Python common_layers.flatten4d3d函数代码示例

def lstm_seq2seq_internal_bid_encoder(inputs, targets, hparams, train):
  """The basic LSTM seq2seq model with bidirectional encoder."""
  with tf.variable_scope("lstm_seq2seq_bid_encoder"):
    if inputs is not None:
      inputs_length = common_layers.length_from_embedding(inputs)
      # Flatten inputs.
      inputs = common_layers.flatten4d3d(inputs)
      # LSTM encoder.
      _, final_encoder_state = lstm_bid_encoder(
          inputs, inputs_length, hparams, train, "encoder")
    else:
      inputs_length = None
      final_encoder_state = None
    # LSTM decoder.
    shifted_targets = common_layers.shift_right(targets)
    # Add 1 to account for the padding added to the left from shift_right
    targets_length = common_layers.length_from_embedding(shifted_targets) + 1
    hparams_decoder = copy.copy(hparams)
    hparams_decoder.hidden_size = 2 * hparams.hidden_size
    decoder_outputs, _ = lstm(
        common_layers.flatten4d3d(shifted_targets),
        targets_length,
        hparams_decoder,
        train,
        "decoder",
        initial_state=final_encoder_state)
    return tf.expand_dims(decoder_outputs, axis=2)

  def body(self, features):
    hparams = self._hparams
    targets = features["targets"]
    inputs = features["inputs"]
    target_space = features["target_space_id"]

    inputs = common_layers.flatten4d3d(inputs)
    targets = common_layers.flatten4d3d(targets)

    (encoder_input, encoder_self_attention_bias,
     encoder_decoder_attention_bias) = (transformer.transformer_prepare_encoder(
         inputs, target_space, hparams))
    (decoder_input,
     decoder_self_attention_bias) = transformer.transformer_prepare_decoder(
         targets, hparams)

    encoder_input = tf.nn.dropout(encoder_input,
                                  1.0 - hparams.layer_prepostprocess_dropout)
    decoder_input = tf.nn.dropout(decoder_input,
                                  1.0 - hparams.layer_prepostprocess_dropout)
    encoder_output = transformer_revnet_encoder(
        encoder_input, encoder_self_attention_bias, hparams)

    decoder_output = transformer_revnet_decoder(
        decoder_input, encoder_output, decoder_self_attention_bias,
        encoder_decoder_attention_bias, hparams)
    decoder_output = tf.expand_dims(decoder_output, 2)

    return decoder_output

def lstm_seq2seq_internal(inputs, targets, hparams, train):
  """The basic LSTM seq2seq model, main step used for training."""
  with tf.variable_scope("lstm_seq2seq"):
    if inputs is not None:
      inputs_length = common_layers.length_from_embedding(inputs)
      # Flatten inputs.
      inputs = common_layers.flatten4d3d(inputs)

      # LSTM encoder.
      inputs = tf.reverse_sequence(inputs, inputs_length, seq_axis=1)
      _, final_encoder_state = lstm(inputs, inputs_length, hparams, train,
                                    "encoder")
    else:
      final_encoder_state = None

    # LSTM decoder.
    shifted_targets = common_layers.shift_right(targets)
    # Add 1 to account for the padding added to the left from shift_right
    targets_length = common_layers.length_from_embedding(shifted_targets) + 1
    decoder_outputs, _ = lstm(
        common_layers.flatten4d3d(shifted_targets),
        targets_length,
        hparams,
        train,
        "decoder",
        initial_state=final_encoder_state)
    return tf.expand_dims(decoder_outputs, axis=2)

  def body(self, features):
    hp = self.hparams
    # pylint: disable=eval-used
    if hp.image_input_type == "image":
      image_feat = vqa_layers.image_embedding(
          features["inputs"],
          model_fn=eval(hp.image_model_fn),
          trainable=hp.train_resnet,
          is_training=hp.mode == tf.estimator.ModeKeys.TRAIN)
    else:
      image_feat = features["inputs"]

    image_feat = common_layers.flatten4d3d(image_feat)
    image_feat = common_layers.dense(image_feat, hp.hidden_size)
    utils.collect_named_outputs("norms", "image_feat_after_proj",
                                tf.norm(image_feat, axis=-1))

    question = common_layers.flatten4d3d(features["question"])
    utils.collect_named_outputs("norms", "question_embedding",
                                tf.norm(question, axis=-1))
    (encoder_input, encoder_self_attention_bias,
     encoder_decoder_attention_bias) = prepare_image_question_encoder(
         image_feat, question, hp)

    encoder_input = tf.nn.dropout(
        encoder_input, keep_prob=1.-hp.layer_prepostprocess_dropout)

    encoder_output, _ = recurrent_transformer_decoder(
        encoder_input, None, encoder_self_attention_bias, None,
        hp, name="encoder")
    utils.collect_named_outputs(
        "norms", "encoder_output", tf.norm(encoder_output, axis=-1))

    # scale query by sqrt(hidden_size)
    query = tf.get_variable("query", [hp.hidden_size]) * hp.hidden_size **0.5
    query = tf.expand_dims(tf.expand_dims(query, axis=0), axis=0)
    batch_size = common_layers.shape_list(encoder_input)[0]
    query = tf.tile(query, [batch_size, 1, 1])
    query = tf.nn.dropout(
        query, keep_prob=1.-hp.layer_prepostprocess_dropout)

    decoder_output, _ = recurrent_transformer_decoder(
        query, encoder_output, None, encoder_decoder_attention_bias,
        hp, name="decoder")
    utils.collect_named_outputs("norms", "decoder_output",
                                tf.norm(decoder_output, axis=-1))

    norm_tensors = utils.convert_collection_to_dict("norms")
    vqa_layers.summarize_tensors(norm_tensors, tag="norms/")

    # Expand dimension 1 and 2
    return tf.expand_dims(decoder_output, axis=1)

def lstm_seq2seq_internal_attention(inputs, targets, hparams, train):
  """LSTM seq2seq model with attention, main step used for training."""
  with tf.variable_scope("lstm_seq2seq_attention"):
    # Flatten inputs.
    inputs = common_layers.flatten4d3d(inputs)
    # LSTM encoder.
    encoder_outputs, final_encoder_state = lstm(
        tf.reverse(inputs, axis=[1]), hparams, train, "encoder")
    # LSTM decoder with attention
    shifted_targets = common_layers.shift_right(targets)
    decoder_outputs, _ = lstm_attention_decoder(
        common_layers.flatten4d3d(shifted_targets), hparams, train, "decoder",
        final_encoder_state, encoder_outputs)
    return tf.expand_dims(decoder_outputs, axis=2)

    def preprocess_targets(targets, i):
      """Performs preprocessing steps on the targets to prepare for the decoder.

      This includes:
        - Embedding the ids.
        - Flattening to 3D tensor.
        - Optionally adding timing signals.

      Args:
        targets: inputs ids to the decoder. [batch_size, 1]
        i: scalar, Step number of the decoding loop.

      Returns:
        Processed targets [batch_size, 1, hidden_dim]
      """
      # _shard_features called to ensure that the variable names match
      targets = self._shard_features({"targets": targets})["targets"]
      with tf.variable_scope(target_modality.name):
        targets = target_modality.targets_bottom_sharded(targets, dp)[0]
      targets = common_layers.flatten4d3d(targets)

      # TODO(llion): Explain! Is this even needed?
      targets = tf.cond(
          tf.equal(i, 0), lambda: tf.zeros_like(targets), lambda: targets)

      if hparams.pos == "timing":
        targets += timing_signal[:, i:i + 1]
      return targets

def slicenet_internal(inputs, targets, target_space, hparams, run_decoder=True):
  """The slicenet model, main step used for training."""
  with tf.variable_scope("slicenet"):
    # Project to hidden size if necessary
    if inputs.get_shape().as_list()[-1] != hparams.hidden_size:
      inputs = common_layers.conv_block(
          inputs,
          hparams.hidden_size, [((1, 1), (3, 3))],
          first_relu=False,
          padding="SAME",
          force2d=True)

    # Flatten inputs and encode.
    inputs = tf.expand_dims(common_layers.flatten4d3d(inputs), axis=2)
    inputs_mask = 1.0 - embedding_to_padding(inputs)
    inputs = common_layers.add_timing_signal(inputs)  # Add position info.
    target_space_emb = embed_target_space(target_space, hparams.hidden_size)
    extra_layers = int(hparams.num_hidden_layers * 1.5)
    inputs_encoded = multi_conv_res(
        inputs, "SAME", "encoder", extra_layers, hparams, mask=inputs_mask)
    if not run_decoder:
      return inputs_encoded
    # Do the middle part.
    decoder_start, similarity_loss = slicenet_middle(
        inputs_encoded, targets, target_space_emb, inputs_mask, hparams)
    # Decode.
    decoder_final = multi_conv_res(
        decoder_start,
        "LEFT",
        "decoder",
        hparams.num_hidden_layers,
        hparams,
        mask=inputs_mask,
        source=inputs_encoded)
    return decoder_final, tf.reduce_mean(similarity_loss)

def transformer_text_encoder(inputs,
                             target_space,
                             hparams,
                             name=None):
  """Transformer text encoder over inputs with unmasked full attention.

  Args:
    inputs: Tensor of shape [batch, length, 1, hparams.hidden_size].
    target_space: int. Used for encoding inputs under a target space id.
    hparams: tf.contrib.training.HParams.
    name: string, variable scope.

  Returns:
    encoder_output: Tensor of shape [batch, length, hparams.hidden_size].
    ed: Tensor of shape [batch, 1, 1, length]. Encoder-decoder attention bias
      for any padded tokens.
  """
  with tf.variable_scope(name, default_name="transformer_text_encoder"):
    inputs = common_layers.flatten4d3d(inputs)
    [
        encoder_input,
        encoder_self_attention_bias,
        ed,
    ] = transformer_layers.transformer_prepare_encoder(
        inputs, target_space=target_space, hparams=hparams)
    encoder_input = tf.nn.dropout(encoder_input, 1.0 - hparams.dropout)
    encoder_output = transformer_layers.transformer_encoder(
        encoder_input, encoder_self_attention_bias, hparams)
    return encoder_output, ed

  def model_fn_body(self, features):
    """Transformer main model_fn.

    Args:
      features: Map of features to the model. Should contain the following:
          "inputs": Transformer inputs [batch_size, input_length, hidden_dim]
          "tragets": Target decoder outputs.
              [batch_size, decoder_length, hidden_dim]
          "target_space_id"

    Returns:
      Final decoder representation. [batch_size, decoder_length, hidden_dim]
    """
    hparams = self._hparams

    inputs = features.get("inputs")
    encoder_output, encoder_decoder_attention_bias = (None, None)
    if inputs is not None:
      target_space = features["target_space_id"]
      encoder_output, encoder_decoder_attention_bias = self.encode(
          inputs, target_space, hparams, features=features)

    targets = features["targets"]
    targets = common_layers.flatten4d3d(targets)

    decoder_input, decoder_self_attention_bias = transformer_prepare_decoder(
        targets, hparams, features=features)

    return self.decode(decoder_input, encoder_output,
                       encoder_decoder_attention_bias,
                       decoder_self_attention_bias, hparams,
                       nonpadding=_features_to_nonpadding(features, "targets"))

def transformer_text_encoder(x,
                             space_id,
                             hparams,
                             name="transformer_text_encoder"):
  """Transformer text encoder over inputs with unmasked full attention.

  Args:
    x: Tensor of shape [batch, length, 1, hparams.hidden_size].
    space_id: int, id.
    hparams: tf.contrib.training.HParams.
    name: string, variable scope.

  Returns:
    encoder_output: Tensor of shape [batch, length, hparams.hidden_size].
    ed: Tensor of shape [batch, 1, 1, length]. Encoder-decoder attention bias
      for any padded tokens.
  """
  with tf.variable_scope(name):
    x = common_layers.flatten4d3d(x)
    (encoder_input, encoder_self_attention_bias,
     ed) = transformer.transformer_prepare_encoder(x, space_id, hparams)
    encoder_input = tf.nn.dropout(encoder_input, 1.0 - hparams.dropout)
    encoder_output = transformer.transformer_encoder(
        encoder_input, encoder_self_attention_bias, hparams)
    return encoder_output, ed

def bytenet_internal(inputs, targets, hparams):
  """ByteNet, main step used for training."""
  with tf.variable_scope("bytenet"):
    # Flatten inputs and extend length by 50%.
    inputs = tf.expand_dims(common_layers.flatten4d3d(inputs), axis=2)
    extend_length = tf.to_int32(0.5 * tf.to_float(tf.shape(inputs)[1]))
    inputs_shape = inputs.shape.as_list()
    inputs = tf.pad(inputs, [[0, 0], [0, extend_length], [0, 0], [0, 0]])
    inputs_shape[1] = None
    inputs.set_shape(inputs_shape)  # Don't lose the other shapes when padding.
    # Pad inputs and targets to be the same length, divisible by 50.
    inputs, targets = common_layers.pad_to_same_length(
        inputs, targets, final_length_divisible_by=50)
    final_encoder = residual_dilated_conv(inputs, hparams.num_block_repeat,
                                          "SAME", "encoder", hparams)

    shifted_targets = common_layers.shift_right(targets)
    kernel = (hparams.kernel_height, hparams.kernel_width)
    decoder_start = common_layers.conv_block(
        tf.concat([final_encoder, shifted_targets], axis=3),
        hparams.hidden_size, [((1, 1), kernel)],
        padding="LEFT")

    return residual_dilated_conv(decoder_start, hparams.num_block_repeat,
                                 "LEFT", "decoder", hparams)

  def encode(self, inputs, target_space, hparams, features=None):
    """Encode transformer inputs.

    Args:
      inputs: Transformer inputs [batch_size, input_length, input_height,
        hidden_dim] which will be flattened along the two spatial dimensions.
      target_space: scalar, target space ID.
      hparams: hyperparmeters for model.
      features: optionally pass the entire features dictionary as well.
        This is needed now for "packed" datasets.

    Returns:
      Tuple of:
          encoder_output: Encoder representation.
              [batch_size, input_length, hidden_dim]
          encoder_decoder_attention_bias: Bias and mask weights for
              encodre-decoder attention. [batch_size, input_length]
    """
    inputs = common_layers.flatten4d3d(inputs)

    encoder_input, self_attention_bias, encoder_decoder_attention_bias = (
        transformer_prepare_encoder(
            inputs, target_space, hparams, features=features))

    encoder_input = tf.nn.dropout(encoder_input,
                                  1.0 - hparams.layer_prepostprocess_dropout)

    encoder_output = transformer_encoder(
        encoder_input, self_attention_bias,
        hparams, nonpadding=features_to_nonpadding(features, "inputs"),
        save_weights_to=self.attention_weights)

    return encoder_output, encoder_decoder_attention_bias

def decode_transformer(encoder_output,
                       encoder_decoder_attention_bias,
                       targets,
                       hparams,
                       name,
                       task=None):
  """Original Transformer decoder."""
  with tf.variable_scope(name):
    if task is None:
      task = hparams.task
    if task == "translate":
      targets = common_layers.flatten4d3d(targets)

      decoder_input, decoder_self_bias = (
          transformer.transformer_prepare_decoder(targets, hparams))

      decoder_input = tf.nn.dropout(decoder_input,
                                    1.0 - hparams.layer_prepostprocess_dropout)

      decoder_output = transformer.transformer_decoder(
          decoder_input,
          encoder_output,
          decoder_self_bias,
          encoder_decoder_attention_bias,
          hparams)
      decoder_output = tf.expand_dims(decoder_output, axis=2)
    else:
      assert task == "image"
      inputs = None
      # have to reshape targets as b, 32, 32, 3 * hidden size] beacuse otherwise
      # prepare_image will choke
      targets = tf.reshape(targets, [tf.shape(targets)[0], hparams.img_len,
                                     hparams.img_len,
                                     hparams.num_channels*hparams.hidden_size])

      # Prepare decoder inputs and bias.
      decoder_input, _, _, bias = cia.prepare_decoder(targets, hparams)
      # Add class label to decoder input.
      if not hparams.drop_inputs:
        decoder_input += tf.reshape(
            inputs,
            [common_layers.shape_list(targets)[0], 1, 1, hparams.hidden_size])
      decoder_output = cia.transformer_decoder_layers(
          decoder_input,
          None,
          bias,
          hparams.num_decoder_layers or hparams.num_hidden_layers,
          hparams,
          attention_type=hparams.dec_attention_type,
          name="decoder")
    decoder_output_shape = common_layers.shape_list(decoder_output)
    decoder_output = tf.reshape(decoder_output, [decoder_output_shape[0], -1, 1,
                                                 hparams.hidden_size])
    # Expand since t2t expects 4d tensors.
    return decoder_output

  def body(self, features):
    hp = self.hparams
    # pylint: disable=eval-used
    if hp.image_input_type == "image":
      image_feat = vqa_layers.image_embedding(
          features["inputs"],
          model_fn=eval(hp.image_model_fn),
          trainable=hp.train_resnet,
          is_training=hp.mode == tf.estimator.ModeKeys.TRAIN)
    else:
      image_feat = features["inputs"]

    image_feat = common_layers.flatten4d3d(image_feat)
    # image feature self attention
    # image_feat = tf.nn.dropout(
    #     image_feat, keep_prob=1.-hp.layer_prepostprocess_dropout)

    # image_feat = image_feat - tf.reduce_mean(
    #     image_feat, axis=-1, keepdims=True)
    # image_feat = tf.nn.l2_normalize(image_feat, -1)
    # utils.collect_named_outputs("norms", "image_feat_after_l2",
    #                             tf.norm(image_feat, axis=-1))

    image_feat = tf.nn.dropout(image_feat, keep_prob=1.-hp.dropout)

    image_feat = image_encoder(image_feat, hp)
    utils.collect_named_outputs("norms", "image_feat_encoded",
                                tf.norm(image_feat, axis=-1))
    image_feat = common_layers.l2_norm(image_feat)
    utils.collect_named_outputs("norms", "image_feat_encoded_l2",
                                tf.norm(image_feat, axis=-1))

    query = question_encoder(features["question"], hp)
    utils.collect_named_outputs("norms", "query",
                                tf.norm(query, axis=-1))

    image_ave = attn(image_feat, query, hp)
    utils.collect_named_outputs("norms", "image_ave",
                                tf.norm(image_ave, axis=-1))

    image_question = tf.concat([image_ave, query], axis=1)
    utils.collect_named_outputs("norms", "image_question",
                                tf.norm(image_question, axis=-1))

    image_question = tf.nn.dropout(image_question, 1. - hp.dropout)

    output = mlp(image_question, hp)
    utils.collect_named_outputs("norms", "output",
                                tf.norm(output, axis=-1))

    norm_tensors = utils.convert_collection_to_dict("norms")
    vqa_layers.summarize_tensors(norm_tensors, tag="norms/")

    # Expand dimension 1 and 2
    return tf.expand_dims(tf.expand_dims(output, axis=1), axis=2)

def lstm_seq2seq_internal(inputs, targets, hparams, train):
  """The basic LSTM seq2seq model, main step used for training."""
  with tf.variable_scope("lstm_seq2seq"):
    if inputs is not None:
      # Flatten inputs.
      inputs = common_layers.flatten4d3d(inputs)
      # LSTM encoder.
      _, final_encoder_state = lstm(
          tf.reverse(inputs, axis=[1]), hparams, train, "encoder")
    else:
      final_encoder_state = None
    # LSTM decoder.
    shifted_targets = common_layers.shift_right(targets)
    decoder_outputs, _ = lstm(
        common_layers.flatten4d3d(shifted_targets),
        hparams,
        train,
        "decoder",
        initial_state=final_encoder_state)
    return tf.expand_dims(decoder_outputs, axis=2)