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).

[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')

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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)

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(32, 7, 7, 576)
(32, 22)
(32, 22)

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    6  638  108 2645    6 2310    0    0    0    0    0    0    0
    0    0    0    0    0    0    0    0]
[   6  638  108 2645    6 2310    4    0    0    0    0    0    0    0
    0    0    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))

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100%|██████████| 938/938 [00:03<00:00, 259.45it/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)

a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a

in sits

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)

a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a
in in a a a a the
wall beach jumping person in dirty dogs pacifier play art just a a and

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
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100/100 [==============================] - ETA: 0s - loss: 5.0033 - masked_acc: 0.1962
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[libprotobuf ERROR external/com_google_protobuf/src/google/protobuf/text_format.cc:337] Error parsing text-format tensorflow.data.experimental.DistributedSnapshotMetadata: 1:3: Expected identifier, got: 8062394198849137179
a man in a man in a man
a man in a man on a dog field
a man are outside a team plate muddy logs and the while face

100/100 [==============================] - 32s 199ms/step - loss: 5.0033 - masked_acc: 0.1962 - val_loss: 4.6044 - val_masked_acc: 0.2465
Epoch 2/100
100/100 [==============================] - ETA: 0s - loss: 4.6332 - masked_acc: 0.2552

a man in a man in the water
two and white dog in the water
a bright girl is running a

100/100 [==============================] - 8s 80ms/step - loss: 4.6332 - masked_acc: 0.2552 - val_loss: 4.3487 - val_masked_acc: 0.2735
Epoch 3/100
100/100 [==============================] - ETA: 0s - loss: 4.3851 - masked_acc: 0.2787

a man in a red and white and white dog is in the water
a man in a red is of the beach
blanket

100/100 [==============================] - 7s 74ms/step - loss: 4.3851 - masked_acc: 0.2787 - val_loss: 4.1681 - val_masked_acc: 0.2936
Epoch 4/100
 99/100 [============================>.] - ETA: 0s - loss: 4.2094 - masked_acc: 0.3002

a man in a red shirt is in the water
a man is in a blue is on a in a pool
a little girl stands is swimming on and white rock church

100/100 [==============================] - 7s 74ms/step - loss: 4.2084 - masked_acc: 0.3004 - val_loss: 3.9979 - val_masked_acc: 0.3126
Epoch 5/100
 99/100 [============================>.] - ETA: 0s - loss: 4.1069 - masked_acc: 0.3088

a man in a red shirt is running in the water
a man is in a red girl in the water
a wooded hats picture in the ground high over a neon head with a swimming dirt

100/100 [==============================] - 7s 73ms/step - loss: 4.1084 - masked_acc: 0.3088 - val_loss: 3.8942 - val_masked_acc: 0.3252
Epoch 6/100
 99/100 [============================>.] - ETA: 0s - loss: 4.0548 - masked_acc: 0.3124

a man is jumping in the water
a man in a blue shirt is jumping
a young girl wearing a traverse

100/100 [==============================] - 6s 58ms/step - loss: 4.0557 - masked_acc: 0.3123 - val_loss: 3.7970 - val_masked_acc: 0.3266
Epoch 7/100
 99/100 [============================>.] - ETA: 0s - loss: 3.9158 - masked_acc: 0.3274

a man in a blue shirt is running in the water
a man is jumping down a blue water
five people playing air across the river

100/100 [==============================] - 6s 64ms/step - loss: 3.9184 - masked_acc: 0.3271 - val_loss: 3.7825 - val_masked_acc: 0.3363
Epoch 8/100
100/100 [==============================] - ETA: 0s - loss: 3.8581 - masked_acc: 0.3302

a man in a red shirt is jumping in the water
a boy in a blue water
boy in the rides through the water that runs

100/100 [==============================] - 7s 68ms/step - loss: 3.8581 - masked_acc: 0.3302 - val_loss: 3.6629 - val_masked_acc: 0.3360
Epoch 9/100
100/100 [==============================] - ETA: 0s - loss: 3.8192 - masked_acc: 0.3343

a man in a blue shirt is in a pool
a man is wearing a yellow shirt is in the water
a couple a pool

100/100 [==============================] - 6s 64ms/step - loss: 3.8192 - masked_acc: 0.3343 - val_loss: 3.5895 - val_masked_acc: 0.3502
Epoch 10/100
 99/100 [============================>.] - ETA: 0s - loss: 3.7347 - masked_acc: 0.3399

a man in a blue shirt is jumping in the water
a man in a blue jacket and a red kayak in the water
a boy in a black jacket is wears her tree stands in beach

100/100 [==============================] - 8s 80ms/step - loss: 3.7326 - masked_acc: 0.3402 - val_loss: 3.5817 - val_masked_acc: 0.3474
Epoch 11/100
 99/100 [============================>.] - ETA: 0s - loss: 3.6462 - masked_acc: 0.3451

a man in a blue shirt is jumping over a pool
a man in a white shirt with a red blue shirt is riding a blue shirt is running in the ocean
a man in red jacket over a bench with a blue

100/100 [==============================] - 7s 75ms/step - loss: 3.6463 - masked_acc: 0.3448 - val_loss: 3.5439 - val_masked_acc: 0.3475
Epoch 12/100
100/100 [==============================] - ETA: 0s - loss: 3.5961 - masked_acc: 0.3517

a man in a blue shirt is jumping into the water
a man in a blue and a blue hat jumps into the water
a group of black and blue black and white beard is standing in the air with a pool

100/100 [==============================] - 8s 79ms/step - loss: 3.5961 - masked_acc: 0.3517 - val_loss: 3.4749 - val_masked_acc: 0.3523
Epoch 13/100
 99/100 [============================>.] - ETA: 0s - loss: 3.5621 - masked_acc: 0.3490

a man in a blue shirt is jumping into the water
a person is wearing a swimming pool
two young asian boat in the water while hills

100/100 [==============================] - 6s 62ms/step - loss: 3.5626 - masked_acc: 0.3490 - val_loss: 3.4983 - val_masked_acc: 0.3525
Epoch 14/100
 99/100 [============================>.] - ETA: 0s - loss: 3.5247 - masked_acc: 0.3572

a man in a blue shirt is jumping into the water
a girl in a wave
a shopping trees by puddle teammate and white motorcycle sliding to a climbing from front of water

100/100 [==============================] - 7s 68ms/step - loss: 3.5244 - masked_acc: 0.3572 - val_loss: 3.4636 - val_masked_acc: 0.3531
Epoch 15/100
 99/100 [============================>.] - ETA: 0s - loss: 3.4647 - masked_acc: 0.3644

a man in a blue shirt is jumping into the water
a man in a blue shirt is riding a wave
a boy sits near a water

100/100 [==============================] - 6s 63ms/step - loss: 3.4636 - masked_acc: 0.3644 - val_loss: 3.3734 - val_masked_acc: 0.3705
Epoch 16/100
 99/100 [============================>.] - ETA: 0s - loss: 3.4827 - masked_acc: 0.3619

a man in a red shirt is jumping into the water
a man in a red shirt is doing a wave
a man in a yellow corner of water while another little dog in water

100/100 [==============================] - 7s 68ms/step - loss: 3.4814 - masked_acc: 0.3620 - val_loss: 3.3008 - val_masked_acc: 0.3665
Epoch 17/100
100/100 [==============================] - ETA: 0s - loss: 3.4324 - masked_acc: 0.3606

a man in a red shirt is swimming in the water
a group of people are playing in the water
brown and child skateboarding on the body of water camera

100/100 [==============================] - 7s 66ms/step - loss: 3.4324 - masked_acc: 0.3606 - val_loss: 3.3836 - val_masked_acc: 0.3615
Epoch 18/100
 99/100 [============================>.] - ETA: 0s - loss: 3.4043 - masked_acc: 0.3653

a man in a blue shirt is swimming in a pool
a boy in a red wetsuit is swimming in the ocean
a boy in the blue jacket and an outdoor pool on the surfboard

100/100 [==============================] - 7s 72ms/step - loss: 3.4042 - masked_acc: 0.3652 - val_loss: 3.2615 - val_masked_acc: 0.3757
Epoch 19/100
100/100 [==============================] - ETA: 0s - loss: 3.3802 - masked_acc: 0.3670

a man in a blue shirt is swimming pool
a man in a pink swimming pool
a man sits jumping over the water while playing a enjoying a swinging

100/100 [==============================] - 7s 66ms/step - loss: 3.3802 - masked_acc: 0.3670 - val_loss: 3.2385 - val_masked_acc: 0.3717
Epoch 20/100
 99/100 [============================>.] - ETA: 0s - loss: 3.2858 - masked_acc: 0.3739

a man in a blue shirt is swimming in the water
a man in a blue pool
two children sit on a ocean

100/100 [==============================] - 6s 57ms/step - loss: 3.2898 - masked_acc: 0.3739 - val_loss: 3.2971 - val_masked_acc: 0.3684
Epoch 21/100
 99/100 [============================>.] - ETA: 0s - loss: 3.2694 - masked_acc: 0.3760

a man in a yellow shirt is swimming pool
a person is swimming in the water
a large jump into the sand

100/100 [==============================] - 6s 57ms/step - loss: 3.2699 - masked_acc: 0.3761 - val_loss: 3.2614 - val_masked_acc: 0.3669
Epoch 22/100
 99/100 [============================>.] - ETA: 0s - loss: 3.2372 - masked_acc: 0.3776

a man in a red shirt is swimming pool
a man in a red jacket is riding a wave
a man kayaking upside down on a wave

100/100 [==============================] - 6s 62ms/step - loss: 3.2372 - masked_acc: 0.3776 - val_loss: 3.1561 - val_masked_acc: 0.3772
Epoch 23/100
 99/100 [============================>.] - ETA: 0s - loss: 3.2379 - masked_acc: 0.3778

a man in a blue shirt is riding a wave
a surfer is riding on the ocean
a man is surfing on the surfboard through the raft

100/100 [==============================] - 6s 61ms/step - loss: 3.2399 - masked_acc: 0.3777 - val_loss: 3.2144 - val_masked_acc: 0.3701
Epoch 24/100
 99/100 [============================>.] - ETA: 0s - loss: 3.2174 - masked_acc: 0.3778

a man in a red jacket is swimming in the water
a man in a wave
a person on a football rides at her mountain

100/100 [==============================] - 6s 59ms/step - loss: 3.2199 - masked_acc: 0.3779 - val_loss: 3.1902 - val_masked_acc: 0.3698
Epoch 25/100
 99/100 [============================>.] - ETA: 0s - loss: 3.2041 - masked_acc: 0.3785

a man in a red jacket is swimming pool
a man with a red helmet is jumping over a wave
the two children are posing in a water

100/100 [==============================] - 6s 62ms/step - loss: 3.2030 - masked_acc: 0.3788 - val_loss: 3.1596 - val_masked_acc: 0.3723
Epoch 26/100
100/100 [==============================] - ETA: 0s - loss: 3.1657 - masked_acc: 0.3829

a man in a red shirt is jumping into the water
a person in a red jacket is jumping in a wave
a surfer in the snow sits in the ocean

100/100 [==============================] - 7s 67ms/step - loss: 3.1657 - masked_acc: 0.3829 - val_loss: 3.1445 - val_masked_acc: 0.3730
Epoch 27/100
 99/100 [============================>.] - ETA: 0s - loss: 3.1746 - masked_acc: 0.3831

a man in a blue wetsuit is swimming pool
a man in a black wetsuit is swimming pool
a man gets through a wave

100/100 [==============================] - 6s 60ms/step - loss: 3.1732 - masked_acc: 0.3831 - val_loss: 3.0899 - val_masked_acc: 0.3836
Epoch 28/100
 99/100 [============================>.] - ETA: 0s - loss: 3.1347 - masked_acc: 0.3880

a man in a red shirt is surfing
a man and a child in a wave
young boy in a tire is jumping over a wave

100/100 [==============================] - 6s 59ms/step - loss: 3.1336 - masked_acc: 0.3882 - val_loss: 3.1059 - val_masked_acc: 0.3853
Epoch 29/100
100/100 [==============================] - ETA: 0s - loss: 3.0805 - masked_acc: 0.3880

a man in a red shirt is swimming pool
a person in a red hat is sitting on a surfboard in the air
a child swimming in its mouth open surfer in the snow

100/100 [==============================] - 7s 68ms/step - loss: 3.0805 - masked_acc: 0.3880 - val_loss: 3.1021 - val_masked_acc: 0.3820
Epoch 30/100
 99/100 [============================>.] - ETA: 0s - loss: 3.0598 - masked_acc: 0.3921

a man in a red shirt is swimming pool
a surfer in a swimming pool
a man in a ramp

100/100 [==============================] - 6s 60ms/step - loss: 3.0595 - masked_acc: 0.3920 - val_loss: 3.1247 - val_masked_acc: 0.3707
Epoch 31/100
100/100 [==============================] - ETA: 0s - loss: 3.0244 - masked_acc: 0.3965

a man in a red jacket is swimming in the water
a person in a red and white pool
a man in a straw hat is jumping into a uniform in a swimming sofa

100/100 [==============================] - 7s 70ms/step - loss: 3.0244 - masked_acc: 0.3965 - val_loss: 3.0469 - val_masked_acc: 0.3833
Epoch 32/100
100/100 [==============================] - ETA: 0s - loss: 3.0207 - masked_acc: 0.3945

a man in a red shirt is riding a wave
a man in a red surfboard is being wave
a man in a black surfboard in a train

100/100 [==============================] - 6s 64ms/step - loss: 3.0207 - masked_acc: 0.3945 - val_loss: 3.1176 - val_masked_acc: 0.3714
Epoch 33/100
 99/100 [============================>.] - ETA: 0s - loss: 3.0083 - masked_acc: 0.3961

a man in a yellow kayak is riding a wave
a person in a yellow jacket is surfing
a man and a small and yellow surfboard while another man swimming in the water

100/100 [==============================] - 7s 66ms/step - loss: 3.0094 - masked_acc: 0.3962 - val_loss: 3.1063 - val_masked_acc: 0.3822
Epoch 34/100
 99/100 [============================>.] - ETA: 0s - loss: 2.9952 - masked_acc: 0.3964

a man in a blue wetsuit is surfing
a surfer and a woman in a wave
a man in a boy rides a sunset

100/100 [==============================] - 6s 60ms/step - loss: 2.9965 - masked_acc: 0.3963 - val_loss: 3.0172 - val_masked_acc: 0.3882
Epoch 35/100
 99/100 [============================>.] - ETA: 0s - loss: 2.9939 - masked_acc: 0.4000

a man in a red jacket is surfing
a man is surfing a wave
a yellow toy paddling through the pool on the mountain

100/100 [==============================] - 6s 59ms/step - loss: 2.9950 - masked_acc: 0.3997 - val_loss: 3.0631 - val_masked_acc: 0.3782
Epoch 36/100
 99/100 [============================>.] - ETA: 0s - loss: 3.0180 - masked_acc: 0.3923

a man in a yellow shirt is surfing
a man in a red is standing in the ocean
a group of people sit in a pool with a point into a pool

100/100 [==============================] - 7s 65ms/step - loss: 3.0191 - masked_acc: 0.3924 - val_loss: 3.0470 - val_masked_acc: 0.3785
Epoch 37/100
 99/100 [============================>.] - ETA: 0s - loss: 2.9569 - masked_acc: 0.3996

a man in a red jacket is surfing
a surfer in a red shirt and orange kayak in a pool
basketball players leaping in the pool of a wave

100/100 [==============================] - 6s 64ms/step - loss: 2.9554 - masked_acc: 0.3997 - val_loss: 3.0078 - val_masked_acc: 0.3907
Epoch 38/100
100/100 [==============================] - ETA: 0s - loss: 2.9457 - masked_acc: 0.4005

a man in a yellow kayak is surfing
a man in a red jacket is surfing
a skier wearing red bird rides a wave

100/100 [==============================] - 6s 61ms/step - loss: 2.9457 - masked_acc: 0.4005 - val_loss: 3.0194 - val_masked_acc: 0.3911
Epoch 39/100
 99/100 [============================>.] - ETA: 0s - loss: 2.8841 - masked_acc: 0.4071

a man in a red jacket is surfing
a man in a blue jacket is jumping in the ocean
a couple are under a kicked snowed through the beach

100/100 [==============================] - 6s 64ms/step - loss: 2.8827 - masked_acc: 0.4076 - val_loss: 2.9267 - val_masked_acc: 0.3908
Epoch 40/100
 99/100 [============================>.] - ETA: 0s - loss: 2.8689 - masked_acc: 0.4121

a man in a red wetsuit is riding a wave
a man in a black wetsuit and white kayak in the ocean
a surfer in waves boys falls that is interacting in the ocean

100/100 [==============================] - 7s 67ms/step - loss: 2.8684 - masked_acc: 0.4122 - val_loss: 3.0017 - val_masked_acc: 0.3946
Epoch 41/100
 99/100 [============================>.] - ETA: 0s - loss: 2.8619 - masked_acc: 0.4086

a man in a red wetsuit is surfing
a surfer is surfing
santa surfer floats in the surf

100/100 [==============================] - 5s 54ms/step - loss: 2.8635 - masked_acc: 0.4086 - val_loss: 2.9427 - val_masked_acc: 0.3918
Epoch 42/100
 99/100 [============================>.] - ETA: 0s - loss: 2.8763 - masked_acc: 0.4077

a man in a red wetsuit is surfing
a man in a wetsuit is surfing on a wave
helmeted man in a red hand is on a path

100/100 [==============================] - 6s 62ms/step - loss: 2.8766 - masked_acc: 0.4080 - val_loss: 2.9700 - val_masked_acc: 0.3870
Epoch 43/100
 99/100 [============================>.] - ETA: 0s - loss: 2.8604 - masked_acc: 0.4103

a man is surfing
a surfer is riding a wave
a man is as a surfer wearing red wave

100/100 [==============================] - 6s 59ms/step - loss: 2.8645 - masked_acc: 0.4097 - val_loss: 2.9140 - val_masked_acc: 0.3993
Epoch 44/100
 99/100 [============================>.] - ETA: 0s - loss: 2.8393 - masked_acc: 0.4101

a man in a yellow kayak is surfing
a man in a wetsuit is surfing on a wave
surfer in a life jackets in the ocean

100/100 [==============================] - 6s 60ms/step - loss: 2.8380 - masked_acc: 0.4105 - val_loss: 2.9495 - val_masked_acc: 0.3932
Epoch 45/100
 99/100 [============================>.] - ETA: 0s - loss: 2.8527 - masked_acc: 0.4104

a surfer in a yellow kayak
a surfer is surfing a wave
a woman on a surfboard

100/100 [==============================] - 5s 53ms/step - loss: 2.8519 - masked_acc: 0.4103 - val_loss: 2.9480 - val_masked_acc: 0.3902
Epoch 46/100
 99/100 [============================>.] - ETA: 0s - loss: 2.8380 - masked_acc: 0.4088

a man in a yellow shirt is surfing
a girl in a yellow shirt is surfing
child with red tracksuit is splashing in the water

100/100 [==============================] - 6s 61ms/step - loss: 2.8342 - masked_acc: 0.4092 - val_loss: 2.8838 - val_masked_acc: 0.3980
Epoch 47/100
100/100 [==============================] - ETA: 0s - loss: 2.8178 - masked_acc: 0.4087

a man in a yellow shirt is surfing
a person in a life jacket is riding a surfboard
a person wearing a orange wetsuit surfs

100/100 [==============================] - 6s 64ms/step - loss: 2.8178 - masked_acc: 0.4087 - val_loss: 2.8597 - val_masked_acc: 0.4050
Epoch 48/100
 99/100 [============================>.] - ETA: 0s - loss: 2.7571 - masked_acc: 0.4190

a man in a yellow shirt is surfing
a man in a blue shirt is riding a wave
a man tricks on the water

100/100 [==============================] - 6s 59ms/step - loss: 2.7561 - masked_acc: 0.4193 - val_loss: 2.9148 - val_masked_acc: 0.4001
Epoch 49/100
 99/100 [============================>.] - ETA: 0s - loss: 2.7213 - masked_acc: 0.4226

a man in a blue wetsuit is surfing
a man in a white shirt and orange surfboard
a man in a yellow trunks is wearing a red slide

100/100 [==============================] - 6s 61ms/step - loss: 2.7226 - masked_acc: 0.4220 - val_loss: 2.9517 - val_masked_acc: 0.3948
Epoch 50/100
 99/100 [============================>.] - ETA: 0s - loss: 2.7381 - masked_acc: 0.4212

a man in a wetsuit is surfing
a man on a surfboard in the ocean
boys waves through the shore of a surfboard

100/100 [==============================] - 6s 57ms/step - loss: 2.7375 - masked_acc: 0.4210 - val_loss: 2.9522 - val_masked_acc: 0.3936
Epoch 51/100
 99/100 [============================>.] - ETA: 0s - loss: 2.7772 - masked_acc: 0.4176

a man in a yellow kayak is surfing
a man in a yellow wetsuit is surfing
a man is pulling a surfboard by through the water

100/100 [==============================] - 6s 59ms/step - loss: 2.7751 - masked_acc: 0.4179 - val_loss: 2.8938 - val_masked_acc: 0.3973
Epoch 52/100
 99/100 [============================>.] - ETA: 0s - loss: 2.7135 - masked_acc: 0.4260

a man in a yellow shirt is surfing
a man wearing a blue shirt is surfing
a man they are in a wave

100/100 [==============================] - 6s 59ms/step - loss: 2.7155 - masked_acc: 0.4255 - val_loss: 2.9382 - val_masked_acc: 0.3906

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 0x7f71e44b3d30>

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 0x7f70f8404670>

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 yellow shirt is surfing'

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, 9, 49]), TensorShape([1, 2, 9, 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=(9,), dtype=float32, numpy=
array([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