def __init__(self, model_path):
self.model = load_model(model_path)
self.uses_hist_equalization = get_hdf5_attr(
model_path, 'decoder_uses_hist_equalization', True)
self.distribution = DistributionCollection.from_hdf5(model_path)
self._predict = predict_wrapper(self.model.predict, self.model.output_names)
self.model._make_predict_function()
def __init__(self, model_path):
self.model = load_model(model_path)
self.uses_hist_equalization = get_hdf5_attr(
model_path, 'decoder_uses_hist_equalization', True)
self.distribution = DistributionCollection.from_hdf5(model_path)
self._predict = predict_wrapper(self.model.predict,
self.model.output_names)
self.model._make_predict_function()
def run(g_weights_fname, d_weights_fname, selected_outputs, nb_samples,
out_fname):
generator = load_model(g_weights_fname, render_gan_custom_objects())
discriminator = load_model(d_weights_fname, render_gan_custom_objects())
generator._make_predict_function()
discriminator._make_predict_function()
dist_json = get_hdf5_attr(g_weights_fname, 'distribution').decode('utf-8')
dist = diktya.distributions.load_from_json(dist_json)
os.makedirs(os.path.dirname(out_fname), exist_ok=True)
dset = DistributionHDF5Dataset(out_fname,
mode='w',
nb_samples=nb_samples,
distribution=dist)
batch_size = 100
avialable_datasets = [
name for name in generator.output_names if name != 'labels'
]
print("Avialable outputs: " + ", ".join(avialable_datasets))
generator_predict = predict_wrapper(
lambda x: generator.predict(x, batch_size), generator.output_names)
def sample_generator():
z_shape = get_layer(generator.inputs[0]).batch_input_shape
while True:
z = np.random.uniform(-1, 1, (batch_size, ) + z_shape[1:])
outs = generator_predict(z)
raw_labels = outs.pop('labels')
pos = 0
labels = np.zeros(len(raw_labels), dtype=dist.norm_dtype)
for name, size in dist.norm_nb_elems.items():
labels[name] = raw_labels[:, pos:pos + size]
pos += size
deleted_keys = []
if selected_outputs != 'all':
for name in list(outs.keys()):
if name not in selected_outputs:
del outs[name]
deleted_keys.append(name)
if not outs:
raise Exception(
"Got no outputs. Removed {}. Selected outputs {}".format(
deleted_keys, selected_outputs))
outs['labels'] = labels
outs['discriminator'] = discriminator.predict(outs['fake'])
yield outs
bar = progressbar.ProgressBar(max_value=nb_samples)
for batch in sample_generator():
pos = dset.append(**batch)
bar.update(pos)
if pos >= nb_samples:
break
dset.close()
print("Saved dataset with fakes and labels to: {}".format(out_fname))
def run(g_weights_fname, d_weights_fname, selected_outputs, nb_samples, out_fname):
generator = load_model(g_weights_fname, render_gan_custom_objects())
discriminator = load_model(d_weights_fname, render_gan_custom_objects())
generator._make_predict_function()
discriminator._make_predict_function()
dist_json = get_hdf5_attr(g_weights_fname, 'distribution').decode('utf-8')
dist = diktya.distributions.load_from_json(dist_json)
os.makedirs(os.path.dirname(out_fname), exist_ok=True)
dset = DistributionHDF5Dataset(out_fname, mode='w', nb_samples=nb_samples,
distribution=dist)
batch_size = 100
avialable_datasets = [name for name in generator.output_names
if name != 'labels']
print("Avialable outputs: " + ", ".join(avialable_datasets))
generator_predict = predict_wrapper(lambda x: generator.predict(x, batch_size),
generator.output_names)
def sample_generator():
z_shape = get_layer(generator.inputs[0]).batch_input_shape
while True:
z = np.random.uniform(-1, 1, (batch_size, ) + z_shape[1:])
outs = generator_predict(z)
raw_labels = outs.pop('labels')
pos = 0
labels = np.zeros(len(raw_labels), dtype=dist.norm_dtype)
for name, size in dist.norm_nb_elems.items():
labels[name] = raw_labels[:, pos:pos+size]
pos += size
deleted_keys = []
if selected_outputs != 'all':
for name in list(outs.keys()):
if name not in selected_outputs:
del outs[name]
deleted_keys.append(name)
if not outs:
raise Exception("Got no outputs. Removed {}. Selected outputs {}"
.format(deleted_keys, selected_outputs))
outs['labels'] = labels
outs['discriminator'] = discriminator.predict(outs['fake'])
yield outs
bar = progressbar.ProgressBar(max_value=nb_samples)
for batch in sample_generator():
pos = dset.append(**batch)
bar.update(pos)
if pos >= nb_samples:
break
dset.close()
print("Saved dataset with fakes and labels to: {}".format(out_fname))
def train_callbacks(rendergan,
output_dir,
nb_visualise,
real_hdf5_fname,
distribution,
lr_schedule=None,
overwrite=False):
save_gan_cb = SaveGAN(rendergan,
join(output_dir, "models/{epoch:03d}/{name}.hdf5"),
every_epoch=10,
hdf5_attrs=get_distribution_hdf5_attrs(distribution))
nb_score = 1000
sample_fn = predict_wrapper(
rendergan.sample_generator_given_z.predict,
rendergan.sample_generator_given_z_output_names)
real = next(train_data_generator(real_hdf5_fname, nb_score, 1))['data']
vis_cb = VisualiseTag3dAndFake(nb_samples=nb_visualise // 2,
output_dir=join(output_dir,
'visualise_tag3d_fake'),
show=False,
preprocess=lambda x: np.clip(x, -1, 1))
vis_all = VisualiseAll(nb_samples=nb_visualise //
len(rendergan.sample_generator_given_z.outputs),
output_dir=join(output_dir, 'visualise_all'),
show=False,
preprocess=lambda x: np.clip(x, -1, 1))
vis_fake_sorted = VisualiseFakesSorted(
nb_samples=nb_visualise,
output_dir=join(output_dir, 'visualise_fakes_sorted'),
show=False,
preprocess=lambda x: np.clip(x, -1, 1))
vis_real_sorted = VisualiseRealsSorted(
nb_samples=nb_visualise,
output_dir=join(output_dir, 'visualise_reals_sorted'),
show=False,
preprocess=lambda x: np.clip(x, -1, 1))
def default_lr_schedule(lr):
return {
200: lr / 4,
250: lr / 4**2,
300: lr / 4**3,
}
def lr_scheduler(opt):
return LearningRateScheduler(opt,
lr_schedule(float(K.get_value(opt.lr))))
if lr_schedule is None:
lr_schedule = default_lr_schedule
g_optimizer = rendergan.gan.g_optimizer
d_optimizer = rendergan.gan.d_optimizer
lr_schedulers = [
lr_scheduler(g_optimizer),
lr_scheduler(d_optimizer),
]
hist_dir = join(output_dir, "history")
os.makedirs(hist_dir, exist_ok=True)
hist = HistoryPerBatch(hist_dir)
def history_plot(e, logs={}):
fig, _ = hist.plot(save_as="{:03d}.png".format(e),
metrics=['g_loss', 'd_loss'])
plt.close(fig) # allows fig to be garbage collected
hist_save = OnEpochEnd(history_plot, every_nth_epoch=20)
sample_outdir = join(output_dir, 'samples')
os.makedirs(sample_outdir, exist_ok=True)
store_samples_cb = StoreSamples(sample_outdir, distribution, overwrite)
dscore_outdir = join(output_dir, 'd_score_hist')
os.makedirs(dscore_outdir, exist_ok=True)
dscore = DScoreHistogram(dscore_outdir)
nb_sample = max(nb_score, nb_visualise)
sample_cb = SampleGAN(sample_fn,
rendergan.discriminator.predict,
rendergan.gan.random_z(nb_sample),
real,
callbacks=[
vis_cb, vis_fake_sorted, vis_all,
vis_real_sorted, dscore, store_samples_cb
])
return [sample_cb, save_gan_cb, hist, hist_save] + lr_schedulers
def train_callbacks(rendergan, output_dir, nb_visualise, real_hdf5_fname,
distribution, lr_schedule=None, overwrite=False):
save_gan_cb = SaveGAN(rendergan, join(output_dir, "models/{epoch:03d}/{name}.hdf5"),
every_epoch=10, hdf5_attrs=get_distribution_hdf5_attrs(distribution))
nb_score = 1000
sample_fn = predict_wrapper(rendergan.sample_generator_given_z.predict,
rendergan.sample_generator_given_z_output_names)
real = next(train_data_generator(real_hdf5_fname, nb_score, 1))['data']
vis_cb = VisualiseTag3dAndFake(
nb_samples=nb_visualise // 2,
output_dir=join(output_dir, 'visualise_tag3d_fake'),
show=False,
preprocess=lambda x: np.clip(x, -1, 1)
)
vis_all = VisualiseAll(
nb_samples=nb_visualise // len(rendergan.sample_generator_given_z.outputs),
output_dir=join(output_dir, 'visualise_all'),
show=False,
preprocess=lambda x: np.clip(x, -1, 1))
vis_fake_sorted = VisualiseFakesSorted(
nb_samples=nb_visualise,
output_dir=join(output_dir, 'visualise_fakes_sorted'),
show=False,
preprocess=lambda x: np.clip(x, -1, 1))
vis_real_sorted = VisualiseRealsSorted(
nb_samples=nb_visualise,
output_dir=join(output_dir, 'visualise_reals_sorted'),
show=False,
preprocess=lambda x: np.clip(x, -1, 1))
def default_lr_schedule(lr):
return {
200: lr / 4,
250: lr / 4**2,
300: lr / 4**3,
}
def lr_scheduler(opt):
return LearningRateScheduler(opt, lr_schedule(float(K.get_value(opt.lr))))
if lr_schedule is None:
lr_schedule = default_lr_schedule
g_optimizer = rendergan.gan.g_optimizer
d_optimizer = rendergan.gan.d_optimizer
lr_schedulers = [
lr_scheduler(g_optimizer),
lr_scheduler(d_optimizer),
]
hist_dir = join(output_dir, "history")
os.makedirs(hist_dir, exist_ok=True)
hist = HistoryPerBatch(hist_dir)
def history_plot(e, logs={}):
fig, _ = hist.plot(save_as="{:03d}.png".format(e), metrics=['g_loss', 'd_loss'])
plt.close(fig) # allows fig to be garbage collected
hist_save = OnEpochEnd(history_plot, every_nth_epoch=20)
sample_outdir = join(output_dir, 'samples')
os.makedirs(sample_outdir, exist_ok=True)
store_samples_cb = StoreSamples(sample_outdir, distribution, overwrite)
dscore_outdir = join(output_dir, 'd_score_hist')
os.makedirs(dscore_outdir, exist_ok=True)
dscore = DScoreHistogram(dscore_outdir)
nb_sample = max(nb_score, nb_visualise)
sample_cb = SampleGAN(sample_fn, rendergan.discriminator.predict,
rendergan.gan.random_z(nb_sample), real,
callbacks=[vis_cb, vis_fake_sorted, vis_all, vis_real_sorted,
dscore, store_samples_cb])
return [sample_cb, save_gan_cb, hist, hist_save] + lr_schedulers
