), 1, 16)
# Generate latent vectors (frame, image, channel, component).
print 'Generating latent vectors...'
latents = np.random.randn(num_frames, np.prod(image_grid_size),
*net.G.input_shape[1:]).astype(np.float32)
latents = scipy.ndimage.gaussian_filter(latents, [filter_frames] +
[0] * len(net.G.input_shape),
mode='wrap')
latents /= np.sqrt(np.mean(latents**2))
# Create video.
print 'Generating video...'
result_subdir = misc.create_result_subdir(config.result_dir,
config.run_desc)
def make_frame(t):
frame_idx = np.clip(int(np.round(t * video_fps)), 0, num_frames - 1)
images = net.gen_fn(latents[frame_idx],
net.example_labels[:latents.shape[1]])
grid = misc.create_image_grid(images, grid_size=image_grid_size)
if zoom != 1: grid = scipy.ndimage.zoom(grid, [1, zoom, zoom], order=0)
grid = grid.clip(0, 255).transpose(1, 2, 0) # CHW => HWC
if grid.shape[2] == 1: grid = grid.repeat(3, 2) # grayscale => RGB
return grid
video = moviepy.editor.VideoClip(make_frame,
duration=float(num_frames) / video_fps)
video.write_videofile(os.path.join(
result_subdir,
os.path.basename(result_subdir) + '.mp4'),
fps=video_fps,
codec='libx264',
bitrate=video_bitrate)
# Done.
print 'Done.'
with open(os.path.join(result_subdir, '_video-done.txt'), 'wt'):
pass
def predict_gan():
separate_funcs = False
drange_net = [-1,1]
drange_viz = [-1,1]
image_grid_size = (1 ,1)
image_grid_type = 'default'
resume_network = './pre-trained_weight' # adding the ./ to define the pre-trained-weight folder at root level
np.random.seed(config.random_seed)
if resume_network:
print("Resuming weight from:"+resume_network)
G = Generator(num_channels=3, resolution=128, label_size=0, **config.G)
G = load_G_weights(G,resume_network,True)
print(G.summary())
# Misc init.
if image_grid_type == 'default':
if image_grid_size is None:
w, h = G.output_shape[1], G.output_shape[2]
print("w:%d,h:%d"%(w,h))
image_grid_size = np.clip(int(1920 // w), 3, 16).astype('int'), np.clip(1080 / h, 2, 16).astype('int')
print("image_grid_size:",image_grid_size)
else:
raise ValueError('Invalid image_grid_type', image_grid_type)
result_subdir = misc.create_result_subdir('pre-trained_result', config.run_desc)
for i in range(1,6):
snapshot_fake_latents = random_latents(np.prod(image_grid_size), G.input_shape)
snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images, os.path.join(result_subdir, 'pre-trained_%03d.png'%i), drange=drange_viz, grid_size=image_grid_size)
# use streamlit to show images generated
# st.image(os.path.join(result_subdir, 'pre-trained_%03d.png'%i))
st.header('IG Post #' + str(i))
im = Image.open(os.path.join(result_subdir, 'pre-trained_%03d.png'%i))
st.image(im.resize((512, 512), Image.ANTIALIAS)) # with gpt2, the images are generated too slow
# call gpt2-simple on a pre-trained weight
gen_file = os.path.join(result_subdir,'gpt2_gentext_{:%Y%m%d_%H%M%S}.txt'.format(datetime.utcnow()))
gpt2.generate_to_file(sess, destination_path=gen_file, run_name='tree_run1')
# read contents of generated text
with open(gen_file, 'r') as content:
st.write(content.read())
def predict_gan():
separate_funcs = False
drange_net = [-1, 1]
drange_viz = [-1, 1]
image_grid_size = (1, 1)
image_grid_type = 'default'
resume_network = './pre-trained_weight' # adding the ./ to define the pre-trained-weight folder at root level
np.random.seed(config.random_seed)
if resume_network:
print("Resuming weight from:" + resume_network)
G = Generator(num_channels=3, resolution=128, label_size=0, **config.G)
G = load_G_weights(G, resume_network, True)
print(G.summary())
# Misc init.
if image_grid_type == 'default':
if image_grid_size is None:
w, h = G.output_shape[1], G.output_shape[2]
print("w:%d,h:%d" % (w, h))
image_grid_size = np.clip(int(1920 // w), 3,
16).astype('int'), np.clip(
1080 / h, 2, 16).astype('int')
print("image_grid_size:", image_grid_size)
else:
raise ValueError('Invalid image_grid_type', image_grid_type)
result_subdir = misc.create_result_subdir('pre-trained_result',
config.run_desc)
for i in range(1, 6):
snapshot_fake_latents = random_latents(np.prod(image_grid_size),
G.input_shape)
snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images,
os.path.join(result_subdir,
'pre-trained_%03d.png' % i),
drange=drange_viz,
grid_size=image_grid_size)
def predict_gan():
separate_funcs = False
drange_net = [-1,1]
drange_viz = [-1,1]
image_grid_size = None
image_grid_type = 'default'
resume_network = path2preTrainedWeights
#np.random.seed(config.random_seed)
if resume_network:
print("Resuming weight from:"+resume_network)
G = Generator(num_channels=3, resolution=resolution, label_size=0, **config.G)
#G = load_G_weights(G,resume_network,True)
D = Discriminator(num_channels=3, resolution=resolution, label_size=0, **config.D)
G,D = load_GD_weights(G,D,resume_network,True)
print(G.summary())
print(D.summary())
# Misc init.
if image_grid_type == 'default':
if image_grid_size is None:
w, h = G.output_shape[1], G.output_shape[2]
print("w:%d,h:%d"%(w,h))
image_grid_size = np.clip(int(1920 // w), 3, 16).astype('int'), np.clip(1080 / h, 2, 16).astype('int')
image_grid_size=1,1
print("image_grid_size:",image_grid_size)
else:
raise ValueError('Invalid image_grid_type', image_grid_type)
result_subdir = misc.create_result_subdir('pre-trained_result', config.run_desc)
for i in range(1,numOfGenImages):
snapshot_fake_latents = random_latents(np.prod(image_grid_size), G.input_shape)
snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
snapshot_fake_scores = D.predict_on_batch(snapshot_fake_images)[0,0,0,0]
misc.save_image_grid(snapshot_fake_images, os.path.join(result_subdir, 'pre-trained_%03d.png'%i), drange=drange_viz, grid_size=image_grid_size)
def train_gan(separate_funcs=False,
D_training_repeats=1,
G_learning_rate_max=0.0010,
D_learning_rate_max=0.0010,
G_smoothing=0.999,
adam_beta1=0.0,
adam_beta2=0.99,
adam_epsilon=1e-8,
minibatch_default=16,
minibatch_overrides={},
rampup_kimg=40,
rampdown_kimg=0,
lod_initial_resolution=4,
lod_training_kimg=400,
lod_transition_kimg=400,
total_kimg=10000,
dequantize_reals=False,
gdrop_beta=0.9,
gdrop_lim=0.5,
gdrop_coef=0.2,
gdrop_exp=2.0,
drange_net=[-1, 1],
drange_viz=[-1, 1],
image_grid_size=None,
tick_kimg_default=50,
tick_kimg_overrides={
32: 20,
64: 10,
128: 10,
256: 5,
512: 2,
1024: 1
},
image_snapshot_ticks=4,
network_snapshot_ticks=40,
image_grid_type='default',
resume_network_pkl=None,
resume_kimg=0.0,
resume_time=0.0):
# Load dataset and build networks.
training_set, drange_orig = load_dataset()
if resume_network_pkl:
print 'Resuming', resume_network_pkl
G, D, _ = misc.load_pkl(
os.path.join(config.result_dir, resume_network_pkl))
else:
G = network.Network(num_channels=training_set.shape[1],
resolution=training_set.shape[2],
label_size=training_set.labels.shape[1],
**config.G)
D = network.Network(num_channels=training_set.shape[1],
resolution=training_set.shape[2],
label_size=training_set.labels.shape[1],
**config.D)
Gs = G.create_temporally_smoothed_version(beta=G_smoothing,
explicit_updates=True)
misc.print_network_topology_info(G.output_layers)
misc.print_network_topology_info(D.output_layers)
# Setup snapshot image grid.
if image_grid_type == 'default':
if image_grid_size is None:
w, h = G.output_shape[3], G.output_shape[2]
image_grid_size = np.clip(1920 / w, 3,
16), np.clip(1080 / h, 2, 16)
example_real_images, snapshot_fake_labels = training_set.get_random_minibatch(
np.prod(image_grid_size), labels=True)
snapshot_fake_latents = random_latents(np.prod(image_grid_size),
G.input_shape)
elif image_grid_type == 'category':
W = training_set.labels.shape[1]
H = W if image_grid_size is None else image_grid_size[1]
image_grid_size = W, H
snapshot_fake_latents = random_latents(W * H, G.input_shape)
snapshot_fake_labels = np.zeros((W * H, W),
dtype=training_set.labels.dtype)
example_real_images = np.zeros((W * H, ) + training_set.shape[1:],
dtype=training_set.dtype)
for x in xrange(W):
snapshot_fake_labels[x::W, x] = 1.0
indices = np.arange(
training_set.shape[0])[training_set.labels[:, x] != 0]
for y in xrange(H):
example_real_images[x + y * W] = training_set.h5_lods[0][
np.random.choice(indices)]
else:
raise ValueError('Invalid image_grid_type', image_grid_type)
# Theano input variables and compile generation func.
print 'Setting up Theano...'
real_images_var = T.TensorType('float32', [False] *
len(D.input_shape))('real_images_var')
real_labels_var = T.TensorType(
'float32', [False] * len(training_set.labels.shape))('real_labels_var')
fake_latents_var = T.TensorType('float32', [False] *
len(G.input_shape))('fake_latents_var')
fake_labels_var = T.TensorType(
'float32', [False] * len(training_set.labels.shape))('fake_labels_var')
G_lrate = theano.shared(np.float32(0.0))
D_lrate = theano.shared(np.float32(0.0))
gen_fn = theano.function([fake_latents_var, fake_labels_var],
Gs.eval_nd(fake_latents_var,
fake_labels_var,
ignore_unused_inputs=True),
on_unused_input='ignore')
# Misc init.
resolution_log2 = int(np.round(np.log2(G.output_shape[2])))
initial_lod = max(
resolution_log2 - int(np.round(np.log2(lod_initial_resolution))), 0)
cur_lod = 0.0
min_lod, max_lod = -1.0, -2.0
fake_score_avg = 0.0
if config.D.get('mbdisc_kernels', None):
print 'Initializing minibatch discrimination...'
if hasattr(D, 'cur_lod'): D.cur_lod.set_value(np.float32(initial_lod))
D.eval(real_images_var, deterministic=False, init=True)
init_layers = lasagne.layers.get_all_layers(D.output_layers)
init_updates = [
update for layer in init_layers
for update in getattr(layer, 'init_updates', [])
]
init_fn = theano.function(inputs=[real_images_var],
outputs=None,
updates=init_updates)
init_reals = training_set.get_random_minibatch(500, lod=initial_lod)
init_reals = misc.adjust_dynamic_range(init_reals, drange_orig,
drange_net)
init_fn(init_reals)
del init_reals
# Save example images.
snapshot_fake_images = gen_fn(snapshot_fake_latents, snapshot_fake_labels)
result_subdir = misc.create_result_subdir(config.result_dir,
config.run_desc)
misc.save_image_grid(example_real_images,
os.path.join(result_subdir, 'reals.png'),
drange=drange_orig,
grid_size=image_grid_size)
misc.save_image_grid(snapshot_fake_images,
os.path.join(result_subdir, 'fakes%06d.png' % 0),
drange=drange_viz,
grid_size=image_grid_size)
# Training loop.
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
tick_train_out = []
train_start_time = tick_start_time - resume_time
while cur_nimg < total_kimg * 1000:
# Calculate current LOD.
cur_lod = initial_lod
if lod_training_kimg or lod_transition_kimg:
tlod = (cur_nimg / 1000.0) / (lod_training_kimg +
lod_transition_kimg)
cur_lod -= np.floor(tlod)
if lod_transition_kimg:
cur_lod -= max(
1.0 + (np.fmod(tlod, 1.0) - 1.0) *
(lod_training_kimg + lod_transition_kimg) /
lod_transition_kimg, 0.0)
cur_lod = max(cur_lod, 0.0)
# Look up resolution-dependent parameters.
cur_res = 2**(resolution_log2 - int(np.floor(cur_lod)))
minibatch_size = minibatch_overrides.get(cur_res, minibatch_default)
tick_duration_kimg = tick_kimg_overrides.get(cur_res,
tick_kimg_default)
# Update network config.
lrate_coef = misc.rampup(cur_nimg / 1000.0, rampup_kimg)
lrate_coef *= misc.rampdown_linear(cur_nimg / 1000.0, total_kimg,
rampdown_kimg)
G_lrate.set_value(np.float32(lrate_coef * G_learning_rate_max))
D_lrate.set_value(np.float32(lrate_coef * D_learning_rate_max))
if hasattr(G, 'cur_lod'): G.cur_lod.set_value(np.float32(cur_lod))
if hasattr(D, 'cur_lod'): D.cur_lod.set_value(np.float32(cur_lod))
# Setup training func for current LOD.
new_min_lod, new_max_lod = int(np.floor(cur_lod)), int(
np.ceil(cur_lod))
if min_lod != new_min_lod or max_lod != new_max_lod:
print 'Compiling training funcs...'
min_lod, max_lod = new_min_lod, new_max_lod
# Pre-process reals.
real_images_expr = real_images_var
if dequantize_reals:
rnd = theano.sandbox.rng_mrg.MRG_RandomStreams(
lasagne.random.get_rng().randint(1, 2147462579))
epsilon_noise = rnd.uniform(size=real_images_expr.shape,
low=-0.5,
high=0.5,
dtype='float32')
real_images_expr = T.cast(
real_images_expr, 'float32'
) + epsilon_noise # match original implementation of Improved Wasserstein
real_images_expr = misc.adjust_dynamic_range(
real_images_expr, drange_orig, drange_net)
if min_lod > 0: # compensate for shrink_based_on_lod
real_images_expr = T.extra_ops.repeat(real_images_expr,
2**min_lod,
axis=2)
real_images_expr = T.extra_ops.repeat(real_images_expr,
2**min_lod,
axis=3)
# Optimize loss.
G_loss, D_loss, real_scores_out, fake_scores_out = evaluate_loss(
G, D, min_lod, max_lod, real_images_expr, real_labels_var,
fake_latents_var, fake_labels_var, **config.loss)
G_updates = adam(G_loss,
G.trainable_params(),
learning_rate=G_lrate,
beta1=adam_beta1,
beta2=adam_beta2,
epsilon=adam_epsilon).items()
D_updates = adam(D_loss,
D.trainable_params(),
learning_rate=D_lrate,
beta1=adam_beta1,
beta2=adam_beta2,
epsilon=adam_epsilon).items()
# Compile training funcs.
if not separate_funcs:
GD_train_fn = theano.function([
real_images_var, real_labels_var, fake_latents_var,
fake_labels_var
], [G_loss, D_loss, real_scores_out, fake_scores_out],
updates=G_updates + D_updates +
Gs.updates,
on_unused_input='ignore')
else:
D_train_fn = theano.function([
real_images_var, real_labels_var, fake_latents_var,
fake_labels_var
], [G_loss, D_loss, real_scores_out, fake_scores_out],
updates=D_updates,
on_unused_input='ignore')
G_train_fn = theano.function(
[fake_latents_var, fake_labels_var], [],
updates=G_updates + Gs.updates,
on_unused_input='ignore')
# Invoke training funcs.
if not separate_funcs:
assert D_training_repeats == 1
mb_reals, mb_labels = training_set.get_random_minibatch(
minibatch_size,
lod=cur_lod,
shrink_based_on_lod=True,
labels=True)
mb_train_out = GD_train_fn(
mb_reals, mb_labels,
random_latents(minibatch_size, G.input_shape),
random_labels(minibatch_size, training_set))
cur_nimg += minibatch_size
tick_train_out.append(mb_train_out)
else:
for idx in xrange(D_training_repeats):
mb_reals, mb_labels = training_set.get_random_minibatch(
minibatch_size,
lod=cur_lod,
shrink_based_on_lod=True,
labels=True)
mb_train_out = D_train_fn(
mb_reals, mb_labels,
random_latents(minibatch_size, G.input_shape),
random_labels(minibatch_size, training_set))
cur_nimg += minibatch_size
tick_train_out.append(mb_train_out)
G_train_fn(random_latents(minibatch_size, G.input_shape),
random_labels(minibatch_size, training_set))
# Fade in D noise if we're close to becoming unstable
fake_score_cur = np.clip(np.mean(mb_train_out[1]), 0.0, 1.0)
fake_score_avg = fake_score_avg * gdrop_beta + fake_score_cur * (
1.0 - gdrop_beta)
gdrop_strength = gdrop_coef * (max(fake_score_avg - gdrop_lim, 0.0)**
gdrop_exp)
if hasattr(D, 'gdrop_strength'):
D.gdrop_strength.set_value(np.float32(gdrop_strength))
# Perform maintenance operations once per tick.
if cur_nimg >= tick_start_nimg + tick_duration_kimg * 1000 or cur_nimg >= total_kimg * 1000:
cur_tick += 1
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
tick_start_time = cur_time
tick_train_avg = tuple(
np.mean(np.concatenate([np.asarray(v).flatten()
for v in vals]))
for vals in zip(*tick_train_out))
tick_train_out = []
# Print progress.
print 'tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-9.1f sec/kimg %-6.1f Dgdrop %-8.4f Gloss %-8.4f Dloss %-8.4f Dreal %-8.4f Dfake %-8.4f' % (
(cur_tick, cur_nimg / 1000.0, cur_lod, minibatch_size,
misc.format_time(cur_time - train_start_time), tick_time,
tick_time / tick_kimg, gdrop_strength) + tick_train_avg)
# Visualize generated images.
if cur_tick % image_snapshot_ticks == 0 or cur_nimg >= total_kimg * 1000:
snapshot_fake_images = gen_fn(snapshot_fake_latents,
snapshot_fake_labels)
misc.save_image_grid(snapshot_fake_images,
os.path.join(
result_subdir,
'fakes%06d.png' % (cur_nimg / 1000)),
drange=drange_viz,
grid_size=image_grid_size)
# Save network snapshot every N ticks.
if cur_tick % network_snapshot_ticks == 0 or cur_nimg >= total_kimg * 1000:
misc.save_pkl(
(G, D, Gs),
os.path.join(
result_subdir,
'network-snapshot-%06d.pkl' % (cur_nimg / 1000)))
# Write final results.
misc.save_pkl((G, D, Gs), os.path.join(result_subdir, 'network-final.pkl'))
training_set.close()
print 'Done.'
with open(os.path.join(result_subdir, '_training-done.txt'), 'wt'):
pass
def train_gan(separate_funcs=False,
D_training_repeats=1,
G_learning_rate_max=0.0010,
D_learning_rate_max=0.0010,
G_smoothing=0.999,
adam_beta1=0.0,
adam_beta2=0.99,
adam_epsilon=1e-8,
minibatch_default=16,
minibatch_overrides={},
rampup_kimg=40 / speed_factor,
rampdown_kimg=0,
lod_initial_resolution=4,
lod_training_kimg=400 / speed_factor,
lod_transition_kimg=400 / speed_factor,
total_kimg=10000 / speed_factor,
dequantize_reals=False,
gdrop_beta=0.9,
gdrop_lim=0.5,
gdrop_coef=0.2,
gdrop_exp=2.0,
drange_net=[-1, 1],
drange_viz=[-1, 1],
image_grid_size=None,
tick_kimg_default=50 / speed_factor,
tick_kimg_overrides={
32: 20,
64: 10,
128: 10,
256: 5,
512: 2,
1024: 1
},
image_snapshot_ticks=1,
network_snapshot_ticks=4,
image_grid_type='default',
resume_network=None,
resume_kimg=0.0,
resume_time=0.0):
training_set, drange_orig = load_dataset()
print("-" * 50)
print("resume_kimg: %s" % resume_kimg)
print("-" * 50)
if resume_network:
print("Resuming weight from:" + resume_network)
G = Generator(num_channels=training_set.shape[3],
resolution=training_set.shape[1],
label_size=training_set.labels.shape[1],
**config.G)
D = Discriminator(num_channels=training_set.shape[3],
resolution=training_set.shape[1],
label_size=training_set.labels.shape[1],
**config.D)
G, D = load_GD_weights(G, D,
os.path.join(config.result_dir, resume_network),
True)
print("pre-trained weights loaded!")
print("-" * 50)
else:
G = Generator(num_channels=training_set.shape[3],
resolution=training_set.shape[1],
label_size=training_set.labels.shape[1],
**config.G)
D = Discriminator(num_channels=training_set.shape[3],
resolution=training_set.shape[1],
label_size=training_set.labels.shape[1],
**config.D)
G_train, D_train = PG_GAN(G, D, config.G['latent_size'], 0,
training_set.shape[1], training_set.shape[3])
print(G.summary())
print(D.summary())
# Misc init.
resolution_log2 = int(np.round(np.log2(G.output_shape[2])))
initial_lod = max(
resolution_log2 - int(np.round(np.log2(lod_initial_resolution))), 0)
cur_lod = 0.0
min_lod, max_lod = -1.0, -2.0
fake_score_avg = 0.0
G_opt = optimizers.Adam(lr=0.0,
beta_1=adam_beta1,
beta_2=adam_beta2,
epsilon=adam_epsilon)
D_opt = optimizers.Adam(lr=0.0,
beta_1=adam_beta1,
beta_2=adam_beta2,
epsilon=adam_epsilon)
if config.loss['type'] == 'wass':
G_loss = wasserstein_loss
D_loss = wasserstein_loss
elif config.loss['type'] == 'iwass':
G_loss = multiple_loss
D_loss = [mean_loss, 'mse']
D_loss_weight = [1.0, config.loss['iwass_lambda']]
G.compile(G_opt, loss=G_loss)
D.trainable = False
G_train.compile(G_opt, loss=G_loss)
D.trainable = True
D_train.compile(D_opt, loss=D_loss, loss_weights=D_loss_weight)
cur_nimg = int(resume_kimg * 1000)
print("-" * 50)
print("resume_kimg: %s" % resume_kimg)
print("current nimg: %s" % cur_nimg)
print("-" * 50)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
tick_train_out = []
train_start_time = tick_start_time - resume_time
if image_grid_type == 'default':
if image_grid_size is None:
w, h = G.output_shape[1], G.output_shape[2]
print("w:%d,h:%d" % (w, h))
image_grid_size = np.clip(int(1920 // w), 3,
16).astype('int'), np.clip(
1080 / h, 2, 16).astype('int')
print("image_grid_size:", image_grid_size)
example_real_images, snapshot_fake_labels = training_set.get_random_minibatch_channel_last(
np.prod(image_grid_size), labels=True)
snapshot_fake_latents = random_latents(np.prod(image_grid_size),
G.input_shape)
else:
raise ValueError('Invalid image_grid_type', image_grid_type)
result_subdir = misc.create_result_subdir(config.result_dir,
config.run_desc)
print("example_real_images.shape:", example_real_images.shape)
misc.save_image_grid(example_real_images,
os.path.join(result_subdir, 'reals.png'),
drange=drange_orig,
grid_size=image_grid_size)
snapshot_fake_latents = random_latents(np.prod(image_grid_size),
G.input_shape)
snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images,
os.path.join(result_subdir,
'fakes%06d.png' % (cur_nimg / 1000)),
drange=drange_viz,
grid_size=image_grid_size)
nimg_h = 0
while cur_nimg < total_kimg * 1000:
# Calculate current LOD.
cur_lod = initial_lod
if lod_training_kimg or lod_transition_kimg:
tlod = (cur_nimg / (1000.0 / speed_factor)) / (lod_training_kimg +
lod_transition_kimg)
cur_lod -= np.floor(tlod)
if lod_transition_kimg:
cur_lod -= max(
1.0 + (np.fmod(tlod, 1.0) - 1.0) *
(lod_training_kimg + lod_transition_kimg) /
lod_transition_kimg, 0.0)
cur_lod = max(cur_lod, 0.0)
# Look up resolution-dependent parameters.
cur_res = 2**(resolution_log2 - int(np.floor(cur_lod)))
minibatch_size = minibatch_overrides.get(cur_res, minibatch_default)
tick_duration_kimg = tick_kimg_overrides.get(cur_res,
tick_kimg_default)
# Update network config.
lrate_coef = rampup(cur_nimg / 1000.0, rampup_kimg)
lrate_coef *= rampdown_linear(cur_nimg / 1000.0, total_kimg,
rampdown_kimg)
K.set_value(G.optimizer.lr,
np.float32(lrate_coef * G_learning_rate_max))
K.set_value(G_train.optimizer.lr,
np.float32(lrate_coef * G_learning_rate_max))
K.set_value(D_train.optimizer.lr,
np.float32(lrate_coef * D_learning_rate_max))
if hasattr(G_train, 'cur_lod'):
K.set_value(G_train.cur_lod, np.float32(cur_lod))
if hasattr(D_train, 'cur_lod'):
K.set_value(D_train.cur_lod, np.float32(cur_lod))
new_min_lod, new_max_lod = int(np.floor(cur_lod)), int(
np.ceil(cur_lod))
if min_lod != new_min_lod or max_lod != new_max_lod:
min_lod, max_lod = new_min_lod, new_max_lod
# train D
d_loss = None
for idx in range(D_training_repeats):
mb_reals, mb_labels = training_set.get_random_minibatch_channel_last(
minibatch_size,
lod=cur_lod,
shrink_based_on_lod=True,
labels=True)
mb_latents = random_latents(minibatch_size, G.input_shape)
mb_labels_rnd = random_labels(minibatch_size, training_set)
if min_lod > 0: # compensate for shrink_based_on_lod
mb_reals = np.repeat(mb_reals, 2**min_lod, axis=1)
mb_reals = np.repeat(mb_reals, 2**min_lod, axis=2)
mb_fakes = G.predict_on_batch([mb_latents])
epsilon = np.random.uniform(0, 1, size=(minibatch_size, 1, 1, 1))
interpolation = epsilon * mb_reals + (1 - epsilon) * mb_fakes
mb_reals = misc.adjust_dynamic_range(mb_reals, drange_orig,
drange_net)
d_loss, d_diff, d_norm = D_train.train_on_batch(
[mb_fakes, mb_reals, interpolation], [
np.ones((minibatch_size, 1, 1, 1)),
np.ones((minibatch_size, 1))
])
d_score_real = D.predict_on_batch(mb_reals)
d_score_fake = D.predict_on_batch(mb_fakes)
print("%d real score: %d fake score: %d" %
(idx, np.mean(d_score_real), np.mean(d_score_fake)))
print("-" * 50)
cur_nimg += minibatch_size
#train G
mb_latents = random_latents(minibatch_size, G.input_shape)
mb_labels_rnd = random_labels(minibatch_size, training_set)
g_loss = G_train.train_on_batch([mb_latents], (-1) * np.ones(
(mb_latents.shape[0], 1, 1, 1)))
print("%d images [D loss: %f] [G loss: %f]" %
(cur_nimg, d_loss, g_loss))
print("-" * 50)
fake_score_cur = np.clip(np.mean(d_loss), 0.0, 1.0)
fake_score_avg = fake_score_avg * gdrop_beta + fake_score_cur * (
1.0 - gdrop_beta)
gdrop_strength = gdrop_coef * (max(fake_score_avg - gdrop_lim, 0.0)**
gdrop_exp)
if hasattr(D, 'gdrop_strength'):
K.set_value(D.gdrop_strength, np.float32(gdrop_strength))
if cur_nimg >= tick_start_nimg + tick_duration_kimg * 1000 or cur_nimg >= total_kimg * 1000:
cur_tick += 1
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
tick_start_time = cur_time
tick_train_avg = tuple(
np.mean(np.concatenate([np.asarray(v).flatten()
for v in vals]))
for vals in zip(*tick_train_out))
tick_train_out = []
# Visualize generated images.
if cur_tick % image_snapshot_ticks == 0 or cur_nimg >= total_kimg * 1000:
snapshot_fake_images = G.predict_on_batch(
snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images,
os.path.join(
result_subdir,
'fakes%06d.png' % (cur_nimg / 1000)),
drange=drange_viz,
grid_size=image_grid_size)
if cur_tick % network_snapshot_ticks == 0 or cur_nimg >= total_kimg * 1000:
save_GD_weights(
G, D,
os.path.join(result_subdir,
'network-snapshot-%06d' % (cur_nimg / 1000)))
save_GD(G, D, os.path.join(result_subdir, 'network-final'))
training_set.close()
print('Done.')
def train_gan(
separate_funcs=False,
D_training_repeats=1,
G_learning_rate_max=0.0010,
D_learning_rate_max=0.0010,
G_smoothing=0.999,
adam_beta1=0.0,
adam_beta2=0.99,
adam_epsilon=1e-8,
minibatch_default=16,
minibatch_overrides={},
rampup_kimg=40 / speed_factor,
rampdown_kimg=0,
lod_initial_resolution=4,
lod_training_kimg=400 / speed_factor,
lod_transition_kimg=400 / speed_factor,
total_kimg=10000 / speed_factor,
dequantize_reals=False,
gdrop_beta=0.9,
gdrop_lim=0.5,
gdrop_coef=0.2,
gdrop_exp=2.0,
drange_net=[-1, 1],
drange_viz=[-1, 1],
image_grid_size=None,
tick_kimg_default=50 / speed_factor,
tick_kimg_overrides={
32: 20,
64: 10,
128: 10,
256: 5,
512: 2,
1024: 1
},
image_snapshot_ticks=1,
network_snapshot_ticks=4,
image_grid_type='default',
resume_network='000-celeba/network-snapshot-000488',
#resume_network = None,
resume_kimg=511.6,
resume_time=0.0):
training_set, drange_orig = load_dataset()
#print("training_set.shape:",training_set.shape)
if resume_network:
print("Resuming weight from:" + resume_network)
G = Generator(num_channels=training_set.shape[3],
resolution=training_set.shape[1],
label_size=training_set.labels.shape[1],
**config.G)
D = Discriminator(num_channels=training_set.shape[3],
resolution=training_set.shape[1],
label_size=training_set.labels.shape[1],
**config.D)
G, D = load_GD_weights(G, D,
os.path.join(config.result_dir, resume_network),
True)
else:
G = Generator(num_channels=training_set.shape[3],
resolution=training_set.shape[1],
label_size=training_set.labels.shape[1],
**config.G)
D = Discriminator(num_channels=training_set.shape[3],
resolution=training_set.shape[1],
label_size=training_set.labels.shape[1],
**config.D)
#missing Gs
G_train, D_train = PG_GAN(G, D, config.G['latent_size'], 0,
training_set.shape[1], training_set.shape[3])
#G_tmp = Model(inputs=[G.get_input_at(0)],
# outputs = [G.get_layer('G6bPN').output])
print(G.summary())
print(D.summary())
#print(pg_GAN.summary())
# Misc init.
resolution_log2 = int(np.round(np.log2(G.output_shape[2])))
initial_lod = max(
resolution_log2 - int(np.round(np.log2(lod_initial_resolution))), 0)
cur_lod = 0.0
min_lod, max_lod = -1.0, -2.0
fake_score_avg = 0.0
G_opt = optimizers.Adam(lr=0.0,
beta_1=adam_beta1,
beta_2=adam_beta2,
epsilon=adam_epsilon)
D_opt = optimizers.Adam(lr=0.0,
beta_1=adam_beta1,
beta_2=adam_beta2,
epsilon=adam_epsilon)
# GAN_opt = optimizers.Adam(lr = 0.0,beta_1 = 0.0,beta_2 = 0.99)
if config.loss['type'] == 'wass':
G_loss = wasserstein_loss
D_loss = wasserstein_loss
elif config.loss['type'] == 'iwass':
G_loss = multiple_loss
D_loss = [mean_loss, 'mse']
D_loss_weight = [1.0, config.loss['iwass_lambda']]
G.compile(G_opt, loss=G_loss)
D.trainable = False
G_train.compile(G_opt, loss=G_loss)
D.trainable = True
D_train.compile(D_opt, loss=D_loss, loss_weights=D_loss_weight)
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
tick_train_out = []
train_start_time = tick_start_time - resume_time
#real_image_input = Input((training_set.shape[2],training_set.shape[2],training_set.shape[-1]),name = "real_image_input")
#real_label_input = Input((training_set.labels.shape[1]),name = "real_label_input")
#fake_latent_input = Input((config.G['latent_size']),name = "fake_latent_input")
#fake_labels_input = Input((training_set.labels.shape[1]),name = "fake_label_input")
if image_grid_type == 'default':
if image_grid_size is None:
w, h = G.output_shape[1], G.output_shape[2]
print("w:%d,h:%d" % (w, h))
image_grid_size = np.clip(int(1920 // w), 3,
16).astype('int'), np.clip(
1080 / h, 2, 16).astype('int')
print("image_grid_size:", image_grid_size)
example_real_images, snapshot_fake_labels = training_set.get_random_minibatch_channel_last(
np.prod(image_grid_size), labels=True)
snapshot_fake_latents = random_latents(np.prod(image_grid_size),
G.input_shape)
else:
raise ValueError('Invalid image_grid_type', image_grid_type)
#print("image_grid_size:",image_grid_size)
result_subdir = misc.create_result_subdir(config.result_dir,
config.run_desc)
#snapshot_fake_images = gen_fn(snapshot_fake_latents, snapshot_fake_labels)
#result_subdir = misc.create_result_subdir(config.result_dir, config.run_desc)
#('example_real_images.shape:', (120, 3, 128, 128))
#print("example_real_images:",example_real_images)
print("example_real_images.shape:", example_real_images.shape)
misc.save_image_grid(example_real_images,
os.path.join(result_subdir, 'reals.png'),
drange=drange_orig,
grid_size=image_grid_size)
#misc.save_image_grid(snapshot_fake_images, os.path.join(result_subdir, 'fakes%06d.png' % 0), drange=drange_viz, grid_size=image_grid_size)
#NINweight = G.get_layer('Glod0NIN').get_weights()[0]
#print("Glod0NIN weight:",NINweight)
snapshot_fake_latents = random_latents(np.prod(image_grid_size),
G.input_shape)
snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images,
os.path.join(result_subdir,
'fakes%06d.png' % (cur_nimg / 1000)),
drange=drange_viz,
grid_size=image_grid_size)
nimg_h = 0
while cur_nimg < total_kimg * 1000:
# Calculate current LOD.
cur_lod = initial_lod
if lod_training_kimg or lod_transition_kimg:
tlod = (cur_nimg / (1000.0 / speed_factor)) / (lod_training_kimg +
lod_transition_kimg)
cur_lod -= np.floor(tlod)
if lod_transition_kimg:
cur_lod -= max(
1.0 + (np.fmod(tlod, 1.0) - 1.0) *
(lod_training_kimg + lod_transition_kimg) /
lod_transition_kimg, 0.0)
cur_lod = max(cur_lod, 0.0)
# Look up resolution-dependent parameters.
cur_res = 2**(resolution_log2 - int(np.floor(cur_lod)))
minibatch_size = minibatch_overrides.get(cur_res, minibatch_default)
tick_duration_kimg = tick_kimg_overrides.get(cur_res,
tick_kimg_default)
# Update network config.
lrate_coef = rampup(cur_nimg / 1000.0, rampup_kimg)
lrate_coef *= rampdown_linear(cur_nimg / 1000.0, total_kimg,
rampdown_kimg)
#G_lrate.set_value(np.float32(lrate_coef * G_learning_rate_max))
K.set_value(G.optimizer.lr,
np.float32(lrate_coef * G_learning_rate_max))
K.set_value(G_train.optimizer.lr,
np.float32(lrate_coef * G_learning_rate_max))
#D_lrate.set_value(np.float32(lrate_coef * D_learning_rate_max))
K.set_value(D_train.optimizer.lr,
np.float32(lrate_coef * D_learning_rate_max))
if hasattr(G_train, 'cur_lod'):
K.set_value(G_train.cur_lod, np.float32(cur_lod))
if hasattr(D_train, 'cur_lod'):
K.set_value(D_train.cur_lod, np.float32(cur_lod))
new_min_lod, new_max_lod = int(np.floor(cur_lod)), int(
np.ceil(cur_lod))
if min_lod != new_min_lod or max_lod != new_max_lod:
min_lod, max_lod = new_min_lod, new_max_lod
# # Pre-process reals.
# real_images_expr = real_images_var
# if dequantize_reals:
# epsilon_noise = K.random_uniform_variable(real_image_input.shape(), low=-0.5, high=0.5, dtype='float32', seed=np.random.randint(1, 2147462579))
# epsilon_noise = rnd.uniform(size=real_images_expr.shape, low=-0.5, high=0.5, dtype='float32')
# real_images_expr = T.cast(real_images_expr, 'float32') + epsilon_noise # match original implementation of Improved Wasserstein
# real_images_expr = misc.adjust_dynamic_range(real_images_expr, drange_orig, drange_net)
# if min_lod > 0: # compensate for shrink_based_on_lod
# real_images_expr = T.extra_ops.repeat(real_images_expr, 2**min_lod, axis=2)
# real_images_expr = T.extra_ops.repeat(real_images_expr, 2**min_lod, axis=3)
# train D
d_loss = None
for idx in range(D_training_repeats):
mb_reals, mb_labels = training_set.get_random_minibatch_channel_last(
minibatch_size,
lod=cur_lod,
shrink_based_on_lod=True,
labels=True)
mb_latents = random_latents(minibatch_size, G.input_shape)
mb_labels_rnd = random_labels(minibatch_size, training_set)
if min_lod > 0: # compensate for shrink_based_on_lod
mb_reals = np.repeat(mb_reals, 2**min_lod, axis=1)
mb_reals = np.repeat(mb_reals, 2**min_lod, axis=2)
mb_fakes = G.predict_on_batch([mb_latents])
epsilon = np.random.uniform(0, 1, size=(minibatch_size, 1, 1, 1))
interpolation = epsilon * mb_reals + (1 - epsilon) * mb_fakes
mb_reals = misc.adjust_dynamic_range(mb_reals, drange_orig,
drange_net)
d_loss, d_diff, d_norm = D_train.train_on_batch(
[mb_fakes, mb_reals, interpolation], [
np.ones((minibatch_size, 1, 1, 1)),
np.ones((minibatch_size, 1))
])
d_score_real = D.predict_on_batch(mb_reals)
d_score_fake = D.predict_on_batch(mb_fakes)
print("real score: %d fake score: %d" %
(np.mean(d_score_real), np.mean(d_score_fake)))
#d_loss_real = D.train_on_batch(mb_reals, -np.ones((mb_reals.shape[0],1,1,1)))
#d_loss_fake = D.train_on_batch(mb_fakes, np.ones((mb_fakes.shape[0],1,1,1)))
#d_loss = 0.5 * np.add(d_loss_fake, d_loss_real)
cur_nimg += minibatch_size
#train G
mb_latents = random_latents(minibatch_size, G.input_shape)
mb_labels_rnd = random_labels(minibatch_size, training_set)
#if cur_nimg//100 !=nimg_h:
# snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
# print(np.mean(snapshot_fake_images,axis=-1))
# G_lod = G_tmp.predict(snapshot_fake_latents)
# print("G6bPN:",np.mean(G_lod,axis=-1))
# misc.save_image_grid(snapshot_fake_images, os.path.join(result_subdir, 'fakes%06d_beforeGtrain.png' % (cur_nimg)), drange=drange_viz, grid_size=image_grid_size)
g_loss = G_train.train_on_batch([mb_latents], (-1) * np.ones(
(mb_latents.shape[0], 1, 1, 1)))
print("%d [D loss: %f] [G loss: %f]" % (cur_nimg, d_loss, g_loss))
#print(cur_nimg)
#print(g_loss)
#print(d_loss)
# Fade in D noise if we're close to becoming unstable
#if cur_nimg//100 !=nimg_h:
# nimg_h=cur_nimg//100
# snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
# print(np.mean(snapshot_fake_images,axis=-1))
# G_lod = G_tmp.predict(snapshot_fake_latents)
# print("G6bPN:",np.mean(G_lod,axis=-1))
# NINweight = G.get_layer('Glod0NIN').get_weights()[0]
# print(NINweight)
# misc.save_image_grid(snapshot_fake_images, os.path.join(result_subdir, 'fakes%06d.png' % (cur_nimg)), drange=drange_viz, grid_size=image_grid_size)
fake_score_cur = np.clip(np.mean(d_loss), 0.0, 1.0)
fake_score_avg = fake_score_avg * gdrop_beta + fake_score_cur * (
1.0 - gdrop_beta)
gdrop_strength = gdrop_coef * (max(fake_score_avg - gdrop_lim, 0.0)**
gdrop_exp)
if hasattr(D, 'gdrop_strength'):
K.set_value(D.gdrop_strength, np.float32(gdrop_strength))
if cur_nimg >= tick_start_nimg + tick_duration_kimg * 1000 or cur_nimg >= total_kimg * 1000:
cur_tick += 1
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
tick_start_time = cur_time
tick_train_avg = tuple(
np.mean(np.concatenate([np.asarray(v).flatten()
for v in vals]))
for vals in zip(*tick_train_out))
tick_train_out = []
'''
# Print progress.
print ('tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-9.1f sec/kimg %-6.1f Dgdrop %-8.4f Gloss %-8.4f Dloss %-8.4f Dreal %-8.4f Dfake %-8.4f' % (
(cur_tick, cur_nimg / 1000.0, cur_lod, minibatch_size, format_time(cur_time - train_start_time), tick_time, tick_time / tick_kimg, gdrop_strength) + tick_train_avg))
'''
# Visualize generated images.
if cur_tick % image_snapshot_ticks == 0 or cur_nimg >= total_kimg * 1000:
snapshot_fake_images = G.predict_on_batch(
snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images,
os.path.join(
result_subdir,
'fakes%06d.png' % (cur_nimg / 1000)),
drange=drange_viz,
grid_size=image_grid_size)
if cur_tick % network_snapshot_ticks == 0 or cur_nimg >= total_kimg * 1000:
save_GD_weights(
G, D,
os.path.join(result_subdir,
'network-snapshot-%06d' % (cur_nimg / 1000)))
save_GD(G, D, os.path.join(result_subdir, 'network-final'))
training_set.close()
print('Done.')
def train_progressive_gan(
G_smoothing=0.999, # Exponential 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=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.
maintenance_start_time = time.time()
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**config.dataset)
unlabeled_training_set = dataset.load_dataset(
data_dir=config.unlabeled_data_dir,
verbose=True,
**config.unlabeled_dataset)
# 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...')
print("Training-Set Label Size: ", training_set.label_size)
print("Unlabeled-Training-Set Label Size: ",
unlabeled_training_set.label_size)
G = tfutil.Network('G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**config.G)
D = tfutil.Network('D',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**config.D)
Gs = G.clone('Gs')
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=G_smoothing)
G.print_layers()
D.print_layers()
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'):
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 // config.num_gpus
reals, labels = training_set.get_minibatch_tf()
unlabeled_reals, _ = unlabeled_training_set.get_minibatch_tf()
reals_split = tf.split(reals, config.num_gpus)
unlabeled_reals_split = tf.split(unlabeled_reals, config.num_gpus)
labels_split = tf.split(labels, config.num_gpus)
G_opt = tfutil.Optimizer(name='TrainG',
learning_rate=lrate_in,
**config.G_opt)
G_opt_pggan = tfutil.Optimizer(name='TrainG_pggan',
learning_rate=lrate_in,
**config.G_opt)
D_opt_pggan = tfutil.Optimizer(name='TrainD_pggan',
learning_rate=lrate_in,
**config.D_opt)
D_opt = tfutil.Optimizer(name='TrainD',
learning_rate=lrate_in,
**config.D_opt)
print("CUDA_VISIBLE_DEVICES: ", os.environ['CUDA_VISIBLE_DEVICES'])
for gpu in range(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_gpu = process_reals(reals_split[gpu], lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
unlabeled_reals_gpu = process_reals(
unlabeled_reals_split[gpu], lod_in, mirror_augment,
unlabeled_training_set.dynamic_range, drange_net)
labels_gpu = labels_split[gpu]
with tf.name_scope('G_loss'), tf.control_dependencies(
lod_assign_ops):
G_loss = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
unlabeled_reals=unlabeled_reals_gpu,
**config.G_loss)
with tf.name_scope('G_loss_pggan'), tf.control_dependencies(
lod_assign_ops):
G_loss_pggan = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
**config.G_loss_pggan)
with tf.name_scope('D_loss'), tf.control_dependencies(
lod_assign_ops):
D_loss = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
reals=reals_gpu,
labels=labels_gpu,
unlabeled_reals=unlabeled_reals_gpu,
**config.D_loss)
with tf.name_scope('D_loss_pggan'), tf.control_dependencies(
lod_assign_ops):
D_loss_pggan = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
unlabeled_reals=unlabeled_reals_gpu,
**config.D_loss_pggan)
G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
G_opt_pggan.register_gradients(tf.reduce_mean(G_loss_pggan),
G_gpu.trainables)
D_opt_pggan.register_gradients(tf.reduce_mean(D_loss_pggan),
D_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
print('GPU %d loaded!' % gpu)
G_train_op = G_opt.apply_updates()
G_train_op_pggan = G_opt_pggan.apply_updates()
D_train_op_pggan = D_opt_pggan.apply_updates()
D_train_op = D_opt.apply_updates()
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = setup_snapshot_image_grid(
G, training_set, **config.grid)
sched = TrainingSchedule(total_kimg * TrainingSpeedInt, training_set,
**config.sched)
grid_fakes = Gs.run(grid_latents,
grid_labels,
minibatch_size=sched.minibatch // config.num_gpus)
print('Setting up result dir...')
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
misc.save_image_grid(grid_reals,
os.path.join(result_subdir, 'reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
misc.save_image_grid(grid_fakes,
os.path.join(result_subdir, 'fakes%06d.png' % 0),
drange=drange_net,
grid_size=grid_size)
summary_log = tf.compat.v1.summary.FileWriter(result_subdir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms()
D.setup_weight_histograms()
print("Start Time: ",
datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
print('Training...')
cur_nimg = int(resume_kimg * TrainingSpeedInt)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
train_start_time = tick_start_time - resume_time
prev_lod = -1.0
while cur_nimg < total_kimg * TrainingSpeedInt:
# Choose training parameters and configure training ops.
sched = TrainingSchedule(cur_nimg, training_set, **config.sched)
sched2 = TrainingSchedule(cur_nimg, unlabeled_training_set,
**config.sched)
training_set.configure(sched.minibatch, sched.lod)
unlabeled_training_set.configure(sched2.minibatch, sched2.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()
G_opt_pggan.reset_optimizer_state()
D_opt_pggan.reset_optimizer_state()
prev_lod = sched.lod
# Run training ops.
for repeat in range(minibatch_repeats):
for _ in range(D_repeats):
# Run the Pggan loss if lod != 0 else run SSL loss with feature matching
if sched.lod == 0:
tfutil.run(
[D_train_op, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch
})
else:
tfutil.run(
[D_train_op_pggan, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch
})
cur_nimg += sched.minibatch
#tmp = min(tick_start_nimg + sched.tick_kimg * TrainingSpeedInt, total_kimg * TrainingSpeedInt)
#print("Tick progress: {}/{}".format(cur_nimg, tmp), end="\r", flush=True)
# Run the Pggan loss if lod != 0 else run SSL loss with feature matching
if sched.lod == 0:
tfutil.run(
[G_train_op], {
lod_in: sched.lod,
lrate_in: sched.G_lrate,
minibatch_in: sched.minibatch
})
else:
tfutil.run(
[G_train_op_pggan], {
lod_in: sched.lod,
lrate_in: sched.G_lrate,
minibatch_in: sched.minibatch
})
# Perform maintenance tasks once per tick.
done = (cur_nimg >= total_kimg * TrainingSpeedInt)
if cur_nimg >= tick_start_nimg + sched.tick_kimg * TrainingSpeedInt or done:
cur_tick += 1
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / TrainingSpeedFloat
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
total_time = cur_time - train_start_time
maintenance_time = tick_start_time - maintenance_start_time
maintenance_start_time = cur_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 %.1f date %s'
% (tfutil.autosummary('Progress/tick', cur_tick),
tfutil.autosummary('Progress/kimg',
cur_nimg / TrainingSpeedFloat),
tfutil.autosummary('Progress/lod', sched.lod),
tfutil.autosummary('Progress/minibatch', sched.minibatch),
misc.format_time(
tfutil.autosummary('Timing/total_sec', total_time)),
tfutil.autosummary('Timing/sec_per_tick', tick_time),
tfutil.autosummary('Timing/sec_per_kimg',
tick_time / tick_kimg),
tfutil.autosummary(
'Timing/maintenance_sec', maintenance_time),
datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
tfutil.autosummary('Timing/total_hours',
total_time / (60.0 * 60.0))
tfutil.autosummary('Timing/total_days',
total_time / (24.0 * 60.0 * 60.0))
#######################
# VALIDATION ACCURACY #
#######################
# example ndim = 512 for an image that is 512x512 pixels
# All images for SSL-PGGAN must be square
ndim = 256
correct = 0
guesses = 0
dir_tuple = (config.validation_dog, config.validation_cat)
# If guessed the wrong class seeing if there is a bias
FP_RATE = [[0], [0]]
# For each class
for indx, directory in enumerate(dir_tuple):
# Go through every image that needs to be tested
for filename in os.listdir(directory):
guesses += 1
#tensor = np.zeros((1, 3, 512, 512))
print(filename)
img = np.asarray(PIL.Image.open(directory +
filename)).reshape(
3, ndim, ndim)
img = np.expand_dims(
img, axis=0) # makes the image (1,3,512,512)
K_logits_out, fake_logit_out, features_out = test_discriminator(
D, img)
#print("K Logits Out:",K_logits_out.eval())
sample_probs = tf.nn.softmax(K_logits_out)
#print("Softmax Output:", sample_probs.eval())
label = np.argmax(sample_probs.eval()[0], axis=0)
if label == indx:
correct += 1
else:
FP_RATE[indx][0] += 1
print("-----------------------------------")
print("GUESSED LABEL: ", label)
print("CORRECT LABEL: ", indx)
validation = (correct / guesses)
print("Total Correct: ", correct, "\n", "Total Guesses: ",
guesses, "\n", "Percent correct: ", validation)
print("False Positives: Dog, Cat", FP_RATE)
print()
tfutil.autosummary('Accuracy/Validation', (correct / guesses))
tfutil.save_summaries(summary_log, cur_nimg)
# Save snapshots.
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents,
grid_labels,
minibatch_size=sched.minibatch //
config.num_gpus)
misc.save_image_grid(grid_fakes,
os.path.join(
result_subdir, 'fakes%06d.png' %
(cur_nimg // TrainingSpeedInt)),
drange=drange_net,
grid_size=grid_size)
if cur_tick % network_snapshot_ticks == 0 or done:
misc.save_pkl((G, D, Gs),
os.path.join(
result_subdir, 'network-snapshot-%06d.pkl' %
(cur_nimg // TrainingSpeedInt)))
# Record start time of the next tick.
tick_start_time = time.time()
# Write final results.
misc.save_pkl((G, D, Gs), os.path.join(result_subdir, 'network-final.pkl'))
summary_log.close()
open(os.path.join(result_subdir, '_training-done.txt'), 'wt').close()
def train_detector(
minibatch_repeats=4, # Number of minibatches to run before adjusting training parameters.
total_kimg=1, # Total length of the training, measured in thousands of real images.
drange_net=[
-1, 1
], # Dynamic range used when feeding image data to the networks.
snapshot_size=16, # Size of the snapshot image
snapshot_ticks=2**13, # Number of images before maintenance
image_snapshot_ticks=1, # How often to export image snapshots?
network_snapshot_ticks=1, # 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=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.
maintenance_start_time = time.time()
# Load the datasets
training_set = dataset.load_dataset(tfrecord=config.tfrecord_train,
verbose=True,
**config.dataset)
testing_set = dataset.load_dataset(tfrecord=config.tfrecord_test,
verbose=True,
repeat=False,
shuffle_mb=0,
**config.dataset)
testing_set_len = len(testing_set)
# TODO: data augmentation
# TODO: testing set
# Construct networks.
with tf.device('/gpu:0'):
if resume_run_id is not None: # TODO: save methods
network_pkl = misc.locate_network_pkl(resume_run_id,
resume_snapshot)
print('Loading networks from "%s"...' % network_pkl)
N = misc.load_pkl(network_pkl)
else:
print('Constructing the network...'
) # TODO: better network (like lod-wise network)
N = tfutil.Network('N',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
**config.N)
N.print_layers()
print('Building TensorFlow graph...')
# Training set up
with tf.name_scope('Inputs'):
lrate_in = tf.placeholder(tf.float32, name='lrate_in', shape=[])
# minibatch_in = tf.placeholder(tf.int32, name='minibatch_in', shape=[])
reals, labels, bboxes = training_set.get_minibatch_tf(
) # TODO: increase the size of the batch by several loss computation and mean
N_opt = tfutil.Optimizer(name='TrainN',
learning_rate=lrate_in,
**config.N_opt)
with tf.device('/gpu:0'):
reals, labels, gt_outputs, gt_ref = pre_process(
reals, labels, bboxes, training_set.dynamic_range,
[0, training_set.shape[-2]], drange_net)
with tf.name_scope('N_loss'): # TODO: loss inadapted
N_loss = tfutil.call_func_by_name(N=N,
reals=reals,
gt_outputs=gt_outputs,
gt_ref=gt_ref,
**config.N_loss)
N_opt.register_gradients(tf.reduce_mean(N_loss), N.trainables)
N_train_op = N_opt.apply_updates()
# Testing set up
with tf.device('/gpu:0'):
test_reals_tf, test_labels_tf, test_bboxes_tf = testing_set.get_minibatch_tf(
)
test_reals_tf, test_labels_tf, test_gt_outputs_tf, test_gt_ref_tf = pre_process(
test_reals_tf, test_labels_tf, test_bboxes_tf,
testing_set.dynamic_range, [0, testing_set.shape[-2]], drange_net)
with tf.name_scope('N_test_loss'):
test_loss = tfutil.call_func_by_name(N=N,
reals=test_reals_tf,
gt_outputs=test_gt_outputs_tf,
gt_ref=test_gt_ref_tf,
is_training=False,
**config.N_loss)
print('Setting up result dir...')
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
summary_log = tf.summary.FileWriter(result_subdir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
N.setup_weight_histograms()
test_reals, _, test_bboxes = testing_set.get_minibatch_np(snapshot_size)
misc.save_img_bboxes(test_reals,
test_bboxes,
os.path.join(result_subdir, 'reals.png'),
snapshot_size,
adjust_range=False)
test_reals = misc.adjust_dynamic_range(test_reals,
training_set.dynamic_range,
drange_net)
test_preds, _ = N.run(test_reals, minibatch_size=snapshot_size)
misc.save_img_bboxes(test_reals, test_preds,
os.path.join(result_subdir, 'fakes.png'),
snapshot_size)
print('Training...')
if resume_run_id is None:
tfutil.run(tf.global_variables_initializer())
cur_nimg = 0
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
train_start_time = tick_start_time
# Choose training parameters and configure training ops.
sched = TrainingSchedule(cur_nimg, training_set, **config.sched)
training_set.configure(sched.minibatch)
_train_loss = 0
while cur_nimg < total_kimg * 1000:
# Run training ops.
# for _ in range(minibatch_repeats):
_, loss = tfutil.run([N_train_op, N_loss], {lrate_in: sched.N_lrate})
_train_loss += loss
cur_nimg += sched.minibatch
# Perform maintenance tasks once per tick.
if (cur_nimg >= total_kimg * 1000) or (cur_nimg % snapshot_ticks == 0
and cur_nimg > 0):
cur_tick += 1
cur_time = time.time()
# tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
_train_loss = _train_loss / (cur_nimg - tick_start_nimg)
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
total_time = cur_time - train_start_time
maintenance_time = tick_start_time - maintenance_start_time
maintenance_start_time = cur_time
testing_set.configure(sched.minibatch)
_test_loss = 0
# testing_set_len = 1 # TMP
for _ in range(0, testing_set_len, sched.minibatch):
_test_loss += tfutil.run(test_loss)
_test_loss /= testing_set_len
# Report progress. # TODO: improved report display
print(
'tick %-5d kimg %-6.1f time %-10s sec/tick %-3.1f maintenance %-7.2f train_loss %.4f test_loss %.4f'
% (tfutil.autosummary('Progress/tick', cur_tick),
tfutil.autosummary('Progress/kimg', cur_nimg / 1000),
misc.format_time(
tfutil.autosummary('Timing/total_sec', total_time)),
tfutil.autosummary('Timing/sec_per_tick', tick_time),
tfutil.autosummary('Timing/maintenance', maintenance_time),
tfutil.autosummary('TrainN/train_loss', _train_loss),
tfutil.autosummary('TrainN/test_loss', _test_loss)))
tfutil.autosummary('Timing/total_hours',
total_time / (60.0 * 60.0))
tfutil.autosummary('Timing/total_days',
total_time / (24.0 * 60.0 * 60.0))
tfutil.save_summaries(summary_log, cur_nimg)
if cur_tick % image_snapshot_ticks == 0:
test_bboxes, test_refs = N.run(test_reals,
minibatch_size=snapshot_size)
misc.save_img_bboxes_ref(
test_reals, test_bboxes, test_refs,
os.path.join(result_subdir,
'fakes%06d.png' % (cur_nimg // 1000)),
snapshot_size)
if cur_tick % network_snapshot_ticks == 0:
misc.save_pkl(
N,
os.path.join(
result_subdir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000)))
_train_loss = 0
# Record start time of the next tick.
tick_start_time = time.time()
# Write final results.
# misc.save_pkl(N, os.path.join(result_subdir, 'network-final.pkl'))
summary_log.close()
open(os.path.join(result_subdir, '_training-done.txt'), 'wt').close()
def train_gan(
separate_funcs=False,
D_training_repeats=1,
G_learning_rate_max=0.0010,
D_learning_rate_max=0.0010,
G_smoothing=0.999,
adam_beta1=0.0,
adam_beta2=0.99,
adam_epsilon=1e-8,
minibatch_default=16,
minibatch_overrides={},
rampup_kimg=40 / speed_factor,
rampdown_kimg=0,
lod_initial_resolution=4,
lod_training_kimg=400 / speed_factor,
lod_transition_kimg=400 / speed_factor,
#lod_training_kimg = 40,
#lod_transition_kimg = 40,
total_kimg=10000 / speed_factor,
dequantize_reals=False,
gdrop_beta=0.9,
gdrop_lim=0.5,
gdrop_coef=0.2,
gdrop_exp=2.0,
drange_net=[-1, 1],
drange_viz=[-1, 1],
image_grid_size=None,
#tick_kimg_default = 1,
tick_kimg_default=50 / speed_factor,
tick_kimg_overrides={
32: 20,
64: 10,
128: 10,
256: 5,
512: 2,
1024: 1
},
image_snapshot_ticks=4,
network_snapshot_ticks=40,
image_grid_type='default',
#resume_network_pkl = '006-celeb128-progressive-growing/network-snapshot-002009.pkl',
resume_network_pkl=None,
resume_kimg=0,
resume_time=0.0):
# Load dataset and build networks.
training_set, drange_orig = load_dataset()
print "*************** test the format of dataset ***************"
print training_set
print drange_orig
# training_set是dataset模块解析h5之后的对象，
# drange_orig 为training_set.get_dynamic_range()
if resume_network_pkl:
print 'Resuming', resume_network_pkl
G, D, _ = misc.load_pkl(
os.path.join(config.result_dir, resume_network_pkl))
else:
G = network.Network(num_channels=training_set.shape[1],
resolution=training_set.shape[2],
label_size=training_set.labels.shape[1],
**config.G)
D = network.Network(num_channels=training_set.shape[1],
resolution=training_set.shape[2],
label_size=training_set.labels.shape[1],
**config.D)
Gs = G.create_temporally_smoothed_version(beta=G_smoothing,
explicit_updates=True)
# G,D对象可以由misc解析pkl之后生成，也可以由network模块构造
print G
print D
#misc.print_network_topology_info(G.output_layers)
#misc.print_network_topology_info(D.output_layers)
# Setup snapshot image grid.
# 设置中途输出图片的格式
if image_grid_type == 'default':
if image_grid_size is None:
w, h = G.output_shape[3], G.output_shape[2]
image_grid_size = np.clip(1920 / w, 3,
16), np.clip(1080 / h, 2, 16)
example_real_images, snapshot_fake_labels = training_set.get_random_minibatch(
np.prod(image_grid_size), labels=True)
snapshot_fake_latents = random_latents(np.prod(image_grid_size),
G.input_shape)
else:
raise ValueError('Invalid image_grid_type', image_grid_type)
# Theano input variables and compile generation func.
print 'Setting up Theano...'
real_images_var = T.TensorType('float32', [False] *
len(D.input_shape))('real_images_var')
# <class 'theano.tensor.var.TensorVariable'>
# print type(real_images_var),real_images_var
real_labels_var = T.TensorType(
'float32', [False] * len(training_set.labels.shape))('real_labels_var')
fake_latents_var = T.TensorType('float32', [False] *
len(G.input_shape))('fake_latents_var')
fake_labels_var = T.TensorType(
'float32', [False] * len(training_set.labels.shape))('fake_labels_var')
# 带有_var的均为输入张量
G_lrate = theano.shared(np.float32(0.0))
D_lrate = theano.shared(np.float32(0.0))
# share语法就是用来设定默认值的，返回复制的对象
gen_fn = theano.function([fake_latents_var, fake_labels_var],
Gs.eval_nd(fake_latents_var,
fake_labels_var,
ignore_unused_inputs=True),
on_unused_input='ignore')
# gen_fn 是一个函数，输入为：[fake_latents_var, fake_labels_var],
# 输出位：Gs.eval_nd(fake_latents_var, fake_labels_var, ignore_unused_inputs=True),
#生成函数
# Misc init.
#读入当前分辨率
resolution_log2 = int(np.round(np.log2(G.output_shape[2])))
#lod 精细度
initial_lod = max(
resolution_log2 - int(np.round(np.log2(lod_initial_resolution))), 0)
cur_lod = 0.0
min_lod, max_lod = -1.0, -2.0
fake_score_avg = 0.0
# Save example images.
snapshot_fake_images = gen_fn(snapshot_fake_latents, snapshot_fake_labels)
result_subdir = misc.create_result_subdir(config.result_dir,
config.run_desc)
misc.save_image_grid(example_real_images,
os.path.join(result_subdir, 'reals.png'),
drange=drange_orig,
grid_size=image_grid_size)
misc.save_image_grid(snapshot_fake_images,
os.path.join(result_subdir, 'fakes%06d.png' % 0),
drange=drange_viz,
grid_size=image_grid_size)
# Training loop.
# 这里才是主训练入口
# 注意在训练过程中不会跳出最外层while循环，因此更换分辨率等操作必然在while循环里
#现有图片数
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
tick_train_out = []
train_start_time = tick_start_time - resume_time
while cur_nimg < total_kimg * 1000:
# Calculate current LOD.
#计算当前精细度
cur_lod = initial_lod
if lod_training_kimg or lod_transition_kimg:
tlod = (cur_nimg / (1000.0 / speed_factor)) / (lod_training_kimg +
lod_transition_kimg)
cur_lod -= np.floor(tlod)
if lod_transition_kimg:
cur_lod -= max(
1.0 + (np.fmod(tlod, 1.0) - 1.0) *
(lod_training_kimg + lod_transition_kimg) /
lod_transition_kimg, 0.0)
cur_lod = max(cur_lod, 0.0)
# Look up resolution-dependent parameters.
cur_res = 2**(resolution_log2 - int(np.floor(cur_lod)))
# 当前分辨率
minibatch_size = minibatch_overrides.get(cur_res, minibatch_default)
tick_duration_kimg = tick_kimg_overrides.get(cur_res,
tick_kimg_default)
# Update network config.
# 更新网络结构
lrate_coef = misc.rampup(cur_nimg / 1000.0, rampup_kimg)
lrate_coef *= misc.rampdown_linear(cur_nimg / 1000.0, total_kimg,
rampdown_kimg)
G_lrate.set_value(np.float32(lrate_coef * G_learning_rate_max))
D_lrate.set_value(np.float32(lrate_coef * D_learning_rate_max))
if hasattr(G, 'cur_lod'): G.cur_lod.set_value(np.float32(cur_lod))
if hasattr(D, 'cur_lod'): D.cur_lod.set_value(np.float32(cur_lod))
# Setup training func for current LOD.
new_min_lod, new_max_lod = int(np.floor(cur_lod)), int(
np.ceil(cur_lod))
#print " cur_lod%f\n min_lod %f\n new_min_lod %f\n max_lod %f\n new_max_lod %f\n"%(cur_lod,min_lod,new_min_lod,max_lod,new_max_lod)
if min_lod != new_min_lod or max_lod != new_max_lod:
print 'Compiling training funcs...'
min_lod, max_lod = new_min_lod, new_max_lod
# Pre-process reals.
real_images_expr = real_images_var
if dequantize_reals:
rnd = theano.sandbox.rng_mrg.MRG_RandomStreams(
lasagne.random.get_rng().randint(1, 2147462579))
epsilon_noise = rnd.uniform(size=real_images_expr.shape,
low=-0.5,
high=0.5,
dtype='float32')
real_images_expr = T.cast(
real_images_expr, 'float32'
) + epsilon_noise # match original implementation of Improved Wasserstein
real_images_expr = misc.adjust_dynamic_range(
real_images_expr, drange_orig, drange_net)
if min_lod > 0: # compensate for shrink_based_on_lod
real_images_expr = T.extra_ops.repeat(real_images_expr,
2**min_lod,
axis=2)
real_images_expr = T.extra_ops.repeat(real_images_expr,
2**min_lod,
axis=3)
# Optimize loss.
G_loss, D_loss, real_scores_out, fake_scores_out = evaluate_loss(
G, D, min_lod, max_lod, real_images_expr, real_labels_var,
fake_latents_var, fake_labels_var, **config.loss)
G_updates = adam(G_loss,
G.trainable_params(),
learning_rate=G_lrate,
beta1=adam_beta1,
beta2=adam_beta2,
epsilon=adam_epsilon).items()
D_updates = adam(D_loss,
D.trainable_params(),
learning_rate=D_lrate,
beta1=adam_beta1,
beta2=adam_beta2,
epsilon=adam_epsilon).items()
D_train_fn = theano.function([
real_images_var, real_labels_var, fake_latents_var,
fake_labels_var
], [G_loss, D_loss, real_scores_out, fake_scores_out],
updates=D_updates,
on_unused_input='ignore')
G_train_fn = theano.function([fake_latents_var, fake_labels_var],
[],
updates=G_updates + Gs.updates,
on_unused_input='ignore')
for idx in xrange(D_training_repeats):
mb_reals, mb_labels = training_set.get_random_minibatch(
minibatch_size,
lod=cur_lod,
shrink_based_on_lod=True,
labels=True)
print "******* test minibatch"
print "mb_reals"
print idx, D_training_repeats
print mb_reals.shape, mb_labels.shape
#print mb_reals
print "mb_labels"
#print mb_labels
mb_train_out = D_train_fn(
mb_reals, mb_labels,
random_latents(minibatch_size, G.input_shape),
random_labels(minibatch_size, training_set))
cur_nimg += minibatch_size
tick_train_out.append(mb_train_out)
G_train_fn(random_latents(minibatch_size, G.input_shape),
random_labels(minibatch_size, training_set))
# Fade in D noise if we're close to becoming unstable
fake_score_cur = np.clip(np.mean(mb_train_out[1]), 0.0, 1.0)
fake_score_avg = fake_score_avg * gdrop_beta + fake_score_cur * (
1.0 - gdrop_beta)
gdrop_strength = gdrop_coef * (max(fake_score_avg - gdrop_lim, 0.0)**
gdrop_exp)
if hasattr(D, 'gdrop_strength'):
D.gdrop_strength.set_value(np.float32(gdrop_strength))
# Perform maintenance operations once per tick.
if cur_nimg >= tick_start_nimg + tick_duration_kimg * 1000 or cur_nimg >= total_kimg * 1000:
cur_tick += 1
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
tick_start_time = cur_time
tick_train_avg = tuple(
np.mean(np.concatenate([np.asarray(v).flatten()
for v in vals]))
for vals in zip(*tick_train_out))
tick_train_out = []
# Print progress.
print 'tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-9.1f sec/kimg %-6.1f Dgdrop %-8.4f Gloss %-8.4f Dloss %-8.4f Dreal %-8.4f Dfake %-8.4f' % (
(cur_tick, cur_nimg / 1000.0, cur_lod, minibatch_size,
misc.format_time(cur_time - train_start_time), tick_time,
tick_time / tick_kimg, gdrop_strength) + tick_train_avg)
# Visualize generated images.
if cur_tick % image_snapshot_ticks == 0 or cur_nimg >= total_kimg * 1000:
snapshot_fake_images = gen_fn(snapshot_fake_latents,
snapshot_fake_labels)
misc.save_image_grid(snapshot_fake_images,
os.path.join(
result_subdir,
'fakes%06d.png' % (cur_nimg / 1000)),
drange=drange_viz,
grid_size=image_grid_size)
# Save network snapshot every N ticks.
if cur_tick % network_snapshot_ticks == 0 or cur_nimg >= total_kimg * 1000:
misc.save_pkl(
(G, D, Gs),
os.path.join(
result_subdir,
'network-snapshot-%06d.pkl' % (cur_nimg / 1000)))
# Write final results.
misc.save_pkl((G, D, Gs), os.path.join(result_subdir, 'network-final.pkl'))
training_set.close()
print 'Done.'
with open(os.path.join(result_subdir, '_training-done.txt'), 'wt'):
pass
def train_classifier(
smoothing=0.999, # Exponential running average of encoder weights.
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=25000, # Total length of the training, measured in thousands of real images.
lr_mirror_augment=True, # Enable mirror augment?
ud_mirror_augment=False, # Enable up-down 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?
save_tf_graph=False, # Include full TensorFlow computation graph in the tfevents file?
save_weight_histograms=False
): # Include weight histograms in the tfevents file?
maintenance_start_time = time.time()
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**config.training_set)
validation_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**config.validation_set)
network_snapshot_ticks = total_kimg // 100 # How often to export network snapshots?
# Construct networks.
with tf.device('/gpu:0'):
try:
network_pkl = misc.locate_network_pkl()
resume_kimg, resume_time = misc.resume_kimg_time(network_pkl)
print('Loading networks from "%s"...' % network_pkl)
EG, D_rec, EGs = misc.load_pkl(network_pkl)
except:
print('Constructing networks...')
resume_kimg = 0.0
resume_time = 0.0
EG = tfutil.Network('EG',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**config.EG)
D_rec = tfutil.Network('D_rec',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
**config.D_rec)
EGs = EG.clone('EGs')
EGs_update_op = EGs.setup_as_moving_average_of(EG, beta=smoothing)
EG.print_layers()
D_rec.print_layers()
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'):
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 // config.num_gpus
reals, labels = training_set.get_minibatch_tf()
reals_split = tf.split(reals, config.num_gpus)
labels_split = tf.split(labels, config.num_gpus)
EG_opt = tfutil.Optimizer(name='TrainEG',
learning_rate=lrate_in,
**config.EG_opt)
D_rec_opt = tfutil.Optimizer(name='TrainD_rec',
learning_rate=lrate_in,
**config.D_rec_opt)
for gpu in range(config.num_gpus):
with tf.name_scope('GPU%d' % gpu), tf.device('/gpu:%d' % gpu):
EG_gpu = EG if gpu == 0 else EG.clone(EG.name + '_shadow_%d' % gpu)
D_rec_gpu = D_rec if gpu == 0 else D_rec.clone(D_rec.name +
'_shadow_%d' % gpu)
reals_fade_gpu, reals_orig_gpu = process_reals(
reals_split[gpu], lod_in, lr_mirror_augment, ud_mirror_augment,
training_set.dynamic_range, drange_net)
labels_gpu = labels_split[gpu]
with tf.name_scope('EG_loss'):
EG_loss = tfutil.call_func_by_name(EG=EG_gpu,
D_rec=D_rec_gpu,
reals_orig=reals_orig_gpu,
labels=labels_gpu,
**config.EG_loss)
with tf.name_scope('D_rec_loss'):
D_rec_loss = tfutil.call_func_by_name(
EG=EG_gpu,
D_rec=D_rec_gpu,
D_rec_opt=D_rec_opt,
minibatch_size=minibatch_split,
reals_orig=reals_orig_gpu,
**config.D_rec_loss)
EG_opt.register_gradients(tf.reduce_mean(EG_loss),
EG_gpu.trainables)
D_rec_opt.register_gradients(tf.reduce_mean(D_rec_loss),
D_rec_gpu.trainables)
EG_train_op = EG_opt.apply_updates()
D_rec_train_op = D_rec_opt.apply_updates()
print('Setting up snapshot image grid...')
grid_size, train_reals, train_labels = setup_snapshot_image_grid(
training_set, drange_net, [450, 10], **config.grid)
grid_size, val_reals, val_labels = setup_snapshot_image_grid(
validation_set, drange_net, [450, 10], **config.grid)
sched = TrainingSchedule(total_kimg * 1000, training_set, **config.sched)
train_recs, train_fingerprints, train_logits = EGs.run(
train_reals, minibatch_size=sched.minibatch // config.num_gpus)
train_preds = np.argmax(train_logits, axis=1)
train_gt = np.argmax(train_labels, axis=1)
train_acc = np.float32(np.sum(train_gt == train_preds)) / np.float32(
len(train_gt))
print('Training Accuracy = %f' % train_acc)
val_recs, val_fingerprints, val_logits = EGs.run(
val_reals, minibatch_size=sched.minibatch // config.num_gpus)
val_preds = np.argmax(val_logits, axis=1)
val_gt = np.argmax(val_labels, axis=1)
val_acc = np.float32(np.sum(val_gt == val_preds)) / np.float32(len(val_gt))
print('Validation Accuracy = %f' % val_acc)
print('Setting up result dir...')
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
misc.save_image_grid(train_reals[::30, :, :, :],
os.path.join(result_subdir, 'train_reals.png'),
drange=drange_net,
grid_size=[15, 10])
misc.save_image_grid(train_recs[::30, :, :, :],
os.path.join(result_subdir, 'train_recs-init.png'),
drange=drange_net,
grid_size=[15, 10])
misc.save_image_grid(train_fingerprints[::30, :, :, :],
os.path.join(result_subdir,
'train_fingerrints-init.png'),
drange=drange_net,
grid_size=[15, 10])
misc.save_image_grid(val_reals[::30, :, :, :],
os.path.join(result_subdir, 'val_reals.png'),
drange=drange_net,
grid_size=[15, 10])
misc.save_image_grid(val_recs[::30, :, :, :],
os.path.join(result_subdir, 'val_recs-init.png'),
drange=drange_net,
grid_size=[15, 10])
misc.save_image_grid(val_fingerprints[::30, :, :, :],
os.path.join(result_subdir,
'val_fingerrints-init.png'),
drange=drange_net,
grid_size=[15, 10])
est_fingerprints = np.transpose(
EGs.vars['Conv_fingerprints/weight'].eval(), axes=[3, 2, 0, 1])
misc.save_image_grid(
est_fingerprints,
os.path.join(result_subdir, 'est_fingerrints-init.png'),
drange=[np.amin(est_fingerprints),
np.amax(est_fingerprints)],
grid_size=[est_fingerprints.shape[0], 1])
summary_log = tf.summary.FileWriter(result_subdir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
EG.setup_weight_histograms()
D_rec.setup_weight_histograms()
print('Training...')
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
train_start_time = tick_start_time - resume_time
prev_lod = -1.0
while cur_nimg < total_kimg * 1000:
# Choose training parameters and configure training ops.
sched = TrainingSchedule(cur_nimg, training_set, **config.sched)
training_set.configure(sched.minibatch, 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):
EG_opt.reset_optimizer_state()
D_rec_opt.reset_optimizer_state()
prev_lod = sched.lod
# Run training ops.
for repeat in range(minibatch_repeats):
tfutil.run(
[D_rec_train_op], {
lod_in: sched.lod,
lrate_in: sched.lrate,
minibatch_in: sched.minibatch
})
tfutil.run(
[EG_train_op], {
lod_in: sched.lod,
lrate_in: sched.lrate,
minibatch_in: sched.minibatch
})
tfutil.run([EGs_update_op], {})
cur_nimg += 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
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
total_time = cur_time - train_start_time
maintenance_time = tick_start_time - maintenance_start_time
maintenance_start_time = cur_time
# Report progress.
print(
'tick %-5d kimg %-8.1f lod %-5.2f resolution %-4d minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %.1f'
% (tfutil.autosummary('Progress/tick', cur_tick),
tfutil.autosummary('Progress/kimg', cur_nimg / 1000.0),
tfutil.autosummary('Progress/lod', sched.lod),
tfutil.autosummary('Progress/resolution', sched.resolution),
tfutil.autosummary('Progress/minibatch', sched.minibatch),
misc.format_time(
tfutil.autosummary('Timing/total_sec', total_time)),
tfutil.autosummary('Timing/sec_per_tick', tick_time),
tfutil.autosummary('Timing/sec_per_kimg',
tick_time / tick_kimg),
tfutil.autosummary('Timing/maintenance_sec',
maintenance_time)))
tfutil.autosummary('Timing/total_hours',
total_time / (60.0 * 60.0))
tfutil.autosummary('Timing/total_days',
total_time / (24.0 * 60.0 * 60.0))
tfutil.save_summaries(summary_log, cur_nimg)
# Print accuracy.
if cur_tick % image_snapshot_ticks == 0 or done:
train_recs, train_fingerprints, train_logits = EGs.run(
train_reals,
minibatch_size=sched.minibatch // config.num_gpus)
train_preds = np.argmax(train_logits, axis=1)
train_gt = np.argmax(train_labels, axis=1)
train_acc = np.float32(np.sum(
train_gt == train_preds)) / np.float32(len(train_gt))
print('Training Accuracy = %f' % train_acc)
val_recs, val_fingerprints, val_logits = EGs.run(
val_reals,
minibatch_size=sched.minibatch // config.num_gpus)
val_preds = np.argmax(val_logits, axis=1)
val_gt = np.argmax(val_labels, axis=1)
val_acc = np.float32(np.sum(val_gt == val_preds)) / np.float32(
len(val_gt))
print('Validation Accuracy = %f' % val_acc)
misc.save_image_grid(train_recs[::30, :, :, :],
os.path.join(result_subdir,
'train_recs-final.png'),
drange=drange_net,
grid_size=[15, 10])
misc.save_image_grid(train_fingerprints[::30, :, :, :],
os.path.join(
result_subdir,
'train_fingerrints-final.png'),
drange=drange_net,
grid_size=[15, 10])
misc.save_image_grid(val_recs[::30, :, :, :],
os.path.join(result_subdir,
'val_recs-final.png'),
drange=drange_net,
grid_size=[15, 10])
misc.save_image_grid(val_fingerprints[::30, :, :, :],
os.path.join(result_subdir,
'val_fingerrints-final.png'),
drange=drange_net,
grid_size=[15, 10])
est_fingerprints = np.transpose(
EGs.vars['Conv_fingerprints/weight'].eval(),
axes=[3, 2, 0, 1])
misc.save_image_grid(est_fingerprints,
os.path.join(result_subdir,
'est_fingerrints-final.png'),
drange=[
np.amin(est_fingerprints),
np.amax(est_fingerprints)
],
grid_size=[est_fingerprints.shape[0], 1])
if cur_tick % network_snapshot_ticks == 0 or done:
misc.save_pkl(
(EG, D_rec, EGs),
os.path.join(
result_subdir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000)))
# Record start time of the next tick.
tick_start_time = time.time()
# Write final results.
misc.save_pkl((EG, D_rec, EGs),
os.path.join(result_subdir, 'network-final.pkl'))
summary_log.close()
open(os.path.join(result_subdir, '_training-done.txt'), 'wt').close()
def train_progressive_gan(
G_smoothing=0.999, # Exponential 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?
compute_fid_score=False, # Compute FID during training once sched.lod=0.0
minimum_fid_kimg=0, # Compute FID after
fid_snapshot_ticks=1, # How often to compute FID
fid_patience=2, # When to end training based on FID
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.
result_subdir="./"):
maintenance_start_time = time.time()
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**config.dataset)
# Construct networks.
with tf.device('/gpu:0'):
if resume_run_id != "None":
network_pkl = misc.locate_network_pkl(resume_run_id,
resume_snapshot)
resume_pkl_name = os.path.splitext(
os.path.basename(network_pkl))[0]
try:
resume_kimg = int(resume_pkl_name.split('-')[-1])
print('** Setting resume kimg to', resume_kimg, flush=True)
except:
print('** Keeping resume kimg as:', resume_kimg, flush=True)
print('Loading networks from "%s"...' % network_pkl, flush=True)
G, D, Gs = misc.load_pkl(network_pkl)
else:
print('Constructing networks...', flush=True)
G = tfutil.Network('G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**config.G)
D = tfutil.Network('D',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**config.D)
Gs = G.clone('Gs')
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=G_smoothing)
G.print_layers()
D.print_layers()
print('Building TensorFlow graph...', flush=True)
with tf.name_scope('Inputs'):
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 // config.num_gpus
reals, labels = training_set.get_minibatch_tf()
reals_split = tf.split(reals, config.num_gpus)
labels_split = tf.split(labels, config.num_gpus)
G_opt = tfutil.Optimizer(name='TrainG',
learning_rate=lrate_in,
**config.G_opt)
D_opt = tfutil.Optimizer(name='TrainD',
learning_rate=lrate_in,
**config.D_opt)
for gpu in range(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_gpu = process_reals(reals_split[gpu], lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
labels_gpu = labels_split[gpu]
with tf.name_scope('G_loss'):
G_loss = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
**config.G_loss)
with tf.name_scope('D_loss'):
D_loss = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
reals=reals_gpu,
labels=labels_gpu,
**config.D_loss)
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()
print('Setting up snapshot image grid...', flush=True)
grid_size, grid_reals, grid_labels, grid_latents = setup_snapshot_image_grid(
G, training_set, **config.grid)
sched = TrainingSchedule(total_kimg * 1000, training_set, **config.sched)
grid_fakes = Gs.run(grid_latents,
grid_labels,
minibatch_size=sched.minibatch // config.num_gpus)
print('Setting up result dir...', flush=True)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
misc.save_image_grid(grid_reals,
os.path.join(result_subdir, 'reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
misc.save_image_grid(grid_fakes,
os.path.join(result_subdir, 'fakes%06d.png' % 0),
drange=drange_net,
grid_size=grid_size)
summary_log = tf.summary.FileWriter(result_subdir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms()
D.setup_weight_histograms()
print('Training...', flush=True)
# FID patience parameters:
fid_list = []
fid_steps = 0
fid_stop = False
fid_patience_step = 0
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
train_start_time = tick_start_time - resume_time
prev_lod = -1.0
while cur_nimg < total_kimg * 1000:
# Choose training parameters and configure training ops.
sched = TrainingSchedule(cur_nimg, training_set, **config.sched)
training_set.configure(sched.minibatch, 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 repeat in range(minibatch_repeats):
for _ in range(D_repeats):
tfutil.run(
[D_train_op, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch
})
cur_nimg += sched.minibatch
tfutil.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
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
total_time = cur_time - train_start_time
maintenance_time = tick_start_time - maintenance_start_time
maintenance_start_time = cur_time
if (compute_fid_score
== True) and (cur_tick % fid_snapshot_ticks
== 0) and (sched.lod == 0.0) and (
cur_nimg >= minimum_fid_kimg * 1000):
fid = compute_fid(Gs=Gs,
minibatch_size=sched.minibatch,
dataset_obj=training_set,
iter_number=cur_nimg / 1000,
lod=0.0,
num_images=10000,
printing=False)
fid_list.append(fid)
# Report progress without FID.
print(
'tick %-5d kimg %-8.1f lod %-5.2f minibatch %-4d time %-12s sec/tick %-7.1f sec/kimg %-7.2f maintenance %.1f'
% (tfutil.autosummary('Progress/tick', cur_tick),
tfutil.autosummary('Progress/kimg', cur_nimg / 1000.0),
tfutil.autosummary('Progress/lod', sched.lod),
tfutil.autosummary('Progress/minibatch', sched.minibatch),
misc.format_time(
tfutil.autosummary('Timing/total_sec', total_time)),
tfutil.autosummary('Timing/sec_per_tick', tick_time),
tfutil.autosummary('Timing/sec_per_kimg',
tick_time / tick_kimg),
tfutil.autosummary('Timing/maintenance_sec',
maintenance_time)),
flush=True)
tfutil.autosummary('Timing/total_hours',
total_time / (60.0 * 60.0))
tfutil.autosummary('Timing/total_days',
total_time / (24.0 * 60.0 * 60.0))
tfutil.save_summaries(summary_log, cur_nimg)
# Save image snapshots.
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents,
grid_labels,
minibatch_size=sched.minibatch //
config.num_gpus)
misc.save_image_grid(grid_fakes,
os.path.join(
result_subdir,
'fakes%06d.png' % (cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
# Save network snapshots
if cur_tick % network_snapshot_ticks == 0 or done or (
compute_fid_score
== True) and (cur_tick % fid_snapshot_ticks == 0) and (
cur_nimg >= minimum_fid_kimg * 1000):
misc.save_pkl(
(G, D, Gs),
os.path.join(
result_subdir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000)))
# End training based on FID patience
if (compute_fid_score
== True) and (cur_tick % fid_snapshot_ticks
== 0) and (sched.lod == 0.0) and (
cur_nimg >= minimum_fid_kimg * 1000):
fid_patience_step += 1
if len(fid_list) == 1:
fid_patience_step = 0
misc.save_pkl((G, D, Gs),
os.path.join(result_subdir,
'network-final-full-conv.pkl'))
print(
"Save network-final-full-conv for FID: %.3f at kimg %-8.1f."
% (fid_list[-1], cur_nimg // 1000),
flush=True)
else:
if fid_list[-1] < np.min(fid_list[:-1]):
fid_patience_step = 0
misc.save_pkl(
(G, D, Gs),
os.path.join(result_subdir,
'network-final-full-conv.pkl'))
print(
"Save network-final-full-conv for FID: %.3f at kimg %-8.1f."
% (fid_list[-1], cur_nimg // 1000),
flush=True)
else:
print("No improvement for FID: %.3f at kimg %-8.1f." %
(fid_list[-1], cur_nimg // 1000),
flush=True)
if fid_patience_step == fid_patience:
fid_stop = True
print("Training stopped due to FID early-stopping.",
flush=True)
cur_nimg = total_kimg * 1000
# Record start time of the next tick.
tick_start_time = time.time()
# Write final results.
# Save final only if FID-Stopping has not happend:
if fid_stop == False:
fid = compute_fid(Gs=Gs,
minibatch_size=sched.minibatch,
dataset_obj=training_set,
iter_number=cur_nimg / 1000,
lod=0.0,
num_images=10000,
printing=False)
print("Final FID: %.3f at kimg %-8.1f." % (fid, cur_nimg // 1000),
flush=True)
### save final FID to .csv file in result_parent_dir
csv_file = os.path.join(
os.path.dirname(os.path.dirname(result_subdir)),
"results_full_conv.csv")
list_to_append = [
result_subdir.split("/")[-2] + "/" + result_subdir.split("/")[-1],
fid
]
with open(csv_file, 'a') as f_object:
writer_object = writer(f_object)
writer_object.writerow(list_to_append)
f_object.close()
misc.save_pkl((G, D, Gs),
os.path.join(result_subdir,
'network-final-full-conv.pkl'))
print("Save network-final-full-conv.", flush=True)
summary_log.close()
open(os.path.join(result_subdir, '_training-done.txt'), 'wt').close()
def train_progressive_gan(
G_smoothing = 0.999, # Exponential 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 = 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.
maintenance_start_time = time.time()
training_set = dataset.load_dataset(data_dir=config.data_dir, verbose=True, **config.dataset)
# 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 = tfutil.Network('G', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **config.G)
D = tfutil.Network('D', num_channels=training_set.shape[0], resolution=training_set.shape[1], label_size=training_set.label_size, **config.D)
Gs = G.clone('Gs')
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=G_smoothing)
G.print_layers(); D.print_layers()
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'):
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 // config.num_gpus
reals, labels = training_set.get_minibatch_tf()
reals_split = tf.split(reals, config.num_gpus)
labels_split = tf.split(labels, config.num_gpus)
G_opt = tfutil.Optimizer(name='TrainG', learning_rate=lrate_in, **config.G_opt)
D_opt = tfutil.Optimizer(name='TrainD', learning_rate=lrate_in, **config.D_opt)
for gpu in range(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_gpu = process_reals(reals_split[gpu], lod_in, mirror_augment, training_set.dynamic_range, drange_net)
labels_gpu = labels_split[gpu]
with tf.name_scope('G_loss'), tf.control_dependencies(lod_assign_ops):
G_loss = tfutil.call_func_by_name(G=G_gpu, D=D_gpu, opt=G_opt, training_set=training_set, minibatch_size=minibatch_split, **config.G_loss)
with tf.name_scope('D_loss'), tf.control_dependencies(lod_assign_ops):
D_loss = tfutil.call_func_by_name(G=G_gpu, D=D_gpu, opt=D_opt, training_set=training_set, minibatch_size=minibatch_split, reals=reals_gpu, labels=labels_gpu, **config.D_loss)
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()
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = setup_snapshot_image_grid(G, training_set, **config.grid)
sched = TrainingSchedule(total_kimg * 1000, training_set, **config.sched)
grid_fakes = Gs.run(grid_latents, grid_labels, minibatch_size=sched.minibatch//config.num_gpus)
print('Setting up result dir...')
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
misc.save_image_grid(grid_reals, os.path.join(result_subdir, 'reals.png'), drange=training_set.dynamic_range, grid_size=grid_size)
misc.save_image_grid(grid_fakes, os.path.join(result_subdir, 'fakes%06d.png' % 0), drange=drange_net, grid_size=grid_size)
summary_log = tf.summary.FileWriter(result_subdir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms(); D.setup_weight_histograms()
print('Training...')
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
train_start_time = tick_start_time - resume_time
prev_lod = -1.0
while cur_nimg < total_kimg * 1000:
# Choose training parameters and configure training ops.
sched = TrainingSchedule(cur_nimg, training_set, **config.sched)
training_set.configure(sched.minibatch, 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 repeat in range(minibatch_repeats):
for _ in range(D_repeats):
tfutil.run([D_train_op, Gs_update_op], {lod_in: sched.lod, lrate_in: sched.D_lrate, minibatch_in: sched.minibatch})
cur_nimg += sched.minibatch
tfutil.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
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
total_time = cur_time - train_start_time
maintenance_time = tick_start_time - maintenance_start_time
maintenance_start_time = cur_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 %.1f' % (
tfutil.autosummary('Progress/tick', cur_tick),
tfutil.autosummary('Progress/kimg', cur_nimg / 1000.0),
tfutil.autosummary('Progress/lod', sched.lod),
tfutil.autosummary('Progress/minibatch', sched.minibatch),
misc.format_time(tfutil.autosummary('Timing/total_sec', total_time)),
tfutil.autosummary('Timing/sec_per_tick', tick_time),
tfutil.autosummary('Timing/sec_per_kimg', tick_time / tick_kimg),
tfutil.autosummary('Timing/maintenance_sec', maintenance_time)))
tfutil.autosummary('Timing/total_hours', total_time / (60.0 * 60.0))
tfutil.autosummary('Timing/total_days', total_time / (24.0 * 60.0 * 60.0))
tfutil.save_summaries(summary_log, cur_nimg)
# Save snapshots.
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents, grid_labels, minibatch_size=sched.minibatch//config.num_gpus)
misc.save_image_grid(grid_fakes, os.path.join(result_subdir, 'fakes%06d.png' % (cur_nimg // 1000)), drange=drange_net, grid_size=grid_size)
if cur_tick % network_snapshot_ticks == 0 or done:
misc.save_pkl((G, D, Gs), os.path.join(result_subdir, 'network-snapshot-%06d.pkl' % (cur_nimg // 1000)))
# Record start time of the next tick.
tick_start_time = time.time()
# Write final results.
misc.save_pkl((G, D, Gs), os.path.join(result_subdir, 'network-final.pkl'))
summary_log.close()
open(os.path.join(result_subdir, '_training-done.txt'), 'wt').close()
def train_progressive_gan(
G_smoothing=0.999, # Exponential 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=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.
maintenance_start_time = time.time()
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**config.dataset)
#resume_run_id = '/dresden/users/mk1391/evl/pggan_logs/logs_celeba128cc/fsg16_results_0/000-pgan-celeba-preset-v2-2gpus-fp32/network-snapshot-010211.pkl'
resume_with_new_nets = False
# Construct networks.
with tf.device('/gpu:0'):
if resume_run_id is None or resume_with_new_nets:
print('Constructing networks...')
G = tfutil.Network('G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**config.G)
D = tfutil.Network('D',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**config.D)
Gs = G.clone('Gs')
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)
rG, rD, rGs = misc.load_pkl(network_pkl)
if resume_with_new_nets:
G.copy_vars_from(rG)
D.copy_vars_from(rD)
Gs.copy_vars_from(rGs)
else:
G = rG
D = rD
Gs = rGs
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=G_smoothing)
G.print_layers()
D.print_layers()
### pyramid draw fsg (comment out for actual training to happen)
#draw_gen_fsg(Gs, 10, os.path.join(config.result_dir, 'pggan_fsg_draw.png'))
#print('>>> done printing fsgs.')
#return
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'):
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 // config.num_gpus
reals, labels = training_set.get_minibatch_tf()
reals_split = tf.split(reals, config.num_gpus)
labels_split = tf.split(labels, config.num_gpus)
G_opt = tfutil.Optimizer(name='TrainG',
learning_rate=lrate_in,
**config.G_opt)
D_opt = tfutil.Optimizer(name='TrainD',
learning_rate=lrate_in,
**config.D_opt)
for gpu in range(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_gpu = process_reals(reals_split[gpu], lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
labels_gpu = labels_split[gpu]
with tf.name_scope('G_loss'), tf.control_dependencies(
lod_assign_ops):
G_loss = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
**config.G_loss)
with tf.name_scope('D_loss'), tf.control_dependencies(
lod_assign_ops):
D_loss = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
reals=reals_gpu,
labels=labels_gpu,
**config.D_loss)
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()
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = setup_snapshot_image_grid(
G, training_set, **config.grid)
### shift reals
print('>>> reals shape: ', grid_reals.shape)
fc_x = 0.5
fc_y = 0.5
im_size = grid_reals.shape[-1]
kernel_loc = 2.*np.pi*fc_x * np.arange(im_size).reshape((1, 1, im_size)) + \
2.*np.pi*fc_y * np.arange(im_size).reshape((1, im_size, 1))
kernel_cos = np.cos(kernel_loc)
kernel_sin = np.sin(kernel_loc)
reals_t = (grid_reals / 255.) * 2. - 1
reals_t *= kernel_cos
grid_reals_sh = np.rint(
(reals_t + 1.) * 255. / 2.).clip(0, 255).astype(np.uint8)
### end shift reals
sched = TrainingSchedule(total_kimg * 1000, training_set, **config.sched)
grid_fakes = Gs.run(grid_latents,
grid_labels,
minibatch_size=sched.minibatch // config.num_gpus)
### fft drawing
#sys.path.insert(1, '/home/mahyar/CV_Res/ganist')
#from fig_draw import apply_fft_win
#data_size = 1000
#latents = np.random.randn(data_size, *Gs.input_shapes[0][1:])
#labels = np.zeros([latents.shape[0]] + Gs.input_shapes[1][1:])
#g_samples = Gs.run(latents, labels, minibatch_size=sched.minibatch//config.num_gpus)
#g_samples = g_samples.transpose(0, 2, 3, 1)
#print('>>> g_samples shape: {}'.format(g_samples.shape))
#apply_fft_win(g_samples, 'fft_pggan_hann.png')
### end fft drawing
print('Setting up result dir...')
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
misc.save_image_grid(grid_reals,
os.path.join(result_subdir, 'reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
### drawing shifted real images
misc.save_image_grid(grid_reals_sh,
os.path.join(result_subdir, 'reals_sh.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
misc.save_image_grid(grid_fakes,
os.path.join(result_subdir, 'fakes%06d.png' % 0),
drange=drange_net,
grid_size=grid_size)
### drawing shifted fake images
misc.save_image_grid(grid_fakes * kernel_cos,
os.path.join(result_subdir, 'fakes%06d_sh.png' % 0),
drange=drange_net,
grid_size=grid_size)
summary_log = tf.summary.FileWriter(result_subdir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms()
D.setup_weight_histograms()
#### True cosine fft eval
#fft_data_size = 1000
#im_size = training_set.shape[1]
#freq_centers = [(64/128., 64/128.)]
#true_samples = sample_true(training_set, fft_data_size, dtype=training_set.dtype, batch_size=32).transpose(0, 2, 3, 1) / 255. * 2. - 1.
#true_fft, true_fft_hann, true_hist = cosine_eval(true_samples, 'true', freq_centers, log_dir=result_subdir)
#fractal_eval(true_samples, f'koch_snowflake_true', result_subdir)
print('Training...')
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
train_start_time = tick_start_time - resume_time
prev_lod = -1.0
while cur_nimg < total_kimg * 1000:
# Choose training parameters and configure training ops.
sched = TrainingSchedule(cur_nimg, training_set, **config.sched)
training_set.configure(sched.minibatch, 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 repeat in range(minibatch_repeats):
for _ in range(D_repeats):
tfutil.run(
[D_train_op, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch
})
cur_nimg += sched.minibatch
tfutil.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
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
total_time = cur_time - train_start_time
maintenance_time = tick_start_time - maintenance_start_time
maintenance_start_time = cur_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 %.1f'
% (tfutil.autosummary('Progress/tick', cur_tick),
tfutil.autosummary('Progress/kimg', cur_nimg / 1000.0),
tfutil.autosummary('Progress/lod', sched.lod),
tfutil.autosummary('Progress/minibatch', sched.minibatch),
misc.format_time(
tfutil.autosummary('Timing/total_sec', total_time)),
tfutil.autosummary('Timing/sec_per_tick', tick_time),
tfutil.autosummary('Timing/sec_per_kimg',
tick_time / tick_kimg),
tfutil.autosummary('Timing/maintenance_sec',
maintenance_time)))
tfutil.autosummary('Timing/total_hours',
total_time / (60.0 * 60.0))
tfutil.autosummary('Timing/total_days',
total_time / (24.0 * 60.0 * 60.0))
tfutil.save_summaries(summary_log, cur_nimg)
# Save snapshots.
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents,
grid_labels,
minibatch_size=sched.minibatch //
config.num_gpus)
misc.save_image_grid(grid_fakes,
os.path.join(
result_subdir,
'fakes%06d.png' % (cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
### drawing shifted fake images
misc.save_image_grid(
grid_fakes * kernel_cos,
os.path.join(result_subdir,
'fakes%06d_sh.png' % (cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
### drawing fsg
#draw_gen_fsg(Gs, 10, os.path.join(config.result_dir, 'fakes%06d_fsg_draw.png' % (cur_nimg // 1000)))
### Gen fft eval
#gen_samples = sample_gen(Gs, fft_data_size).transpose(0, 2, 3, 1)
#print(f'>>> fake_samples: max={np.amax(grid_fakes)} min={np.amin(grid_fakes)}')
#print(f'>>> gen_samples: max={np.amax(gen_samples)} min={np.amin(gen_samples)}')
#misc.save_image_grid(gen_samples[:25], os.path.join(result_subdir, 'fakes%06d_gsample.png' % (cur_nimg // 1000)), drange=drange_net, grid_size=grid_size)
#cosine_eval(gen_samples, f'gen_{cur_nimg//1000:06d}', freq_centers, log_dir=result_subdir, true_fft=true_fft, true_fft_hann=true_fft_hann, true_hist=true_hist)
#fractal_eval(gen_samples, f'koch_snowflake_fakes{cur_nimg//1000:06d}', result_subdir)
if cur_tick % network_snapshot_ticks == 0 or done:
misc.save_pkl(
(G, D, Gs),
os.path.join(
result_subdir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000)))
# Record start time of the next tick.
tick_start_time = time.time()
# Write final results.
misc.save_pkl((G, D, Gs), os.path.join(result_subdir, 'network-final.pkl'))
summary_log.close()
open(os.path.join(result_subdir, '_training-done.txt'), 'wt').close()
'Use grabcut algorithm on the face mask to better segment the foreground',
type=bool)
parser.add_argument('--scale_mask',
default=1.5,
help='Look over a wider section of foreground for grabcut',
type=float)
parser.add_argument('--use_aligned',
default=1,
help='align face before recovery',
type=int)
args = parser.parse_args()
# manual parameters
result_subdir = misc.create_result_subdir('results', 'inference_test')
misc.init_output_logging()
args.aligned_dir = os.path.join(result_subdir, args.aligned_dir)
args.dlatent_dir = os.path.join(result_subdir, args.dlatent_dir)
args.dlabel_dir = os.path.join(result_subdir, args.dlabel_dir)
args.generated_images_dir = os.path.join(result_subdir,
args.generated_images_dir)
if os.path.exists(args.aligned_dir) == False:
os.mkdir(args.aligned_dir)
# initialize TensorFlow
print('Initializing TensorFlow...')
env = EasyDict() # Environment variables, set by the main program in train.py.
env.TF_CPP_MIN_LOG_LEVEL = '1' # Print warnings and errors, but disable debug info.
def train_gan(
images_dir1,
images_dir2,
batch_size,
img_shape = (32,32,3),
D_training_repeats = 1,
G_learning_rate_max = 0.0010,
D_learning_rate_max = 0.0010,
G_smoothing = 0.999,
adam_beta1 = 0.0,
adam_beta2 = 0.99,
adam_epsilon = 1e-8,
minibatch_default = 16,
rampup_kimg = 40/speed_factor,
rampdown_kimg = 0,
lod_initial_resolution = 4,
lod_training_kimg = 400/speed_factor,
lod_transition_kimg = 400/speed_factor,
total_kimg = 10000/speed_factor,
dequantize_reals = False,
gdrop_beta = 0.9,
gdrop_lim = 0.5,
gdrop_coef = 0.2,
gdrop_exp = 2.0,
drange_net = [-1,1],
drange_viz = [-1,1],
image_grid_size = None,
tick_kimg_default = 50/speed_factor,
tick_kimg_overrides = {32:20, 64:10, 128:10, 256:5, 512:2, 1024:1},
image_snapshot_ticks = 1,
network_snapshot_ticks = 4,
image_grid_type = 'default',
#resume_network = '000-celeba/network-snapshot-000488',
resume_network = None,
resume_kimg = 0.0,
resume_time = 0.0):
# if resume_network:
# print("Resuming weight from:"+resume_network)
# G = Generator(num_channels=training_set.shape[3], resolution=training_set.shape[1], label_size=training_set.labels.shape[1], **config.G)
# D = Discriminator(num_channels=training_set.shape[3], resolution=training_set.shape[1], label_size=training_set.labels.shape[1], **config.D)
# G,D = load_GD_weights(G,D,os.path.join(config.result_dir,resume_network),True)
# else:
E_G = Encoder_Generator(num_channels=img_shape[2], resolution=img_shape[0], **config.G)
D = Discriminator(num_channels=img_shape[2], resolution=img_shape[0], **config.D)
E_twin_G_twin = new_batch_norm(E_G)
D_twin = new_batch_norm(D)
E = extract_encoder(E_G)
E_twin = extract_encoder(E_twin_G_twin)
E_twin_G = replace_batch_norm(E_G, E_twin_G_twin, apply='encoder')
E_G_twin = replace_batch_norm(E_G, E_twin_G_twin, apply='generator')
E_G_twin_E_twin = Sequential([E_G_twin, E_twin])
E_G_D = Sequential([E_G, D])
E_G_twin_D_twin = Sequential([E_G_twin, D_twin])
E_twin_G_E = Sequential([E_twin_G, E])
E_twin_G_D = Sequential([E_twin_G, D])
E_twin_G_twin_D_twin = Sequential([E_twin_G_twin, D_twin])
# Misc init.
resolution_log2 = int(np.round(np.log2(E_G.output_shape[2])))
initial_lod = max(resolution_log2 - int(np.round(np.log2(lod_initial_resolution))), 0)
cur_lod = 0.0
min_lod, max_lod = -1.0, -2.0
fake_score_avg = 0.0
D.trainable = False
D_twin.trainable = False
E_G_D.compile(optimizers.Adam(lr=0.0, beta_1=adam_beta1, beta_2=adam_beta2, epsilon=adam_epsilon),
loss=adversarial_loss)
E_twin_G_D.compile(optimizers.Adam(lr=0.0, beta_1=adam_beta1, beta_2=adam_beta2, epsilon=adam_epsilon),
loss=adversarial_loss)
E_G_twin_D_twin.compile(optimizers.Adam(lr=0.0, beta_1=adam_beta1, beta_2=adam_beta2, epsilon=adam_epsilon),
loss=adversarial_loss)
E_twin_G_twin_D_twin.compile(optimizers.Adam(lr=0.0, beta_1=adam_beta1, beta_2=adam_beta2, epsilon=adam_epsilon),
loss=adversarial_loss)
D.trainable = True
D_twin.trainable = True
D.compile(optimizers.Adam(lr=0.0, beta_1=adam_beta1, beta_2=adam_beta2, epsilon=adam_epsilon),
loss=adversarial_loss)
D_twin.compile(optimizers.Adam(lr=0.0, beta_1=adam_beta1, beta_2=adam_beta2, epsilon=adam_epsilon),
loss=adversarial_loss)
E_G.compile(optimizers.Adam(lr=0.0, beta_1=adam_beta1, beta_2=adam_beta2, epsilon=adam_epsilon),
loss=cycle_consistency_loss)
E_twin_G_twin.compile(optimizers.Adam(lr=0.0, beta_1=adam_beta1, beta_2=adam_beta2, epsilon=adam_epsilon),
loss=cycle_consistency_loss)
E_twin_G_E.compile(optimizers.Adam(lr=0.0, beta_1=adam_beta1, beta_2=adam_beta2, epsilon=adam_epsilon),
loss=semantic_consistency_loss)
E_G_twin_E_twin.compile(optimizers.Adam(lr=0.0, beta_1=adam_beta1, beta_2=adam_beta2, epsilon=adam_epsilon),
loss=semantic_consistency_loss)
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
tick_train_out = []
# Set up generators
data_generator1 = DataGenerator(images_dir=images_dir1)
data_generator2 = DataGenerator(images_dir=images_dir2)
generator1 = data_generator1.generate(batch_size=2, img_size=2**resolution_log2)
generator2 = data_generator2.generate(batch_size=2, img_size=2**resolution_log2)
real_1 = next(generator1)
real_2 = next(generator2)
result_subdir = misc.create_result_subdir(config.result_dir, config.run_desc)
print("real_1.shape:", real_1.shape)
print("real_2.shape:", real_2.shape)
misc.save_image_grid_twin(real_1, real_2, os.path.join(result_subdir, 'reals.png'))
nimg_h = 0
valid = np.ones((batch_size, 1, 1, 1))
fake = np.zeros((batch_size, 1, 1, 1))
while cur_nimg < total_kimg * 1000:
# Calculate current LOD.
cur_lod = initial_lod
if lod_training_kimg or lod_transition_kimg:
tlod = (cur_nimg / (1000.0/speed_factor)) / (lod_training_kimg + lod_transition_kimg)
cur_lod -= np.floor(tlod)
if lod_transition_kimg:
cur_lod -= max(1.0 + (np.fmod(tlod, 1.0) - 1.0) * (lod_training_kimg + lod_transition_kimg) / lod_transition_kimg, 0.0)
cur_lod = max(cur_lod, 0.0)
# Look up resolution-dependent parameters.
cur_res = 2 ** (resolution_log2 - int(np.floor(cur_lod)))
tick_duration_kimg = tick_kimg_overrides.get(cur_res, tick_kimg_default)
# Update network config.
lrate_coef = rampup(cur_nimg / 1000.0, rampup_kimg)
lrate_coef *= rampdown_linear(cur_nimg / 1000.0, total_kimg, rampdown_kimg)
models = [E_G_D, E_twin_G_D, E_G_twin_D_twin, E_twin_G_twin_D_twin, D, D_twin, E_G, E_twin_G_twin,
E_twin_G_E, E_G_twin_E_twin]
learning_rate_max = 0.001
for model in models:
K.set_value(model.optimizer.lr, np.float32(lrate_coef * learning_rate_max))
if hasattr(model, 'cur_lod'): K.set_value(model.cur_lod,np.float32(cur_lod))
new_min_lod, new_max_lod = int(np.floor(cur_lod)), int(np.ceil(cur_lod))
if min_lod != new_min_lod or max_lod != new_max_lod:
min_lod, max_lod = new_min_lod, new_max_lod
generator1 = data_generator1.generate(batch_size=batch_size, img_size=cur_res, min_lod=min_lod)
generator2 = data_generator2.generate(batch_size=batch_size, img_size=cur_res, min_lod=min_lod)
# create image to check the training process
real_1 = next(generator1)
real_2 = next(generator2)
fake_2 = E_G_twin.predict_on_batch(real_1)
fake_1 = E_twin_G.predict_on_batch(real_2)
misc.save_image_grid_twin(real_1, fake_2, os.path.join(result_subdir, 'fakes_dog%06d.png' % (cur_nimg / 1000)))
misc.save_image_grid_twin(real_2, fake_1, os.path.join(result_subdir, 'fakes_celeb%06d.png' % (cur_nimg / 1000)))
cur_nimg += batch_size
################################################################################################################
# train D
images1 = next(generator1)
img_fakes = E_G.predict_on_batch([images1])
d_true = D.train_on_batch(images1, valid)
d_fake = D.train_on_batch(img_fakes, fake)
#train E_G_D
g_loss = E_G_D.train_on_batch(images1, valid)
#print ("%d [D loss: %f] [G loss: %f]" % (cur_nimg, np.mean(d_true, d_fake), g_loss))
################################################################################################################
# train D_twin
images2 = next(generator2)
img_fakes = E_twin_G_twin.predict_on_batch([images2])
d_true = D_twin.train_on_batch(images2, valid)
d_fake = D_twin.train_on_batch(img_fakes, fake)
#train E_twin_G_twin_D_twin
g_loss = E_twin_G_twin_D_twin.train_on_batch(images2, valid)
#print ("%d [D loss: %f] [G loss: %f]" % (cur_nimg, np.mean(d_true, d_fake), g_loss))
#train EG on cycle consistency
E_G.train_on_batch(images1, images1)
# train E_twin_G_twin on cycle consistency
E_twin_G_twin.train_on_batch(images2, images2)
# train E_G_twin an D_twin on discriminator loss
img_fakes = E_G_twin.predict_on_batch([images1])
d_true = D_twin.train_on_batch(images2, valid)
d_fake = D_twin.train_on_batch(img_fakes, fake)
g_loss = E_G_twin_D_twin.train_on_batch(images1, valid)
# train E_twin_G and D on discriminator loss
img_fakes = E_twin_G.predict_on_batch([images2])
d_true = D.train_on_batch(images1, valid)
d_fake = D.train_on_batch(img_fakes, fake)
g_loss = E_twin_G_D.train_on_batch(images2, valid)
# train E_G_twin on semantic consistency
semantic = E.predict_on_batch(images1)
E_G_twin_E_twin.train_on_batch(images1, semantic)
# train E_twin_G on semantic consistency
semantic = E_twin.predict_on_batch(images2)
E_twin_G_E.train_on_batch(images2, semantic)
1/0
fake_score_cur = np.clip(np.mean(d_loss), 0.0, 1.0)
fake_score_avg = fake_score_avg * gdrop_beta + fake_score_cur * (1.0 - gdrop_beta)
if cur_nimg >= tick_start_nimg + tick_duration_kimg * 1000 or cur_nimg >= total_kimg * 1000:
cur_tick += 1
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
tick_start_time = cur_time
tick_train_avg = tuple(np.mean(np.concatenate([np.asarray(v).flatten() for v in vals])) for vals in zip(*tick_train_out))
tick_train_out = []
# Visualize generated images.
if cur_tick % image_snapshot_ticks == 0 or cur_nimg >= total_kimg * 1000:
snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images, os.path.join(result_subdir, 'fakes%06d.png' % (cur_nimg / 1000)), drange=drange_viz, grid_size=image_grid_size)
if cur_tick % network_snapshot_ticks == 0 or cur_nimg >= total_kimg * 1000:
save_GD_weights(G,D,os.path.join(result_subdir, 'network-snapshot-%06d' % (cur_nimg / 1000)))
save_GD(G,D,os.path.join(result_subdir, 'network-final'))
training_set.close()
print('Done.')
def train_progressive_gan(
G_smoothing=0.999, # Exponential 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=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.
maintenance_start_time = time.time()
training_set = dataset.load_dataset(data_dir=config.data_dir,
verbose=True,
**config.dataset)
# 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, E = misc.load_pkl(network_pkl)
else:
print('Constructing networks...')
G = tfutil.Network('G',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**config.G)
D = tfutil.Network('D',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**config.D)
E = tfutil.Network('E',
num_channels=training_set.shape[0],
resolution=training_set.shape[1],
label_size=training_set.label_size,
**config.E)
Gs = G.clone('Gs')
Gs_update_op = Gs.setup_as_moving_average_of(G, beta=G_smoothing)
G.print_layers()
D.print_layers()
E.print_layers()
print('Building TensorFlow graph...')
with tf.name_scope('Inputs'):
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 // config.num_gpus
reals, labels = training_set.get_minibatch_tf()
reals_split = tf.split(reals, config.num_gpus)
labels_split = tf.split(labels, config.num_gpus)
G_opt = tfutil.Optimizer(name='TrainG',
learning_rate=lrate_in,
**config.G_opt)
D_opt = tfutil.Optimizer(name='TrainD',
learning_rate=lrate_in,
**config.D_opt)
E_opt = tfutil.Optimizer(name='TrainE',
learning_rate=lrate_in,
**config.E_opt)
for gpu in range(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')
E_gpu = E if gpu == 0 else E.clone(E.name + '_shadow')
lod_assign_ops = [
tf.assign(G_gpu.find_var('lod'), lod_in),
tf.assign(D_gpu.find_var('lod'), lod_in),
tf.assign(E_gpu.find_var('lod'), lod_in)
]
reals_gpu = process_reals(reals_split[gpu], lod_in, mirror_augment,
training_set.dynamic_range, drange_net)
labels_gpu = labels_split[gpu]
with tf.name_scope('G_loss'), tf.control_dependencies(
lod_assign_ops):
G_loss = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
E=E_gpu,
opt=G_opt,
training_set=training_set,
minibatch_size=minibatch_split,
**config.G_loss)
with tf.name_scope('D_loss'), tf.control_dependencies(
lod_assign_ops):
D_loss = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
E=E_gpu,
opt=D_opt,
training_set=training_set,
minibatch_size=minibatch_split,
reals=reals_gpu,
labels=labels_gpu,
**config.D_loss)
with tf.name_scope('E_loss'), tf.control_dependencies(
lod_assign_ops):
E_loss = tfutil.call_func_by_name(
G=G_gpu,
D=D_gpu,
E=E_gpu,
opt=E_opt,
training_set=training_set,
reals=reals_gpu,
minibatch_size=minibatch_split,
**config.E_loss)
G_opt.register_gradients(tf.reduce_mean(G_loss), G_gpu.trainables)
D_opt.register_gradients(tf.reduce_mean(D_loss), D_gpu.trainables)
E_opt.register_gradients(tf.reduce_mean(E_loss), E_gpu.trainables)
G_train_op = G_opt.apply_updates()
D_train_op = D_opt.apply_updates()
E_train_op = E_opt.apply_updates()
#sys.exit(0)
print('Setting up snapshot image grid...')
grid_size, grid_reals, grid_labels, grid_latents = setup_snapshot_image_grid(
G, training_set, **config.grid)
sched = TrainingSchedule(total_kimg * 1000, training_set, **config.sched)
grid_fakes = Gs.run(grid_latents,
grid_labels,
minibatch_size=sched.minibatch // config.num_gpus)
print('Setting up result dir...')
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
misc.save_image_grid(grid_reals,
os.path.join(result_subdir, 'reals.png'),
drange=training_set.dynamic_range,
grid_size=grid_size)
misc.save_image_grid(grid_fakes,
os.path.join(result_subdir, 'fakes%06d.png' % 0),
drange=drange_net,
grid_size=grid_size)
summary_log = tf.summary.FileWriter(result_subdir)
if save_tf_graph:
summary_log.add_graph(tf.get_default_graph())
if save_weight_histograms:
G.setup_weight_histograms()
D.setup_weight_histograms()
print('Training...')
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
train_start_time = tick_start_time - resume_time
prev_lod = -1.0
while cur_nimg < total_kimg * 1000:
# Choose training parameters and configure training ops.
sched = TrainingSchedule(cur_nimg, training_set, **config.sched)
training_set.configure(sched.minibatch, 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()
E_opt.reset_optimizer_state()
prev_lod = sched.lod
# Run training ops.
for repeat in range(minibatch_repeats):
for _ in range(D_repeats):
tfutil.run(
[D_train_op, Gs_update_op], {
lod_in: sched.lod,
lrate_in: sched.D_lrate,
minibatch_in: sched.minibatch
})
cur_nimg += sched.minibatch
tfutil.run(
[G_train_op, E_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
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
total_time = cur_time - train_start_time
maintenance_time = tick_start_time - maintenance_start_time
maintenance_start_time = cur_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 %.1f'
% (tfutil.autosummary('Progress/tick', cur_tick),
tfutil.autosummary('Progress/kimg', cur_nimg / 1000.0),
tfutil.autosummary('Progress/lod', sched.lod),
tfutil.autosummary('Progress/minibatch', sched.minibatch),
misc.format_time(
tfutil.autosummary('Timing/total_sec', total_time)),
tfutil.autosummary('Timing/sec_per_tick', tick_time),
tfutil.autosummary('Timing/sec_per_kimg',
tick_time / tick_kimg),
tfutil.autosummary('Timing/maintenance_sec',
maintenance_time)))
tfutil.autosummary('Timing/total_hours',
total_time / (60.0 * 60.0))
tfutil.autosummary('Timing/total_days',
total_time / (24.0 * 60.0 * 60.0))
tfutil.save_summaries(summary_log, cur_nimg)
# Save snapshots.
if cur_tick % image_snapshot_ticks == 0 or done:
grid_fakes = Gs.run(grid_latents,
grid_labels,
minibatch_size=sched.minibatch //
config.num_gpus)
misc.save_image_grid(grid_fakes,
os.path.join(
result_subdir,
'fakes%06d.png' % (cur_nimg // 1000)),
drange=drange_net,
grid_size=grid_size)
misc.save_all_res(training_set.shape[1],
Gs,
result_subdir,
50,
minibatch_size=sched.minibatch //
config.num_gpus)
if cur_tick % network_snapshot_ticks == 0 or done:
misc.save_pkl(
(G, D, Gs, E),
os.path.join(
result_subdir,
'network-snapshot-%06d.pkl' % (cur_nimg // 1000)))
# Record start time of the next tick.
tick_start_time = time.time()
# Write final results.
misc.save_pkl((G, D, Gs, E),
os.path.join(result_subdir, 'network-final.pkl'))
summary_log.close()
open(os.path.join(result_subdir, '_training-done.txt'), 'wt').close()
def train_gan(
separate_funcs=False,
D_training_repeats=1,
G_learning_rate_max=0.0010,
D_learning_rate_max=0.0010,
G_smoothing=0.999,
adam_beta1=0.0,
adam_beta2=0.99,
adam_epsilon=1e-8,
minibatch_default=16,
minibatch_overrides={},
rampup_kimg=40/speed_factor,
rampdown_kimg=0,
lod_initial_resolution=4,
lod_training_kimg=400/speed_factor,
lod_transition_kimg=400/speed_factor,
total_kimg=10000/speed_factor,
dequantize_reals=False,
gdrop_beta=0.9,
gdrop_lim=0.5,
gdrop_coef=0.2,
gdrop_exp=2.0,
drange_net=[-1, 1],
drange_viz=[-1, 1],
image_grid_size=None,
tick_kimg_default=50/speed_factor,
tick_kimg_overrides={32: 20, 64: 10, 128: 10, 256: 5, 512: 2, 1024: 1},
image_snapshot_ticks=1,
network_snapshot_ticks=4,
image_grid_type='default',
# resume_network = '000-celeba/network-snapshot-000488',
resume_network=None,
resume_kimg=0.0,
resume_time=0.0):
training_set, drange_orig = load_dataset()
G, G_train,\
D, D_train = build_trainable_model(resume_network,
training_set.shape[3],
training_set.shape[1],
training_set.labels.shape[1],
adam_beta1, adam_beta2,
adam_epsilon)
# Misc init.
resolution_log2 = int(np.round(np.log2(G.output_shape[2])))
initial_lod = max(resolution_log2 -
int(np.round(np.log2(lod_initial_resolution))), 0)
cur_lod = 0.0
min_lod, max_lod = -1.0, -2.0
fake_score_avg = 0.0
cur_nimg = int(resume_kimg * 1000)
cur_tick = 0
tick_start_nimg = cur_nimg
tick_start_time = time.time()
train_start_time = tick_start_time - resume_time
if image_grid_type == 'default':
if image_grid_size is None:
w, h = G.output_shape[1], G.output_shape[2]
print('w:%d,h:%d' % (w, h))
image_grid_size = (np.clip(int(1920 // w), 3, 16).astype('int'),
np.clip(1080 / h, 2, 16).astype('int'))
print('image_grid_size:', image_grid_size)
example_real_images, snapshot_fake_labels = \
training_set.get_random_minibatch_channel_last(
np.prod(image_grid_size), labels=True)
snapshot_fake_latents = random_latents(
np.prod(image_grid_size), G.input_shape)
else:
raise ValueError('Invalid image_grid_type', image_grid_type)
result_subdir = misc.create_result_subdir(
config.result_dir, config.run_desc)
result_subdir = Path(result_subdir)
print('example_real_images.shape:', example_real_images.shape)
misc.save_image_grid(example_real_images,
str(result_subdir / 'reals.png'),
drange=drange_orig, grid_size=image_grid_size)
snapshot_fake_latents = random_latents(
np.prod(image_grid_size), G.input_shape)
snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images,
str(result_subdir/f'fakes{cur_nimg//1000:06}.png'),
drange=drange_viz, grid_size=image_grid_size)
# nimg_h = 0
while cur_nimg < total_kimg * 1000:
# Calculate current LOD.
cur_lod = initial_lod
if lod_training_kimg or lod_transition_kimg:
tlod = (cur_nimg / (1000.0/speed_factor)) / \
(lod_training_kimg + lod_transition_kimg)
cur_lod -= np.floor(tlod)
if lod_transition_kimg:
cur_lod -= max(1.0 + (np.fmod(tlod, 1.0) - 1.0) *
(lod_training_kimg + lod_transition_kimg) /
lod_transition_kimg,
0.0)
cur_lod = max(cur_lod, 0.0)
# Look up resolution-dependent parameters.
cur_res = 2 ** (resolution_log2 - int(np.floor(cur_lod)))
minibatch_size = minibatch_overrides.get(cur_res, minibatch_default)
tick_duration_kimg = tick_kimg_overrides.get(
cur_res, tick_kimg_default)
# Update network config.
lrate_coef = rampup(cur_nimg / 1000.0, rampup_kimg)
lrate_coef *= rampdown_linear(cur_nimg /
1000.0, total_kimg, rampdown_kimg)
K.set_value(G.optimizer.lr, np.float32(
lrate_coef * G_learning_rate_max))
K.set_value(G_train.optimizer.lr, np.float32(
lrate_coef * G_learning_rate_max))
K.set_value(D_train.optimizer.lr, np.float32(
lrate_coef * D_learning_rate_max))
if hasattr(G_train, 'cur_lod'):
K.set_value(G_train.cur_lod, np.float32(cur_lod))
if hasattr(D_train, 'cur_lod'):
K.set_value(D_train.cur_lod, np.float32(cur_lod))
new_min_lod, new_max_lod = int(
np.floor(cur_lod)), int(np.ceil(cur_lod))
if min_lod != new_min_lod or max_lod != new_max_lod:
min_lod, max_lod = new_min_lod, new_max_lod
# train D
d_loss = None
for idx in range(D_training_repeats):
mb_reals, mb_labels = \
training_set.get_random_minibatch_channel_last(
minibatch_size, lod=cur_lod, shrink_based_on_lod=True,
labels=True)
mb_latents = random_latents(minibatch_size, G.input_shape)
# compensate for shrink_based_on_lod
if min_lod > 0:
mb_reals = np.repeat(mb_reals, 2**min_lod, axis=1)
mb_reals = np.repeat(mb_reals, 2**min_lod, axis=2)
mb_fakes = G.predict_on_batch([mb_latents])
epsilon = np.random.uniform(0, 1, size=(minibatch_size, 1, 1, 1))
interpolation = epsilon*mb_reals + (1-epsilon)*mb_fakes
mb_reals = misc.adjust_dynamic_range(
mb_reals, drange_orig, drange_net)
d_loss, d_diff, d_norm = \
D_train.train_on_batch([mb_fakes, mb_reals, interpolation],
[np.ones((minibatch_size, 1, 1, 1)),
np.ones((minibatch_size, 1))])
d_score_real = D.predict_on_batch(mb_reals)
d_score_fake = D.predict_on_batch(mb_fakes)
print('real score: %d fake score: %d' %
(np.mean(d_score_real), np.mean(d_score_fake)))
cur_nimg += minibatch_size
# train G
mb_latents = random_latents(minibatch_size, G.input_shape)
g_loss = G_train.train_on_batch(
[mb_latents], (-1)*np.ones((mb_latents.shape[0], 1, 1, 1)))
print('%d [D loss: %f] [G loss: %f]' % (cur_nimg, d_loss, g_loss))
fake_score_cur = np.clip(np.mean(d_loss), 0.0, 1.0)
fake_score_avg = fake_score_avg * gdrop_beta + \
fake_score_cur * (1.0 - gdrop_beta)
gdrop_strength = gdrop_coef * \
(max(fake_score_avg - gdrop_lim, 0.0) ** gdrop_exp)
if hasattr(D, 'gdrop_strength'):
K.set_value(D.gdrop_strength, np.float32(gdrop_strength))
is_complete = cur_nimg >= total_kimg * 1000
is_generate_a_lot = \
cur_nimg >= tick_start_nimg + tick_duration_kimg * 1000
if is_generate_a_lot or is_complete:
cur_tick += 1
cur_time = time.time()
tick_kimg = (cur_nimg - tick_start_nimg) / 1000.0
tick_start_nimg = cur_nimg
tick_time = cur_time - tick_start_time
print(f'tick time: {tick_time}')
print(f'tick image: {tick_kimg}k')
tick_start_time = cur_time
# Visualize generated images.
is_image_snapshot_ticks = cur_tick % image_snapshot_ticks == 0
if is_image_snapshot_ticks or is_complete:
snapshot_fake_images = G.predict_on_batch(
snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images,
str(result_subdir /
f'fakes{cur_nimg // 1000:06}.png'),
drange=drange_viz,
grid_size=image_grid_size)
if cur_tick % network_snapshot_ticks == 0 or is_complete:
save_GD_weights(G, D, str(
result_subdir / f'network-snapshot-{cur_nimg // 1000:06}'))
break
save_GD(G, D, str(result_subdir/'network-final'))
training_set.close()
print('Done.')
train_complete_time = time.time()-train_start_time
print(f'training time: {train_complete_time}')
