'--output',
action='store',
type='string',
dest='outputDir',
default='train_simple/',
help='output directory')
parser.add_option('-c',
'--config',
action='store',
type='string',
dest='config',
default='train_config_threelayer.yml',
help='configuration file')
(options, args) = parser.parse_args()
yamlConfig = parse_config(options.config)
if os.path.isdir(options.outputDir):
#raise Exception('output directory must not exists yet')
raw_input(
"Warning: output directory exists. Press Enter to continue...")
else:
os.mkdir(options.outputDir)
X_train_val, X_test, y_train_val, y_test, labels = get_features(
options, yamlConfig)
model_constraint = getattr(models, yamlConfig['KerasModelRetrain'])
# Instantiate new model with added custom constraints
if 'L1RegR' in yamlConfig:
#X_test = X_test.reshape(10000, 784)
X_test = X_test.astype('float32')
X_test /= 255
Y_test = to_categorical(y_test, nclasses)
configs = [str(config) for config in options.configs.split(',')]
df = pd.DataFrame()
fpr = {}
tpr = {}
auc1 = {}
for cfg in configs:
config = parse_config(cfg)
if 'cnn' in config['KerasModel']:
img_rows, img_cols = 28, 28
if config['DataFormat'] == 'channels_first':
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
else:
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
else:
X_test = X_test.reshape(10000, 784)
if config['KerasLoss'] == 'squared_hinge':
Y_test = to_categorical(y_test, nclasses) * 2 - 1
outdir = config['OutputDir']
if options.fashionMNIST:
outdir = 'fashionMNIST_' + outdir
model_name = config['KerasModel']
generated_image = G(
[gan_input_particle, gan_input_energy, gan_input_noise])
d_proba = D([generated_image, gan_input_particle, gan_input_energy])
GAN = Model(input=[gan_input_particle, gan_input_energy, gan_input_noise],
output=d_proba)
# temporarily making D untrainable so the summary shows that D won't be trainale during the GAN.
# We reset it during training, and the freeze it every cycle.
train.make_trainable(D, False)
GAN.compile(optimizer=train.optimizer(), loss=train.wasser_loss)
print 'GAN'
print GAN.summary()
train.make_trainable(D, True)
return GAN
if __name__ == '__main__':
# parse args
path2config = sys.argv[1]
args = train.parse_config(path2config)
# This model has 5x5 hard coded
assert args['cropped_width'] == 5
G = local_generator(args)
D = local_discriminator(args)
GAN = gan(G, D, args)
train.train(G, D, GAN, args)
uem_file = os.path.join(TEST_DATA_PATH, 'uem')
der_uem = score_der_uem(
hyp=os.path.join(args.model_dir,
'final_{}_hyp.rttm'.format(args.checkpoint)),
ref=os.path.join(args.model_dir,
'final_{}_ref.rttm'.format(args.checkpoint)),
outfile=os.path.join(args.model_dir, 'final_hyp_uem.derlog'),
uem=uem_file,
collar=0.25)
rpkl['full_der_uem'] = der_uem
print('({}) Full test DER (uem): {}'.format(args.cfg,
rpkl['full_der_uem']))
pickle.dump(rpkl, open(results_pkl, 'wb'))
if __name__ == "__main__":
args = parse_args()
args = parse_config(args)
uvloop.install()
rpkl_path = os.path.join(args.model_dir, 'results.p')
if not os.path.isfile(rpkl_path):
print('No results.p found')
else:
device = torch.device('cuda')
TEST_DATA_PATH = args.diar_data
generator = setup()
extract_and_diarize(generator, TEST_DATA_PATH, device)
