def __init__(self):
super().__init__()
# stores the current probability of an image being augmented
self.probability = tf.Variable(0.0)
# the corresponding augmentation names from the paper are shown above each layer
# the authors show (see figure 4), that the blitting and geometric augmentations
# are the most helpful in the low-data regime
self.augmenter = keras.Sequential(
[
layers.InputLayer(input_shape=(image_size, image_size, 3)),
# blitting/x-flip:
layers.RandomFlip("horizontal"),
# blitting/integer translation:
layers.RandomTranslation(
height_factor=max_translation,
width_factor=max_translation,
interpolation="nearest",
),
# geometric/rotation:
layers.RandomRotation(factor=max_rotation),
# geometric/isotropic and anisotropic scaling:
layers.RandomZoom(height_factor=(-max_zoom, 0.0),
width_factor=(-max_zoom, 0.0)),
],
name="adaptive_augmenter",
)
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
# Translate
translation_factor = utils.add_to_hp(self.translation_factor, hp)
if translation_factor not in [0, (0, 0)]:
height_factor, width_factor = self._get_fraction_value(
translation_factor
)
output_node = layers.RandomTranslation(height_factor, width_factor)(
output_node
)
# Flip
horizontal_flip = self.horizontal_flip
if horizontal_flip is None:
horizontal_flip = hp.Boolean("horizontal_flip", default=True)
vertical_flip = self.vertical_flip
if self.vertical_flip is None:
vertical_flip = hp.Boolean("vertical_flip", default=True)
if not horizontal_flip and not vertical_flip:
flip_mode = ""
elif horizontal_flip and vertical_flip:
flip_mode = "horizontal_and_vertical"
elif horizontal_flip and not vertical_flip:
flip_mode = "horizontal"
elif not horizontal_flip and vertical_flip:
flip_mode = "vertical"
if flip_mode != "":
output_node = layers.RandomFlip(mode=flip_mode)(output_node)
# Rotate
rotation_factor = utils.add_to_hp(self.rotation_factor, hp)
if rotation_factor != 0:
output_node = layers.RandomRotation(rotation_factor)(output_node)
# Zoom
zoom_factor = utils.add_to_hp(self.zoom_factor, hp)
if zoom_factor not in [0, (0, 0)]:
height_factor, width_factor = self._get_fraction_value(zoom_factor)
# TODO: Add back RandomZoom when it is ready.
# output_node = layers.RandomZoom(
# height_factor, width_factor)(output_node)
# Contrast
contrast_factor = utils.add_to_hp(self.contrast_factor, hp)
if contrast_factor not in [0, (0, 0)]:
output_node = layers.RandomContrast(contrast_factor)(output_node)
return output_node
def build_model_augment() -> keras.Sequential:
# build a sequential model
model = keras.Sequential()
# add a data augmentation layer
data_augmentation = keras.Sequential([ # random flip horizontally
layers.RandomFlip("horizontal"),
# random flip vertically
layers.RandomFlip("vertical"),
# random rotation at most 0.2 degrees
layers.RandomRotation(0.2),
# random zoom at most 0.2 times difference
layers.RandomZoom(0.2),
# random contrast at most 0.1 difference
layers.RandomContrast(0.1)
])
model.add(data_augmentation)
# add the first convolutional layer, with ReLU as activation funtion and same padding
model.add(
keras.layers.Conv2D(32, (3, 3),
activation="relu",
padding="same",
input_shape=(32, 32, 3))) # [32,32, 32]
# add a maxpooling layer to reduce the dimension
model.add(keras.layers.MaxPooling2D(2, 2)) # [16, 16, 32]
# add the second convolutional layer, with ReLU as activation funtion and same padding
model.add(
keras.layers.Conv2D(64, (3, 3), activation="relu",
padding="same")) # [16, 16, 64]
# add a maxpooling layer to reduce the dimension
model.add(keras.layers.MaxPooling2D(2, 2)) # [8, 8, 64]
# add the third convolutional layer, with ReLU as activation funtion and same padding
model.add(
keras.layers.Conv2D(128, (3, 3), activation="relu",
padding="same")) # [8, 8, 128]
# add a maxpooling layer to reduce the dimension
model.add(keras.layers.MaxPooling2D(2, 2)) # [4, 4, 128]
# add a flatten layer to make the data into a 1-dimensional array
model.add(keras.layers.Flatten()) # [1024, ]
# add a fully connected layer, with ReLU as activation function
model.add(keras.layers.Dense(128, activation="relu"))
# add a fully connected layer, the output size is the same as the number of classes and
# softmax as activation function to do multiclass classification
model.add(keras.layers.Dense(10, activation="softmax"))
# compile the model, use SDG as the optimizer and categorical crossentropy as loss function
model.compile(optimizer=optimizers.SGD(learning_rate=1e-3, momentum=0.9),
loss="categorical_crossentropy",
metrics=["accuracy"])
# return the model
return model
ax = plt.subplot(3, 3, i + 1)
plt.imshow(image.numpy().astype("uint8"))
plt.title("{}".format(format_label(label)))
plt.axis("off")
"""
### Data augmentation
We can use the preprocessing layers APIs for image augmentation.
"""
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
img_augmentation = Sequential(
[
layers.RandomRotation(factor=0.15),
layers.RandomTranslation(height_factor=0.1, width_factor=0.1),
layers.RandomFlip(),
layers.RandomContrast(factor=0.1),
],
name="img_augmentation",
)
"""
This `Sequential` model object can be used both as a part of
the model we later build, and as a function to preprocess
data before feeding into the model. Using them as function makes
it easy to visualize the augmented images. Here we plot 9 examples
of augmentation result of a given figure.
"""
for image, label in ds_train.take(1):
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(int(labels[i]))
plt.axis("off")
"""
## Using image data augmentation
When you don't have a large image dataset, it's a good practice to artificially
introduce sample diversity by applying random yet realistic transformations to the
training images, such as random horizontal flipping or small random rotations. This
helps expose the model to different aspects of the training data while slowing down
overfitting.
"""
data_augmentation = keras.Sequential(
[layers.RandomFlip("horizontal"), layers.RandomRotation(0.1),]
)
"""
Let's visualize what the augmented samples look like, by applying `data_augmentation`
repeatedly to the first image in the dataset:
"""
plt.figure(figsize=(10, 10))
for images, _ in train_ds.take(1):
for i in range(9):
augmented_images = data_augmentation(images)
ax = plt.subplot(3, 3, i + 1)
plt.imshow(augmented_images[0].numpy().astype("uint8"))
plt.axis("off")
)
# have a look at some of the images:
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(16):
ax = plt.subplot(4, 4, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
#plt.title(int(labels[i]))
plt.title(int(np.where(labels[i] == 1)[0]))
plt.axis("off")
data_augmentation = keras.Sequential([
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.1),
])
# have a look at what the image augmentation is doing:
plt.figure(figsize=(10, 10))
for images, _ in train_ds.take(1):
for i in range(9):
augmented_images = data_augmentation(images)
ax = plt.subplot(3, 3, i + 1)
plt.imshow(augmented_images[0].numpy().astype("uint8"))
plt.axis("off")
# Configure the dataset for performance
# Let's make sure to use buffered prefetching so we can yield data from disk without having I/O becoming blocking:
#train_ds = train_ds.prefetch( buffer_size=32 )
#val_ds = val_ds.prefetch( buffer_size=32 )
strip_chars = "!\"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"
strip_chars = strip_chars.replace("<", "")
strip_chars = strip_chars.replace(">", "")
vectorization = TextVectorization(
max_tokens=VOCAB_SIZE,
output_mode="int",
output_sequence_length=SEQ_LENGTH,
standardize=custom_standardization,
)
vectorization.adapt(text_data)
# Data augmentation for image data
image_augmentation = keras.Sequential([
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.2),
layers.RandomContrast(0.3),
])
"""
## Building a `tf.data.Dataset` pipeline for training
We will generate pairs of images and corresponding captions using a `tf.data.Dataset` object.
The pipeline consists of two steps:
1. Read the image from the disk
2. Tokenize all the five captions corresponding to the image
"""
def decode_and_resize(img_path, size=IMAGE_SIZE):
img = tf.io.read_file(img_path)
projection_dim * 2,
projection_dim,
] # Size of the transformer layers
transformer_layers = 8
# Size of the dense layers of the final classifier
mlp_head_units = [2048, 1024]
"""
## Use data augmentation
"""
data_augmentation = keras.Sequential(
[
layers.Normalization(),
layers.Resizing(image_size, image_size),
layers.RandomFlip("horizontal"),
layers.RandomRotation(factor=0.02),
layers.RandomZoom(height_factor=0.2, width_factor=0.2),
],
name="data_augmentation",
)
# Compute the mean and the variance of the training data for normalization.
data_augmentation.layers[0].adapt(x_train)
"""
## Implement multilayer perceptron (MLP)
"""
def mlp(x, hidden_units, dropout_rate):
for units in hidden_units:
x = layers.Dense(units, activation=tf.nn.gelu)(x)
x = layers.Dropout(dropout_rate)(x)
print(f"Image shape: {metadata.features['image'].shape}")
print(f"Training images: {metadata.splits['train'].num_examples}")
print(f"Test images: {metadata.splits['test'].num_examples}")
"""
## Use Data Augmentation
We will rescale the data to `[0, 1]` and perform simple augmentations to our data.
"""
rescale = layers.Rescaling(1.0 / 255)
data_augmentation = tf.keras.Sequential(
[
layers.RandomFlip("horizontal_and_vertical"),
layers.RandomRotation(0.3),
layers.RandomZoom(0.2),
]
)
def prepare(ds, shuffle=False, augment=False):
# Rescale dataset
ds = ds.map(lambda x, y: (rescale(x), y), num_parallel_calls=AUTOTUNE)
if shuffle:
ds = ds.shuffle(1024)
# Batch dataset
ds = ds.batch(batch_size)
def main():
# Download the dataset
# Using the Flickr8K dataset for this tutorial. This dataset
# comprises over 8,000 images, that are each paired with five
# different captions.
#!wget -q https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_Dataset.zip
#!wget -q https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_text.zip
#!unzip -qq Flickr8k_Dataset.zip
#!unzip -qq Flickr8k_text.zip
#!rm Flickr8k_Dataset.zip Flickr8k_text.zip
# Path to images.
IMAGES_PATH = "Flicker8k_Dataset"
# Desired image dimensions.
IMAGE_SIZE = (299, 299)
# Vocabulary size.
VOCAB_SIZE = 10000
# Fixed length allowed for any sequence.
SEQ_LENGTH = 25
# Dimension for the image embeddings and token embeddings.
EMBED_DIM = 512
# Pre-layer units in the feed-forward network.
FF_DIM = 512
# Other training parameters.
BATCH_SIZE = 64
EPOCHS = 30
AUTOTUNE = tf.data.AUTOTUNE
# Preparing the dataset.
def load_captions_data(filename):
# Load captions (text) data and maps them to corresponding
# images.
# @param: filename, path to the text file containing caption
# data.
# @return: caption_mapping, dictionary mapping image names and
# the corresponding captions.
# @return: text_data, list containing all the available
# captions.
with open(filename) as caption_file:
caption_data = caption_file.readlines()
caption_mapping = {}
text_data = []
images_to_skip = set()
for line in caption_data:
line = line.rstrip("\n")
# IMage name and captions are separated using a tab.
img_name, caption = line.split("\t")
# Each image is repeated five times for the five
# different captions. Each image name has a suffix
# '#(caption_number)'.
img_name = img_name.split("#")[0]
img_name = os.path.join(IMAGES_PATH, img_name.strip())
# Remove captions that are either too short or too
# long.
tokens = caption.strip().split()
if len(tokens) < 5 or len(tokens) > SEQ_LENGTH:
images_to_skip.add(img_name)
continue
if img_name.endswith("jpg") and img_name not in images_to_skip:
# Add a start and end token to each caption.
caption = "<start>" + caption.strip() + "<end>"
text_data.append(caption)
if img_name in caption_mapping:
caption_mapping[img_name].append(caption)
else:
caption_mapping[img_name] = [caption]
for img_name in images_to_skip:
if img_name in caption_mapping:
del caption_mapping[img_name]
return caption_mapping, text_data
def train_val_split(caption_data, train_size=0.8, shuffle=True):
# Split the captioning dataset into train and validation sets.
# @param: caption_data (dict), dictionary containing the mapped
# data.
# @param: train_size (float), fraction of all the full dataset
# to use as training data.
# @param: shuffle (bool), whether to shuffle the dataset before
# splitting.
# @return: training and validation datasets as two separated
# dicts.
# 1) Get the list of all image names.
all_images = list(caption_data.keys())
# 2) Shuffle if necessary.
if shuffle:
np.random.shuffle(all_images)
# 3) Split into training and validation sets.
train_size = int(len(caption_data) * train_size)
training_data = {
img_name: caption_data[img_name]
for img_name in all_images[:train_size]
}
validation_data = {
img_name: caption_data[img_name]
for img_name in all_images[train_size:]
}
# 4) Return the splits.
return training_data, validation_data
# Load the dataset.
captions_mapping, text_data = load_captions_data("Flickr8k.token.txt")
# Split the dataset into training and validation sets.
train_data, valid_data = train_val_split(captions_mapping)
print("Number of training samples: ", len(train_data))
print("Number of validation samples: ", len(valid_data))
# Vectorizing the text data
# Use the TextVectorization layer to vectorize the text data, that
# is to say, to turn the original strings into integer sequences
# where each integer represents the index of a word in a
# vocabulary. Use a custom string standardization scheme (in this
# case, strip punctuation characters except < and >) and the
# default splitting scheme (split on whitespace).
def custom_standardization(input_string):
lowercase = tf.strings.lower(input_string)
return tf.strings.regex_replace(lowercase,
"[%s]" % re.escape(strip_chars), "")
strip_chars = "!\"#$%&'()*+,-./;<=>?@[\]^_`{|}~"
strip_chars = strip_chars.replace("<", "")
strip_chars = strip_chars.replace(">", "")
vectorization = TextVectorization(
max_tokens=VOCAB_SIZE,
output_mode="int",
output_sequence_length=SEQ_LENGTH,
standardize=custom_standardization,
)
vectorization.adapt(text_data)
# Data augmentation for image data.
image_augmentation = keras.Sequential([
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.2),
layers.RandomContrast(0.3),
])
# Building a tf.data.Dataset pipeline for training
# Generate pairs of images and corresponding captions using a
# tf.data.Dataset object. The pipeline consists of two steps:
# 1) Read the image from the disk.
# 2) Tokenize all the five captions corresponding to the image.
def decode_and_resize(img_path):
img = tf.io.read_file(img_path)
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.resize(img, IMAGE_SIZE)
img = tf.image.convert_image_dtype(img, tf.float32)
return img
def process_input(img_path, captions):
return decode_and_resize(img_path), vectorization(captions)
def make_dataset(images, captions):
'''
if split == "train":
img_dataset = tf.data.Dataset.from_tensor_slices(images).map(
read_train_image, num_parallel_calls=AUTOTUNE
)
else:
img_dataset = tf.data.Dataset.from_tensor_slices(images).map(
read_valid_image, num_parallel_calls=AUTOTUNE
)
cap_dataset = tf.data.Dataset.from_tensor_slices(captions).map(
vectorization, num_parallel_calls=AUTOTUNE
)
dataset = tf.data.Dataset.zip((img_dataset, cap_dataset))
dataset = dataset.batch(BATCH_SIZE).shuffle(256).prefetch(AUTOTUNE)
return dataset
'''
dataset = tf.data.Dataset.from_tensor_slices((images, captions))
dataset = dataset.shuffle(len(images))
dataset = dataset.map(process_input, num_parallel_calls=AUTOTUNE)
dataset = dataset.batch(BATCH_SIZE).prefetch(AUTOTUNE)
return dataset
# Pass the list of images and the list of corresponding captions.
train_dataset = make_dataset(list(train_data.keys()),
list(train_data.values()))
valid_dataset = make_dataset(list(valid_data.keys()),
list(valid_data.values()))
# Building the model
# The image captioning architecture consists of three models:
# 1) A CNN: Used to extract the image features.
# 2) A TransformerEncoder: The extracted image features are then
# passed to a Transformer based encoder that generates a new
# representation of the inputs.
# 3) A TransformerDecoder: This model takes the encoder output and
# the text data (sequences) as inputs and tries to learn to
# generate the caption.
def get_cnn_model():
base_model = efficientnet.EfficientNetB0(
input_shape=(*IMAGE_SIZE, 3),
include_top=False,
weights="imagenet",
)
# Freeze the feature extractor.
base_model.trainable = False
base_model_out = base_model.output
base_model_out = layers.Reshape(
(-1, base_model_out.shape[-1]))(base_model_out)
cnn_model = keras.models.Model(base_model.input, base_model_out)
return cnn_model
class TransformerEncoderBlock(layers.Layer):
def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.dense_dim = dense_dim
self.num_heads = num_heads
self.attention_1 = layers.MultiHeadAttention(num_heads=num_heads,
key_dim=embed_dim,
dropout=0.0)
self.layernorm1 = layers.LayerNormalization()
self.layernorm2 = layers.LayerNormalization()
self.dense_1 = layers.Dense(embed_dim, activation="relu")
def call(self, inputs, training, mask=None):
inputs = self.layernorm1(inputs)
inputs = self.dense_1(inputs)
attention_output_1 = self.attention_1(
query=inputs,
value=inputs,
key=inputs,
attention_mask=None,
training=training,
)
out_1 = self.layernorm2(inputs + attention_output_1)
return out_1
class PositionalEmbedding(layers.Layer):
def __init__(self, sequence_length, vocab_size, embed_dim, **kwargs):
super().__init__(**kwargs)
self.token_embeddings = layers.Embedding(input_dim=vocab_size,
output_dim=embed_dim)
self.position_embeddings = layers.Embedding(
input_dim=sequence_length, output_dim=embed_dim)
self.sequence_length = sequence_length
self.vocab_size = vocab_size
self.embed_dim = embed_dim
self.embed_scale = tf.math.sqrt(tf.cast(embed_dim, tf.float32))
def call(self, inputs):
length = tf.shape(inputs)[-1]
positions = tf.range(start=0, limit=length, delta=1)
embedded_tokens = self.token_embeddings(inputs)
embedded_tokens = embedded_tokens * self.embed_scale
embedded_positions = self.position_embeddings(positions)
return embedded_tokens + embedded_positions
def compute_mask(self, inputs, mask=None):
return tf.math.not_equal(inputs, 0)
class TransformerDecoderBlock(layers.Layer):
def __init__(self, embed_dim, ff_dim, num_heads, **kwargs):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.ff_dim = ff_dim
self.num_heads = num_heads
self.attention_1 = layers.MultiHeadAttention(num_heads=num_heads,
key_dim=embed_dim,
dropout=0.1)
self.attention_2 = layers.MultiHeadAttention(num_heads=num_heads,
key_dim=embed_dim,
dropout=0.1)
self.ffn_layer_1 = layers.Dense(ff_dim, activation="relu")
self.ffn_layer_2 = layers.Dense(embed_dim)
self.layernorm_1 = layers.LayerNormalization()
self.layernorm_2 = layers.LayerNormalization()
self.layernorm_3 = layers.LayerNormalization()
self.embedding = PositionalEmbedding(embed_dim=EMBED_DIM,
sequence_length=SEQ_LENGTH,
vocab_size=VOCAB_SIZE)
self.out = layers.Dense(VOCAB_SIZE, activation="softmax")
self.dropout_1 = layers.Dropout(0.3)
self.dropout_2 = layers.Dropout(0.5)
self.supports_masking = True
def call(self, inputs, encoder_outputs, training, mask=None):
inputs = self.embedding(inputs)
causal_mask = self.get_causal_attention_mask(inputs)
if mask is not None:
padding_mask = tf.cast(mask[:, :, tf.newaxis], dtype=tf.int32)
combined_mask = tf.cast(mask[:, tf.newaxis, :], dtype=tf.int32)
combined_mask = tf.minimum(combined_mask, causal_mask)
attention_output_1 = self.attention_1(
query=inputs,
value=inputs,
key=inputs,
attention_mask=combined_mask,
training=training,
)
out_1 = self.layernorm_1(inputs + attention_output_1)
attention_output_2 = self.attention_2(
query=out_1,
value=encoder_outputs,
key=encoder_outputs,
attention_mask=padding_mask,
training=training,
)
out_2 = self.layernorm_2(out_1 + attention_output_2)
ffn_out = self.ffn_layer_1(out_2)
ffn_out = self.dropout_1(ffn_out, training=training)
ffn_out = self.ffn_layer_2(ffn_out)
ffn_out = self.layernorm_3(ffn_out + out_2, training=training)
ffn_out = self.dropout_2(ffn_out, training=training)
preds = self.out(ffn_out)
return preds
def get_causal_attention_mask(self, inputs):
input_shape = tf.shape(inputs)
batch_size, sequence_length = input_shape[0], input_shape[1]
i = tf.range(sequence_length)[:, tf.newaxis]
j = tf.range(sequence_length)
mask = tf.cast(i >= j, dtype="int32")
mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))
mult = tf.concat(
[
tf.expand_dims(batch_size, -1),
tf.constant([1, 1], dtype=tf.int32)
],
axis=0,
)
return tf.tile(mask, mult)
class ImageCaptioningModel(keras.Model):
def __init__(self,
cnn_model,
encoder,
decoder,
num_captions_per_image=5,
image_aug=None):
super().__init__()
self.cnn_model = cnn_model
self.encoder = encoder
self.decoder = decoder
self.loss_tracker = keras.metrics.Mean(name="loss")
self.acc_tracker = keras.metrics.Mean(name="accuracy")
self.num_captions_per_image = num_captions_per_image
self.image_aug = image_aug
def calculate_loss(self, y_true, y_pred, mask):
loss = self.loss(y_true, y_pred)
mask = tf.cast(mask, dtype=loss.dtype)
loss *= mask
return tf.reduce_sum(loss) / tf.reduce_sum(mask)
def calculate_accuracy(self, y_true, y_pred, mask):
accuracy = tf.equal(y_true, tf.argmax(y_pred, axis=2))
accuracy = tf.math.logical_and(mask, accuracy)
accuracy = tf.cast(accuracy, dtype=tf.float32)
mask = tf.cast(mask, dtype=tf.float32)
return tf.reduce_sum(accuracy) / tf.reduce_sum(mask)
def _compute_caption_loss_and_acc(self,
img_embed,
batch_seq,
training=True):
encoder_out = self.encoder(img_embed, training=training)
batch_seq_inp = batch_seq[:, :-1]
batch_seq_true = batch_seq[:, 1:]
mask = tf.math.not_equal(batch_seq_true, 0)
batch_seq_pred = self.decoder(batch_seq_inp,
encoder_out,
training=training,
mask=mask)
loss = self.calculate_loss(batch_seq_true, batch_seq_pred, mask)
acc = self.calculate_accuracy(batch_seq_true, batch_seq_pred, mask)
return loss, acc
def train_step(self, batch_data):
batch_img, batch_seq = batch_data
batch_loss = 0
batch_acc = 0
if self.image_aug:
batch_img = self.image_aug(batch_img)
# 1) Get image embeddings.
img_embed = self.cnn_model(batch_img)
# 2) Pass each of the five captions one by one to the
# decoder along with the encoder outputs and compute the
# loss as well as accuracy for each caption.
for i in range(self.num_captions_per_image):
with tf.GradientTape() as tape:
loss, acc = self._compute_caption_loss_and_acc(
img_embed, batch_seq[:, i, :], training=True)
# 3) Update loss and accuracy.
batch_loss += loss
batch_acc += acc
# 4) Get the list of all the trainable weights.
train_vars = (self.encoder.trainable_variables +
self.decoder.trainable_variables)
# 5) Get the gradients.
grads = tape.gradient(loss, train_vars)
# 6) Update the trainable weights.
self.optimizer.apply_gradients(zip(grads, train_vars))
# 7) Update the trackers.
batch_acc /= float(self.num_captions_per_image)
self.loss_tracker.update_state(batch_loss)
self.acc_tracker.update_state(batch_acc)
# 8) Return the loss and accuracy values.
return {
"loss": self.loss_tracker.result(),
"acc": self.acc_tracker.result()
}
def test_step(self, batch_data):
batch_img, batch_seq = batch_data
batch_loss = 0
batch_acc = 0
# 1) Get image embeddings.
img_embed = self.cnn_model(batch_img)
# 2) Pass each of the five captions one by one to the
# decoder along with the encoder outputs and compute the
# loss as well as accuracy for each caption.
for i in range(self.num_captions_per_image):
loss, acc = self._compute_caption_loss_and_acc(img_embed,
batch_seq[:,
i, :],
training=False)
# 3) Update loss and accuracy.
batch_loss += loss
batch_acc += acc
batch_acc /= float(self.num_captions_per_image)
# 4) Update the trackers.
self.loss_tracker.update_state(batch_loss)
self.acc_tracker.update_state(batch_acc)
# 8) Return the loss and accuracy values.
return {
"loss": self.loss_tracker.result(),
"acc": self.acc_tracker.result()
}
@property
def metrics(self):
# List the metrics here so the 'reset_states()' can be
# called automatically.
return [self.loss_tracker, self.acc_tracker]
cnn_model = get_cnn_model()
encoder = TransformerEncoderBlock(embed_dim=EMBED_DIM,
dense_dim=FF_DIM,
num_heads=1)
decoder = TransformerDecoderBlock(embed_dim=EMBED_DIM,
ff_dim=FF_DIM,
num_heads=2)
caption_model = ImageCaptioningModel(
cnn_model=cnn_model,
encoder=encoder,
decoder=decoder,
image_aug=image_augmentation,
)
# Model training
# Define the loss function.
cross_entropy = keras.losses.SparseCategoricalCrossentropy(
from_logits=False, reduction="none")
# Early stopping criteria.
early_stopping = keras.callbacks.EarlyStopping(patience=3,
restore_best_weights=True)
# Learning Rate Scheduler for the optimizer.
class LRSchedule(keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, post_warmup_learning_rate, warmup_steps):
super().__init__()
self.post_warmup_learning_rate = post_warmup_learning_rate
self.warmup_steps = warmup_steps
def __call__(self, step):
global_step = tf.cast(step, tf.float32)
warmup_steps = tf.cast(self.warmup_steps, tf.float32)
warmup_progress = global_step / warmup_steps
warmup_learning_rate = self.post_warmup_learning_rate * warmup_progress
return tf.cond(
global_step < warmup_steps,
lambda: warmup_learning_rate,
lambda: self.post_warmup_learning_rate,
)
# Create a learning rate schedule.
num_train_steps = len(train_dataset) * EPOCHS
num_warmup_steps = num_train_steps // 15
lr_schedule = LRSchedule(post_warmup_learning_rate=1e-4,
warmup_steps=num_warmup_steps)
# Compile the model.
caption_model.compile(optimizer=keras.optimizers.Adam(lr_schedule),
loss=cross_entropy)
# Fit the model.
caption_model.fit(
train_dataset,
epochs=EPOCHS,
validation_data=valid_dataset,
callbacks=[early_stopping],
)
# Check sample predictions
'''
vocab = vectorization.get_vocabulary()
index_lookup = dict(zip(range(len(vocab)), vocab))
max_decoded_sentence_length = SEQ_LENGTH - 1
valid_images = list(valid_data.keys())
def generate_caption():
# Select a random image from the validation dataset.
sample_img = np.random.choice(valid_images)
# Read the image from the disk.
sample_img = decode_and_resize(sample_img)
img = sample_img.numpy().clip(0, 255).astype(np.uint8)
plt.imshow(img)
plt.show()
# Pass the image to the CNN.
img = tf.expand_dims(sample_img, 0)
img = caption_model.cnn_model(img)
# Pass the image features to the Transformer encoder.
encoded_img = caption_model.encoder(img, training=False)
# Generate the caption using the Transformer decoder.
decoded_caption = "<start>"
for i in range(max_decoded_sentence_length):
tokenized_caption = vectorization([decoded_caption])[:, :-1]
mask = tf.math.not_equal(tokenized_caption, 0)
predictions = caption_model.decoder(
tokenized_caption, encoded_img, training=False, mask=mask
)
sampled_token_index = np.argmax(predictions[0, i, :])
sampled_token = index_lookup[sampled_token_index]
if sampled_token == " <end>":
break
decoded_caption += " " + sampled_token
decoded_caption = decoded_caption.replace("<start>", "")
decoded_caption = decoded_caption.replace("<end>", "").strip()
print("Predicted Caption: ", decoded_caption)
# Check predictions for a few samples.
generate_caption()
generate_caption()
generate_caption()
'''
# End Notes
# Notice that the model starts to generate reasonable captions
# after a few epochs. To keep this example easily runnable, it has
# been trained with a few constraints, like a minimal number of
# attention heads. To improve predictions, try changing these
# training settings and find a good model for your use case.
# Exit the program.
exit(0)
data_preprocessing.layers[-1].adapt(x_data)
"""
## Data augmentation
Unlike simCLR, which randomly picks a single data augmentation function to apply to an input
image, we apply a set of data augmentation functions randomly to the input image.
(You can experiment with other image augmentation techniques by following
the [data augmentation tutorial](https://www.tensorflow.org/tutorials/images/data_augmentation).)
"""
data_augmentation = keras.Sequential([
layers.RandomTranslation(height_factor=(-0.2, 0.2),
width_factor=(-0.2, 0.2),
fill_mode="nearest"),
layers.RandomFlip(mode="horizontal"),
layers.RandomRotation(factor=0.15, fill_mode="nearest"),
layers.RandomZoom(height_factor=(-0.3, 0.1),
width_factor=(-0.3, 0.1),
fill_mode="nearest"),
])
"""
Display a random image
"""
image_idx = np.random.choice(range(x_data.shape[0]))
image = x_data[image_idx]
image_class = classes[y_data[image_idx][0]]
plt.figure(figsize=(3, 3))
plt.imshow(x_data[image_idx].astype("uint8"))
plt.title(image_class)
_ = plt.axis("off")
