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import keras
from keras import layers
import tensorflow as tf
IMAGE_SIZE = (299, 299)
VOCAB_SIZE = 10000
SEQ_LENGTH = 25
EMBED_DIM = 512
FF_DIM = 512
image_augmentation = keras.Sequential(
[
keras.layers.RandomFlip("horizontal"),
keras.layers.RandomRotation(0.2),
keras.layers.RandomContrast(0.3),
]
)
@keras.saving.register_keras_serializable()
def get_cnn_model():
base_model = keras.applications.efficientnet.EfficientNetB0(
input_shape=(*IMAGE_SIZE, 3),
include_top=False,
weights="imagenet"
)
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
@keras.saving.register_keras_serializable()
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 get_config(self):
base_config = super().get_config()
config = {
"embed_dim": self.embed_dim,
"dense_dim": self.dense_dim,
"num_heads": self.num_heads,
}
return {**base_config, **config}
def call(self, inputs, training):
inputs = self.layernorm_1(inputs)
inputs = self.dense_1(inputs)
attention_output_1 = self.attention_1(
query=inputs,
value=inputs,
key=inputs,
training=training,
)
out_1 = self.layernorm_2(inputs + attention_output_1)
return out_1
@keras.saving.register_keras_serializable()
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, mask_zero=True
)
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.add = layers.Add()
def get_config(self):
base_config = super().get_config()
config = {
"sequence_length": self.sequence_length,
"vocab_size": self.vocab_size,
"embed_dim": self.embed_dim,
}
return {**base_config, **config}
def call(self, seq):
seq = self.token_embeddings(seq)
x = tf.range(tf.shape(seq)[1])
x = x[tf.newaxis, :]
x = self.position_embeddings(x)
return self.add([seq, x])
@keras.saving.register_keras_serializable()
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 get_config(self):
base_config = super().get_config()
config = {
"embed_dim": self.embed_dim,
"ff_dim": self.ff_dim,
"num_heads": self.num_heads,
}
return {**base_config, **config}
def call(self, inputs, encoder_outputs, training, mask=None):
inputs = self.embedding(inputs)
attention_output_1 = self.attention_1(
query=inputs,
value=inputs,
key=inputs,
training=training,
use_causal_mask=True
)
out_1 = self.layernorm_1(inputs + attention_output_1)
attention_output_2 = self.attention_2(
query=out_1,
value=encoder_outputs,
key=encoder_outputs,
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
@keras.saving.register_keras_serializable()
class ImageCaptioningModel(keras.Model):
def __init__(
self,
cnn_model,
encoder,
decoder,
image_aug=None,
**kwargs
):
super().__init__(**kwargs)
self.cnn_model = cnn_model
self.encoder = encoder
self.decoder = decoder
self.image_aug = image_aug
def get_config(self):
base_config = super().get_config()
config = {
"cnn_model": self.cnn_model,
"encoder": self.encoder,
"decoder": self.decoder,
"image_aug": self.image_aug,
}
return {**base_config, **config}
@classmethod
def from_config(cls, config):
# Note that you can also use [`keras.saving.deserialize_keras_object`](/api/models/model_saving_apis/serialization_utils#deserializekerasobject-function) here
config["cnn_model"] = keras.saving.deserialize_keras_object(
config["cnn_model"])
config["encoder"] = keras.saving.deserialize_keras_object(
config["encoder"])
config["decoder"] = keras.saving.deserialize_keras_object(
config["decoder"])
config["image_aug"] = keras.saving.deserialize_keras_object(
config["image_aug"])
# Instantiate the ImageCaptioningModel with the remaining configuration
return cls(**config)
def call(self, inputs, training):
img, caption = inputs
if self.image_aug:
img = self.image_aug(img)
img_embed = self.cnn_model(img)
encoder_out = self.encoder(img_embed, training=training)
pred = self.decoder(caption, encoder_out, training=training)
return pred
@keras.saving.register_keras_serializable()
class LRSchedule(keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, post_warmup_learning_rate, warmup_steps, **kwargs):
super().__init__(**kwargs)
self.post_warmup_learning_rate = post_warmup_learning_rate
self.warmup_steps = warmup_steps
def get_config(self):
config = {
"post_warmup_learning_rate": self.post_warmup_learning_rate,
"warmup_steps": self.warmup_steps,
}
return config
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,
)
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