def setup_snapshot_image_grid(training_set, drange_net, grid_size=None,
size = '1080p', # '1080p' = to be viewed on 1080p display, '4k' = to be viewed on 4k display.
layout = 'random'): # 'random' = grid contents are selected randomly, 'row_per_class' = each row corresponds to one class label.
# Select size.
if grid_size is None:
if size == '1080p':
gw = np.clip(1920 // training_set.shape[2], 3, 32)
gh = np.clip(1080 // training_set.shape[1], 2, 32)
if size == '4k':
gw = np.clip(3840 // training_set.shape[2], 7, 32)
gh = np.clip(2160 // training_set.shape[1], 4, 32)
else:
gw = grid_size[0]
gh = grid_size[1]
# Fill in reals and labels.
reals = np.zeros([gw * gh] + training_set.shape, dtype=np.float32)
labels = np.zeros([gw * gh, training_set.label_size], dtype=training_set.label_dtype)
for idx in range(gw * gh):
x = idx % gw; y = idx // gw
while True:
real, label = training_set.get_minibatch_np(1)
real = real.astype(np.float32)
real = misc.adjust_dynamic_range(real, training_set.dynamic_range, drange_net)
if layout == 'row_per_class' and training_set.label_size > 0:
if label[0, y % training_set.label_size] == 0.0:
continue
reals[idx] = real[0]
labels[idx] = label[0]
break
return (gw, gh), reals, labels
def process_reals(x, lod, mirror_augment, drange_data, drange_net):
with tf.name_scope('ProcessReals'):
with tf.name_scope('DynamicRange'):
x = tf.cast(x, tf.float32)
x = misc.adjust_dynamic_range(x, drange_data, drange_net)
if mirror_augment:
with tf.name_scope('MirrorAugment'):
s = tf.shape(x)
mask = tf.random_uniform([s[0], 1, 1, 1], 0.0, 1.0)
mask = tf.tile(mask, [1, s[1], s[2], s[3]])
x = tf.where(mask < 0.5, x, tf.reverse(x, axis=[3]))
with tf.name_scope(
'FadeLOD'
): # Smooth crossfade between consecutive levels-of-detail.
s = tf.shape(x)
y = tf.reshape(x, [-1, s[1], s[2] // 2, 2, s[3] // 2, 2])
y = tf.reduce_mean(y, axis=[3, 5], keepdims=True)
y = tf.tile(y, [1, 1, 1, 2, 1, 2])
y = tf.reshape(y, [-1, s[1], s[2], s[3]])
x = tfutil.lerp(x, y, lod - tf.floor(lod))
with tf.name_scope(
'UpscaleLOD'
): # Upscale to match the expected input/output size of the networks.
s = tf.shape(x)
factor = tf.cast(2**tf.floor(lod), tf.int32)
x = tf.reshape(x, [-1, s[1], s[2], 1, s[3], 1])
x = tf.tile(x, [1, 1, 1, factor, 1, factor])
x = tf.reshape(x, [-1, s[1], s[2] * factor, s[3] * factor])
return x
def __init__(self, filename=None, model=None):
if model is not None:
# error
return
_, _, G = misc.load_pkl(filename)
self.net = Net()
self.net.G = G
self.have_compiled = False
self.net.labels_var = T.TensorType('float32',
[False] * 512)('labels_var')
# experiment
num_example_latents = 10
self.net.example_latents = train.random_latents(
num_example_latents, self.net.G.input_shape)
self.net.example_labels = self.net.example_latents
self.net.latents_var = T.TensorType(
'float32',
[False] * len(self.net.example_latents.shape))('latents_var')
self.net.labels_var = T.TensorType(
'float32',
[False] * len(self.net.example_latents.shape))('labels_var')
self.net.images_expr = self.net.G.eval(self.net.latents_var,
self.net.labels_var,
ignore_unused_inputs=True)
self.net.images_expr = misc.adjust_dynamic_range(
self.net.images_expr, [-1, 1], [0, 1])
train.imgapi_compile_gen_fn(self.net)
self.invert_models = def_invert_models(self.net,
layer='conv4',
alpha=0.002)
def imgapi_load_net(run_id, snapshot=None, random_seed=1000, num_example_latents=1000, load_dataset=True, compile_gen_fn=True):
class Net: pass
net = Net()
net.result_subdir = misc.locate_result_subdir(run_id)
net.network_pkl = misc.locate_network_pkl(net.result_subdir, snapshot)
_, _, net.G = misc.load_pkl(net.network_pkl)
# Generate example latents and labels.
np.random.seed(random_seed)
net.example_latents = random_latents(num_example_latents, net.G.input_shape)
net.example_labels = np.zeros((num_example_latents, 0), dtype=np.float32)
net.dynamic_range = [0, 255]
if load_dataset:
imgapi_load_dataset(net)
# Compile Theano func.
net.latents_var = T.TensorType('float32', [False] * len(net.example_latents.shape))('latents_var')
net.labels_var = T.TensorType('float32', [False] * len(net.example_labels.shape)) ('labels_var')
if hasattr(net.G, 'cur_lod'):
net.lod = net.G.cur_lod.get_value()
net.images_expr = net.G.eval(net.latents_var, net.labels_var, min_lod=net.lod, max_lod=net.lod, ignore_unused_inputs=True)
else:
net.lod = 0.0
net.images_expr = net.G.eval(net.latents_var, net.labels_var, ignore_unused_inputs=True)
net.images_expr = misc.adjust_dynamic_range(net.images_expr, [-1,1], net.dynamic_range)
if compile_gen_fn:
imgapi_compile_gen_fn(net)
return net
def process_reals(x, lod, mirror_augment, drange_data, drange_net):
with tf.name_scope('ProcessReals'):
with tf.name_scope('DynamicRange'):
x = tf.cast(x, tf.float32)
x = misc.adjust_dynamic_range(x, drange_data, drange_net)
if mirror_augment:
with tf.name_scope('MirrorAugment'):
s = tf.shape(x)
mask = tf.random_uniform([s[0], 1, 1, 1], 0.0, 1.0)
mask = tf.tile(mask, [1, s[1], s[2], s[3]])
x = tf.where(mask < 0.5, x, tf.reverse(x, axis=[3]))
with tf.name_scope('FadeLOD'): # Smooth crossfade between consecutive levels-of-detail.
s = tf.shape(x)
y = tf.reshape(x, [-1, s[1], s[2]//2, 2, s[3]//2, 2])
y = tf.reduce_mean(y, axis=[3, 5], keepdims=True)
y = tf.tile(y, [1, 1, 1, 2, 1, 2])
y = tf.reshape(y, [-1, s[1], s[2], s[3]])
x = tfutil.lerp(x, y, lod - tf.floor(lod))
with tf.name_scope('UpscaleLOD'): # Upscale to match the expected input/output size of the networks.
s = tf.shape(x)
factor = tf.cast(2 ** tf.floor(lod), tf.int32)
x = tf.reshape(x, [-1, s[1], s[2], 1, s[3], 1])
x = tf.tile(x, [1, 1, 1, factor, 1, factor])
x = tf.reshape(x, [-1, s[1], s[2] * factor, s[3] * factor])
return x
def train(self):
while self.sche.cur_img < 1000 * self.total_kimg:
real = self.dataloader.get_batch().cuda()
real = propress_real(real, self.sche.lod, self.sche.phase)
for repeat in range(self.D_repeat):
loss_d = self.update_D(real, self.sche.batchsize)
#self.Gs.update_Gs(self.G)
self.sche.cur_img += self.sche.batchsize
loss_g = self.update_G(self.sche.batchsize)
self.sche.cur_img += self.sche.batchsize
print("loss_d:%.2f loss_g:%.2f" % (loss_d, loss_g))
self.writer.add_scalar('Loss/loss_d', loss_d, self.sche.cur_img)
self.writer.add_scalar('Loss/loss_g', loss_g, self.sche.cur_img)
if self.sche.tick % self.sche.save_kimg_tick.get(
self.sche.phase, 1) == 0:
with torch.no_grad():
img = self.G(self.z, self.sche.lod, self.sche.phase,
self.sche.TRANSITION)
img = upscale_phase(img, self.sche.phase,
self.sche.max_resolution)
os.makedirs(os.path.join(self.tag_dir, "images"),
exist_ok=True)
save_image_grid(
adjust_dynamic_range(img, [-1, 1], [0, 1]).clamp(0, 1),
os.path.join(
self.tag_dir, "images", "phase%d_cur%d.jpg" %
(self.sche.phase, self.sche.last_cur_phase_kimg)))
save_image_grid(
adjust_dynamic_range(
upscale_phase(real[:24], self.sche.phase,
self.sche.max_resolution), [-1, 1],
[0, 1]).clamp(0, 1),
os.path.join(
self.tag_dir, "images", "phase%d_cur%d_real.jpg" %
(self.sche.phase, self.sche.last_cur_phase_kimg)))
self.sche.update()
if self.sche.NEW_PHASE is True:
self.save_model(self.sche.phase)
self.G.Add_To_RGB_Layer(self.sche.phase)
self.D.Add_From_RGB_Layer(self.sche.phase)
self.renew()
import theano
import lasagne
import dataset
import network
from theano import tensor as T
import config
import misc
import numpy as np
import scipy.ndimage
_, _, G = misc.load_pkl("network-snapshot-009041.pkl")
class Net: pass
net = Net()
net.G = G
import train
num_example_latents = 10
net.example_latents = train.random_latents(num_example_latents, net.G.input_shape)
net.example_labels = net.example_latents
net.latents_var = T.TensorType('float32', [False] * len(net.example_latents.shape))('latents_var')
net.labels_var = T.TensorType('float32', [False] * len(net.example_latents.shape)) ('labels_var')
print("HIYA", net.example_latents[:1].shape, net.example_labels[:1].shape)
net.images_expr = net.G.eval(net.latents_var, net.labels_var, ignore_unused_inputs=True)
net.images_expr = misc.adjust_dynamic_range(net.images_expr, [-1,1], [0,1])
train.imgapi_compile_gen_fn(net)
images = net.gen_fn(net.example_latents[:1], net.example_labels[:1])
misc.save_image(images[0], "fake4c.png", drange=[0,1])
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 process_colors(x):
with tf.name_scope('ProcessColors'):
x = misc.adjust_dynamic_range(x, [0, 1], [-1, 1])
return x
def process_masks(x):
with tf.name_scope('ProcessMasks'):
x = tf.cast(x, tf.float32)
x = misc.adjust_dynamic_range(x, [0, 255], [-1, 1])
return x
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 pre_process(
imgs, # Images to pre-process
coords, # Coords corresponding
bboxes, # Bounding boxes
#threshold, #
drange_imgs, # Dynamic range for the images (Typically: [0, 255])
drange_coords, # Dynamic range for the coordinates (Typically: [0, image.shape[0]])
drange_net, # Dynamic range for the network (Typically: [-1, 1])
mirror_augment=False, # Should mirror augment be applied?
random_dw_conv=False, # Apply a random depthwise convolution to this input image?
horizontal_flip=False,
):
with tf.name_scope('ProcessReals'):
imgs = tf.cast(imgs, tf.float32)
coords = tf.cast(coords, tf.float32)
with tf.name_scope('DynamicRange'):
imgs = misc.adjust_dynamic_range(imgs, drange_imgs, drange_net)
coords = misc.adjust_dynamic_range(coords, drange_coords,
drange_net)
if mirror_augment:
with tf.name_scope('MirrorAugment'):
s = tf.shape(imgs)
mask = tf.random_uniform([s[0], 1, 1, 1], 0.0, 1.0)
mask = tf.tile(mask, [1, s[1], s[2], s[3]])
imgs = tf.where(mask < 0.5, imgs, tf.reverse(imgs, axis=[3]))
if random_dw_conv:
with tf.name_scope('RandomDWConv'):
# Parameters of the augmentation:
m = 0.5
filt = 2 * m * tf.random_uniform((3, 3, 3, 1)) - m
imgs = (tf.nn.depthwise_conv2d(imgs,
filt,
strides=[1, 1, 1, 1],
padding='SAME',
data_format='NCHW') +
imgs) / (1 + m)
if horizontal_flip:
with tf.name_scope('HorizontalFlip'):
rand = np.random.random()
if rand > 0.5:
imgs = tf.image.flip_left_right(imgs)
mult = tf.constant([-1, 1, -1, 1, -1, 1])
coords *= mult
# net_bboxes = tf.constant(generate_output(64, np.arange(4,17,2)), tf.float32) # TODO: architecture independance
with tf.name_scope('ProcessBboxes'):
bboxes = tf.cast(bboxes, tf.float32)
bboxes_transformed = tf.map_fn(
lambda bbox: bboxes_fn(bbox, net_bboxes, config.threshold), bboxes)
refinement = tf.map_fn(lambda bbox: bbox_refinement(bbox, net_bboxes),
bboxes) # TODO: optimize refinement
# Should refinement be normalized?
# net_bboxes.shape = [561, 4]
# refinement.shape = [?, 561, 4]
# bboxes = tf.map_fn(lambda bbox: IoU(bbox, net_bboxes_area, config.threshold), bboxes)
# Remove the irrelevent boxes
# mask = tf.greater(tf.math.reduce_sum(bboxes,axis=-1),0)
# bboxes = tf.boolean_mask(bboxes, mask)
# imgs = tf.boolean_mask(imgs,mask)
# coords = tf.boolean_mask(coords,mask)
return imgs, coords, bboxes_transformed, refinement
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,
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}')
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 classify(model_path, testing_data_path):
labels_1 = [
'CelebA_real_data', 'ProGAN_generated_data', 'SNGAN_generated_data',
'CramerGAN_generated_data', 'MMDGAN_generated_data'
]
labels_2 = [
'CelebA_real_data', 'ProGAN_seed_0_generated_data ',
'ProGAN_seed_1_generated_data', 'ProGAN_seed_2_generated_data',
'ProGAN_seed_3_generated_data', 'ProGAN_seed_4_generated_data',
'ProGAN_seed_5_generated_data', 'ProGAN_seed_6_generated_data',
'ProGAN_seed_7_generated_data', 'ProGAN_seed_8_generated_data',
'ProGAN_seed_9_generated_data'
]
print('Loading network...')
C_im = misc.load_network_pkl(model_path)
if testing_data_path.endswith('.png') or testing_data_path.endswith(
'.jpg'):
im = np.array(PIL.Image.open(testing_data_path)).astype(
np.float32) / 255.0
if len(im.shape) < 3:
im = np.dstack([im, im, im])
if im.shape[2] == 4:
im = im[:, :, :3]
if im.shape[0] != 128:
im = skimage.transform.resize(im, (128, 128))
im = np.transpose(misc.adjust_dynamic_range(im, [0, 1], [-1, 1]),
axes=[2, 0, 1])
im = np.reshape(im, [1] + list(im.shape))
logits = C_im.run(im,
minibatch_size=1,
num_gpus=1,
out_dtype=np.float32)
idx = np.argmax(np.squeeze(logits))
if logits.shape[1] == len(labels_1):
labels = list(labels_1)
elif logits.shape[1] == len(labels_2):
labels = list(labels_2)
print('The input image is predicted as being sampled from %s' %
labels[idx])
elif os.path.isdir(testing_data_path):
count_dict = None
name_list = sorted(os.listdir(testing_data_path))
length = len(name_list)
for (count0, name) in enumerate(name_list):
im = np.array(PIL.Image.open('%s/%s' %
(testing_data_path, name))).astype(
np.float32) / 255.0
if len(im.shape) < 3:
im = np.dstack([im, im, im])
if im.shape[2] == 4:
im = im[:, :, :3]
if im.shape[0] != 128:
im = skimage.transform.resize(im, (128, 128))
im = np.transpose(misc.adjust_dynamic_range(im, [0, 1], [-1, 1]),
axes=[2, 0, 1])
im = np.reshape(im, [1] + list(im.shape))
logits = C_im.run(im,
minibatch_size=1,
num_gpus=1,
out_dtype=np.float32)
idx = np.argmax(np.squeeze(logits))
if logits.shape[1] == len(labels_1):
labels = list(labels_1)
elif logits.shape[1] == len(labels_2):
labels = list(labels_2)
if count_dict is None:
count_dict = {}
for label in labels:
count_dict[label] = 0
count_dict[labels[idx]] += 1
print(
'Classifying %d/%d images: %s: predicted as being sampled from %s'
% (count0, length, name, labels[idx]))
for label in labels:
print(
'The percentage of images sampled from %s is %d/%d = %.2f%%' %
(label, count_dict[label], length,
float(count_dict[label]) / float(length) * 100.0))
