models.py

import os
import re
import numpy as np
import matplotlib.pyplot as plt

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.applications import efficientnet
#import efficientnet
from tensorflow.keras.layers import TextVectorization


seed = 111
np.random.seed(seed)
tf.random.set_seed(seed)


# Path to the 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

# Per-layer units in the feed-forward network
FF_DIM = 512

# Other training parameters
BATCH_SIZE = 64
EPOCHS = 30
AUTOTUNE = tf.data.AUTOTUNE

strip_chars = "!\"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"
strip_chars = strip_chars.replace("<", "")
strip_chars = strip_chars.replace(">", "")

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 custom_standardization(input_string):
    lowercase = tf.strings.lower(input_string)
    return tf.strings.regex_replace(lowercase, "[%s]" % re.escape(strip_chars), "")

vectorization = TextVectorization(
    max_tokens=VOCAB_SIZE,
    output_mode="int",
    output_sequence_length=SEQ_LENGTH,
    standardize=custom_standardization,
)
def load_captions_data(filename):
    """Loads captions (text) data and maps them to corresponding images.

    Args:
        filename: Path to the text file containing caption data.

    Returns:
        caption_mapping: Dictionary mapping image names and the corresponding captions
        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())

            # We will remove caption that are either too short to 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:
                # We will add a start and an 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


# Load the dataset
captions_mapping, text_data = load_captions_data("Flickr8k.token.txt")

vectorization.adapt(text_data)

def process_input(img_path, captions):
    return decode_and_resize(img_path), vectorization(captions)


def make_dataset(images, captions):
    dataset = tf.data.Dataset.from_tensor_slices((images, captions))
    dataset = dataset.shuffle(BATCH_SIZE * 8)
    dataset = dataset.map(process_input, num_parallel_calls=AUTOTUNE)
    dataset = dataset.batch(BATCH_SIZE).prefetch(AUTOTUNE)

    return dataset


def train_val_split(caption_data, train_size=0.8, shuffle=True):
    """Split the captioning dataset into train and validation sets.

    Args:
        caption_data (dict): Dictionary containing the mapped caption data
        train_size (float): Fraction of all the full dataset to use as training data
        shuffle (bool): Whether to shuffle the dataset before splitting

    Returns:
        Traning 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



# 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))

train_dataset = make_dataset(list(train_data.keys()), list(train_data.values()))

valid_dataset = make_dataset(list(valid_data.keys()), list(valid_data.values()))

def get_cnn_model():
    base_model = efficientnet.EfficientNetB0(
        input_shape=(*IMAGE_SIZE, 3), include_top=False, weights="imagenet",
    )
    # We freeze our 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

def load_captions_data(filename):
    """Loads captions (text) data and maps them to corresponding images.

    Args:
        filename: Path to the text file containing caption data.

    Returns:
        caption_mapping: Dictionary mapping image names and the corresponding captions
        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())

            # We will remove caption that are either too short to 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:
                # We will add a start and an 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.

    Args:
        caption_data (dict): Dictionary containing the mapped caption data
        train_size (float): Fraction of all the full dataset to use as training data
        shuffle (bool): Whether to shuffle the dataset before splitting

    Returns:
        Traning 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))




# Data augmentation for image data
image_augmentation = keras.Sequential(
    [
        layers.RandomFlip("horizontal"),
        layers.RandomRotation(0.2),
        layers.RandomContrast(0.3),
    ]
)

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.layernorm_1 = layers.LayerNormalization()
        self.layernorm_2 = layers.LayerNormalization()
        self.dense_1 = layers.Dense(embed_dim, activation="relu")

    def call(self, inputs, training, mask=None):
        inputs = self.layernorm_1(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.layernorm_2(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 batch loss and batch 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)

        # 5. Return the loss and accuracy values
        return {"loss": self.loss_tracker.result(), "acc": self.acc_tracker.result()}

    @property
    def metrics(self):
        # We need to list our 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)
# new_model = ImageCaptioningModel(
#     cnn_model=cnn_model, encoder=encoder, decoder=decoder, image_aug=image_augmentation,
# )

# new_model.load_weights('model_weights.h5')