def get_conditional_topo_samples(generator, m, n, condition_sampler_fn, embedding_file=DEFAULT_EMBEDDING_FILE):
if isinstance(generator, basestring):
generator = load_generator_from_file(generator)
conditional_batch = generator.condition_space.make_theano_batch()
conditional_data = condition_sampler_fn(generator, m, n, embedding_file=embedding_file)
topo_samples_batch = generator.sample(conditional_batch)
topo_sample_f = theano.function([conditional_batch], topo_samples_batch)
topo_samples = topo_sample_f(conditional_data).swapaxes(0, 3)
return topo_samples, conditional_data
def get_conditional_topo_samples(generator,
m,
n,
condition_sampler_fn,
embedding_file=DEFAULT_EMBEDDING_FILE):
if isinstance(generator, basestring):
generator = load_generator_from_file(generator)
conditional_batch = generator.condition_space.make_theano_batch()
conditional_data = condition_sampler_fn(generator,
m,
n,
embedding_file=embedding_file)
topo_samples_batch = generator.sample(conditional_batch)
topo_sample_f = theano.function([conditional_batch], topo_samples_batch)
topo_samples = topo_sample_f(conditional_data).swapaxes(0, 3)
return topo_samples, conditional_data
embeddings = np.load(args.embedding_file)['arr_0']
if args.axes is None:
args.axes = range(embeddings.shape[1])
else:
args.axes = [int(x) for x in args.axes.strip().split(',')]
condition_dim = embeddings.shape[1]
m, n = len(args.axes), 10
shift = 7.5
# Prepare generator
generator = util.load_generator_from_file(args.model_path)
noise_batch = generator.noise_space.make_theano_batch()
conditional_batch = generator.condition_space.make_theano_batch()
topo_sample_f = theano.function([noise_batch, conditional_batch],
generator.dropout_fprop((noise_batch, conditional_batch))[0])
# Sample some noise data -- this needs to be shared between orig and mod
# sample pairs
noise_data = generator.get_noise((n, generator.noise_dim)).eval()
# Begin modifying axes
base_conditional_data = args.conditional_sampler(generator, n, 1,
embedding_file=args.embedding_file)
print 'Mean for each axis:'
pprint.pprint(zip(args.axes, base_conditional_data[:, args.axes].mean(axis=1)))
help='Comma-separated list of axes to modify')
args = parser.parse_args()
embeddings = np.load(args.embedding_file)['arr_0']
if args.axes is None:
args.axes = range(embeddings.shape[1])
else:
args.axes = [int(x) for x in args.axes.strip().split(',')]
condition_dim = embeddings.shape[1]
m, n = len(args.axes), 10
shift = 7.5
# Prepare generator
generator = util.load_generator_from_file(args.model_path)
noise_batch = generator.noise_space.make_theano_batch()
conditional_batch = generator.condition_space.make_theano_batch()
topo_sample_f = theano.function([noise_batch, conditional_batch],
generator.dropout_fprop(
(noise_batch, conditional_batch))[0])
# Sample some noise data -- this needs to be shared between orig and mod
# sample pairs
noise_data = generator.get_noise((n, generator.noise_dim)).eval()
# Begin modifying axes
base_conditional_data = args.conditional_sampler(
generator, n, 1, embedding_file=args.embedding_file)
print 'Mean for each axis:'
pprint.pprint(zip(args.axes, base_conditional_data[:, args.axes].mean(axis=1)))
