def _build_generator_given_z_offset_and_labels(self):
labels = Input(shape=self.labels_shape, name='input_labels')
z_offset = Input(shape=(self.z_dim_offset, ), name='input_z_offset')
outputs = OrderedDict()
labels_without_bits = Subtensor(self.nb_bits,
self.labels_shape[0],
axis=1)(labels)
# build tag3d tensors
tag3d, tag3d_depth_map = self.tag3d_network(labels)
tag3d_segmented = Segmentation(threshold=-0.08,
smooth_threshold=0.2,
sigma=1.5,
name='segmentation')(tag3d)
tag3d_segmented_blur = GaussianBlur(sigma=3.0)(tag3d_segmented)
# get generator params
blur_factor, lights, background, details = \
simple_gan_generator(self.generator_units, z_offset, labels_without_bits,
tag3d_depth_map, tag3d, depth=self.generator_depth)
tag3d_blur = BlendingBlur(sigma=1)([tag3d, blur_factor])
tag3d_lightin = AddLighting(scale_factor=0.85,
shift_factor=0.75)([tag3d_blur] + lights)
fake_without_noise = Background(name='bg')(
[background, tag3d_lightin, tag3d_segmented_blur])
details_high_pass = HighPass(4, nb_steps=4)(details)
fake = InBounds(-1.0,
1.0)(merge([details_high_pass, fake_without_noise],
mode='sum'))
outputs = [
('tag3d', tag3d),
('tag3d_blur', tag3d_blur),
('tag3d_lightin', tag3d_lightin),
('fake_without_noise', fake_without_noise),
('fake', fake),
]
outputs = OrderedDict([(name, name_tensor(x, name))
for name, x in outputs])
self.generator_given_z_and_labels = Model([z_offset, labels], [fake])
self.sample_generator_given_z_and_labels_output_names = list(
outputs.keys())
self.sample_generator_given_z_and_labels = Model([z_offset, labels],
list(
outputs.values()))
def test_simple_gan_generator():
nb_units = 2
bs = 8
z = Input(batch_shape=(bs, 50))
labels = Input(batch_shape=(bs, 22))
depth_map = Input(batch_shape=(bs, 1, 16, 16))
tag3d = Input(batch_shape=(bs, 1, 64, 64))
blur, (light_sb, light_sw, light_t), background, details = \
simple_gan_generator(nb_units, z, labels, depth_map, tag3d, depth=2)
inputs = [z, labels, depth_map, tag3d]
fn = K.function([K.learning_phase(), z, labels, depth_map, tag3d],
[blur, light_sb, light_sw, light_t, background, details])
out = fn([1] + [np.random.sample(get_layer(x).output_shape) for x in inputs])
assert out[0].shape == (bs, 1)
for i, out_arr in enumerate(out[1:]):
assert out_arr.shape == (bs, 1, 64, 64), i + 1
def test_simple_gan_generator():
nb_units = 2
bs = 8
z = Input(batch_shape=(bs, 50))
labels = Input(batch_shape=(bs, 22))
depth_map = Input(batch_shape=(bs, 1, 16, 16))
tag3d = Input(batch_shape=(bs, 1, 64, 64))
blur, (light_sb, light_sw, light_t), background, details = \
simple_gan_generator(nb_units, z, labels, depth_map, tag3d, depth=2)
inputs = [z, labels, depth_map, tag3d]
fn = K.function([K.learning_phase(), z, labels, depth_map, tag3d],
[blur, light_sb, light_sw, light_t, background, details])
out = fn([1] +
[np.random.sample(get_layer(x).output_shape) for x in inputs])
assert out[0].shape == (bs, 1)
for i, out_arr in enumerate(out[1:]):
assert out_arr.shape == (bs, 1, 64, 64), i + 1
def _build_generator_given_z_offset_and_labels(self):
labels = Input(shape=self.labels_shape, name='input_labels')
z_offset = Input(shape=(self.z_dim_offset,), name='input_z_offset')
outputs = OrderedDict()
labels_without_bits = Subtensor(self.nb_bits, self.labels_shape[0], axis=1)(labels)
# build tag3d tensors
tag3d, tag3d_depth_map = self.tag3d_network(labels)
tag3d_segmented = Segmentation(threshold=-0.08, smooth_threshold=0.2,
sigma=1.5, name='segmentation')(tag3d)
tag3d_segmented_blur = GaussianBlur(sigma=3.0)(tag3d_segmented)
# get generator params
blur_factor, lights, background, details = \
simple_gan_generator(self.generator_units, z_offset, labels_without_bits,
tag3d_depth_map, tag3d, depth=self.generator_depth)
tag3d_blur = BlendingBlur(sigma=1)([tag3d, blur_factor])
tag3d_lightin = AddLighting(scale_factor=0.85, shift_factor=0.75)([tag3d_blur] + lights)
fake_without_noise = Background(name='bg')(
[background, tag3d_lightin, tag3d_segmented_blur])
details_high_pass = HighPass(4, nb_steps=4)(details)
fake = InBounds(-1.0, 1.0)(merge([details_high_pass, fake_without_noise], mode='sum'))
outputs = [
('tag3d', tag3d),
('tag3d_blur', tag3d_blur),
('tag3d_lightin', tag3d_lightin),
('fake_without_noise', fake_without_noise),
('fake', fake),
]
outputs = OrderedDict([(name, name_tensor(x, name)) for name, x in outputs])
self.generator_given_z_and_labels = Model([z_offset, labels], [fake])
self.sample_generator_given_z_and_labels_output_names = list(outputs.keys())
self.sample_generator_given_z_and_labels = Model([z_offset, labels],
list(outputs.values()))
