Training and Saving the models

training.py

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+import tensorflow as tf
+import os
+import json
+import pandas as pd
+import re
+import numpy as np
+import time
+import matplotlib.pyplot as plt
+import collections
+import random
+import requests
+import json
+import pickle
+from math import sqrt
+from PIL import Image
+from tqdm.auto import tqdm
+
+DATASET_PATH = './coco2017/'
+MAX_LENGTH = 40
+MAX_VOCABULARY = 12000
+BATCH_SIZE = 64
+BUFFER_SIZE = 1000
+EMBEDDING_DIM = 512
+UNITS = 512
+EPOCHS = 5
+
+with open(f'{DATASET_PATH}/annotations/captions_train2017.json', 'r') as f:
+    data = json.load(f)
+    data = data['annotations']
+
+img_cap_pairs = []
+
+for sample in data:
+    img_name = '%012d.jpg' % sample['image_id']
+    img_cap_pairs.append([img_name, sample['caption']])
+
+captions = pd.DataFrame(img_cap_pairs, columns=['image', 'caption'])
+captions['image'] = captions['image'].apply(
+    lambda x: f'{DATASET_PATH}/train2017/{x}'
+)
+captions = captions.sample(70000)
+captions = captions.reset_index(drop=True)
+captions.head()
+
+def preprocessing(text):
+    text = text.lower()
+    text = re.sub(r'[^\w\s]', '', text)
+    text = re.sub('\s+', ' ', text)
+    text = text.strip()
+    text = '[start] ' + text + ' [end]'
+    return text
+  
+captions['caption'] = captions['caption'].apply(preprocessing)
+captions.head()
+
+tokenizer = tf.keras.layers.TextVectorization(
+    max_tokens=MAX_VOCABULARY,
+    standardize=None,
+    output_sequence_length=MAX_LENGTH)
+
+tokenizer.adapt(captions['caption'])
+
+pickle.dump(tokenizer.get_vocabulary(), open('./image-caption-generator/vocabulary/vocab_coco.file', 'wb'))
+
+word2idx = tf.keras.layers.StringLookup(
+  mask_token = "",
+  vocabulary = tokenizer.get_vocabulary()
+)
+
+idx2word = tf.keras.layers.StringLookup(
+  mask_token = "",
+  vocabulary = tokenizer.get_vocabulary(),
+  invert = True
+)
+
+img_to_cap_vector = collections.defaultdict(list)
+for img, cap in zip(captions['image'], captions['caption']):
+  img_to_cap_vector[img].append(cap)
+  
+img_keys = list(img_to_cap_vector.keys())
+random.shuffle(img_keys)
+
+slice_index = int(len(img_keys)*0.8)
+img_name_train_keys, img_name_test_keys = (img_keys[:slice_index], img_keys[slice_index:])
+
+train_img = []
+train_caption = []
+for imgt in img_name_train_keys:
+  capt_len = len(img_to_cap_vector[imgt])
+  train_img.extend([imgt]*capt_len)
+  train_caption.extend(img_to_cap_vector[imgt])
+  
+test_img = []
+test_caption = []
+for imgtest in img_name_test_keys:
+  capv_len = len(img_to_cap_vector[imgtest])
+  test_img.extend([imgtest]*capv_len)
+  test_caption.extend(img_to_cap_vector[imgtest])
+  
+len(train_img), len(train_caption), len(test_img), len(test_caption)
+
+def load_data(img_path, caption):
+  img = tf.io.read_file(img_path)
+  img = tf.io.decode_jpeg(img, channels=3)
+  img = tf.keras.layers.Resizing(299, 299)(img)
+  img = tf.keras.applications.inception_v3.preprocess_input(img)
+  caption = tokenizer(caption)
+  return img, caption
+
+train_dataset = tf.data.Dataset.from_tensor_slices((train_img,train_caption))
+
+train_dataset = train_dataset.map(load_data, num_parallel_calls = tf.data.AUTOTUNE).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
+
+test_dataset = tf.data.Dataset.from_tensor_slices((test_img,test_caption))
+
+test_dataset = test_dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
+
+image_augmentation = tf.keras.Sequential(
+    [
+        tf.keras.layers.RandomFlip("horizontal"),
+        tf.keras.layers.RandomRotation(0.2),
+        tf.keras.layers.RandomContrast(0.3),
+    ]
+)
+
+def CNN_Encoder():
+    inception_v3 = tf.keras.applications.InceptionV3(
+        include_top=False,
+        weights='imagenet'
+    )
+
+    output = inception_v3.output
+    output = tf.keras.layers.Reshape(
+        (-1, output.shape[-1]))(output)
+
+    cnn_model = tf.keras.models.Model(inception_v3.input, output)
+    return cnn_model
+  
+
+class TransformerEncoderLayer(tf.keras.layers.Layer):
+
+    def __init__(self, embed_dim, num_heads):
+        super().__init__()
+        self.layer_norm_1 = tf.keras.layers.LayerNormalization()
+        self.layer_norm_2 = tf.keras.layers.LayerNormalization()
+        self.attention = tf.keras.layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim)
+        self.dense = tf.keras.layers.Dense(embed_dim, activation="relu")
+    
+
+    def call(self, x, training):
+        x = self.layer_norm_1(x)
+        x = self.dense(x)
+
+        attn_output = self.attention(
+            query=x,
+            value=x,
+            key=x,
+            attention_mask=None,
+            training=training
+        )
+
+        x = self.layer_norm_2(x + attn_output)
+        return x
+      
+
+class Embeddings(tf.keras.layers.Layer):
+
+    def __init__(self, vocab_size, embed_dim, max_len):
+        super().__init__()
+        self.token_embeddings = tf.keras.layers.Embedding(
+            vocab_size, embed_dim)
+        self.position_embeddings = tf.keras.layers.Embedding(
+            max_len, embed_dim, input_shape=(None, max_len))
+    
+
+    def call(self, input_ids):
+        length = tf.shape(input_ids)[-1]
+        position_ids = tf.range(start=0, limit=length, delta=1)
+        position_ids = tf.expand_dims(position_ids, axis=0)
+
+        token_embeddings = self.token_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+
+        return token_embeddings + position_embeddings
+      
+class TransformerDecoderLayer(tf.keras.layers.Layer):
+
+    def __init__(self, embed_dim, units, num_heads):
+        super().__init__()
+        self.embedding = Embeddings(
+            tokenizer.vocabulary_size(), embed_dim, MAX_LENGTH)
+
+        self.attention_1 = tf.keras.layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim, dropout=0.1
+        )
+        self.attention_2 = tf.keras.layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim, dropout=0.1
+        )
+
+        self.layernorm_1 = tf.keras.layers.LayerNormalization()
+        self.layernorm_2 = tf.keras.layers.LayerNormalization()
+        self.layernorm_3 = tf.keras.layers.LayerNormalization()
+
+        self.ffn_layer_1 = tf.keras.layers.Dense(units, activation="relu")
+        self.ffn_layer_2 = tf.keras.layers.Dense(embed_dim)
+
+        self.out = tf.keras.layers.Dense(tokenizer.vocabulary_size(), activation="softmax")
+
+        self.dropout_1 = tf.keras.layers.Dropout(0.3)
+        self.dropout_2 = tf.keras.layers.Dropout(0.5)
+    
+
+    def call(self, input_ids, encoder_output, training, mask=None):
+        embeddings = self.embedding(input_ids)
+
+        combined_mask = None
+        padding_mask = None
+        
+        if mask is not None:
+            causal_mask = self.get_causal_attention_mask(embeddings)
+            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)
+
+        attn_output_1 = self.attention_1(
+            query=embeddings,
+            value=embeddings,
+            key=embeddings,
+            attention_mask=combined_mask,
+            training=training
+        )
+
+        out_1 = self.layernorm_1(embeddings + attn_output_1)
+
+        attn_output_2 = self.attention_2(
+            query=out_1,
+            value=encoder_output,
+            key=encoder_output,
+            attention_mask=padding_mask,
+            training=training
+        )
+
+        out_2 = self.layernorm_2(out_1 + attn_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)
+        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(tf.keras.Model):
+
+    def __init__(self, cnn_model, encoder, decoder, image_aug=None):
+        super().__init__()
+        self.cnn_model = cnn_model
+        self.encoder = encoder
+        self.decoder = decoder
+        self.image_aug = image_aug
+        self.loss_tracker = tf.keras.metrics.Mean(name="loss")
+        self.acc_tracker = tf.keras.metrics.Mean(name="accuracy")
+
+
+    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_loss_and_acc(self, img_embed, captions, training=True):
+        encoder_output = self.encoder(img_embed, training=True)
+        y_input = captions[:, :-1]
+        y_true = captions[:, 1:]
+        mask = (y_true != 0)
+        y_pred = self.decoder(
+            y_input, encoder_output, training=True, mask=mask
+        )
+        loss = self.calculate_loss(y_true, y_pred, mask)
+        acc = self.calculate_accuracy(y_true, y_pred, mask)
+        return loss, acc
+
+    
+    def train_step(self, batch):
+        imgs, captions = batch
+
+        if self.image_aug:
+            imgs = self.image_aug(imgs)
+        
+        img_embed = self.cnn_model(imgs)
+
+        with tf.GradientTape() as tape:
+            loss, acc = self.compute_loss_and_acc(
+                img_embed, captions
+            )
+    
+        train_vars = (
+            self.encoder.trainable_variables + self.decoder.trainable_variables
+        )
+        grads = tape.gradient(loss, train_vars)
+        self.optimizer.apply_gradients(zip(grads, train_vars))
+        self.loss_tracker.update_state(loss)
+        self.acc_tracker.update_state(acc)
+
+        return {"loss": self.loss_tracker.result(), "acc": self.acc_tracker.result()}
+    
+
+    def test_step(self, batch):
+        imgs, captions = batch
+
+        img_embed = self.cnn_model(imgs)
+
+        loss, acc = self.compute_loss_and_acc(
+            img_embed, captions, training=False
+        )
+
+        self.loss_tracker.update_state(loss)
+        self.acc_tracker.update_state(acc)
+
+        return {"loss": self.loss_tracker.result(), "acc": self.acc_tracker.result()}
+
+    @property
+    def metrics(self):
+        return [self.loss_tracker, self.acc_tracker]
+      
+
+encoder = TransformerEncoderLayer(EMBEDDING_DIM, 1)
+decoder = TransformerDecoderLayer(EMBEDDING_DIM, UNITS, 8)
+
+cnn_model = CNN_Encoder()
+caption_model = ImageCaptioningModel(
+    cnn_model=cnn_model, encoder=encoder, decoder=decoder, image_aug=image_augmentation,
+)
+
+
+cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(
+    from_logits=False, reduction="none"
+)
+
+early_stopping = tf.keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)
+
+caption_model.compile(
+    optimizer=tf.keras.optimizers.Adam(),
+    loss=cross_entropy
+)
+
+history = caption_model.fit(
+    train_dataset,
+    epochs=EPOCHS,
+    validation_data=val_dataset,
+    callbacks=[early_stopping]
+)
+
+caption_model.save_weights('./image-caption-generator/models/trained_coco_weights.h5')
+  
+  
+