Image-Captioning / model.py
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import pickle
import tensorflow as tf
import pandas as pd
import numpy as np
# CONTANTS
MAX_LENGTH = 40
VOCABULARY_SIZE = 10000
BATCH_SIZE = 32
BUFFER_SIZE = 1000
EMBEDDING_DIM = 512
UNITS = 512
# LOADING DATA
vocab = pickle.load(open('saved_models/vocab.file', 'rb'))
tokenizer = tf.keras.layers.TextVectorization(
max_tokens=VOCABULARY_SIZE,
standardize=None,
output_sequence_length=MAX_LENGTH,
vocabulary=vocab
)
idx2word = tf.keras.layers.StringLookup(
mask_token="",
vocabulary=tokenizer.get_vocabulary(),
invert=True)
# MODEL
def CNN_Encoder():
inception_v3 = tf.keras.applications.InceptionV3(
include_top=False,
weights='imagenet'
)
inception_v3.trainable = False
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)
img = tf.keras.layers.Resizing(299, 299)(img)
img = img / 255.
return img
def generate_caption(img, caption_model):
if isinstance(img, str):
img = load_image_from_path(img)
if isinstance(img, np.ndarray):
img = tf.convert_to_tensor(img)
img = tf.expand_dims(img, axis=0)
img_embed = caption_model.cnn_model(img)
img_encoded = caption_model.encoder(img_embed, training=False)
y_inp = '[start]'
for i in range(MAX_LENGTH-1):
tokenized = tokenizer([y_inp])[:, :-1]
mask = tf.cast(tokenized != 0, tf.int32)
pred = caption_model.decoder(
tokenized, img_encoded, training=False, mask=mask)
pred_idx = np.argmax(pred[0, i, :])
pred_word = idx2word(pred_idx).numpy().decode('utf-8')
if pred_word == '[end]':
break
y_inp += ' ' + pred_word
y_inp = y_inp.replace('[start] ', '')
return y_inp
def get_caption_model():
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=None,
)
def call_fn(batch, training):
return batch
caption_model.call = call_fn
sample_x, sample_y = tf.random.normal((1, 299, 299, 3)), tf.zeros((1, 40))
caption_model((sample_x, sample_y))
sample_img_embed = caption_model.cnn_model(sample_x)
sample_enc_out = caption_model.encoder(sample_img_embed, training=False)
caption_model.decoder(sample_y, sample_enc_out, training=False)
caption_model.load_weights('saved_models\image_captioning_transformer_weights.h5')
return caption_model