Exemplo n.º 1
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    def _build_generator_given_z(self):
        z = Input(shape=(self.z_dim, ), name='z')

        z_bits = Subtensor(*self.pos_z_bits, axis=1)(z)
        z_labels = Subtensor(*self.pos_z_labels, axis=1)(z)
        z_offset = Subtensor(*self.pos_z_offset, axis=1)(z)
        bits = ThresholdBits()(z_bits)
        nb_labels_without_bits = self.labels_shape[0] - self.nb_bits
        generated_labels = get_label_generator(
            z_labels,
            self.generator_units,
            nb_output_units=nb_labels_without_bits)

        labels_normed = NormSinCosAngle(0)(generated_labels)
        labels = concat([bits, labels_normed], name='labels')
        fake = self.generator_given_z_and_labels([z_offset, labels])
        self.generator_given_z = Model([z], [fake])

        sample_tensors = self.sample_generator_given_z_and_labels(
            [z_offset, labels])
        sample_tensors = [
            name_tensor(t, n) for t, n in zip(
                sample_tensors,
                self.sample_generator_given_z_and_labels.output_names)
        ]
        self.sample_generator_given_z_output_names = ['labels'] + \
            self.sample_generator_given_z_and_labels_output_names
        self.sample_generator_given_z = Model([z], [labels] + sample_tensors)
Exemplo n.º 2
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def tag3d_network_dense(input, nb_units=64, nb_dense_units=[512, 512],
                        depth=2, nb_output_channels=1, trainable=True):
    n = nb_units

    def conv(n, repeats=None):
        def normal(shape, name=None):
            return keras.initializations.normal(shape, scale=0.01, name=name)

        if repeats is None:
            repeats = depth
        return [
            [
                Convolution2D(n, 3, 3, border_mode='same', init='he_normal'),
                Activation('relu')
            ] for _ in range(repeats)
        ]

    base = sequential([
        [
            Dense(nb_dense, activation='relu')
            for nb_dense in nb_dense_units
        ],
        Dense(8*n*4*4),
        Activation('relu'),
        Reshape((8*n, 4, 4,)),
        conv(8*n),
        UpSampling2D(),  # 8x8
        conv(4*n),
        UpSampling2D(),  # 16x16
        conv(2*n),
    ], ns='tag3d_gen.base', trainable=trainable)(input)

    tag3d = sequential([
        conv(2*n),
        UpSampling2D(),  # 32x32
        conv(n),
        UpSampling2D(),  # 64x64
        conv(n, 1),
        Convolution2D(1, 3, 3, border_mode='same', init='he_normal'),
    ], ns='tag3d', trainable=trainable)(base)

    depth_map = sequential([
        conv(n // 2, depth - 1),
        Convolution2D(1, 3, 3, border_mode='same', init='he_normal'),
    ], ns='depth_map', trainable=trainable)(base)

    return name_tensor(tag3d, 'tag3d'), name_tensor(depth_map, 'depth_map')
Exemplo n.º 3
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    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()))
Exemplo n.º 4
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    def _build_generator_given_z(self):
        z = Input(shape=(self.z_dim,), name='z')

        z_bits = Subtensor(*self.pos_z_bits, axis=1)(z)
        z_labels = Subtensor(*self.pos_z_labels, axis=1)(z)
        z_offset = Subtensor(*self.pos_z_offset, axis=1)(z)
        bits = ThresholdBits()(z_bits)
        nb_labels_without_bits = self.labels_shape[0] - self.nb_bits
        generated_labels = get_label_generator(
            z_labels, self.generator_units, nb_output_units=nb_labels_without_bits)

        labels_normed = NormSinCosAngle(0)(generated_labels)
        labels = concat([bits, labels_normed], name='labels')
        fake = self.generator_given_z_and_labels([z_offset, labels])
        self.generator_given_z = Model([z], [fake])

        sample_tensors = self.sample_generator_given_z_and_labels([z_offset, labels])
        sample_tensors = [name_tensor(t, n)
                          for t, n in zip(sample_tensors,
                                          self.sample_generator_given_z_and_labels.output_names)]
        self.sample_generator_given_z_output_names = ['labels'] + \
            self.sample_generator_given_z_and_labels_output_names
        self.sample_generator_given_z = Model([z], [labels] + sample_tensors)
Exemplo n.º 5
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    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()))
Exemplo n.º 6
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def tag3d_network_dense(input,
                        nb_units=64,
                        nb_dense_units=[512, 512],
                        depth=2,
                        nb_output_channels=1,
                        trainable=True):
    n = nb_units

    def conv(n, repeats=None):
        def normal(shape, name=None):
            return keras.initializations.normal(shape, scale=0.01, name=name)

        if repeats is None:
            repeats = depth
        return [[
            Convolution2D(n, 3, 3, border_mode='same', init='he_normal'),
            Activation('relu')
        ] for _ in range(repeats)]

    base = sequential(
        [
            [
                Dense(nb_dense, activation='relu')
                for nb_dense in nb_dense_units
            ],
            Dense(8 * n * 4 * 4),
            Activation('relu'),
            Reshape((
                8 * n,
                4,
                4,
            )),
            conv(8 * n),
            UpSampling2D(),  # 8x8
            conv(4 * n),
            UpSampling2D(),  # 16x16
            conv(2 * n),
        ],
        ns='tag3d_gen.base',
        trainable=trainable)(input)

    tag3d = sequential(
        [
            conv(2 * n),
            UpSampling2D(),  # 32x32
            conv(n),
            UpSampling2D(),  # 64x64
            conv(n, 1),
            Convolution2D(1, 3, 3, border_mode='same', init='he_normal'),
        ],
        ns='tag3d',
        trainable=trainable)(base)

    depth_map = sequential([
        conv(n // 2, depth - 1),
        Convolution2D(1, 3, 3, border_mode='same', init='he_normal'),
    ],
                           ns='depth_map',
                           trainable=trainable)(base)

    return name_tensor(tag3d, 'tag3d'), name_tensor(depth_map, 'depth_map')
Exemplo n.º 7
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    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()))
Exemplo n.º 8
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    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()))