Python tensorflow.squeeze() Examples

def _aspect_preserving_resize(image, smallest_side):
  """Resize images preserving the original aspect ratio.

  Args:
    image: A 3-D image `Tensor`.
    smallest_side: A python integer or scalar `Tensor` indicating the size of
      the smallest side after resize.

  Returns:
    resized_image: A 3-D tensor containing the resized image.
  """
  smallest_side = tf.convert_to_tensor(smallest_side, dtype=tf.int32)

  shape = tf.shape(image)
  height = shape[0]
  width = shape[1]
  new_height, new_width = _smallest_size_at_least(height, width, smallest_side)
  image = tf.expand_dims(image, 0)
  resized_image = tf.image.resize_bilinear(image, [new_height, new_width],
                                           align_corners=False)
  resized_image = tf.squeeze(resized_image)
  resized_image.set_shape([None, None, 3])
  return resized_image 

def eval_image(image, height, width, scope=None):
  """Prepare one image for evaluation.

  Args:
    image: 3-D float Tensor
    height: integer
    width: integer
    scope: Optional scope for name_scope.
  Returns:
    3-D float Tensor of prepared image.
  """
  with tf.name_scope(values=[image, height, width], name=scope,
                     default_name='eval_image'):
    # Crop the central region of the image with an area containing 87.5% of
    # the original image.
    image = tf.image.central_crop(image, central_fraction=0.875)

    # Resize the image to the original height and width.
    image = tf.expand_dims(image, 0)
    image = tf.image.resize_bilinear(image, [height, width],
                                     align_corners=False)
    image = tf.squeeze(image, [0])
    return image 

def __call__(self, input):
        with tf.variable_scope(self.name, reuse=self._reuse):
            if not self._reuse:
                print('\033[93m'+self.name+'\033[0m')
            _ = input
            num_channel = [32, 64, 128, 256, 256, 512]
            num_layer = np.ceil(np.log2(min(_.shape.as_list()[1:3]))).astype(np.int)
            for i in range(num_layer):
                ch = num_channel[i] if i < len(num_channel) else 512
                _ = conv2d(_, ch, self._is_train, info=not self._reuse,
                           norm=self._norm_type, name='conv{}'.format(i+1))
            _ = conv2d(_, int(num_channel[i]/4), self._is_train, k=1, s=1,
                       info=not self._reuse, norm='None', name='conv{}'.format(i+2))
            _ = conv2d(_, self._num_class+1, self._is_train, k=1, s=1, info=not self._reuse,
                       activation_fn=None, norm='None',
                       name='conv{}'.format(i+3))
            _ = tf.squeeze(_)
            if not self._reuse: 
                log.info('discriminator output {}'.format(_.shape.as_list()))
            self._reuse = True
            self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.name)
            return tf.nn.sigmoid(_), _ 

def get_prob(self, state):
        """
            ### PROBLEM 3
            ### YOUR CODE HERE
        
            args:
                state: np array (batch_size, ob_dim)

            TODO:
                likelihood: 
                    evaluate the discriminator D(x,x) on the same input
                prob:
                    compute the probability density of x from the discriminator
                    likelihood (see homework doc)
        """
        likelihood = self.get_likelihood(state, state)
        # avoid divide by 0 and log(0)
        likelihood = np.clip(np.squeeze(likelihood), 1e-5, 1-1e-5)
        prob = (1 - likelihood) / likelihood
        return prob 

def simulate(self, action):
    with tf.name_scope("environment/simulate"):  # Do we need this?
      initializer = (tf.zeros(self.old_shape, dtype=tf.float32),
                     tf.fill((len(self),), 0.0), tf.fill((len(self),), False))

      def not_done_step(a, _):
        reward, done = self._batch_env.simulate(action)
        with tf.control_dependencies([reward, done]):
          r0 = self._batch_env.observ + 0
          r1 = tf.add(a[1], reward)
          r2 = tf.logical_or(a[2], done)
          return (r0, r1, r2)

      simulate_ret = tf.scan(not_done_step, tf.range(self.skip),
                             initializer=initializer, parallel_iterations=1,
                             infer_shape=False)
      observations, rewards, dones = simulate_ret
      split_observations = tf.split(observations, self.skip, axis=0)
      split_observations = [tf.squeeze(o, axis=0) for o in split_observations]
      observation = tf.concat(split_observations, axis=-1)
      with tf.control_dependencies([self._observ.assign(observation)]):
        return tf.identity(rewards[-1, ...]), tf.identity(dones[-1, ...]) 

def set_precision(predictions, labels,
                  weights_fn=common_layers.weights_nonzero):
  """Precision of set predictions.

  Args:
    predictions : A Tensor of scores of shape [batch, nlabels].
    labels: A Tensor of int32s giving true set elements,
      of shape [batch, seq_length].
    weights_fn: A function to weight the elements.

  Returns:
    hits: A Tensor of shape [batch, nlabels].
    weights: A Tensor of shape [batch, nlabels].
  """
  with tf.variable_scope("set_precision", values=[predictions, labels]):
    labels = tf.squeeze(labels, [2, 3])
    weights = weights_fn(labels)
    labels = tf.one_hot(labels, predictions.shape[-1])
    labels = tf.reduce_max(labels, axis=1)
    labels = tf.cast(labels, tf.bool)
    return tf.to_float(tf.equal(labels, predictions)), weights 

def set_recall(predictions, labels, weights_fn=common_layers.weights_nonzero):
  """Recall of set predictions.

  Args:
    predictions : A Tensor of scores of shape [batch, nlabels].
    labels: A Tensor of int32s giving true set elements,
      of shape [batch, seq_length].
    weights_fn: A function to weight the elements.

  Returns:
    hits: A Tensor of shape [batch, nlabels].
    weights: A Tensor of shape [batch, nlabels].
  """
  with tf.variable_scope("set_recall", values=[predictions, labels]):
    labels = tf.squeeze(labels, [2, 3])
    weights = weights_fn(labels)
    labels = tf.one_hot(labels, predictions.shape[-1])
    labels = tf.reduce_max(labels, axis=1)
    labels = tf.cast(labels, tf.bool)
    return tf.to_float(tf.equal(labels, predictions)), weights 

def rouge_l_fscore(predictions, labels, **unused_kwargs):
  """ROUGE scores computation between labels and predictions.

  This is an approximate ROUGE scoring method since we do not glue word pieces
  or decode the ids and tokenize the output.

  Args:
    predictions: tensor, model predictions
    labels: tensor, gold output.

  Returns:
    rouge_l_fscore: approx rouge-l f1 score.
  """
  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
  # Convert the outputs and labels to a [batch_size, input_length] tensor.
  outputs = tf.squeeze(outputs, axis=[-1, -2])
  labels = tf.squeeze(labels, axis=[-1, -2])
  rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (outputs, labels),
                               tf.float32)
  return rouge_l_f_score, tf.constant(1.0) 

def rouge_2_fscore(predictions, labels, **unused_kwargs):
  """ROUGE-2 F1 score computation between labels and predictions.

  This is an approximate ROUGE scoring method since we do not glue word pieces
  or decode the ids and tokenize the output.

  Args:
    predictions: tensor, model predictions
    labels: tensor, gold output.

  Returns:
    rouge2_fscore: approx rouge-2 f1 score.
  """

  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
  # Convert the outputs and labels to a [batch_size, input_length] tensor.
  outputs = tf.squeeze(outputs, axis=[-1, -2])
  labels = tf.squeeze(labels, axis=[-1, -2])
  rouge_2_f_score = tf.py_func(rouge_n, (outputs, labels), tf.float32)
  return rouge_2_f_score, tf.constant(1.0) 

def bleu_score(predictions, labels, **unused_kwargs):
  """BLEU score computation between labels and predictions.

  An approximate BLEU scoring method since we do not glue word pieces or
  decode the ids and tokenize the output. By default, we use ngram order of 4
  and use brevity penalty. Also, this does not have beam search.

  Args:
    predictions: tensor, model predictions
    labels: tensor, gold output.

  Returns:
    bleu: int, approx bleu score
  """
  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
  # Convert the outputs and labels to a [batch_size, input_length] tensor.
  outputs = tf.squeeze(outputs, axis=[-1, -2])
  labels = tf.squeeze(labels, axis=[-1, -2])

  bleu = tf.py_func(compute_bleu, (labels, outputs), tf.float32)
  return bleu, tf.constant(1.0) 

def decode(self, bottleneck):
    """Auto-decode from the bottleneck and return the result."""
    # Get the shape from bottleneck and num channels.
    shape = common_layers.shape_list(bottleneck)
    try:
      num_channels = self.hparams.problem.num_channels
    except AttributeError:
      num_channels = 1
    dummy_targets = tf.zeros(shape[:-1] + [num_channels])
    # Set the bottleneck to decode.
    if len(shape) > 4:
      bottleneck = tf.squeeze(bottleneck, axis=[1])
    bottleneck = 2 * bottleneck - 1  # Be -1/1 instead of 0/1.
    self._cur_bottleneck_tensor = bottleneck
    # Run decoding.
    res = self.infer({"targets": dummy_targets})
    self._cur_bottleneck_tensor = None
    return res 

def body(self, features):
    # Remove dropout if not training
    hparams = self._hparams
    targets = features["targets"]
    targets = tf.squeeze(targets, 2)

    (decoder_input, decoder_self_attention_bias) = attention_lm_prepare_decoder(
        targets, hparams)

    decoder_input = tf.nn.dropout(decoder_input,
                                  1.0 - hparams.layer_prepostprocess_dropout)
    decoder_output = attention_lm_decoder(decoder_input,
                                          decoder_self_attention_bias, hparams)
    decoder_output = tf.expand_dims(decoder_output, 2)

    return decoder_output 

def infer(self,
            features=None,
            decode_length=50,
            beam_size=1,
            top_beams=1,
            alpha=0.0,
            use_tpu=False):
    """Predict."""
    del decode_length, beam_size, top_beams, alpha, use_tpu
    assert features is not None
    logits, _ = self(features)  # pylint: disable=not-callable
    assert len(logits.get_shape()) == 5
    logits = tf.squeeze(logits, [1, 2, 3])
    log_probs = common_layers.log_prob_from_logits(logits)
    predictions, scores = common_layers.argmax_with_score(log_probs)
    return {
        "outputs": predictions,
        "scores": scores,
    } 

def _create_greedy_infer_model(self):
    """Creates model for greedy inference testing.

    Returns:
      model: A t2t model.
      features: An map of string to tensor.
    """
    model, features = get_model(transformer.transformer_small())

    out_logits, _ = model(features)
    out_logits = tf.squeeze(out_logits, axis=[2, 3])
    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
        logits=tf.reshape(out_logits, [-1, VOCAB_SIZE]),
        labels=tf.reshape(features["targets"], [-1]))
    loss = tf.reduce_mean(loss)
    apply_grad = tf.train.AdamOptimizer(0.001).minimize(loss)

    with self.test_session():
      tf.global_variables_initializer().run()
      for _ in range(100):
        apply_grad.run()

    model.set_mode(tf.estimator.ModeKeys.PREDICT)

    return model, features 

def testGreedyTPUSlowVsFast(self):
    if not tf_version_has_inplace_ops():
      return

    decode_length = 3

    model, features = self._create_greedy_infer_model()

    with tf.variable_scope(tf.get_variable_scope(), reuse=True):
      slow_result = model._slow_greedy_infer_tpu(
          features, decode_length)["outputs"]
      slow_result = tf.squeeze(slow_result, axis=[2, 3])

      fast_result = model._greedy_infer(
          features, decode_length, use_tpu=True)["outputs"]

    with self.test_session():
      slow_res = slow_result.eval()
      fast_res = fast_result.eval()

    self.assertEqual(fast_res.shape,
                     (BATCH_SIZE, INPUT_LENGTH + decode_length))
    self.assertAllClose(fast_res, slow_res) 

def bottom_simple(self, x, name, reuse):
    with tf.variable_scope(name, reuse=reuse):
      # Ensure the inputs are 3-D
      if len(x.get_shape()) == 4:
        x = tf.squeeze(x, axis=3)
      while len(x.get_shape()) < 3:
        x = tf.expand_dims(x, axis=-1)

      var = self._get_weights()
      x = common_layers.dropout_no_scaling(
          x, 1.0 - self._model_hparams.symbol_dropout)
      ret = common_layers.gather(var, x)
      if self._model_hparams.multiply_embedding_mode == "sqrt_depth":
        ret *= self._body_input_depth**0.5
      ret *= tf.expand_dims(tf.to_float(tf.not_equal(x, 0)), -1)
      return ret 

def loss(self, top_out, targets):
    """Compute the CTC loss."""
    logits = top_out
    with tf.name_scope("ctc_loss", values=[logits, targets]):
      # For CTC we assume targets are 1d, [batch, length, 1, 1] here.
      targets_shape = targets.get_shape().as_list()
      assert len(targets_shape) == 4
      assert targets_shape[2] == 1
      assert targets_shape[3] == 1
      targets = tf.squeeze(targets, axis=[2, 3])
      logits = tf.squeeze(logits, axis=[2, 3])
      targets_mask = 1 - tf.to_int32(tf.equal(targets, 0))
      targets_lengths = tf.reduce_sum(targets_mask, axis=1)
      sparse_targets = tf.keras.backend.ctc_label_dense_to_sparse(
          targets, targets_lengths)
      xent = tf.nn.ctc_loss(
          sparse_targets,
          logits,
          targets_lengths,
          time_major=False,
          preprocess_collapse_repeated=False,
          ctc_merge_repeated=False)
      weights = self.targets_weights_fn(targets)  # pylint: disable=not-callable
      return tf.reduce_sum(xent), tf.reduce_sum(weights) 

def get_channel_embeddings(self,
                             io_depth,
                             targets,
                             hidden_size,
                             name="channel"):
    """Get separate embedding for each of the channels."""
    targets_split = tf.split(targets, io_depth, axis=3)
    rgb_embedding_var = tf.get_variable("rgb_target_emb_%s" % name,
                                        [256 * io_depth, hidden_size])
    rgb_embedding_var = tf.identity(rgb_embedding_var)
    rgb_embedding_var *= float(hidden_size)**0.5
    channel_target_embs = []
    for i in range(io_depth):
      # Adding the channel offsets to get the right embedding since the
      # embedding tensor has shape 256 * io_depth, hidden_size
      target_ids = tf.squeeze(targets_split[i], axis=3) + i * 256
      target_embs = common_layers.gather(rgb_embedding_var, target_ids)
      channel_target_embs.append(target_embs)

    return tf.concat(channel_target_embs, axis=-1) 

def top_1_tpu(inputs):
  """find max and argmax over the last dimension.

  Works well on TPU

  Args:
    inputs: A tensor with shape [..., depth]

  Returns:
    values: a Tensor with shape [...]
    indices: a Tensor with shape [...]
  """
  inputs_max = tf.reduce_max(inputs, axis=-1, keepdims=True)
  mask = tf.to_int32(tf.equal(inputs_max, inputs))
  index = tf.range(tf.shape(inputs)[-1]) * mask
  return tf.squeeze(inputs_max, -1), tf.reduce_max(index, axis=-1) 

def call(self, inputs, **kwargs):
        if K.ndim(inputs) != 2:
            raise ValueError(
                "Unexpected inputs dimensions %d, expect to be 2 dimensions" % (K.ndim(inputs)))

        x_0 = tf.expand_dims(inputs, axis=2)
        x_l = x_0
        for i in range(self.layer_num):
            xl_w = tf.tensordot(x_l, self.kernels[i], axes=(1, 0))
            dot_ = tf.matmul(x_0, xl_w)
            x_l = dot_ + self.bias[i] + x_l
        x_l = tf.squeeze(x_l, axis=2)
        return x_l 

def call(self, inputs, **kwargs):
        if K.ndim(inputs) != 3:
            raise ValueError(
                "Unexpected inputs dimensions %d, expect to be 3 dimensions" % (K.ndim(inputs)))

        querys = tf.tensordot(inputs, self.W_Query,
                              axes=(-1, 0))  # None F D*head_num
        keys = tf.tensordot(inputs, self.W_key, axes=(-1, 0))
        values = tf.tensordot(inputs, self.W_Value, axes=(-1, 0))

        # head_num None F D
        querys = tf.stack(tf.split(querys, self.head_num, axis=2))
        keys = tf.stack(tf.split(keys, self.head_num, axis=2))
        values = tf.stack(tf.split(values, self.head_num, axis=2))

        inner_product = tf.matmul(
            querys, keys, transpose_b=True)  # head_num None F F
        self.normalized_att_scores = tf.nn.softmax(inner_product)

        result = tf.matmul(self.normalized_att_scores,
                           values)  # head_num None F D
        result = tf.concat(tf.split(result, self.head_num, ), axis=-1)
        result = tf.squeeze(result, axis=0)  # None F D*head_num

        if self.use_res:
            result += tf.tensordot(inputs, self.W_Res, axes=(-1, 0))
        result = tf.nn.relu(result)

        return result 

def spectrogram2wav(spectrogram, n_iter=hparams.griffin_lim_iters, n_fft=(hparams.num_freq - 1) * 2,
                    win_length=int(hparams.frame_length_ms / 1000 * hparams.sample_rate),
                    hop_length=int(hparams.frame_shift_ms / 1000 * hparams.sample_rate)):
    '''Converts spectrogram into a waveform using Griffin-lim's raw.
    '''

    def invert_spectrogram(spectrogram):
        '''
        spectrogram: [t, f]
        '''
        spectrogram = tf.expand_dims(spectrogram, 0)
        inversed = tf.contrib.signal.inverse_stft(spectrogram, win_length, hop_length, n_fft)
        squeezed = tf.squeeze(inversed, 0)
        return squeezed

    spectrogram = tf.transpose(spectrogram)

    spectrogram = tf.cast(spectrogram, dtype=tf.complex64)  # [t, f]
    X_best = tf.identity(spectrogram)
    for i in range(n_iter):
        X_t = invert_spectrogram(X_best)
        est = tf.contrib.signal.stft(X_t, win_length, hop_length, n_fft, pad_end=False)  # (1, T, n_fft/2+1)
        phase = est / tf.cast(tf.maximum(1e-8, tf.abs(est)), tf.complex64)  # [t, f]
        X_best = spectrogram * phase  # [t, t]
    X_t = invert_spectrogram(X_best)
    y = tf.real(X_t)

    return y 

def predict_rewards(self,input_):

        with tf.variable_scope("encoder"):

            Encoder = Attentive_encoder(self.config)
            encoder_output = Encoder.encode(input_)
            frame = tf.reduce_mean(encoder_output, 1) # [Batch size, Sequence Length, Num_neurons] to [Batch size, Num_neurons]

        with tf.variable_scope("ffn"):
            # ffn 1
            h0 = tf.layers.dense(frame, self.num_neurons, activation=tf.nn.relu, kernel_initializer=self.initializer)
            # ffn 2
            w1 =tf.get_variable("w1", [self.num_neurons, 1], initializer=self.initializer)
            b1 = tf.Variable(self.init_baseline, name="b1")
            self.predictions = tf.squeeze(tf.matmul(h0, w1)+b1) 

def preprocess_for_eval(image, height, width,
                        central_fraction=0.875, scope=None):
  """Prepare one image for evaluation.

  If height and width are specified it would output an image with that size by
  applying resize_bilinear.

  If central_fraction is specified it would crop the central fraction of the
  input image.

  Args:
    image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
      [0, 1], otherwise it would converted to tf.float32 assuming that the range
      is [0, MAX], where MAX is largest positive representable number for
      int(8/16/32) data type (see `tf.image.convert_image_dtype` for details)
    height: integer
    width: integer
    central_fraction: Optional Float, fraction of the image to crop.
    scope: Optional scope for name_scope.
  Returns:
    3-D float Tensor of prepared image.
  """
  with tf.name_scope(scope, 'eval_image', [image, height, width]):
    if image.dtype != tf.float32:
      image = tf.image.convert_image_dtype(image, dtype=tf.float32)
    # Crop the central region of the image with an area containing 87.5% of
    # the original image.
    if central_fraction:
      image = tf.image.central_crop(image, central_fraction=central_fraction)

    if height and width:
      # Resize the image to the specified height and width.
      image = tf.expand_dims(image, 0)
      image = tf.image.resize_bilinear(image, [height, width],
                                       align_corners=False)
      image = tf.squeeze(image, [0])
    image = tf.subtract(image, 0.5)
    image = tf.multiply(image, 2.0)
    return image 

def _kl_divergence_with_logits(q_logits, p_logits, weights):
  """Returns weighted KL divergence between distributions q and p.

  Args:
    q_logits: logits for 1st argument of KL divergence shape
              [num_timesteps * batch_size, num_classes] if num_classes > 2, and
              [num_timesteps * batch_size] if num_classes == 2.
    p_logits: logits for 2nd argument of KL divergence with same shape q_logits.
    weights: 1-D float tensor with shape [num_timesteps * batch_size].
             Elements should be 1.0 only on end of sequences

  Returns:
    KL: float scalar.
  """
  # For logistic regression
  if FLAGS.num_classes == 2:
    q = tf.nn.sigmoid(q_logits)
    kl = (-tf.nn.sigmoid_cross_entropy_with_logits(logits=q_logits, labels=q) +
          tf.nn.sigmoid_cross_entropy_with_logits(logits=p_logits, labels=q))
    kl = tf.squeeze(kl)

  # For softmax regression
  else:
    q = tf.nn.softmax(q_logits)
    kl = tf.reduce_sum(
        q * (tf.nn.log_softmax(q_logits) - tf.nn.log_softmax(p_logits)), 1)

  num_labels = tf.reduce_sum(weights)
  num_labels = tf.where(tf.equal(num_labels, 0.), 1., num_labels)

  kl.get_shape().assert_has_rank(1)
  weights.get_shape().assert_has_rank(1)
  loss = tf.identity(tf.reduce_sum(weights * kl) / num_labels, name='kl')
  return loss 

def _split_bidir_tokens(batch):
  tokens = batch.sequences[data_utils.SequenceWrapper.F_TOKEN_ID]
  # Tokens have shape [batch, time, 2]
  # forward and reverse have shape [batch, time].
  forward, reverse = [
      tf.squeeze(t, axis=[2]) for t in tf.split(tokens, 2, axis=2)
  ]
  return forward, reverse 

def predictions(logits):
  """Class prediction from logits."""
  inner_dim = logits.get_shape().as_list()[-1]
  with tf.name_scope('predictions'):
    # For binary classification
    if inner_dim == 1:
      pred = tf.cast(tf.greater(tf.squeeze(logits), 0.5), tf.int64)
    # For multi-class classification
    else:
      pred = tf.argmax(logits, 1)
    return pred 

def model(self, collection, message, key=None):
    """The model for Alice, Bob, and Eve.  If key=None, the first FC layer
    takes only the message as inputs.  Otherwise, it uses both the key
    and the message.

    Args:
      collection:  The graph keys collection to add new vars to.
      message:  The input message to process.
      key:  The input key (if any) to use.
    """

    if key is not None:
      combined_message = tf.concat(axis=1, values=[message, key])
    else:
      combined_message = message

    # Ensure that all variables created are in the specified collection.
    with tf.contrib.framework.arg_scope(
        [tf.contrib.layers.fully_connected, tf.contrib.layers.conv2d],
        variables_collections=[collection]):

      fc = tf.contrib.layers.fully_connected(
          combined_message,
          TEXT_SIZE + KEY_SIZE,
          biases_initializer=tf.constant_initializer(0.0),
          activation_fn=None)

      # Perform a sequence of 1D convolutions (by expanding the message out to 2D
      # and then squeezing it back down).
      fc = tf.expand_dims(fc, 2)
      # 2,1 -> 1,2
      conv = tf.contrib.layers.conv2d(
          fc, 2, 2, 2, 'SAME', activation_fn=tf.nn.sigmoid)
      # 1,2 -> 1, 2
      conv = tf.contrib.layers.conv2d(
          conv, 2, 1, 1, 'SAME', activation_fn=tf.nn.sigmoid)
      # 1,2 -> 1, 1
      conv = tf.contrib.layers.conv2d(
          conv, 1, 1, 1, 'SAME', activation_fn=tf.nn.tanh)
      conv = tf.squeeze(conv, 2)
      return conv 

def _maybe_apply_dropout(self, inputs, stride):
    # The |inputs| are rank 4 (one 1xN "image" per sequence).  Squeeze out and
    # restore the singleton image height, so dropout is applied to the normal
    # rank 3 batched input tensor.
    inputs = tf.squeeze(inputs, [1])
    inputs = maybe_apply_dropout(inputs, self._dropout_rate,
                                 self._attrs['dropout_per_sequence'], stride)
    inputs = tf.expand_dims(inputs, 1)
    return inputs 

def reframe_box_masks_to_image_masks(box_masks, boxes, image_height,
                                     image_width):
  """Transforms the box masks back to full image masks.

  Embeds masks in bounding boxes of larger masks whose shapes correspond to
  image shape.

  Args:
    box_masks: A tf.float32 tensor of size [num_masks, mask_height, mask_width].
    boxes: A tf.float32 tensor of size [num_masks, 4] containing the box
           corners. Row i contains [ymin, xmin, ymax, xmax] of the box
           corresponding to mask i. Note that the box corners are in
           normalized coordinates.
    image_height: Image height. The output mask will have the same height as
                  the image height.
    image_width: Image width. The output mask will have the same width as the
                 image width.

  Returns:
    A tf.float32 tensor of size [num_masks, image_height, image_width].
  """
  # TODO: Make this a public function.
  def transform_boxes_relative_to_boxes(boxes, reference_boxes):
    boxes = tf.reshape(boxes, [-1, 2, 2])
    min_corner = tf.expand_dims(reference_boxes[:, 0:2], 1)
    max_corner = tf.expand_dims(reference_boxes[:, 2:4], 1)
    transformed_boxes = (boxes - min_corner) / (max_corner - min_corner)
    return tf.reshape(transformed_boxes, [-1, 4])

  box_masks = tf.expand_dims(box_masks, axis=3)
  num_boxes = tf.shape(box_masks)[0]
  unit_boxes = tf.concat(
      [tf.zeros([num_boxes, 2]), tf.ones([num_boxes, 2])], axis=1)
  reverse_boxes = transform_boxes_relative_to_boxes(unit_boxes, boxes)
  image_masks = tf.image.crop_and_resize(image=box_masks,
                                         boxes=reverse_boxes,
                                         box_ind=tf.range(num_boxes),
                                         crop_size=[image_height, image_width],
                                         extrapolation_value=0.0)
  return tf.squeeze(image_masks, axis=3)