示例#1
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  def test_scalar_concat_4d_images_and_scalar(self):
    x = tf.constant(
        [[[[1, 2], [4, 5]], [[7, 8], [10, 11]]], [[[0, 0], [-1, -2]],
                                                  [[1, -2], [2, 5]]]],
        dtype=tf.float32)
    with self.test_session(use_gpu=True) as sess:
      output_np = sess.run(layers.scalar_concat(x, 7))

    expected_np = [[[[1, 2, 7], [4, 5, 7]], [[7, 8, 7], [10, 11, 7]]],
                   [[[0, 0, 7], [-1, -2, 7]], [[1, -2, 7], [2, 5, 7]]]]

    self.assertNDArrayNear(output_np, expected_np, 1.0e-5)
示例#2
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def discriminator(x,
                  progress,
                  num_filters_fn,
                  resolution_schedule,
                  num_blocks=None,
                  kernel_size=3,
                  scope='progressive_gan_discriminator',
                  reuse=None):
    """Discriminator network for the progressive GAN model.

  Args:
    x: A `Tensor`of NHWC format representing images of size `resolution`.
    progress: A scalar float `Tensor` of training progress.
    num_filters_fn: A function that maps `block_id` to # of filters for the
        block.
    resolution_schedule: An object of `ResolutionSchedule`.
    num_blocks: An integer of number of blocks. None means maximum number of
        blocks, i.e. `resolution.schedule.num_resolutions`. Defaults to None.
    kernel_size: An integer of convolution kernel size.
    scope: A string or variable scope.
    reuse: Whether to reuse `scope`. Defaults to None which means to inherit
        the reuse option of the parent scope.

  Returns:
    A `Tensor` of model output and a dictionary of model end points.
  """
    if num_blocks is None:
        num_blocks = resolution_schedule.num_resolutions

    def _conv2d(scope, x, kernel_size, filters, padding='SAME'):
        return layers.custom_conv2d(x=x,
                                    filters=filters,
                                    kernel_size=kernel_size,
                                    padding=padding,
                                    activation=tf.nn.leaky_relu,
                                    he_initializer_slope=0.0,
                                    scope=scope)

    def _from_rgb(x, block_id):
        return _conv2d('from_rgb', x, 1, num_filters_fn(block_id))

    end_points = {}

    with tf.variable_scope(scope, reuse=reuse):
        x0 = x
        end_points['rgb'] = x0

        lods = []
        for block_id in range(num_blocks, 0, -1):
            with tf.variable_scope(block_name(block_id)):
                scale = resolution_schedule.scale_factor(block_id)
                lod = layers.downscale(x0, scale)
                end_points['downscaled_rgb_{}'.format(block_id)] = lod
                lod = _from_rgb(lod, block_id)
                # alpha_i is used to replace lod_select.
                alpha = _discriminator_alpha(block_id, progress)
                end_points['alpha_{}'.format(block_id)] = alpha
            lods.append((lod, alpha))

        lods_iter = iter(lods)
        x, _ = lods_iter.__next__()
        for block_id in range(num_blocks, 1, -1):
            with tf.variable_scope(block_name(block_id)):
                x = _conv2d('conv0', x, kernel_size, num_filters_fn(block_id))
                x = _conv2d('conv1', x, kernel_size,
                            num_filters_fn(block_id - 1))
                x = layers.downscale(x, resolution_schedule.scale_base)
                lod, alpha = lods_iter.__next__()
                x = alpha * lod + (1.0 - alpha) * x

        with tf.variable_scope(block_name(1)):
            x = layers.scalar_concat(x, layers.minibatch_mean_stddev(x))
            x = _conv2d('conv0', x, kernel_size, num_filters_fn(1))
            x = _conv2d('conv1', x, resolution_schedule.start_resolutions,
                        num_filters_fn(0), 'VALID')
            end_points['last_conv'] = x
            logits = layers.custom_dense(x=x, units=1, scope='logits')
            end_points['logits'] = logits

    return logits, end_points
示例#3
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 def minibatch_stats(x):
     return layers.scalar_concat(x, layers.minibatch_mean_variance(x))
示例#4
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 def test_scalar_concat_invalid_input_throws_exception(self):
   with self.assertRaises(ValueError):
     layers.scalar_concat(tf.constant(1.2), 2.0)
示例#5
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def discriminator(x,
                  progress,
                  num_filters_fn,
                  resolution_schedule,
                  num_blocks=None,
                  kernel_size=3,
                  scope='progressive_gan_discriminator',
                  reuse=None):
  """Discriminator network for the progressive GAN model.

  Args:
    x: A `Tensor`of NHWC format representing images of size `resolution`.
    progress: A scalar float `Tensor` of training progress.
    num_filters_fn: A function that maps `block_id` to # of filters for the
        block.
    resolution_schedule: An object of `ResolutionSchedule`.
    num_blocks: An integer of number of blocks. None means maximum number of
        blocks, i.e. `resolution.schedule.num_resolutions`. Defaults to None.
    kernel_size: An integer of convolution kernel size.
    scope: A string or variable scope.
    reuse: Whether to reuse `scope`. Defaults to None which means to inherit
        the reuse option of the parent scope.

  Returns:
    A `Tensor` of model output and a dictionary of model end points.
  """
  if num_blocks is None:
    num_blocks = resolution_schedule.num_resolutions

  def _conv2d(scope, x, kernel_size, filters, padding='SAME'):
    return layers.custom_conv2d(
        x=x,
        filters=filters,
        kernel_size=kernel_size,
        padding=padding,
        activation=tf.nn.leaky_relu,
        he_initializer_slope=0.0,
        scope=scope)

  def _from_rgb(x, block_id):
    return _conv2d('from_rgb', x, 1, num_filters_fn(block_id))

  end_points = {}

  with tf.variable_scope(scope, reuse=reuse):
    x0 = x
    end_points['rgb'] = x0

    lods = []
    for block_id in range(num_blocks, 0, -1):
      with tf.variable_scope(block_name(block_id)):
        scale = resolution_schedule.scale_factor(block_id)
        lod = layers.downscale(x0, scale)
        end_points['downscaled_rgb_{}'.format(block_id)] = lod
        lod = _from_rgb(lod, block_id)
        # alpha_i is used to replace lod_select.
        alpha = _discriminator_alpha(block_id, progress)
        end_points['alpha_{}'.format(block_id)] = alpha
      lods.append((lod, alpha))

    lods_iter = iter(lods)
    x, _ = lods_iter.next()
    for block_id in range(num_blocks, 1, -1):
      with tf.variable_scope(block_name(block_id)):
        x = _conv2d('conv0', x, kernel_size, num_filters_fn(block_id))
        x = _conv2d('conv1', x, kernel_size, num_filters_fn(block_id - 1))
        x = layers.downscale(x, resolution_schedule.scale_base)
        lod, alpha = lods_iter.next()
        x = alpha * lod + (1.0 - alpha) * x

    with tf.variable_scope(block_name(1)):
      x = layers.scalar_concat(x, layers.minibatch_mean_stddev(x))
      x = _conv2d('conv0', x, kernel_size, num_filters_fn(1))
      x = _conv2d('conv1', x, resolution_schedule.start_resolutions,
                  num_filters_fn(0), 'VALID')
      end_points['last_conv'] = x
      logits = layers.custom_dense(x=x, units=1, scope='logits')
      end_points['logits'] = logits

  return logits, end_points