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Consideration-based Picture Captioning with Keras

By Jules Jackson
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Consideration-based Picture Captioning with Keras


In picture captioning, an algorithm is given a picture and tasked with producing a wise caption. It’s a difficult job for a number of causes, not the least being that it entails a notion of saliency or relevance. For this reason current deep studying approaches largely embody some “consideration” mechanism (generally even multiple) to assist specializing in related picture options.

On this put up, we reveal a formulation of picture captioning as an encoder-decoder downside, enhanced by spatial consideration over picture grid cells. The concept comes from a current paper on Neural Picture Caption Era with Visible Consideration (Xu et al. 2015), and employs the identical sort of consideration algorithm as detailed in our put up on machine translation.

We’re porting Python code from a current Google Colaboratory pocket book, utilizing Keras with TensorFlow keen execution to simplify our lives.

Stipulations

The code proven right here will work with the present CRAN variations of tensorflow, keras, and tfdatasets.
Examine that you just’re utilizing not less than model 1.9 of TensorFlow. If that isn’t the case, as of this writing, this

will get you model 1.10.

When loading libraries, please ensure you’re executing the primary 4 strains on this precise order.
We’d like to ensure we’re utilizing the TensorFlow implementation of Keras (tf.keras in Python land), and we’ve got to allow keen execution earlier than utilizing TensorFlow in any manner.

No must copy-paste any code snippets – you’ll discover the whole code (so as essential for execution) right here: eager-image-captioning.R.

The dataset

MS-COCO (“Frequent Objects in Context”) is certainly one of, maybe the, reference dataset in picture captioning (object detection and segmentation, too).
We’ll be utilizing the coaching photographs and annotations from 2014 – be warned, relying in your location, the obtain can take a lengthy time.

After unpacking, let’s outline the place the photographs and captions are.

annotation_file  "train2014/annotations/captions_train2014.json"
image_path  "train2014/train2014"

The annotations are in JSON format, and there are 414113 of them! Fortunately for us we didn’t must obtain that many photographs – each picture comes with 5 completely different captions, for higher generalizability.

annotations  fromJSON(file = annotation_file)
annot_captions  annotations[[4]]

num_captions  size(annot_captions)

We retailer each annotations and picture paths in lists, for later loading.

all_captions  vector(mode = "record", size = num_captions)
all_img_names  vector(mode = "record", size = num_captions)

for (i in seq_len(num_captions)) {
  caption  paste0(" ",
                    annot_captions[[i]][["caption"]],
                    " "
                    )
  image_id  annot_captions[[i]][["image_id"]]
  full_coco_image_path  sprintf(
    "%s/COCO_train2014_percent012d.jpg",
    image_path,
    image_id
  )
  all_img_names[[i]]  full_coco_image_path
  all_captions[[i]]  caption
}

Relying in your computing atmosphere, you’ll for positive wish to prohibit the variety of examples used.
This put up will use 30000 captioned photographs, chosen randomly, and put aside 20% for validation.

Under, we take random samples, cut up into coaching and validation components. The companion code will even retailer the indices on disk, so you possibly can choose up on verification and evaluation later.

num_examples  30000

random_sample  pattern(1:num_captions, measurement = num_examples)
train_indices  pattern(random_sample, measurement = size(random_sample) * 0.8)
validation_indices  setdiff(random_sample, train_indices)

sample_captions  all_captions[random_sample]
sample_images  all_img_names[random_sample]
train_captions  all_captions[train_indices]
train_images  all_img_names[train_indices]
validation_captions  all_captions[validation_indices]
validation_images  all_img_names[validation_indices]

Interlude

Earlier than actually diving into the technical stuff, let’s take a second to mirror on this job.
In typical image-related deep studying walk-throughs, we’re used to seeing well-defined issues – even when in some instances, the answer could also be exhausting. Take, for instance, the stereotypical canine vs. cat downside. Some canine might appear to be cats and a few cats might appear to be canine, however that’s about it: All in all, within the standard world we dwell in, it ought to be a kind of binary query.

If, alternatively, we ask folks to explain what they see in a scene, it’s to be anticipated from the outset that we’ll get completely different solutions. Nonetheless, how a lot consensus there’s will very a lot rely upon the concrete dataset we’re utilizing.

Let’s check out some picks from the very first 20 coaching objects sampled randomly above.

Figure from MS-COCO 2014

Now this picture doesn’t go away a lot room for resolution what to give attention to, and obtained a really factual caption certainly: “There’s a plate with one slice of bacon a half of orange and bread.” If the dataset had been all like this, we’d suppose a machine studying algorithm ought to do fairly effectively right here.

Choosing one other one from the primary 20:

Figure from MS-COCO 2014

What could be salient info to you right here? The caption supplied goes “A smiling little boy has a checkered shirt.”
Is the look of the shirt as vital as that? You may as effectively give attention to the surroundings, – and even one thing on a very completely different stage: The age of the photograph, or it being an analog one.

Let’s take a ultimate instance.

From MS-COCO 2014

What would you say about this scene? The official label we sampled right here is “A bunch of individuals posing in a humorous manner for the digital camera.” Effectively …

Please don’t neglect that for every picture, the dataset consists of 5 completely different captions (though our n = 30000 samples most likely gained’t).
So this isn’t saying the dataset is biased – in no way. As a substitute, we wish to level out the ambiguities and difficulties inherent within the job. Really, given these difficulties, it’s all of the extra wonderful that the duty we’re tackling right here – having a community robotically generate picture captions – ought to be attainable in any respect!

Now let’s see how we are able to do that.

For the encoding a part of our encoder-decoder community, we are going to make use of InceptionV3 to extract picture options. In precept, which options to extract is as much as experimentation, – right here we simply use the final layer earlier than the totally linked high:

image_model  application_inception_v3(
  include_top = FALSE,
  weights = "imagenet"
)

For a picture measurement of 299×299, the output can be of measurement (batch_size, 8, 8, 2048), that’s, we’re making use of 2048 characteristic maps.

InceptionV3 being a “large mannequin,” the place each cross via the mannequin takes time, we wish to precompute options prematurely and retailer them on disk.
We’ll use tfdatasets to stream photographs to the mannequin. This implies all our preprocessing has to make use of tensorflow features: That’s why we’re not utilizing the extra acquainted image_load from keras under.

Our customized load_image will learn in, resize and preprocess the photographs as required to be used with InceptionV3:

load_image  perform(image_path) {
  img 
    tf$read_file(image_path) %>%
    tf$picture$decode_jpeg(channels = 3) %>%
    tf$picture$resize_images(c(299L, 299L)) %>%
    tf$keras$purposes$inception_v3$preprocess_input()
  record(img, image_path)
}

Now we’re prepared to avoid wasting the extracted options to disk. The (batch_size, 8, 8, 2048)-sized options can be flattened to (batch_size, 64, 2048). The latter form is what our encoder, quickly to be mentioned, will obtain as enter.

preencode  distinctive(sample_images) %>% unlist() %>% type()
num_unique  size(preencode)

# adapt this based on your system's capacities  
batch_size_4save  1
image_dataset 
  tensor_slices_dataset(preencode) %>%
  dataset_map(load_image) %>%
  dataset_batch(batch_size_4save)
  
save_iter  make_iterator_one_shot(image_dataset)
  
until_out_of_range({
  
  save_count  save_count + batch_size_4save
  batch_4save  save_iter$get_next()
  img  batch_4save[[1]]
  path  batch_4save[[2]]
  batch_features  image_model(img)
  batch_features  tf$reshape(
    batch_features,
    record(dim(batch_features)[1], -1L, dim(batch_features)[4]
  )
                               )
  for (i in 1:dim(batch_features)[1]) {
    np$save(path[i]$numpy()$decode("utf-8"),
            batch_features[i, , ]$numpy())
  }
    
})

Earlier than we get to the encoder and decoder fashions although, we have to handle the captions.

Processing the captions

We’re utilizing keras text_tokenizer and the textual content processing features texts_to_sequences and pad_sequences to rework ascii textual content right into a matrix.

# we are going to use the 5000 most frequent phrases solely
top_k  5000
tokenizer  text_tokenizer(
  num_words = top_k,
  oov_token = "",
  filters = '!"#$%&()*+.,-/:;=?@[]^_`~ ')
tokenizer$fit_on_texts(sample_captions)

train_captions_tokenized 
  tokenizer %>% texts_to_sequences(train_captions)
validation_captions_tokenized 
  tokenizer %>% texts_to_sequences(validation_captions)

# pad_sequences will use 0 to pad all captions to the identical size
tokenizer$word_index[""]  0

# create a lookup dataframe that permits us to go in each instructions
word_index_df  knowledge.body(
  phrase = tokenizer$word_index %>% names(),
  index = tokenizer$word_index %>% unlist(use.names = FALSE),
  stringsAsFactors = FALSE
)
word_index_df  word_index_df %>% organize(index)

decode_caption  perform(textual content) {
  paste(map(textual content, perform(quantity)
    word_index_df %>%
      filter(index == quantity) %>%
      choose(phrase) %>%
      pull()),
    collapse = " ")
}

# pad all sequences to the identical size (the utmost size, in our case)
# might experiment with shorter padding (truncating the very longest captions)
caption_lengths  map(
  all_captions[1:num_examples],
  perform(c) str_split(c," ")[[1]] %>% size()
  ) %>% unlist()
max_length  fivenum(caption_lengths)[5]

train_captions_padded   pad_sequences(
  train_captions_tokenized,
  maxlen = max_length,
  padding = "put up",
  truncating = "put up"
)

validation_captions_padded  pad_sequences(
  validation_captions_tokenized,
  maxlen = max_length,
  padding = "put up",
  truncating = "put up"
)

Loading the information for coaching

Now that we’ve taken care of pre-extracting the options and preprocessing the captions, we’d like a solution to stream them to our captioning mannequin. For that, we’re utilizing tensor_slices_dataset from tfdatasets, passing within the record of paths to the photographs and the preprocessed captions. Loading the photographs is then carried out as a TensorFlow graph operation (utilizing tf$pyfunc).

The unique Colab code additionally shuffles the information on each iteration. Relying in your {hardware}, this may increasingly take a very long time, and given the scale of the dataset it’s not strictly essential to get cheap outcomes. (The outcomes reported under had been obtained with out shuffling.)

batch_size  10
buffer_size  num_examples

map_func  perform(img_name, cap) {
  p  paste0(img_name$decode("utf-8"), ".npy")
  img_tensor  np$load(p)
  img_tensor  tf$forged(img_tensor, tf$float32)
  record(img_tensor, cap)
}

train_dataset 
  tensor_slices_dataset(record(train_images, train_captions_padded)) %>%
  dataset_map(
    perform(item1, item2) tf$py_func(map_func, record(item1, item2), record(tf$float32, tf$int32))
  ) %>%
  # optionally shuffle the dataset
  # dataset_shuffle(buffer_size) %>%
  dataset_batch(batch_size)

Captioning mannequin

The mannequin is principally the identical as that mentioned within the machine translation put up. Please discuss with that article for an evidence of the ideas, in addition to an in depth walk-through of the tensor shapes concerned at each step. Right here, we offer the tensor shapes as feedback within the code snippets, for fast overview/comparability.

Nevertheless, should you develop your individual fashions, with keen execution you possibly can merely insert debugging/logging statements at arbitrary locations within the code – even in mannequin definitions. So you possibly can have a perform

maybecat  perform(context, x) {
  if (debugshapes) {
    identify  enexpr(x)
    dims  paste0(dim(x), collapse = " ")
    cat(context, ": form of ", identify, ": ", dims, "n", sep = "")
  }
}

And should you now set

you possibly can hint – not solely tensor shapes, however precise tensor values via your fashions, as proven under for the encoder. (We don’t show any debugging statements after that, however the pattern code has many extra.)

Encoder

Now it’s time to outline some some sizing-related hyperparameters and housekeeping variables:

# for encoder output
embedding_dim  256
# decoder (LSTM) capability
gru_units  512
# for decoder output
vocab_size  top_k
# variety of characteristic maps gotten from Inception V3
features_shape  2048
# form of consideration options (flattened from 8x8)
attention_features_shape  64

The encoder on this case is only a totally linked layer, taking within the options extracted from Inception V3 (in flattened type, as they had been written to disk), and embedding them in 256-dimensional area.

cnn_encoder  perform(embedding_dim, identify = NULL) {
    
  keras_model_custom(identify = identify, perform(self) {
      
    self$fc  layer_dense(models = embedding_dim, activation = "relu")
      
    perform(x, masks = NULL) {
      # enter form: (batch_size, 64, features_shape)
      maybecat("encoder enter", x)
      # form after fc: (batch_size, 64, embedding_dim)
      x  self$fc(x)
      maybecat("encoder output", x)
      x
    }
  })
}

Consideration module

Not like within the machine translation put up, right here the eye module is separated out into its personal customized mannequin.
The logic is identical although:

attention_module  perform(gru_units, identify = NULL) {
  
  keras_model_custom(identify = identify, perform(self) {
    
    self$W1 = layer_dense(models = gru_units)
    self$W2 = layer_dense(models = gru_units)
    self$V = layer_dense(models = 1)
      
    perform(inputs, masks = NULL) {
      options  inputs[[1]]
      hidden  inputs[[2]]
      # options(CNN_encoder output) form == (batch_size, 64, embedding_dim)
      # hidden form == (batch_size, gru_units)
      # hidden_with_time_axis form == (batch_size, 1, gru_units)
      hidden_with_time_axis  k_expand_dims(hidden, axis = 2)
        
      # rating form == (batch_size, 64, 1)
      rating  self$V(k_tanh(self$W1(options) + self$W2(hidden_with_time_axis)))
      # attention_weights form == (batch_size, 64, 1)
      attention_weights  k_softmax(rating, axis = 2)
      # context_vector form after sum == (batch_size, embedding_dim)
      context_vector  k_sum(attention_weights * options, axis = 2)
        
      record(context_vector, attention_weights)
    }
  })
}

Decoder

The decoder at every time step calls the eye module with the options it bought from the encoder and its final hidden state, and receives again an consideration vector. The eye vector will get concatenated with the present enter and additional processed by a GRU and two totally linked layers, the final of which supplies us the (unnormalized) chances for the subsequent phrase within the caption.

The present enter at every time step right here is the earlier phrase: the proper one throughout coaching (trainer forcing), the final generated one throughout inference.

rnn_decoder  perform(embedding_dim, gru_units, vocab_size, identify = NULL) {
    
  keras_model_custom(identify = identify, perform(self) {
      
    self$gru_units  gru_units
    self$embedding  layer_embedding(input_dim = vocab_size, 
                                      output_dim = embedding_dim)
    self$gru  if (tf$take a look at$is_gpu_available()) {
      layer_cudnn_gru(
        models = gru_units,
        return_sequences = TRUE,
        return_state = TRUE,
        recurrent_initializer = 'glorot_uniform'
      )
    } else {
      layer_gru(
        models = gru_units,
        return_sequences = TRUE,
        return_state = TRUE,
        recurrent_initializer = 'glorot_uniform'
      )
    }
      
    self$fc1  layer_dense(models = self$gru_units)
    self$fc2  layer_dense(models = vocab_size)
      
    self$consideration  attention_module(self$gru_units)
      
    perform(inputs, masks = NULL) {
      x  inputs[[1]]
      options  inputs[[2]]
      hidden  inputs[[3]]
        
      c(context_vector, attention_weights) % 
        self$consideration(record(options, hidden))
        
      # x form after passing via embedding == (batch_size, 1, embedding_dim)
      x  self$embedding(x)
        
      # x form after concatenation == (batch_size, 1, 2 * embedding_dim)
      x  k_concatenate(record(k_expand_dims(context_vector, 2), x))
        
      # passing the concatenated vector to the GRU
      c(output, state) % self$gru(x)
        
      # form == (batch_size, 1, gru_units)
      x  self$fc1(output)
        
      # x form == (batch_size, gru_units)
      x  k_reshape(x, c(-1, dim(x)[[3]]))
        
      # output form == (batch_size, vocab_size)
      x  self$fc2(x)
        
      record(x, state, attention_weights)
        
    }
  })
}

Loss perform, and instantiating all of it

Now that we’ve outlined our mannequin (constructed of three customized fashions), we nonetheless want to really instantiate it (being exact: the 2 courses we are going to entry from exterior, that’s, the encoder and the decoder).

We additionally must instantiate an optimizer (Adam will do), and outline our loss perform (categorical crossentropy).
Be aware that tf$nn$sparse_softmax_cross_entropy_with_logits expects uncooked logits as a substitute of softmax activations, and that we’re utilizing the sparse variant as a result of our labels are usually not one-hot-encoded.

encoder  cnn_encoder(embedding_dim)
decoder  rnn_decoder(embedding_dim, gru_units, vocab_size)

optimizer = tf$prepare$AdamOptimizer()

cx_loss  perform(y_true, y_pred) {
  masks  1 - k_cast(y_true == 0L, dtype = "float32")
  loss  tf$nn$sparse_softmax_cross_entropy_with_logits(
    labels = y_true,
    logits = y_pred
  ) * masks
  tf$reduce_mean(loss)
}

Coaching

Coaching the captioning mannequin is a time-consuming course of, and you’ll for positive wish to save the mannequin’s weights!
How does this work with keen execution?

We create a tf$prepare$Checkpoint object, passing it the objects to be saved: In our case, the encoder, the decoder, and the optimizer. Later, on the finish of every epoch, we are going to ask it to write down the respective weights to disk.

restore_checkpoint  FALSE

checkpoint_dir  "./checkpoints_captions"
checkpoint_prefix  file.path(checkpoint_dir, "ckpt")
checkpoint  tf$prepare$Checkpoint(
  optimizer = optimizer,
  encoder = encoder,
  decoder = decoder
)

As we’re simply beginning to prepare the mannequin, restore_checkpoint is about to false. Later, restoring the weights can be as simple as

if (restore_checkpoint) {
  checkpoint$restore(tf$prepare$latest_checkpoint(checkpoint_dir))
}

The coaching loop is structured identical to within the machine translation case: We loop over epochs, batches, and the coaching targets, feeding within the right earlier phrase at each timestep.
Once more, tf$GradientTape takes care of recording the ahead cross and calculating the gradients, and the optimizer applies the gradients to the mannequin’s weights.
As every epoch ends, we additionally save the weights.

num_epochs  20

if (!restore_checkpoint) {
  for (epoch in seq_len(num_epochs)) {
    
    total_loss  0
    progress  0
    train_iter  make_iterator_one_shot(train_dataset)
    
    until_out_of_range({
      
      batch  iterator_get_next(train_iter)
      loss  0
      img_tensor  batch[[1]]
      target_caption  batch[[2]]
      
      dec_hidden  k_zeros(c(batch_size, gru_units))
      
      dec_input  k_expand_dims(
        rep(record(word_index_df[word_index_df$word == "", "index"]), 
            batch_size)
      )
      
      with(tf$GradientTape() %as% tape, {
        
        options  encoder(img_tensor)
        
        for (t in seq_len(dim(target_caption)[2] - 1)) {
          c(preds, dec_hidden, weights) %
            decoder(record(dec_input, options, dec_hidden))
          loss  loss + cx_loss(target_caption[, t], preds)
          dec_input  k_expand_dims(target_caption[, t])
        }
        
      })
      
      total_loss 
        total_loss + loss / k_cast_to_floatx(dim(target_caption)[2])
      
      variables  c(encoder$variables, decoder$variables)
      gradients  tape$gradient(loss, variables)
      
      optimizer$apply_gradients(purrr::transpose(record(gradients, variables)),
                                global_step = tf$prepare$get_or_create_global_step()
      )
    })
    cat(paste0(
      "nnTotal loss (epoch): ",
      epoch,
      ": ",
      (total_loss / k_cast_to_floatx(buffer_size)) %>% as.double() %>% spherical(4),
      "n"
    ))
    
    checkpoint$save(file_prefix = checkpoint_prefix)
  }
}

Peeking at outcomes

Identical to within the translation case, it’s attention-grabbing to have a look at mannequin efficiency throughout coaching. The companion code has that performance built-in, so you possibly can watch mannequin progress for your self.

The essential perform right here is get_caption: It will get handed the trail to a picture, hundreds it, obtains its options from Inception V3, after which asks the encoder-decoder mannequin to generate a caption. If at any level the mannequin produces the finish image, we cease early. In any other case, we proceed till we hit the predefined most size.

Anderson et al. 2017) use object detection strategies to bottom-up isolate attention-grabbing objects, and an LSTM stack whereby the primary LSTM computes top-down consideration guided by the output phrase generated by the second.

One other attention-grabbing strategy involving consideration is utilizing a multimodal attentive translator (Liu et al. 2017), the place the picture options are encoded and offered in a sequence, such that we find yourself with sequence fashions each on the encoding and the decoding sides.

One other various is so as to add a realized matter to the knowledge enter (Zhu, Xue, and Yuan 2018), which once more is a top-down characteristic present in human cognition.

For those who discover certainly one of these, or yet one more, strategy extra convincing, an keen execution implementation, within the model of the above, will seemingly be a sound manner of implementing it.

Anderson, Peter, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2017. “Backside-up and High-down Consideration for Picture Captioning and VQA.” CoRR abs/1707.07998. http://arxiv.org/abs/1707.07998.
Liu, Chang, Fuchun Solar, Changhu Wang, Feng Wang, and Alan L. Yuille. 2017. “A Multimodal Attentive Translator for Picture Captioning.” CoRR abs/1702.05658. http://arxiv.org/abs/1702.05658.
Xu, Kelvin, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio. 2015. “Present, Attend and Inform: Neural Picture Caption Era with Visible Consideration.” CoRR abs/1502.03044. http://arxiv.org/abs/1502.03044.
Zhu, Zhihao, Zhan Xue, and Zejian Yuan. 2018. “A Subject-Guided Consideration for Picture Captioning.” CoRR abs/1807.03514v1. https://arxiv.org/abs/1807.03514v1.

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