def morph(args):
# Communicator and Context
extension_module = "cudnn"
ctx = get_extension_context(extension_module, type_config=args.type_config)
nn.set_default_context(ctx)
# Args
latent = args.latent
maps = args.maps
batch_size = args.batch_size
image_size = args.image_size
n_classes = args.n_classes
not_sn = args.not_sn
threshold = args.truncation_threshold
# Model
nn.load_parameters(args.model_load_path)
z = nn.Variable([batch_size, latent])
alpha = nn.Variable.from_numpy_array(np.zeros([1, 1]))
beta = (nn.Variable.from_numpy_array(np.ones([1, 1])) - alpha)
y_fake_a = nn.Variable([batch_size])
y_fake_b = nn.Variable([batch_size])
x_fake = generator(z, [y_fake_a, y_fake_b],
maps=maps,
n_classes=n_classes,
test=True,
sn=not_sn,
coefs=[alpha, beta]).apply(persistent=True)
b, c, h, w = x_fake.shape
# Monitor
monitor = Monitor(args.monitor_path)
name = "Morphed Image {} {}".format(args.from_class_id, args.to_class_id)
monitor_image = MonitorImage(name,
monitor,
interval=1,
num_images=1,
normalize_method=normalize_method)
# Morph
images = []
z_data = resample(batch_size, latent, threshold)
z.d = z_data
for i in range(args.n_morphs):
alpha.d = 1.0 * i / args.n_morphs
y_fake_a.d = generate_one_class(args.from_class_id, batch_size)
y_fake_b.d = generate_one_class(args.to_class_id, batch_size)
x_fake.forward(clear_buffer=True)
monitor_image.add(i, x_fake.d)
def generate(args):
# Communicator and Context
extension_module = "cudnn"
ctx = get_extension_context(extension_module, type_config=args.type_config)
nn.set_default_context(ctx)
# Args
latent = args.latent
maps = args.maps
batch_size = args.batch_size
image_size = args.image_size
n_classes = args.n_classes
not_sn = args.not_sn
threshold = args.truncation_threshold
# Model
nn.load_parameters(args.model_load_path)
z = nn.Variable([batch_size, latent])
y_fake = nn.Variable([batch_size])
x_fake = generator(z, y_fake, maps=maps, n_classes=n_classes, test=True, sn=not_sn)\
.apply(persistent=True)
# Generate All
if args.generate_all:
# Monitor
monitor = Monitor(args.monitor_path)
name = "Generated Image Tile All"
monitor_image = MonitorImageTile(name,
monitor,
interval=1,
num_images=args.batch_size,
normalize_method=normalize_method)
# Generate images for all classes
for class_id in range(args.n_classes):
# Generate
z_data = resample(batch_size, latent, threshold)
y_data = generate_one_class(class_id, batch_size)
z.d = z_data
y_fake.d = y_data
x_fake.forward(clear_buffer=True)
monitor_image.add(class_id, x_fake.d)
return
# Generate Indivisually
monitor = Monitor(args.monitor_path)
name = "Generated Image Tile {}".format(
args.class_id) if args.class_id != -1 else "Generated Image Tile"
monitor_image_tile = MonitorImageTile(name,
monitor,
interval=1,
num_images=args.batch_size,
normalize_method=normalize_method)
name = "Generated Image {}".format(
args.class_id) if args.class_id != -1 else "Generated Image"
monitor_image = MonitorImage(name,
monitor,
interval=1,
num_images=args.batch_size,
normalize_method=normalize_method)
z_data = resample(batch_size, latent, threshold)
y_data = generate_random_class(n_classes, batch_size) if args.class_id == -1 else \
generate_one_class(args.class_id, batch_size)
z.d = z_data
y_fake.d = y_data
x_fake.forward(clear_buffer=True)
monitor_image.add(0, x_fake.d)
monitor_image_tile.add(0, x_fake.d)
#just get the exact number of frames from the wav file upscale emot labels to that
#OR!
#just tweak the OUT_FRAMERATE until it works...
#train_audio002_sent10 has 644 frames according to merlin (TODO where can we directly get this number from?)
#emots has 652 after being processed by resample according to in and out frame rates
#srikanth said num of frames should be within 5 to 644
#if we use sox to get sampling rate and number of samples, then we can get a more accurate figure but still not same
#number of samples * sampling rate * desired_frame_rate
# 155610 * (1/48000) * 200
# Out[2]: 648.375
#NB NB NB !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#upsample
sent_data = resample(sent_data, IN_FRAMERATE,
OUT_FRAMERATE) #returns numpy.ndarray
# except:
# raise ValueError(tf, sent_timestamps, len(data), i, start_time, end_time, 'start frame', start_frame, end_frame, sent_data)
sent_data = list(str(float(num)) for num in sent_data)
#create filename
#format tests_audio002_sent1_16bit.lab
if inner_folder == 'test':
renamed_inner_folder = 'tests' #as folder is called 'test' but files have name 'tests'
else:
renamed_inner_folder = inner_folder
file_name = renamed_inner_folder + '_audio' + dialog_num + '_sent' + str(
i) + '_16bit.' + emot_dim
full_path_to_file = OUT_DIR + emot_dim + '/' + file_name
def match(args):
# Context
extension_module = "cudnn"
ctx = get_extension_context(extension_module, device_id=args.device_id,
type_config=args.type_config)
nn.set_default_context(ctx)
# Args
latent = args.latent
maps = args.maps
batch_size = 1
image_size = args.image_size
n_classes = args.n_classes
not_sn = args.not_sn
threshold = args.truncation_threshold
# Model (SAGAN)
nn.load_parameters(args.model_load_path)
z = nn.Variable([batch_size, latent])
y_fake = nn.Variable([batch_size])
x_fake = generator(z, y_fake, maps=maps, n_classes=n_classes, test=True, sn=not_sn)\
.apply(persistent=True)
# Model (Inception model) from nnp file
nnp = NnpLoader(args.nnp_inception_model_load_path)
x, h = get_input_and_output(nnp, batch_size, args.variable_name)
# DataIterator for a given class_id
di = data_iterator_imagenet(args.train_dir, args.dirname_to_label_path,
batch_size=batch_size, n_classes=args.n_classes,
noise=False,
class_id=args.class_id)
# Monitor
monitor = Monitor(args.monitor_path)
name = "Matched Image {}".format(args.class_id)
monitor_image = MonitorImage(name, monitor, interval=1,
num_images=batch_size,
normalize_method=lambda x: (x + 1.) / 2. * 255.)
name = "Matched Image Tile {}".format(args.class_id)
monitor_image_tile = MonitorImageTile(name, monitor, interval=1,
num_images=batch_size + args.top_n,
normalize_method=lambda x: (x + 1.) / 2. * 255.)
# Generate and p(h|x).forward
# generate
z_data = resample(batch_size, latent, threshold)
y_data = generate_one_class(args.class_id, batch_size)
z.d = z_data
y_fake.d = y_data
x_fake.forward(clear_buffer=True)
# p(h|x).forward
x_fake_d = x_fake.d.copy()
x_fake_d = preprocess(
x_fake_d, (args.image_size, args.image_size), args.nnp_preprocess)
x.d = x_fake_d
h.forward(clear_buffer=True)
h_fake_d = h.d.copy()
# Feature matching
norm2_list = []
x_data_list = []
x_data_list.append(x_fake.d)
for i in range(di.size):
# forward for real data
x_d, _ = di.next()
x_data_list.append(x_d)
x_d = preprocess(
x_d, (args.image_size, args.image_size), args.nnp_preprocess)
x.d = x_d
h.forward(clear_buffer=True)
h_real_d = h.d.copy()
# norm computation
axis = tuple(np.arange(1, len(h.shape)).tolist())
norm2 = np.sum((h_real_d - h_fake_d) ** 2.0, axis=axis)
norm2_list.append(norm2)
# Save top-n images
argmins = np.argsort(norm2_list)
for i in range(args.top_n):
monitor_image.add(i, x_data_list[i])
matched_images = np.concatenate(x_data_list)
monitor_image_tile.add(0, matched_images)
