def load_models(self):
"""
Init models.
"""
log('[Training] using %d input channels' % self.train_images.shape[3])
network_units = list(map(int, self.args.network_units.split(',')))
self.encoder = models.LearnedVariationalEncoder(
self.args.latent_space_size,
0,
resolution=(self.train_images.shape[3], self.train_images.shape[1],
self.train_images.shape[2]),
architecture=self.args.network_architecture,
start_channels=self.args.network_channels,
activation=self.args.network_activation,
batch_normalization=not self.args.network_no_batch_normalization,
units=network_units)
self.decoder = models.LearnedDecoder(
self.args.latent_space_size,
resolution=(self.train_images.shape[3], self.train_images.shape[1],
self.train_images.shape[2]),
architecture=self.args.network_architecture,
start_channels=self.args.network_channels,
activation=self.args.network_activation,
batch_normalization=not self.args.network_no_batch_normalization,
units=network_units)
log(self.encoder)
log(self.decoder)
classifier_units = list(map(int,
self.args.classifier_units.split(',')))
self.classifier = models.Classifier(
1,
resolution=(self.train_images.shape[3], self.train_images.shape[1],
self.train_images.shape[2]),
architecture=self.args.classifier_architecture,
activation=self.args.classifier_activation,
batch_normalization=not self.args.
classifier_no_batch_normalization,
start_channels=self.args.classifier_channels,
dropout=self.args.classifier_dropout,
units=classifier_units,
kernel_size=6)
log(self.classifier)
def main(self):
"""
Main which should be overwritten.
"""
test_images = utils.read_hdf5(self.args.test_images_file)
log('[Sampling] read %s' % self.args.test_images_file)
if len(test_images.shape) < 4:
test_images = numpy.expand_dims(test_images, axis=3)
network_units = list(map(int, self.args.network_units.split(',')))
self.decoder = models.LearnedDecoder(
self.args.latent_space_size,
resolution=(test_images.shape[3], test_images.shape[1],
test_images.shape[2]),
architecture=self.args.network_architecture,
start_channels=self.args.network_channels,
activation=self.args.network_activation,
batch_normalization=not self.args.network_no_batch_normalization,
units=network_units)
log(self.decoder)
assert os.path.exists(self.args.decoder_file)
state = State.load(self.args.decoder_file)
log('[Sampling] loaded %s' % self.args.decoder_file)
self.decoder.load_state_dict(state.model)
log('[Sampling] loaded decoder')
if self.args.use_gpu and not cuda.is_cuda(self.decoder):
self.decoder = self.decoder.cuda()
log('[Sampling] model needs %gMiB' %
((cuda.estimate_size(self.decoder)) / (1024 * 1024)))
self.sample()
def main(self):
"""
Main which should be overwritten.
"""
self.train_images = utils.read_hdf5(
self.args.train_images_file).astype(numpy.float32)
log('[Testing] read %s' % self.args.train_images_file)
self.test_images = utils.read_hdf5(self.args.test_images_file).astype(
numpy.float32)
log('[Testing] read %s' % self.args.test_images_file)
# For handling both color and gray images.
if len(self.train_images.shape) < 4:
self.train_images = numpy.expand_dims(self.train_images, axis=3)
self.test_images = numpy.expand_dims(self.test_images, axis=3)
log('[Testing] no color images, adjusted size')
self.resolution = self.train_images.shape[2]
log('[Testing] resolution %d' % self.resolution)
self.train_codes = utils.read_hdf5(self.args.train_codes_file).astype(
numpy.float32)
log('[Testing] read %s' % self.args.train_codes_file)
self.test_codes = utils.read_hdf5(self.args.test_codes_file).astype(
numpy.float32)
log('[Testing] read %s' % self.args.test_codes_file)
self.train_codes = self.train_codes[:, self.args.label_index]
self.test_codes = self.test_codes[:, self.args.label_index]
if self.args.label >= 0:
self.train_images = self.train_images[self.train_codes ==
self.args.label]
self.test_images = self.test_images[self.test_codes ==
self.args.label]
log('[Testing] using %d input channels' % self.test_images.shape[3])
network_units = list(map(int, self.args.network_units.split(',')))
self.encoder = models.LearnedVariationalEncoder(
self.args.latent_space_size,
0,
resolution=(self.train_images.shape[3], self.train_images.shape[1],
self.train_images.shape[2]),
architecture=self.args.network_architecture,
start_channels=self.args.network_channels,
activation=self.args.network_activation,
batch_normalization=not self.args.network_no_batch_normalization,
units=network_units)
self.decoder = models.LearnedDecoder(
self.args.latent_space_size,
resolution=(self.train_images.shape[3], self.train_images.shape[1],
self.train_images.shape[2]),
architecture=self.args.network_architecture,
start_channels=self.args.network_channels,
activation=self.args.network_activation,
batch_normalization=not self.args.network_no_batch_normalization,
units=network_units)
log(self.encoder)
log(self.decoder)
assert os.path.exists(self.args.encoder_file) and os.path.exists(
self.args.decoder_file)
state = State.load(self.args.encoder_file)
log('[Testing] loaded %s' % self.args.encoder_file)
self.encoder.load_state_dict(state.model)
log('[Testing] loaded encoder')
state = State.load(self.args.decoder_file)
log('[Testing] loaded %s' % self.args.decoder_file)
self.decoder.load_state_dict(state.model)
log('[Testing] loaded decoder')
if self.args.use_gpu and not cuda.is_cuda(self.encoder):
self.encoder = self.encoder.cuda()
if self.args.use_gpu and not cuda.is_cuda(self.decoder):
self.decoder = self.decoder.cuda()
log('[Testing] model needs %gMiB' %
((cuda.estimate_size(self.encoder) +
cuda.estimate_size(self.decoder)) / (1024 * 1024)))
self.test()
def load_decoder(self):
"""
Load the decoder.
"""
assert self.args.decoder_files
decoder_files = self.args.decoder_files.split(',')
for decoder_file in decoder_files:
assert os.path.exists(
decoder_file), 'could not find %s' % decoder_file
log('[Training] using %d input channels' % self.train_images.shape[3])
decoder_units = list(map(int, self.args.decoder_units.split(',')))
if len(decoder_files) > 1:
log('[Training] loading multiple decoders')
decoders = []
for i in range(len(decoder_files)):
decoder = models.LearnedDecoder(
self.args.latent_space_size,
resolution=(self.train_images.shape[3],
self.train_images.shape[1],
self.train_images.shape[2]),
architecture=self.args.decoder_architecture,
start_channels=self.args.decoder_channels,
activation=self.args.decoder_activation,
batch_normalization=not self.args.
decoder_no_batch_normalization,
units=decoder_units)
state = State.load(decoder_files[i])
decoder.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(decoder):
decoder = decoder.cuda()
decoders.append(decoder)
decoder.eval()
log('[Training] loaded %s' % decoder_files[i])
self.decoder = models.SelectiveDecoder(
decoders,
resolution=(self.train_images.shape[3],
self.train_images.shape[1],
self.train_images.shape[2]))
else:
log('[Training] loading one decoder')
decoder = models.LearnedDecoder(
self.args.latent_space_size,
resolution=(self.train_images.shape[3],
self.train_images.shape[1],
self.train_images.shape[2]),
architecture=self.args.decoder_architecture,
start_channels=self.args.decoder_channels,
activation=self.args.decoder_activation,
batch_normalization=not self.args.
decoder_no_batch_normalization,
units=decoder_units)
state = State.load(decoder_files[0])
decoder.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(decoder):
decoder = decoder.cuda()
decoder.eval()
log('[Training] read decoder')
self.decoder = decoder
self.decoder_classifier = models.DecoderClassifier(
self.decoder, self.model)
def load_data_and_model(self):
"""
Load data and model.
"""
self.test_images = utils.read_hdf5(self.args.test_images_file).astype(
numpy.float32)
if len(self.test_images.shape) < 4:
self.test_images = numpy.expand_dims(self.test_images, axis=3)
resolution = (self.test_images.shape[3], self.test_images.shape[1],
self.test_images.shape[2])
log('[Visualization] read %s' % self.args.test_images_file)
self.perturbations = utils.read_hdf5(
self.args.perturbations_file).astype(numpy.float32)
self.perturbations = numpy.swapaxes(self.perturbations, 0, 1)
log('[Visualization] read %s' % self.args.perturbations_file)
self.success = utils.read_hdf5(self.args.success_file)
self.success = numpy.swapaxes(self.success, 0, 1)
log('[Visualization] read %s' % self.args.success_file)
self.accuracy = utils.read_hdf5(self.args.accuracy_file)
log('[Visualization] read %s' % self.args.success_file)
self.test_theta = utils.read_hdf5(self.args.test_theta_file).astype(
numpy.float32)
self.test_theta = self.test_theta[:self.perturbations.shape[0]]
log('[Visualization] read %s' % self.args.test_theta_file)
self.test_codes = utils.read_hdf5(self.args.test_codes_file).astype(
numpy.int)
self.test_codes = self.test_codes[:, self.args.label_index]
self.N_class = numpy.max(self.test_codes) + 1
self.test_codes = self.test_codes[:self.perturbations.shape[0]]
log('[Visualization] read %s' % self.args.test_codes_file)
network_units = list(map(int, self.args.network_units.split(',')))
self.classifier = models.Classifier(
self.N_class,
resolution=resolution,
architecture=self.args.network_architecture,
activation=self.args.network_activation,
batch_normalization=not self.args.network_no_batch_normalization,
start_channels=self.args.network_channels,
dropout=self.args.network_dropout,
units=network_units)
assert os.path.exists(
self.args.classifier_file
), 'state file %s not found' % self.args.classifier_file
state = State.load(self.args.classifier_file)
log('[Visualization] read %s' % self.args.classifier_file)
self.classifier.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(self.classifier):
log('[Visualization] classifier is not CUDA')
self.classifier = self.classifier.cuda()
log('[Visualization] loaded classifier')
self.classifier.eval()
log('[Visualization] set classifier to eval')
assert self.args.decoder_files
decoder_files = self.args.decoder_files.split(',')
for decoder_file in decoder_files:
assert os.path.exists(
decoder_file), 'could not find %s' % decoder_file
log('[Visualization] using %d input channels' %
self.test_images.shape[3])
decoder_units = list(map(int, self.args.decoder_units.split(',')))
if len(decoder_files) > 1:
log('[Visualization] loading multiple decoders')
decoders = []
for i in range(len(decoder_files)):
decoder = models.LearnedDecoder(
self.args.latent_space_size,
resolution=resolution,
architecture=self.args.decoder_architecture,
start_channels=self.args.decoder_channels,
activation=self.args.decoder_activation,
batch_normalization=not self.args.
decoder_no_batch_normalization,
units=decoder_units)
state = State.load(decoder_files[i])
decoder.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(decoder):
decoder = decoder.cuda()
decoders.append(decoder)
decoder.eval()
log('[Visualization] loaded %s' % decoder_files[i])
self.decoder = models.SelectiveDecoder(decoders,
resolution=resolution)
else:
log('[Visualization] loading one decoder')
decoder = models.LearnedDecoder(
self.args.latent_space_size,
resolution=resolution,
architecture=self.args.decoder_architecture,
start_channels=self.args.decoder_channels,
activation=self.args.decoder_activation,
batch_normalization=not self.args.
decoder_no_batch_normalization,
units=decoder_units)
state = State.load(decoder_files[0])
decoder.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(decoder):
decoder = decoder.cuda()
decoder.eval()
log('[Visualization] read decoder')
self.decoder = decoder
def load_model(self):
"""
Load model.
"""
assert self.args.decoder_files
decoder_files = self.args.decoder_files.split(',')
for decoder_file in decoder_files:
assert os.path.exists(
decoder_file), 'could not find %s' % decoder_file
decoder_units = list(map(int, self.args.decoder_units.split(',')))
log('[Attack] using %d input channels' % self.test_images.shape[3])
if len(decoder_files) > 1:
log('[Attack] loading multiple decoders')
decoders = []
for i in range(len(decoder_files)):
decoder = models.LearnedDecoder(
self.args.latent_space_size,
resolution=(self.test_images.shape[3],
self.test_images.shape[1],
self.test_images.shape[2]),
architecture=self.args.decoder_architecture,
start_channels=self.args.decoder_channels,
activation=self.args.decoder_activation,
batch_normalization=not self.args.
decoder_no_batch_normalization,
units=decoder_units)
log(decoder)
state = State.load(decoder_files[i])
decoder.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(decoder):
decoder = decoder.cuda()
decoders.append(decoder)
decoder.eval()
log('[Attack] loaded %s' % decoder_files[i])
decoder = models.SelectiveDecoder(
decoders,
resolution=(self.test_images.shape[3],
self.test_images.shape[1],
self.test_images.shape[2]))
else:
log('[Attack] loading one decoder')
decoder = models.LearnedDecoder(
self.args.latent_space_size,
resolution=(self.test_images.shape[3],
self.test_images.shape[1],
self.test_images.shape[2]),
architecture=self.args.decoder_architecture,
start_channels=self.args.decoder_channels,
activation=self.args.decoder_activation,
batch_normalization=not self.args.
decoder_no_batch_normalization,
units=decoder_units)
state = State.load(decoder_files[0])
decoder.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(decoder):
decoder = decoder.cuda()
decoder.eval()
log('[Attack] read decoder')
classifier_units = list(map(int, self.args.network_units.split(',')))
classifier = models.Classifier(
self.N_class,
resolution=(self.test_images.shape[3], self.test_images.shape[1],
self.test_images.shape[2]),
architecture=self.args.network_architecture,
activation=self.args.network_activation,
batch_normalization=not self.args.network_no_batch_normalization,
start_channels=self.args.network_channels,
dropout=self.args.network_dropout,
units=classifier_units)
assert os.path.exists(
self.args.classifier_file
), 'state file %s not found' % self.args.classifier_file
state = State.load(self.args.classifier_file)
log('[Attack] read %s' % self.args.classifier_file)
classifier.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(classifier):
log('[Attack] classifier is not CUDA')
classifier = classifier.cuda()
log('[Attack] loaded classifier')
# !
classifier.eval()
log('[Attack] set classifier to eval')
self.model = models.DecoderClassifier(decoder, classifier)
def load_data(self):
"""
Load data and model.
"""
with logw('[Detection] read %s' % self.args.train_images_file):
self.nearest_neighbor_images = utils.read_hdf5(self.args.train_images_file)
assert len(self.nearest_neighbor_images.shape) == 3
with logw('[Detection] read %s' % self.args.test_images_file):
self.test_images = utils.read_hdf5(self.args.test_images_file)
if len(self.test_images.shape) < 4:
self.test_images = numpy.expand_dims(self.test_images, axis=3)
with logw('[Detection] read %s' % self.args.perturbations_file):
self.perturbations = utils.read_hdf5(self.args.perturbations_file)
assert len(self.perturbations.shape) == 4
with logw('[Detection] read %s' % self.args.success_file):
self.success = utils.read_hdf5(self.args.success_file)
with logw('[Detection] read %s' % self.args.accuracy_file):
self.accuracy = utils.read_hdf5(self.args.accuracy_file)
self.perturbations = numpy.swapaxes(self.perturbations, 0, 1)
num_attempts = self.perturbations.shape[1]
self.test_images = self.test_images[:self.perturbations.shape[0]]
self.train_images = self.nearest_neighbor_images[:self.perturbations.shape[0]]
self.accuracy = self.accuracy[:self.perturbations.shape[0]]
self.perturbations = self.perturbations.reshape((self.perturbations.shape[0]*self.perturbations.shape[1], self.perturbations.shape[2], self.perturbations.shape[3]))
self.success = numpy.swapaxes(self.success, 0, 1)
self.success = self.success.reshape((self.success.shape[0]*self.success.shape[1]))
self.accuracy = numpy.repeat(self.accuracy, num_attempts, axis=0)
self.test_images = numpy.repeat(self.test_images, num_attempts, axis=0)
self.train_images = numpy.repeat(self.train_images, num_attempts, axis=0)
max_samples = self.args.max_samples
self.success = self.success[:max_samples]
self.accuracy = self.accuracy[:max_samples]
self.perturbations = self.perturbations[:max_samples]
self.test_images = self.test_images[:max_samples]
self.train_images = self.train_images[:max_samples]
if self.args.mode == 'true':
assert self.args.database_file
assert self.args.test_codes_file
assert self.args.test_theta_file
self.test_codes = utils.read_hdf5(self.args.test_codes_file)
log('[Detection] read %s' % self.args.test_codes_file)
self.test_theta = utils.read_hdf5(self.args.test_theta_file)
log('[Detection] read %s' % self.args.test_theta_file)
self.test_codes = self.test_codes[:self.perturbations.shape[0]]
self.test_theta = self.test_theta[:self.perturbations.shape[0]]
self.test_codes = numpy.repeat(self.test_codes, num_attempts, axis=0)
self.test_theta = numpy.repeat(self.test_theta, num_attempts, axis=0)
self.test_codes = self.test_codes[:max_samples]
self.test_theta = self.test_theta[:max_samples]
database = utils.read_hdf5(self.args.database_file)
log('[Detection] read %s' % self.args.database_file)
self.N_font = database.shape[0]
self.N_class = database.shape[1]
self.N_theta = self.test_theta.shape[1]
database = database.reshape((database.shape[0]*database.shape[1], database.shape[2], database.shape[3]))
database = torch.from_numpy(database)
if self.args.use_gpu:
database = database.cuda()
database = torch.autograd.Variable(database, False)
self.model = models.AlternativeOneHotDecoder(database, self.N_font, self.N_class, self.N_theta)
self.model.eval()
log('[Detection] initialized decoder')
elif self.args.mode == 'appr':
assert self.args.decoder_files
assert self.args.test_codes_file
assert self.args.test_theta_file
self.test_codes = utils.read_hdf5(self.args.test_codes_file)
log('[Detection] read %s' % self.args.test_codes_file)
self.test_theta = utils.read_hdf5(self.args.test_theta_file)
log('[Detection] read %s' % self.args.test_theta_file)
self.test_codes = self.test_codes[:self.perturbations.shape[0]]
self.test_theta = self.test_theta[:self.perturbations.shape[0]]
self.test_codes = numpy.repeat(self.test_codes, num_attempts, axis=0)
self.test_theta = numpy.repeat(self.test_theta, num_attempts, axis=0)
self.test_codes = self.test_codes[:max_samples]
self.test_theta = self.test_theta[:max_samples]
assert self.args.decoder_files
decoder_files = self.args.decoder_files.split(',')
for decoder_file in decoder_files:
assert os.path.exists(decoder_file), 'could not find %s' % decoder_file
resolution = [1 if len(self.test_images.shape) <= 3 else self.test_images.shape[3], self.test_images.shape[1], self.test_images.shape[2]]
decoder_units = list(map(int, self.args.decoder_units.split(',')))
if len(decoder_files) > 1:
log('[Detection] loading multiple decoders')
decoders = []
for i in range(len(decoder_files)):
decoder = models.LearnedDecoder(self.args.latent_space_size,
resolution=resolution,
architecture=self.args.decoder_architecture,
start_channels=self.args.decoder_channels,
activation=self.args.decoder_activation,
batch_normalization=not self.args.decoder_no_batch_normalization,
units=decoder_units)
state = State.load(decoder_files[i])
decoder.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(decoder):
decoder = decoder.cuda()
decoders.append(decoder)
decoder.eval()
log('[Detection] loaded %s' % decoder_files[i])
self.model = models.SelectiveDecoder(decoders, resolution=resolution)
else:
log('[Detection] loading one decoder')
decoder = models.LearnedDecoder(self.args.latent_space_size,
resolution=resolution,
architecture=self.args.decoder_architecture,
start_channels=self.args.decoder_channels,
activation=self.args.decoder_activation,
batch_normalization=not self.args.decoder_no_batch_normalization,
units=decoder_units)
state = State.load(decoder_files[0])
decoder.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(decoder):
decoder = decoder.cuda()
decoder.eval()
log('[Detection] read decoder')
self.model = decoder
def load_data(self):
"""
Load data and model.
"""
self.test_images = utils.read_hdf5(self.args.test_images_file).astype(numpy.float32)
log('[Testing] read %s' % self.args.test_images_file)
# For handling both color and gray images.
if len(self.test_images.shape) < 4:
self.test_images = numpy.expand_dims(self.test_images, axis=3)
log('[Testing] no color images, adjusted size')
self.resolution = self.test_images.shape[2]
log('[Testing] resolution %d' % self.resolution)
self.train_images = utils.read_hdf5(self.args.train_images_file).astype(numpy.float32)
# !
self.train_images = self.train_images.reshape((self.train_images.shape[0], -1))
log('[Testing] read %s' % self.args.train_images_file)
self.test_theta = utils.read_hdf5(self.args.test_theta_file).astype(numpy.float32)
log('[Testing] read %s' % self.args.test_theta_file)
self.train_theta = utils.read_hdf5(self.args.train_theta_file).astype(numpy.float32)
log('[Testing] read %s' % self.args.train_theta_file)
self.test_codes = utils.read_hdf5(self.args.test_codes_file).astype(numpy.int)
self.test_codes = self.test_codes[:, self.args.label_index]
self.N_class = numpy.max(self.test_codes) + 1
log('[Testing] read %s' % self.args.test_codes_file)
self.accuracy = utils.read_hdf5(self.args.accuracy_file)
log('[Testing] read %s' % self.args.accuracy_file)
self.perturbations = utils.read_hdf5(self.args.perturbations_file).astype(numpy.float32)
self.N_attempts = self.perturbations.shape[0]
assert not numpy.any(self.perturbations != self.perturbations), 'NaN in perturbations'
# First, repeat relevant data.
self.perturbation_theta = numpy.repeat(self.test_theta[:self.perturbations.shape[1]], self.N_attempts, axis=0)
self.perturbation_codes = numpy.repeat(self.test_codes[:self.perturbations.shape[1]], self.N_attempts, axis=0)
self.perturbation_codes = numpy.squeeze(self.perturbation_codes)
self.accuracy = numpy.repeat(self.accuracy[:self.perturbations.shape[1]], self.N_attempts, axis=0)
# Then, reshape the perturbations!
self.perturbations = numpy.swapaxes(self.perturbations, 0, 1)
self.perturbations = self.perturbations.reshape((self.perturbations.shape[0] * self.perturbations.shape[1], -1))
log('[Testing] read %s' % self.args.perturbations_file)
self.success = utils.read_hdf5(self.args.success_file)
self.success = numpy.swapaxes(self.success, 0, 1)
self.success = self.success.reshape((self.success.shape[0] * self.success.shape[1]))
log('[Testing] read %s' % self.args.success_file)
assert self.args.decoder_files
decoder_files = self.args.decoder_files.split(',')
for decoder_file in decoder_files:
assert os.path.exists(decoder_file), 'could not find %s' % decoder_file
log('[Testing] using %d input channels' % self.test_images.shape[3])
decoder_units = list(map(int, self.args.decoder_units.split(',')))
if len(decoder_files) > 1:
log('[Testing] loading multiple decoders')
decoders = []
for i in range(len(decoder_files)):
decoder = models.LearnedDecoder(self.args.latent_space_size, resolution=(self.test_images.shape[3], self.test_images.shape[1], self.test_images.shape[2]),
architecture=self.args.decoder_architecture,
start_channels=self.args.decoder_channels,
activation=self.args.decoder_activation,
batch_normalization=not self.args.decoder_no_batch_normalization,
units=decoder_units)
state = State.load(decoder_files[i])
decoder.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(decoder):
decoder = decoder.cuda()
decoders.append(decoder)
decoder.eval()
log('[Testing] loaded %s' % decoder_files[i])
self.model = models.SelectiveDecoder(decoders, resolution=(self.test_images.shape[3], self.test_images.shape[1], self.test_images.shape[2]))
else:
log('[Testing] loading one decoder')
decoder = models.LearnedDecoder(self.args.latent_space_size, resolution=(self.test_images.shape[3], self.test_images.shape[1], self.test_images.shape[2]),
architecture=self.args.decoder_architecture,
start_channels=self.args.decoder_channels,
activation=self.args.decoder_activation,
batch_normalization=not self.args.decoder_no_batch_normalization,
units=decoder_units)
state = State.load(decoder_files[0])
decoder.load_state_dict(state.model)
if self.args.use_gpu and not cuda.is_cuda(decoder):
decoder = decoder.cuda()
decoder.eval()
log('[Testing] read decoder')
self.model = decoder
