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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import datetime
import numpy as np
import tensorflow as tf
def default_hparams():
return {
'img_x': 224,
'img_y': 224,
'img_ch': 1,
'd_model': 512,
'dff': 2048,
'num_heads': 8,
'num_layers': 6,
'dropout_rate': 0.1
}
def positional_encoding(length, depth):
depth = depth / 2
positions = np.arange(length)[:, np.newaxis] # (seq, 1)
depths = np.arange(depth)[np.newaxis, :] / depth # (1, depth)
angle_rates = 1 / (10000 ** depths) # (1, depth)
angle_rads = positions * angle_rates # (pos, depth)
pos_encoding = np.concatenate(
[np.sin(angle_rads), np.cos(angle_rads)],
axis=-1)
return tf.cast(pos_encoding, dtype=tf.float32)
class PositionalEmbedding(tf.keras.layers.Layer):
def __init__(self, vocab_size, d_model):
super().__init__()
self.d_model = d_model
self.embedding = tf.keras.layers.Embedding(vocab_size, d_model, mask_zero=True)
self.pos_encoding = positional_encoding(length=2048, depth=d_model)
def compute_mask(self, *args, **kwargs):
return self.embedding.compute_mask(*args, **kwargs)
def call(self, x):
length = tf.shape(x)[1]
x = self.embedding(x)
# This factor sets the relative scale of the embedding and positonal_encoding.
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x = x + self.pos_encoding[tf.newaxis, :length, :]
return x
class BaseAttention(tf.keras.layers.Layer):
def __init__(self, **kwargs):
super().__init__()
self.mha = tf.keras.layers.MultiHeadAttention(**kwargs)
self.layernorm = tf.keras.layers.LayerNormalization()
self.add = tf.keras.layers.Add()
class CrossAttention(BaseAttention):
def call(self, x, context):
attn_output, attn_scores = self.mha(
query=x,
key=context,
value=context,
return_attention_scores=True)
# Cache the attention scores for plotting later.
self.last_attn_scores = attn_scores
x = self.add([x, attn_output])
x = self.layernorm(x)
return x
class CausalSelfAttention(BaseAttention):
def call(self, x):
attn_output = self.mha(
query=x,
value=x,
key=x,
use_causal_mask=True)
x = self.add([x, attn_output])
x = self.layernorm(x)
return x
class FeedForward(tf.keras.layers.Layer):
def __init__(self, d_model, dff, dropout_rate=0.1):
super().__init__()
self.seq = tf.keras.Sequential([
tf.keras.layers.Dense(dff, activation='relu'),
tf.keras.layers.Dense(d_model),
tf.keras.layers.Dropout(dropout_rate)
])
self.add = tf.keras.layers.Add()
self.layer_norm = tf.keras.layers.LayerNormalization()
def call(self, x):
x = self.add([x, self.seq(x)])
x = self.layer_norm(x)
return x
class DecoderLayer(tf.keras.layers.Layer):
def __init__(self,
*,
d_model,
num_heads,
dff,
dropout_rate=0.1):
super(DecoderLayer, self).__init__()
self.causal_self_attention = CausalSelfAttention(
num_heads=num_heads,
key_dim=d_model,
dropout=dropout_rate)
self.cross_attention = CrossAttention(
num_heads=num_heads,
key_dim=d_model,
dropout=dropout_rate)
self.ffn = FeedForward(d_model, dff)
def call(self, x, context):
x = self.causal_self_attention(x=x)
x = self.cross_attention(x=x, context=context)
# Cache the last attention scores for plotting later
self.last_attn_scores = self.cross_attention.last_attn_scores
x = self.ffn(x) # Shape `(batch_size, seq_len, d_model)`.
return x
class Encoder(tf.keras.layers.Layer):
def __init__(self, embedding_dim, input_shape, pretrain_weights=None):
super(Encoder, self).__init__()
# shape after fc == (batch_size, nf * nf, embedding_dim)
self.fc = tf.keras.layers.Dense(embedding_dim, activation='relu')
# Use DenseNet-121 as feature extraction model
self.base_model = tf.keras.applications.DenseNet121(
include_top=False, weights=None, input_shape=input_shape)
# Load pre-trained weights if present
if pretrain_weights:
print(f'{datetime.datetime.now()}: I Loading Pretrained DenseNet-121 weights: {pretrain_weights}')
self.base_model.load_weights(pretrain_weights)
else:
print(f'{datetime.datetime.now()}: I No Pretrained DenseNet-121 weights specified')
def call(self, x, **kwargs):
x = self.base_model(x)
# DenseNet-121 output is (batch_size, ?, ?, 1024)
s = tf.shape(x)
x = tf.reshape(x, (s[0], s[1] * s[2], x.shape[3]))
x = self.fc(x)
return x
class Decoder(tf.keras.layers.Layer):
def __init__(self, *, num_layers, d_model, num_heads, dff, vocab_size,
dropout_rate=0.1):
super(Decoder, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.pos_embedding = PositionalEmbedding(vocab_size=vocab_size,
d_model=d_model)
self.dropout = tf.keras.layers.Dropout(dropout_rate)
self.dec_layers = [
DecoderLayer(d_model=d_model, num_heads=num_heads,
dff=dff, dropout_rate=dropout_rate)
for _ in range(num_layers)]
self.last_attn_scores = None
def call(self, x, context):
# `x` is token-IDs shape (batch, target_seq_len)
x = self.pos_embedding(x) # (batch_size, target_seq_len, d_model)
x = self.dropout(x)
for i in range(self.num_layers):
x = self.dec_layers[i](x, context)
self.last_attn_scores = self.dec_layers[-1].last_attn_scores
# The shape of x is (batch_size, target_seq_len, d_model).
return x
class Transformer(tf.keras.Model):
def __init__(self, num_layers, d_model, num_heads, dff,
target_vocab_size, dropout_rate=0.1, input_shape=(224, 224, 1),
classifier_weights=None):
super(Transformer, self).__init__()
self.encoder = Encoder(d_model, input_shape,
pretrain_weights=classifier_weights)
self.decoder = Decoder(num_layers=num_layers, d_model=d_model,
num_heads=num_heads, dff=dff,
vocab_size=target_vocab_size,
dropout_rate=dropout_rate)
self.final_layer = tf.keras.layers.Dense(target_vocab_size)
def call(self, inputs):
# To use a Keras model with `.fit` you must pass all your inputs in the
# first argument.
context, x = inputs
context = self.encoder(context) # (batch_size, context_len, d_model)
x = self.decoder(x, context) # (batch_size, target_len, d_model)
# Final linear layer output.
logits = self.final_layer(x) # (batch_size, target_len, target_vocab_size)
try:
# Drop the keras mask, so it doesn't scale the losses/metrics.
# b/250038731
del logits._keras_mask
except AttributeError:
pass
# Return the final output and the attention weights.
return logits
if __name__ == "__main__":
hparams = default_hparams()
transformer = Transformer(
num_layers=hparams['num_layers'],
d_model=hparams['d_model'],
num_heads=hparams['num_heads'],
dff=hparams['dff'],
target_vocab_size=2048,
dropout_rate=hparams['dropout_rate'])
a=1
image = np.random.rand(1,224,224,1).astype('float32')
text = np.random.randint(0, 2048, size=(1, 27))
output = transformer((image, text))
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