def test_get_offset_front():
a_shape = (22, )
b_shape = (10, )
n = 5
a_input = Input(shape=a_shape)
b_input = Input(shape=b_shape)
output = get_offset_front([a_input, b_input], n)
model = Model([a_input, b_input], output)
model.compile('adam', 'mse')
bs = (64, )
a = np.random.sample(bs + a_shape)
b = np.random.sample(bs + b_shape)
y = np.random.sample(bs + (2 * n, 16, 16))
model.train_on_batch([a, b], y)
def test_get_offset_front():
a_shape = (22, )
b_shape = (10, )
n = 5
a_input = Input(shape=a_shape)
b_input = Input(shape=b_shape)
output = get_offset_front([a_input, b_input], n)
model = Model([a_input, b_input], output)
model.compile('adam', 'mse')
bs = (64, )
a = np.random.sample(bs + a_shape)
b = np.random.sample(bs + b_shape)
y = np.random.sample(bs + (2*n, 16, 16))
model.train_on_batch([a, b], y)
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)
raw_tag3d, tag3d_depth_map = self.tag3d_network(labels)
tag3d = ScaleUnitIntervalTo(-1, 1)(raw_tag3d)
outputs['tag3d'] = tag3d
outputs['tag3d_depth_map'] = tag3d_depth_map
segmentation = Segmentation(threshold=-0.08,
smooth_threshold=0.2,
sigma=1.5,
name='segmentation')
tag3d_downsampled = PyramidReduce()(tag3d)
tag3d_segmented = segmentation(raw_tag3d)
outputs['tag3d_segmented'] = tag3d_segmented
tag3d_segmented_blur = GaussianBlur(sigma=0.66)(tag3d_segmented)
out_offset_front = get_offset_front(
[z_offset, ZeroGradient()(labels_without_bits)],
self.generator_units)
light_depth_map = get_preprocess(tag3d_depth_map,
self.preprocess_units,
nb_conv_layers=2)
light_outs = get_lighting_generator(
[out_offset_front, light_depth_map], self.generator_units)
offset_depth_map = get_preprocess(tag3d_depth_map,
self.preprocess_units,
nb_conv_layers=2)
offset_middle_light = get_preprocess(concat(light_outs),
self.preprocess_units,
resize=['down', 'down'])
offset_middle_tag3d = get_preprocess(tag3d_downsampled,
self.preprocess_units // 2,
resize=['down', ''],
nb_conv_layers=2)
out_offset_middle = get_offset_middle([
out_offset_front, offset_depth_map, offset_middle_light,
offset_middle_tag3d
], self.generator_units)
offset_back_tag3d_downsampled = get_preprocess(tag3d_downsampled,
self.preprocess_units //
2,
nb_conv_layers=2)
offset_back_feature_map, out_offset_back = get_offset_back(
[out_offset_middle, offset_back_tag3d_downsampled],
self.generator_units)
blur_factor = get_blur_factor(out_offset_middle, min=0.25, max=1.)
outputs['blur_factor'] = blur_factor
tag3d_blur = BlendingBlur(sigma=2.0)([tag3d, blur_factor])
outputs['tag3d_blur'] = tag3d_blur
outputs['light_black'] = light_outs[0]
outputs['light_white'] = light_outs[1]
outputs['light_shift'] = light_outs[2]
tag3d_lighten = AddLighting(
scale_factor=0.90, shift_factor=0.90)([tag3d_blur] + light_outs)
tag3d_lighten = InBounds(clip=True, weight=15)(tag3d_lighten)
outputs['tag3d_lighten'] = tag3d_lighten
outputs['background_offset'] = out_offset_back
blending = Background(name='blending')(
[out_offset_back, tag3d_lighten, tag3d_segmented_blur])
outputs['fake_without_noise'] = blending
details = get_details([
blending, tag3d_segmented_blur, tag3d, out_offset_back,
offset_back_feature_map
] + light_outs, self.generator_units)
outputs['details_offset'] = details
details_high_pass = HighPass(3.5, nb_steps=3)(details)
outputs['details_high_pass'] = details_high_pass
fake = InBounds(-2.0, 2.0)(merge([details_high_pass, blending],
mode='sum'))
outputs['fake'] = fake
for name in outputs.keys():
outputs[name] = name_tensor(outputs[name], name)
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 _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)
raw_tag3d, tag3d_depth_map = self.tag3d_network(labels)
tag3d = ScaleUnitIntervalTo(-1, 1)(raw_tag3d)
outputs['tag3d'] = tag3d
outputs['tag3d_depth_map'] = tag3d_depth_map
segmentation = Segmentation(threshold=-0.08, smooth_threshold=0.2,
sigma=1.5, name='segmentation')
tag3d_downsampled = PyramidReduce()(tag3d)
tag3d_segmented = segmentation(raw_tag3d)
outputs['tag3d_segmented'] = tag3d_segmented
tag3d_segmented_blur = GaussianBlur(sigma=0.66)(tag3d_segmented)
out_offset_front = get_offset_front([z_offset, ZeroGradient()(labels_without_bits)],
self.generator_units)
light_depth_map = get_preprocess(tag3d_depth_map, self.preprocess_units,
nb_conv_layers=2)
light_outs = get_lighting_generator([out_offset_front, light_depth_map],
self.generator_units)
offset_depth_map = get_preprocess(tag3d_depth_map, self.preprocess_units,
nb_conv_layers=2)
offset_middle_light = get_preprocess(concat(light_outs), self.preprocess_units,
resize=['down', 'down'])
offset_middle_tag3d = get_preprocess(tag3d_downsampled,
self.preprocess_units // 2,
resize=['down', ''],
nb_conv_layers=2)
out_offset_middle = get_offset_middle(
[out_offset_front, offset_depth_map,
offset_middle_light, offset_middle_tag3d],
self.generator_units)
offset_back_tag3d_downsampled = get_preprocess(tag3d_downsampled,
self.preprocess_units // 2,
nb_conv_layers=2)
offset_back_feature_map, out_offset_back = get_offset_back(
[out_offset_middle, offset_back_tag3d_downsampled], self.generator_units)
blur_factor = get_blur_factor(out_offset_middle, min=0.25, max=1.)
outputs['blur_factor'] = blur_factor
tag3d_blur = BlendingBlur(sigma=2.0)([tag3d, blur_factor])
outputs['tag3d_blur'] = tag3d_blur
outputs['light_black'] = light_outs[0]
outputs['light_white'] = light_outs[1]
outputs['light_shift'] = light_outs[2]
tag3d_lighten = AddLighting(
scale_factor=0.90, shift_factor=0.90)([tag3d_blur] + light_outs)
tag3d_lighten = InBounds(clip=True, weight=15)(tag3d_lighten)
outputs['tag3d_lighten'] = tag3d_lighten
outputs['background_offset'] = out_offset_back
blending = Background(name='blending')([out_offset_back, tag3d_lighten,
tag3d_segmented_blur])
outputs['fake_without_noise'] = blending
details = get_details(
[blending, tag3d_segmented_blur, tag3d, out_offset_back,
offset_back_feature_map] + light_outs, self.generator_units)
outputs['details_offset'] = details
details_high_pass = HighPass(3.5, nb_steps=3)(details)
outputs['details_high_pass'] = details_high_pass
fake = InBounds(-2.0, 2.0)(
merge([details_high_pass, blending], mode='sum'))
outputs['fake'] = fake
for name in outputs.keys():
outputs[name] = name_tensor(outputs[name], name)
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()))
