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 __init__(self, **config):
self.model = load_model(config['model_path'])
# We can't use model.compile because it requires an optimizer and a loss function.
# Since we only use the model for inference, we call the private function
# _make_predict_function(). This is exactly what keras would do otherwise the first
# time model.predict() is called.
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 __init__(self, **config):
self.model = load_model(config['model_path'])
# We can't use model.compile because it requires an optimizer and a loss function.
# Since we only use the model for inference, we call the private function
# _make_predict_function(). This is exactly what keras would do otherwise the first
# time model.predict() is called.
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 load_tag3d_network(weight_fname):
model = load_model(weight_fname)
for layer in model.layers:
layer.trainable = False
return model
def __init__(self,
model_path,
threshold=0.6):
self.saliency_threshold = float(threshold)
self.model = load_model(model_path)
self.model._make_predict_function()
def __init__(self, model_path, threshold=0.6):
self.saliency_threshold = threshold
self.model = load_model(model_path)
self.model._make_predict_function()
def load_tag3d_network(weight_fname):
model = load_model(weight_fname)
for layer in model.layers:
layer.trainable = False
return model
def __init__(self, model_path=None):
if model_path is None:
model_path = os.join.path(os.path.dirname(__file__), '../model/tag_matcher.model')
self.model = load_model(model_path)
self.model._make_predict_function()