Update Models Function

model.py

@@ -10,7 +10,7 @@ EMBEDDING_DIM = 512
 UNITS = 512
 
 
-# LOADING DATA
+#LOAD VOCAB FOLDER
 vocab = pickle.load(open('vocabulary/vocab_coco.file', 'rb'))
 
 tokenizer = tf.keras.layers.TextVectorization(
@@ -25,6 +25,234 @@ idx2word = tf.keras.layers.StringLookup(
     invert = True
 )
 
+# CREATING MODEL BASED ON KERAS
+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]
+
 def load_image_from_path(img_path):
     img = tf.io.read_file(img_path)
     img = tf.io.decode_jpeg(img, channels=3)