Image captioning with visual attention

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Given an image like the example below, your goal is to generate a caption such as "a surfer riding on a wave".

A man surfing, from wikimedia

The model architecture used here is inspired by Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, but has been updated to use a 2-layer Transformer-decoder. To get the most out of this tutorial you should have some experience with text generation, seq2seq models & attention, or transformers.

The model architecture built in this tutorial is shown below. Features are extracted from the image, and passed to the cross-attention layers of the Transformer-decoder.

The model architecture

The transformer decoder is mainly built from attention layers. It uses self-attention to process the sequence being generated, and it uses cross-attention to attend to the image.

By inspecting the attention weights of the cross attention layers you will see what parts of the image the model is looking at as it generates words.

Prediction

This notebook is an end-to-end example. When you run the notebook, it downloads a dataset, extracts and caches the image features, and trains a decoder model. It then uses the model to generate captions on new images.

Setup

apt install --allow-change-held-packages libcudnn8=8.6.0.163-1+cuda11.8
E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)
E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?
pip uninstall -y tensorflow estimator keras
pip install -U tensorflow_text tensorflow tensorflow_datasets
pip install einops

This tutorial uses lots of imports, mostly for loading the dataset(s).

2024-03-10 11:19:31.771491: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-03-10 11:19:31.771538: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-03-10 11:19:31.773124: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

[Optional] Data handling

This section downloads a captions dataset and prepares it for training. It tokenizes the input text, and caches the results of running all the images through a pretrained feature-extractor model. It's not critical to understand everything in this section.

Data ready for training

After those preprocessing steps, here are the datasets:

train_ds = load_dataset('train_cache')
test_ds = load_dataset('test_cache')
train_ds.element_spec
((TensorSpec(shape=(None, 7, 7, 576), dtype=tf.float32, name=None),
  TensorSpec(shape=(None, None), dtype=tf.int64, name=None)),
 TensorSpec(shape=(None, None), dtype=tf.int64, name=None))

The dataset now returns (input, label) pairs suitable for training with keras. The inputs are (images, input_tokens) pairs. The images have been processed with the feature-extractor model. For each location in the input_tokens the model looks at the text so far and tries to predict the next which is lined up at the same location in the labels.

for (inputs, ex_labels) in train_ds.take(1):
  (ex_img, ex_in_tok) = inputs

print(ex_img.shape)
print(ex_in_tok.shape)
print(ex_labels.shape)
(32, 7, 7, 576)
(32, 20)
(32, 20)

The input tokens and the labels are the same, just shifted by 1 step:

print(ex_in_tok[0].numpy())
print(ex_labels[0].numpy())
[  3   2  27  20 109   5   2 210   8  52   2 277 196 123   0   0   0   0
   0   0]
[  2  27  20 109   5   2 210   8  52   2 277 196 123   4   0   0   0   0
   0   0]

A Transformer decoder model

This model assumes that the pretrained image encoder is sufficient, and just focuses on building the text decoder. This tutorial uses a 2-layer Transformer-decoder.

The implementations are almost identical to those in the Transformers tutorial. Refer back to it for more details.

The Transformer encoder and decoder.

The model will be implemented in three main parts:

  1. Input - The token embedding and positional encoding (SeqEmbedding).
  2. Decoder - A stack of transformer decoder layers (DecoderLayer) where each contains:
    1. A causal self attention later (CausalSelfAttention), where each output location can attend to the output so far.
    2. A cross attention layer (CrossAttention) where each output location can attend to the input image.
    3. A feed forward network (FeedForward) layer which further processes each output location independently.
  3. Output - A multiclass-classification over the output vocabulary.

Input

The input text has already been split up into tokens and converted to sequences of IDs.

Remember that unlike a CNN or RNN the Transformer's attention layers are invariant to the order of the sequence. Without some positional input, it just sees an unordered set not a sequence. So in addition to a simple vector embedding for each token ID, the embedding layer will also include an embedding for each position in the sequence.

The SeqEmbedding layer defined below:

  • It looks up the embedding vector for each token.
  • It looks up an embedding vector for each sequence location.
  • It adds the two together.
  • It uses mask_zero=True to initialize the keras-masks for the model.
class SeqEmbedding(tf.keras.layers.Layer):
  def __init__(self, vocab_size, max_length, depth):
    super().__init__()
    self.pos_embedding = tf.keras.layers.Embedding(input_dim=max_length, output_dim=depth)

    self.token_embedding = tf.keras.layers.Embedding(
        input_dim=vocab_size,
        output_dim=depth,
        mask_zero=True)

    self.add = tf.keras.layers.Add()

  def call(self, seq):
    seq = self.token_embedding(seq) # (batch, seq, depth)

    x = tf.range(tf.shape(seq)[1])  # (seq)
    x = x[tf.newaxis, :]  # (1, seq)
    x = self.pos_embedding(x)  # (1, seq, depth)

    return self.add([seq,x])

Decoder

The decoder is a standard Transformer-decoder, it contains a stack of DecoderLayers where each contains three sublayers: a CausalSelfAttention, a CrossAttention, and aFeedForward. The implementations are almost identical to the Transformer tutorial, refer to it for more details.

The CausalSelfAttention layer is below:

class CausalSelfAttention(tf.keras.layers.Layer):
  def __init__(self, **kwargs):
    super().__init__()
    self.mha = tf.keras.layers.MultiHeadAttention(**kwargs)
    # Use Add instead of + so the keras mask propagates through.
    self.add = tf.keras.layers.Add() 
    self.layernorm = tf.keras.layers.LayerNormalization()

  def call(self, x):
    attn = self.mha(query=x, value=x,
                    use_causal_mask=True)
    x = self.add([x, attn])
    return self.layernorm(x)

The CrossAttention layer is below. Note the use of return_attention_scores.

class CrossAttention(tf.keras.layers.Layer):
  def __init__(self,**kwargs):
    super().__init__()
    self.mha = tf.keras.layers.MultiHeadAttention(**kwargs)
    self.add = tf.keras.layers.Add() 
    self.layernorm = tf.keras.layers.LayerNormalization()

  def call(self, x, y, **kwargs):
    attn, attention_scores = self.mha(
             query=x, value=y,
             return_attention_scores=True)

    self.last_attention_scores = attention_scores

    x = self.add([x, attn])
    return self.layernorm(x)

The FeedForward layer is below. Remember that a layers.Dense layer is applied to the last axis of the input. The input will have a shape of (batch, sequence, channels), so it automatically applies pointwise across the batch and sequence axes.

class FeedForward(tf.keras.layers.Layer):
  def __init__(self, units, dropout_rate=0.1):
    super().__init__()
    self.seq = tf.keras.Sequential([
        tf.keras.layers.Dense(units=2*units, activation='relu'),
        tf.keras.layers.Dense(units=units),
        tf.keras.layers.Dropout(rate=dropout_rate),
    ])

    self.layernorm = tf.keras.layers.LayerNormalization()

  def call(self, x):
    x = x + self.seq(x)
    return self.layernorm(x)

Next arrange these three layers into a larger DecoderLayer. Each decoder layer applies the three smaller layers in sequence. After each sublayer the shape of out_seq is (batch, sequence, channels). The decoder layer also returns the attention_scores for later visualizations.

class DecoderLayer(tf.keras.layers.Layer):
  def __init__(self, units, num_heads=1, dropout_rate=0.1):
    super().__init__()

    self.self_attention = CausalSelfAttention(num_heads=num_heads,
                                              key_dim=units,
                                              dropout=dropout_rate)
    self.cross_attention = CrossAttention(num_heads=num_heads,
                                          key_dim=units,
                                          dropout=dropout_rate)
    self.ff = FeedForward(units=units, dropout_rate=dropout_rate)


  def call(self, inputs, training=False):
    in_seq, out_seq = inputs

    # Text input
    out_seq = self.self_attention(out_seq)

    out_seq = self.cross_attention(out_seq, in_seq)

    self.last_attention_scores = self.cross_attention.last_attention_scores

    out_seq = self.ff(out_seq)

    return out_seq

Output

At minimum the output layer needs a layers.Dense layer to generate logit-predictions for each token at each location.

But there are a few other features you can add to make this work a little better:

  1. Handle bad tokens: The model will be generating text. It should never generate a pad, unknown, or start token ('', '[UNK]', '[START]'). So set the bias for these to a large negative value.

  2. Smart initialization: The default initialization of a dense layer will give a model that initially predicts each token with almost uniform likelihood. The actual token distribution is far from uniform. The optimal value for the initial bias of the output layer is the log of the probability of each token. So include an adapt method to count the tokens and set the optimal initial bias. This reduces the initial loss from the entropy of the uniform distribution (log(vocabulary_size)) to the marginal entropy of the distribution (-p*log(p)).

The smart initialization will significantly reduce the initial loss:

output_layer = TokenOutput(tokenizer, banned_tokens=('', '[UNK]', '[START]'))
# This might run a little faster if the dataset didn't also have to load the image data.
output_layer.adapt(train_ds.map(lambda inputs, labels: labels))
100%|██████████| 938/938 [00:02<00:00, 344.92it/s]
Uniform entropy: 8.52
Marginal entropy: 5.29

Build the model

To build the model, you need to combine several parts:

  1. The image feature_extractor and the text tokenizer and.
  2. The seq_embedding layer, to convert batches of token-IDs to vectors (batch, sequence, channels).
  3. The stack of DecoderLayers layers that will process the text and image data.
  4. The output_layer which returns a pointwise prediction of what the next word should be.
class Captioner(tf.keras.Model):
  @classmethod
  def add_method(cls, fun):
    setattr(cls, fun.__name__, fun)
    return fun

  def __init__(self, tokenizer, feature_extractor, output_layer, num_layers=1,
               units=256, max_length=50, num_heads=1, dropout_rate=0.1):
    super().__init__()
    self.feature_extractor = feature_extractor
    self.tokenizer = tokenizer
    self.word_to_index = tf.keras.layers.StringLookup(
        mask_token="",
        vocabulary=tokenizer.get_vocabulary())
    self.index_to_word = tf.keras.layers.StringLookup(
        mask_token="",
        vocabulary=tokenizer.get_vocabulary(),
        invert=True) 

    self.seq_embedding = SeqEmbedding(
        vocab_size=tokenizer.vocabulary_size(),
        depth=units,
        max_length=max_length)

    self.decoder_layers = [
        DecoderLayer(units, num_heads=num_heads, dropout_rate=dropout_rate)
        for n in range(num_layers)]

    self.output_layer = output_layer

When you call the model, for training, it receives an image, txt pair. To make this function more usable, be flexible about the input:

  • If the image has 3 channels run it through the feature_extractor. Otherwise assume that it has been already. Similarly
  • If the text has dtype tf.string run it through the tokenizer.

After that running the model is only a few steps:

  1. Flatten the extracted image features, so they can be input to the decoder layers.
  2. Look up the token embeddings.
  3. Run the stack of DecoderLayers, on the image features and text embeddings.
  4. Run the output layer to predict the next token at each position.
@Captioner.add_method
  def call(self, inputs):
    image, txt = inputs

    if image.shape[-1] == 3:
      # Apply the feature-extractor, if you get an RGB image.
      image = self.feature_extractor(image)

    # Flatten the feature map
    image = einops.rearrange(image, 'b h w c -> b (h w) c')


    if txt.dtype == tf.string:
      # Apply the tokenizer if you get string inputs.
      txt = tokenizer(txt)

    txt = self.seq_embedding(txt)

    # Look at the image
    for dec_layer in self.decoder_layers:
      txt = dec_layer(inputs=(image, txt))

    txt = self.output_layer(txt)

    return txt
model = Captioner(tokenizer, feature_extractor=mobilenet, output_layer=output_layer,
                  units=256, dropout_rate=0.5, num_layers=2, num_heads=2)

Generate captions

Before getting into training, write a bit of code to generate captions. You'll use this to see how training is progressing.

Start by downloading a test image:

image_url = 'https://tensorflow.org/images/surf.jpg'
image_path = tf.keras.utils.get_file('surf.jpg', origin=image_url)
image = load_image(image_path)
Downloading data from https://tensorflow.org/images/surf.jpg
64400/64400 [==============================] - 0s 1us/step

To caption an image with this model:

  • Extract the img_features
  • Initialize the list of output tokens with a [START] token.
  • Pass img_features and tokens into the model.
    • It returns a list of logits.
    • Choose the next token based on those logits.
    • Add it to the list of tokens, and continue the loop.
    • If it generates an '[END]' token, break out of the loop.

So add a "simple" method to do just that:

@Captioner.add_method
def simple_gen(self, image, temperature=1):
  initial = self.word_to_index([['[START]']]) # (batch, sequence)
  img_features = self.feature_extractor(image[tf.newaxis, ...])

  tokens = initial # (batch, sequence)
  for n in range(50):
    preds = self((img_features, tokens)).numpy()  # (batch, sequence, vocab)
    preds = preds[:,-1, :]  #(batch, vocab)
    if temperature==0:
        next = tf.argmax(preds, axis=-1)[:, tf.newaxis]  # (batch, 1)
    else:
        next = tf.random.categorical(preds/temperature, num_samples=1)  # (batch, 1)
    tokens = tf.concat([tokens, next], axis=1) # (batch, sequence) 

    if next[0] == self.word_to_index('[END]'):
      break
  words = index_to_word(tokens[0, 1:-1])
  result = tf.strings.reduce_join(words, axis=-1, separator=' ')
  return result.numpy().decode()

Here are some generated captions for that image, the model's untrained, so they don't make much sense yet:

for t in (0.0, 0.5, 1.0):
  result = model.simple_gen(image, temperature=t)
  print(result)
in a
a in
flowers child a snow

The temperature parameter allows you to interpolate between 3 modes:

  1. Greedy decoding (temperature=0.0) - Chooses the most likely next token at each step.
  2. Random sampling according to the logits (temperature=1.0).
  3. Uniform random sampling (temperature >> 1.0).

Since the model is untrained, and it used the frequency-based initialization, the "greedy" output (first) usually only contains the most common tokens: ['a', '.', '[END]'].

Train

To train the model you'll need several additional components:

  • The Loss and metrics
  • The Optimizer
  • Optional Callbacks

Losses and metrics

Here's an implementation of a masked loss and accuracy:

When calculating the mask for the loss, note the loss < 1e8. This term discards the artificial, impossibly high losses for the banned_tokens.

def masked_loss(labels, preds):  
  loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels, preds)

  mask = (labels != 0) & (loss < 1e8) 
  mask = tf.cast(mask, loss.dtype)

  loss = loss*mask
  loss = tf.reduce_sum(loss)/tf.reduce_sum(mask)
  return loss

def masked_acc(labels, preds):
  mask = tf.cast(labels!=0, tf.float32)
  preds = tf.argmax(preds, axis=-1)
  labels = tf.cast(labels, tf.int64)
  match = tf.cast(preds == labels, mask.dtype)
  acc = tf.reduce_sum(match*mask)/tf.reduce_sum(mask)
  return acc

Callbacks

For feedback during training setup a keras.callbacks.Callback to generate some captions for the surfer image at the end of each epoch.

class GenerateText(tf.keras.callbacks.Callback):
  def __init__(self):
    image_url = 'https://tensorflow.org/images/surf.jpg'
    image_path = tf.keras.utils.get_file('surf.jpg', origin=image_url)
    self.image = load_image(image_path)

  def on_epoch_end(self, epochs=None, logs=None):
    print()
    print()
    for t in (0.0, 0.5, 1.0):
      result = self.model.simple_gen(self.image, temperature=t)
      print(result)
    print()

It generates three output strings, like the earlier example, like before the first is "greedy", choosing the argmax of the logits at each step.

g = GenerateText()
g.model = model
g.on_epoch_end(0)
in a
the
into person his holding dog greyhound and red toddler near on girls water a swampy mouth

Also use callbacks.EarlyStopping to terminate training when the model starts to overfit.

callbacks = [
    GenerateText(),
    tf.keras.callbacks.EarlyStopping(
        patience=5, restore_best_weights=True)]

Train

Configure and execute the training.

model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
           loss=masked_loss,
           metrics=[masked_acc])

For more frequent reporting, use the Dataset.repeat() method, and set the steps_per_epoch and validation_steps arguments to Model.fit.

With this setup on Flickr8k a full pass over the dataset is 900+ batches, but below the reporting-epochs are 100 steps.

history = model.fit(
    train_ds.repeat(),
    steps_per_epoch=100,
    validation_data=test_ds.repeat(),
    validation_steps=20,
    epochs=100,
    callbacks=callbacks)
Epoch 1/100
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1710069637.985768   18357 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
100/100 [==============================] - ETA: 0s - loss: 5.0287 - masked_acc: 0.1999

a man in a man in a
a man in a young
a police dog walking

100/100 [==============================] - 21s 107ms/step - loss: 5.0287 - masked_acc: 0.1999 - val_loss: 4.6129 - val_masked_acc: 0.2416
Epoch 2/100
100/100 [==============================] - ETA: 0s - loss: 4.5972 - masked_acc: 0.2573

a man in a man in a man in a
a man in a little the water
a rock lighting sitting with men in the suit

100/100 [==============================] - 6s 61ms/step - loss: 4.5972 - masked_acc: 0.2573 - val_loss: 4.3570 - val_masked_acc: 0.2722
Epoch 3/100
 99/100 [============================>.] - ETA: 0s - loss: 4.3894 - masked_acc: 0.2740

a man in a red and a red and a man in a red
a woman wearing a blue and water
hiker in the black girls wearing the air

100/100 [==============================] - 6s 59ms/step - loss: 4.3870 - masked_acc: 0.2741 - val_loss: 4.1513 - val_masked_acc: 0.2865
Epoch 4/100
 99/100 [============================>.] - ETA: 0s - loss: 4.2555 - masked_acc: 0.2926

a man in a red and a red and white dog is in the water
a young girl in a blue and a pool with a water
mountains over front of a small path on his of man in the snow

100/100 [==============================] - 6s 63ms/step - loss: 4.2563 - masked_acc: 0.2926 - val_loss: 3.9735 - val_masked_acc: 0.3150
Epoch 5/100
100/100 [==============================] - ETA: 0s - loss: 4.1600 - masked_acc: 0.3023

a man in a red shirt is in the water
a man in a small girl in the snow
a room at a background

100/100 [==============================] - 5s 52ms/step - loss: 4.1600 - masked_acc: 0.3023 - val_loss: 3.9400 - val_masked_acc: 0.3185
Epoch 6/100
100/100 [==============================] - ETA: 0s - loss: 4.0014 - masked_acc: 0.3192

a man in a red shirt is running in the water
a man in a red water
a black shaved boy wearing the young girl in the beach

100/100 [==============================] - 5s 54ms/step - loss: 4.0014 - masked_acc: 0.3192 - val_loss: 3.8855 - val_masked_acc: 0.3175
Epoch 7/100
100/100 [==============================] - ETA: 0s - loss: 3.9390 - masked_acc: 0.3242

a man in a red shirt is jumping in a pool
a person in a pool
three little boy swimming down the air in his front of front of a white raft

100/100 [==============================] - 6s 57ms/step - loss: 3.9390 - masked_acc: 0.3242 - val_loss: 3.7337 - val_masked_acc: 0.3342
Epoch 8/100
100/100 [==============================] - ETA: 0s - loss: 3.8437 - masked_acc: 0.3353

a man in a red shirt is jumping in the water
a man wearing a blue jacket with a pool
a hat stands in a posing as water on near the frisbee

100/100 [==============================] - 6s 56ms/step - loss: 3.8437 - masked_acc: 0.3353 - val_loss: 3.7328 - val_masked_acc: 0.3334
Epoch 9/100
 99/100 [============================>.] - ETA: 0s - loss: 3.7787 - masked_acc: 0.3391

a man in a blue shirt is jumping in the water
a man in a white dog is jumping in the water
a child is jumping arm in the skirt of water

100/100 [==============================] - 6s 56ms/step - loss: 3.7793 - masked_acc: 0.3390 - val_loss: 3.6243 - val_masked_acc: 0.3424
Epoch 10/100
100/100 [==============================] - ETA: 0s - loss: 3.7351 - masked_acc: 0.3356

a man in a blue shirt is jumping over a pool
a man in a blue jacket and white dog is running in the water
a woman on a swimming leaning along a rock wearing a pool

100/100 [==============================] - 6s 61ms/step - loss: 3.7351 - masked_acc: 0.3356 - val_loss: 3.6711 - val_masked_acc: 0.3390
Epoch 11/100
 99/100 [============================>.] - ETA: 0s - loss: 3.6272 - masked_acc: 0.3495

a man in a blue shirt is jumping over a pool
a man in a swimming pool
a boy through a blue is jumping

100/100 [==============================] - 5s 52ms/step - loss: 3.6281 - masked_acc: 0.3497 - val_loss: 3.5704 - val_masked_acc: 0.3461
Epoch 12/100
100/100 [==============================] - ETA: 0s - loss: 3.5918 - masked_acc: 0.3506

a man in a red shirt is in the water
a man in a red and white is jumping off her pool
a red biker in a pool

100/100 [==============================] - 5s 55ms/step - loss: 3.5918 - masked_acc: 0.3506 - val_loss: 3.4961 - val_masked_acc: 0.3544
Epoch 13/100
 99/100 [============================>.] - ETA: 0s - loss: 3.5655 - masked_acc: 0.3532

a man in a red shirt is jumping over a pool
a man in a blue shirt is sitting on a pool
a man on a group of a snowy has a surfboard

100/100 [==============================] - 6s 56ms/step - loss: 3.5675 - masked_acc: 0.3524 - val_loss: 3.4928 - val_masked_acc: 0.3514
Epoch 14/100
100/100 [==============================] - ETA: 0s - loss: 3.5582 - masked_acc: 0.3527

a man in a red shirt is jumping into the water
a boy in a blue shirt is jumping on a wave
a little boy looking playing his ball eating the water

100/100 [==============================] - 5s 54ms/step - loss: 3.5582 - masked_acc: 0.3527 - val_loss: 3.4457 - val_masked_acc: 0.3539
Epoch 15/100
 99/100 [============================>.] - ETA: 0s - loss: 3.5158 - masked_acc: 0.3586

a man in a red shirt is jumping into the water
a snowboarder in a blue shirt is standing in a pool
a dog posing leaps behind after a wave

100/100 [==============================] - 6s 56ms/step - loss: 3.5142 - masked_acc: 0.3589 - val_loss: 3.3525 - val_masked_acc: 0.3597
Epoch 16/100
 99/100 [============================>.] - ETA: 0s - loss: 3.4721 - masked_acc: 0.3602

a man in a red shirt is swimming pool
a man in a red wave
a little dry boy are turn in grass

100/100 [==============================] - 5s 48ms/step - loss: 3.4712 - masked_acc: 0.3605 - val_loss: 3.4128 - val_masked_acc: 0.3568
Epoch 17/100
 99/100 [============================>.] - ETA: 0s - loss: 3.4162 - masked_acc: 0.3643

a man in a red shirt is swimming pool
a man in a red surfboard play in the water
two surfer in a shoreline standing down a snowy day

100/100 [==============================] - 5s 54ms/step - loss: 3.4147 - masked_acc: 0.3647 - val_loss: 3.3201 - val_masked_acc: 0.3658
Epoch 18/100
 99/100 [============================>.] - ETA: 0s - loss: 3.4035 - masked_acc: 0.3645

a man in a red shirt is swimming pool
a man in a red boat in the water
a young black the swimming in a surfboard

100/100 [==============================] - 5s 52ms/step - loss: 3.4030 - masked_acc: 0.3645 - val_loss: 3.3170 - val_masked_acc: 0.3621
Epoch 19/100
100/100 [==============================] - ETA: 0s - loss: 3.3520 - masked_acc: 0.3704

a man in a red shirt is riding a wave
a man in a blue shirt is holding a wave
trucks in black car dancing in hurdle

100/100 [==============================] - 5s 52ms/step - loss: 3.3520 - masked_acc: 0.3704 - val_loss: 3.3185 - val_masked_acc: 0.3654
Epoch 20/100
 99/100 [============================>.] - ETA: 0s - loss: 3.2899 - masked_acc: 0.3726

a man in a red shirt is swimming in a pool
a man in a blue shirt is in a surfboard
a child does her back has the out of a wave

100/100 [==============================] - 6s 56ms/step - loss: 3.2890 - masked_acc: 0.3729 - val_loss: 3.2319 - val_masked_acc: 0.3852
Epoch 21/100
100/100 [==============================] - ETA: 0s - loss: 3.2789 - masked_acc: 0.3754

a man in a red shirt is swimming pool
a man in a yellow surfboard
the biker grinding a wave

100/100 [==============================] - 5s 50ms/step - loss: 3.2789 - masked_acc: 0.3754 - val_loss: 3.2707 - val_masked_acc: 0.3710
Epoch 22/100
100/100 [==============================] - ETA: 0s - loss: 3.2473 - masked_acc: 0.3748

a man in a red shirt is riding a wave
a surfer is in a wave
surfboard from a cellphone in the water while looking through the water

100/100 [==============================] - 5s 54ms/step - loss: 3.2473 - masked_acc: 0.3748 - val_loss: 3.2063 - val_masked_acc: 0.3724
Epoch 23/100
 98/100 [============================>.] - ETA: 0s - loss: 3.2248 - masked_acc: 0.3768

a man in a red shirt is riding a wave
a man in a blue suit is riding a wave
child in the shirt holds rod

100/100 [==============================] - 5s 52ms/step - loss: 3.2282 - masked_acc: 0.3765 - val_loss: 3.1455 - val_masked_acc: 0.3792
Epoch 24/100
100/100 [==============================] - ETA: 0s - loss: 3.2184 - masked_acc: 0.3766

a man in a red shirt is riding a wave
a man in a yellow shirt jumps high in the ocean
a surfer holding a in the wave

100/100 [==============================] - 5s 54ms/step - loss: 3.2184 - masked_acc: 0.3766 - val_loss: 3.1399 - val_masked_acc: 0.3743
Epoch 25/100
 99/100 [============================>.] - ETA: 0s - loss: 3.1842 - masked_acc: 0.3830

a man in a red shirt is swimming in a wave
a man in a red surfboard in the ocean
a child in orange jump around a wave

100/100 [==============================] - 6s 55ms/step - loss: 3.1869 - masked_acc: 0.3827 - val_loss: 3.1132 - val_masked_acc: 0.3845
Epoch 26/100
100/100 [==============================] - ETA: 0s - loss: 3.1839 - masked_acc: 0.3850

a man in a red shirt is riding a wave
a surfer in a red shirt is sitting on a surfboard
a uniforms two children riding an swimming pool with a racing wave in the background

100/100 [==============================] - 6s 56ms/step - loss: 3.1839 - masked_acc: 0.3850 - val_loss: 3.1347 - val_masked_acc: 0.3777
Epoch 27/100
100/100 [==============================] - ETA: 0s - loss: 3.1453 - masked_acc: 0.3838

a man in a blue shirt is swimming in a wave
a surfer swimming in a pool
a child is surfing waits on a wave

100/100 [==============================] - 5s 53ms/step - loss: 3.1453 - masked_acc: 0.3838 - val_loss: 3.0429 - val_masked_acc: 0.3931
Epoch 28/100
 98/100 [============================>.] - ETA: 0s - loss: 3.1545 - masked_acc: 0.3861

a man in a red shirt is surfing on a wave
a person in a red is swimming in a blue and a yellow surfboard
a girl in yellow tshirt is riding riding a surfboard

100/100 [==============================] - 6s 57ms/step - loss: 3.1546 - masked_acc: 0.3861 - val_loss: 3.1243 - val_masked_acc: 0.3729
Epoch 29/100
100/100 [==============================] - ETA: 0s - loss: 3.0604 - masked_acc: 0.3905

a man in a red and yellow wetsuit is surfing
a surfer is in a red and yellow jacket is riding a wave
a wave

100/100 [==============================] - 5s 52ms/step - loss: 3.0604 - masked_acc: 0.3905 - val_loss: 3.0790 - val_masked_acc: 0.3824
Epoch 30/100
100/100 [==============================] - ETA: 0s - loss: 3.0344 - masked_acc: 0.3948

a man in a yellow shirt is surfing on a wave
a man in a yellow and red wetsuit is doing a wave
holding a yellow backpack is on the surfer in the wave

100/100 [==============================] - 6s 55ms/step - loss: 3.0344 - masked_acc: 0.3948 - val_loss: 3.1074 - val_masked_acc: 0.3764
Epoch 31/100
 98/100 [============================>.] - ETA: 0s - loss: 3.0342 - masked_acc: 0.3935

a man in a yellow shirt is surfing
a man in a yellow surfboard
a man surfs on a wave that waves

100/100 [==============================] - 5s 48ms/step - loss: 3.0341 - masked_acc: 0.3933 - val_loss: 3.0822 - val_masked_acc: 0.3767
Epoch 32/100
 99/100 [============================>.] - ETA: 0s - loss: 3.0558 - masked_acc: 0.3958

a man in a red and white and yellow shirt is riding a wave
a man is riding a wave on a wave
a snowboarder jumps off a wave

100/100 [==============================] - 5s 54ms/step - loss: 3.0544 - masked_acc: 0.3959 - val_loss: 3.0930 - val_masked_acc: 0.3745

Plot the loss and accuracy over the training run:

plt.plot(history.history['loss'], label='loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.ylim([0, max(plt.ylim())])
plt.xlabel('Epoch #')
plt.ylabel('CE/token')
plt.legend()
<matplotlib.legend.Legend at 0x7fc3106f96d0>

png

plt.plot(history.history['masked_acc'], label='accuracy')
plt.plot(history.history['val_masked_acc'], label='val_accuracy')
plt.ylim([0, max(plt.ylim())])
plt.xlabel('Epoch #')
plt.ylabel('CE/token')
plt.legend()
<matplotlib.legend.Legend at 0x7fc2d025bee0>

png

Attention plots

Now, using the trained model, run that simple_gen method on the image:

result = model.simple_gen(image, temperature=0.0)
result
'a man in a blue shirt is swimming in a wave'

Split the output back into tokens:

str_tokens = result.split()
str_tokens.append('[END]')

The DecoderLayers each cache the attention scores for their CrossAttention layer. The shape of each attention map is (batch=1, heads, sequence, image):

attn_maps = [layer.last_attention_scores for layer in model.decoder_layers]
[map.shape for map in attn_maps]
[TensorShape([1, 2, 12, 49]), TensorShape([1, 2, 12, 49])]

So stack the maps along the batch axis, then average over the (batch, heads) axes, while splitting the image axis back into height, width:

attention_maps = tf.concat(attn_maps, axis=0)
attention_maps = einops.reduce(
    attention_maps,
    'batch heads sequence (height width) -> sequence height width',
    height=7, width=7,
    reduction='mean')

Now you have a single attention map, for each sequence prediction. The values in each map should sum to 1.

einops.reduce(attention_maps, 'sequence height width -> sequence', reduction='sum')
<tf.Tensor: shape=(12,), dtype=float32, numpy=
array([1.        , 1.        , 1.        , 1.        , 1.        ,

       1.        , 0.99999994, 1.        , 0.99999994, 1.        ,
       1.        , 1.        ], dtype=float32)>

So here is where the model was focusing attention while generating each token of the output:

def plot_attention_maps(image, str_tokens, attention_map):
    fig = plt.figure(figsize=(16, 9))

    len_result = len(str_tokens)

    titles = []
    for i in range(len_result):
      map = attention_map[i]
      grid_size = max(int(np.ceil(len_result/2)), 2)
      ax = fig.add_subplot(3, grid_size, i+1)
      titles.append(ax.set_title(str_tokens[i]))
      img = ax.imshow(image)
      ax.imshow(map, cmap='gray', alpha=0.6, extent=img.get_extent(),
                clim=[0.0, np.max(map)])

    plt.tight_layout()
plot_attention_maps(image/255, str_tokens, attention_maps)

png

Now put that together into a more usable function:

@Captioner.add_method
def run_and_show_attention(self, image, temperature=0.0):
  result_txt = self.simple_gen(image, temperature)
  str_tokens = result_txt.split()
  str_tokens.append('[END]')

  attention_maps = [layer.last_attention_scores for layer in self.decoder_layers]
  attention_maps = tf.concat(attention_maps, axis=0)
  attention_maps = einops.reduce(
      attention_maps,
      'batch heads sequence (height width) -> sequence height width',
      height=7, width=7,
      reduction='mean')

  plot_attention_maps(image/255, str_tokens, attention_maps)
  t = plt.suptitle(result_txt)
  t.set_y(1.05)
run_and_show_attention(model, image)

png

Try it on your own images

For fun, below you're provided a method you can use to caption your own images with the model you've just trained. Keep in mind, it was trained on a relatively small amount of data, and your images may be different from the training data (so be prepared for strange results!)

image_url = 'https://tensorflow.org/images/bedroom_hrnet_tutorial.jpg'
image_path = tf.keras.utils.get_file(origin=image_url)
image = load_image(image_path)

run_and_show_attention(model, image)
Downloading data from https://tensorflow.org/images/bedroom_hrnet_tutorial.jpg
67460/67460 [==============================] - 0s 1us/step

png