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from transformers import TFPreTrainedModel
from tensorflow.keras.models import Model, load_model, Sequential
from tensorflow.keras.layers import Layer, Dense, concatenate, Input, add, Dropout, LayerNormalization, MultiHeadAttention, Embedding
import tensorflow as tf
import numpy as np
import re
import unicodedata
from configuration_bilma import BilmaConfig
# copied from preprocessing.py
BLANK = ' '
RE_OPS = re.I | re.M | re.S
RE_USR = re.compile(r"""@\S+""", RE_OPS)
RE_TAG = re.compile(r"""#\S+""", RE_OPS)
RE_URL = re.compile(r"""(http|ftp|https)://\S+""", RE_OPS)
RE_NUM = re.compile(r"""[-+]?\d+\.?\d*""", RE_OPS)
SYMBOLS_ = "()[]ΒΏ?Β‘!{}~<>|"
SYMBOLS = set(";:,.@\\-\"/" + SYMBOLS_)
# ------------------
# Class declaration
# ------------------
class Bilma(TFPreTrainedModel):
config_class = BilmaConfig
main_input_name = "input_ids"
def __init__(self, config):
super().__init__(config)
#if config.weights == "spanish":
# my_resources = importlib_resources.files("hf_bilma")
# model_file = str((my_resources / "bilma_dataset_small_epoch-1_part-60.h5").joinpath())
# self.model = bm.load(model_file)
#else:
self.model = bilma(num_enc=config.num_encoders,
embed_dim=config.embedding_dim,
max_length=config.max_length,
num_heads=config.num_attention_heads,
ff_dim=config.embedding_dim,
vocab_size=config.vocab_size,
rate=config.drop_rate)
def call(self, tensor):
return self.model(tensor)
#
# Copied from transformer_text.py
# -------------------------------
class EncoderBlock(Layer):
def __init__(self, patch_dim, num_heads, ff_dim, rate=0.1, **kwargs):
super(EncoderBlock, self).__init__(**kwargs)
self.p_d = patch_dim
self.n_h = num_heads
self.f_d = ff_dim
self.rate = rate
self.att = MultiHeadAttention(num_heads=num_heads, key_dim=patch_dim)
self.ffn = Sequential(
#[Conv1D(ff_dim, kernel_size=1, activation=tf.nn.gelu),
# Conv1D(patch_dim, kernel_size=1),]
[Dense(ff_dim, activation=tf.nn.gelu),
Dense(patch_dim),]
)
#self.layernorm0 = LayerNormalization(epsilon=1e-6)
self.layernorm1 = LayerNormalization(epsilon=1e-6)
self.layernorm2 = LayerNormalization(epsilon=1e-6)
self.dropout1 = Dropout(rate)
self.dropout2 = Dropout(rate)
def get_config(self):
config = super(EncoderBlock, self).get_config()
config.update({"patch_dim":self.p_d, "num_heads":self.n_h, "ff_dim":self.f_d, "rate":self.rate})
return config
def call(self, inputs, training=False):
#inputs = self.layernorm0(inputs)
attn_output = self.att(inputs, inputs)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(add([inputs, attn_output]))
ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output, training=training)
return self.layernorm2(add([out1, ffn_output]))
class DecoderBlock(Layer):
def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1, **kwargs):
super(DecoderBlock, self).__init__(**kwargs)
self.e_d = embed_dim
self.n_h = num_heads
self.f_d = ff_dim
self.rate = rate
self.att1 = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
self.att2 = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
self.ffn = Sequential(
#[Conv1D(ff_dim, kernel_size=1, activation=tf.nn.gelu),
# Conv1D(embed_dim, kernel_size=1),]
[Dense(ff_dim, activation=tf.nn.gelu),
Dense(embed_dim),]
)
self.layernorm1 = LayerNormalization(epsilon=1e-6)
self.layernorm2 = LayerNormalization(epsilon=1e-6)
self.dropout1 = Dropout(rate)
self.dropout2 = Dropout(rate)
self.dropout3 = Dropout(rate)
def get_config(self):
config = super(DecoderBlock, self).get_config()
config.update({"embed_dim":self.e_d, "num_heads":self.n_h, "ff_dim":self.f_d, "rate":self.rate})
return config
def call(self, inputs, encoder_output, look_ahead_mask, padding_mask, training=None):
y, attn_output1 = self.att1(inputs, inputs, attention_mask=look_ahead_mask, return_attention_scores=True)
y = self.dropout1(y, training=training)
y = add([inputs, y])
out1 = self.layernorm1(y)
y, attn_encoder = self.att2(out1, encoder_output, attention_mask=padding_mask, return_attention_scores=True)
y = self.dropout2(y, training=training)
y = add([out1, y])
out2 = self.layernorm1(y)
ffn_output = self.ffn(out2)
ffn_output = self.dropout3(ffn_output, training=training)
final_output = self.layernorm2(out2 + ffn_output)
return final_output, attn_output1, attn_encoder
class Encoder(Layer):
def __init__(self, n, embed_dim, max_length, num_heads, ff_dim, rate=0.1, **kwargs):
super(Encoder, self).__init__(**kwargs)
self.n = n
self.embed_dim = embed_dim
self.max_length = max_length
self.n_h = num_heads
self.f_d = ff_dim
self.rate = rate
self._layers = [EncoderBlock(embed_dim, num_heads, ff_dim, rate=0.1) for _ in range(n)]
self.pe = positional_encoding(self.max_length, self.embed_dim)
def get_config(self):
config = super(Encoder, self).get_config()
config.update({"n": self.n, "embed_dim":self.embed_dim, "max_length": self.max_length, "num_heads":self.n_h, "ff_dim":self.f_d, "rate":self.rate})
return config
def call(self, x, training=False):
x *= tf.math.sqrt(tf.cast(self.embed_dim, tf.float32))
x = x + self.pe[:, :tf.shape(x)[1], :]
for layer in self._layers:
x = layer(x, training)
return x
class Decoder(Layer):
def __init__(self, n, embed_dim, max_length, num_heads, ff_dim, rate=0.1, **kwargs):
super(Decoder, self).__init__(**kwargs)
self.n = n
self.embed_dim = embed_dim
self.max_length = max_length
self.n_h = num_heads
self.f_d = ff_dim
self.rate = rate
self._layers = [DecoderBlock(embed_dim, num_heads, ff_dim, rate=0.1) for _ in range(n)]
self.pe = positional_encoding(self.max_length, self.embed_dim)
def get_config(self):
config = super(Decoder, self).get_config()
config.update({"n": self.n, "embed_dim":self.embed_dim, "max_length": self.max_length, "num_heads":self.n_h, "ff_dim":self.f_d, "rate":self.rate})
return config
def call(self, x, encoder_output, look_ahead_mask, padding_mask, training):
x *= tf.math.sqrt(tf.cast(self.embed_dim, tf.float32))
x = x + self.pe[:, :tf.shape(x)[1], :]
for layer in self._layers:
x, self_att, enc_att = layer(x, encoder_output, look_ahead_mask, padding_mask, training)
return x
# =========================================
# M A S K S
# =========================================
def create_padding_mask(seq):
"""
For self-attention
seq shape(bs, max_length, emb_dim)
output shape (bs, max_length, max_length)
"""
mask = tf.cast(tf.not_equal(seq, 0), tf.bool)
mask = tf.reduce_any(mask, 2)
mask = tf.repeat(mask, seq.shape[1], 0)
mask = tf.reshape(mask, (-1,seq.shape[1], seq.shape[1]))
return tf.cast(mask, tf.float32)
def create_cross_padding_mask(seq, target_seq):
"""
For cross-attention
seq shape(bs, k, image_features)
target_seq(bs, max_length, emb_dim)
output shape (bs, max_length, k)
"""
mask = tf.cast(tf.not_equal(target_seq, 0), tf.bool)
mask = tf.reduce_any(mask, 2)
mask = tf.repeat(mask, seq.shape[1], 0)
mask = tf.reshape(mask, (-1, tf.shape(seq)[1], tf.shape(target_seq)[1]))
mask = tf.transpose(mask, [0, 2, 1])
return mask
def create_look_ahead_mask(seq):
"""
seq shape(bs, max_length, emb_dim)
output 2D matrix of shape (bs, max_length, max_length) with ones on the diagonal and below.
"""
size = seq.shape[1]
mask = tf.linalg.band_part(tf.ones((size, size)), -1, 0)
mask = tf.expand_dims(mask, 0)
mask = tf.repeat(mask, tf.shape(seq)[0], 0)
return mask
def create_masks(seq, target_seq):
decoder_mask = create_padding_mask(target_seq)
decoder_mask *= create_look_ahead_mask(target_seq)
cross_att_mask = create_cross_padding_mask(seq, target_seq)
return decoder_mask, cross_att_mask
def create_masks_looking_ahead(seq, target_seq):
decoder_mask = create_padding_mask(target_seq)
cross_att_mask = create_cross_padding_mask(seq, target_seq)
return decoder_mask, cross_att_mask
# =========================================
# P O S I T I O N A L E N C O D I N G
# =========================================
def get_angles(pos, i, d_model):
angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))
return pos * angle_rates
@tf.autograph.experimental.do_not_convert
def positional_encoding(position, d_model):
angle_rads = get_angles(np.arange(position)[:, np.newaxis],
np.arange(d_model)[np.newaxis, :],
d_model)
# apply sin to even indices in the array; 2i
angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])
# apply cos to odd indices in the array; 2i+1
angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])
pos_encoding = angle_rads[np.newaxis, ...]
return tf.cast(pos_encoding, dtype=tf.float32)
class PatchEncoder(Layer):
def __init__(self, num_patches, projection_dim, **kwargs):
super(PatchEncoder, self).__init__(**kwargs)
self.num_patches = num_patches
self.projection_dim = projection_dim
self.projection = Dense(units=projection_dim)
self.position_embedding = Embedding(
input_dim=num_patches, output_dim=projection_dim
)
def get_config(self):
config = super(PatchEncoder, self).get_config()
config.update({"num_patches": self.num_patches, "projection_dim":self.projection_dim})
return config
def call(self, patch):
positions = tf.range(start=0, limit=self.num_patches, delta=1)
encoded = self.projection(patch) + self.position_embedding(positions)
return encoded
# Copied from preprocessing.py
# ----------------------------
def norm_chars(text):
L = []
for u in unicodedata.normalize('NFD', text):
o = ord(u)
if 0x300 <= o and o <= 0x036F:
continue
if u in ('\n', '\r', BLANK, '\t'):
if len(L) == 0:
continue
u = BLANK
if u in SYMBOLS:
if len(L) > 0 and L[-1] != BLANK:
L.append(BLANK)
L.append(u)
L.append(BLANK)
continue
L.append(u)
return "".join(L)
def preprocess(text):
text = RE_URL.sub("_url ", text)
text = RE_USR.sub("_usr ", text)
#text = RE_TAG.sub("_htag ", text)
#text = RE_NUM.sub("0 ", text)
text = re.sub(r"&", "&", text)
text = re.sub(r">", ">", text)
text = re.sub(r"<", "<", text)
#text = norm_chars(text.lower())
text = re.sub(r"j(a|e|i)[jaei]+", r"j\1j\1", text)
text = re.sub(r"h(a|e|i)[haei]+", r"j\1j\1", text)
return re.sub(r"\s+", BLANK, text)
# Copied from wordpiece_tokenizer_ex.py
# -------------------------------------
class Tokenizer():
def __init__(self, vocab_file, unk_token="[UNK]", end_token="[END]", mask_token="[MASK]"):
self.word2idx = {}
self.idx2word = []
c = 0
with open(vocab_file, "r", encoding="utf8") as f:
while True:
line = f.readline()
if not line:
break
self.word2idx[line[0:-1]] = c
self.idx2word.append(line[0:-1])
c += 1
self.n_jobs = 2
self.UNK = unk_token
self.END = end_token
self.MASK = mask_token
def split(self, s):
split = []
i = 0
while i < len(s):
for j in range(i, len(s)):
if (i==j and s[j:j+6] == self.MASK):
split.append(self.MASK)
i = j + 6
break
if (s[j].isalnum()):
continue
if (j==i):
if (s[j] != " "):
split.append(s[i:j+1])
i = j + 1
break
split.append(s[i:j])
i = j
break
else:
split.append(s[i:j+1])
i=j+1
return split
def tokenize(self, S):
#return Parallel(n_jobs=self.n_jobs)(delayed(self._tokenize)(s) for s in S)
return [self._tokenize(s) for s in S]
def detokenize(self, S, human_readable=True):
#return Parallel(n_jobs=self.n_jobs)(delayed(self._detokenize)(s) for s in S)
return [self._detokenize(s, human_readable=human_readable) for s in S]
def _tokenize(self, s):
tokens = []
s = s.rstrip('\n')
for w in self.split(s):
if w in self.word2idx:
tokens.append(self.word2idx[w])
else:
if (len(w)==1):
tokens.append(self.word2idx["[UNK]"])
continue
subtoken = []
l = 0
while len(w)>l:
for i in range(len(w),l-1,-1):
if (w[0: i] in self.word2idx):
subtoken.append(self.word2idx[w[0: i]])
break
if (i == l):
subtoken = [self.word2idx["[UNK]"]]
break
w = "##" + w[i: ]
l = 2
tokens += subtoken
return tokens
def _detokenize(self, tokens, human_readable=True):
sentence = []
start = 0 if human_readable == False else 1
for t in tokens[start:]:
c = self.idx2word[t]
if (human_readable and c == self.END):
break
sentence.append(c)
return sentence
# copied from bilma_model.py
# --------------------------
def loss_function(ignore_id=0):
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')
def loss(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, ignore_id))
loss_ = loss_object(real, pred)
mask = tf.cast(mask, dtype=loss_.dtype)
loss_ *= mask
sum_ = tf.reduce_sum(mask,axis=1)
loss_ = tf.math.divide_no_nan(tf.reduce_sum(loss_, axis=1), sum_)
return loss_
return loss
def accuracy_function(ignore_id=0):
def acc_mlm(real, pred):
accuracies = tf.equal(tf.cast(real, tf.int64), tf.argmax(pred, axis=2))
mask = tf.math.logical_not(tf.math.equal(real, ignore_id))
accuracies = tf.math.logical_and(mask, accuracies)
accuracies = tf.cast(accuracies, dtype=tf.float32)
mask = tf.cast(mask, dtype=tf.float32)
return tf.math.divide_no_nan(tf.reduce_sum(accuracies), tf.reduce_sum(mask))
return acc_mlm
def bilma(num_enc=6, embed_dim=300, max_length=50, num_heads=6, ff_dim=512, vocab_size=9739, rate=0.1):
capt_inputs_ids = Input(shape=(max_length, ), name='capt_input')
capt_embedding = Embedding(vocab_size, embed_dim, mask_zero=False, name="embedding")
capt_inputs = capt_embedding(capt_inputs_ids)
enc = Encoder(num_enc, embed_dim, max_length, num_heads, ff_dim, rate=rate)
enc_output = enc(capt_inputs)
fin_output = Dense(vocab_size, use_bias=True)(enc_output)
caption_model = Model(inputs=capt_inputs_ids, outputs=[fin_output])
return caption_model
def load(model_file):
custom_objects={"EncoderBlock": EncoderBlock,
"Encoder": Encoder,
"loss": loss_function(),
"acc_mlm":accuracy_function(),
}
return load_model(model_file, custom_objects=custom_objects)
class tokenizer():
def __init__(self, vocab_file, max_length):
self.tokenizer = Tokenizer(vocab_file)
self.emo_labels = "β€ππππππππππ‘π’ππ€π₯Ί"
self.max_length = max_length
self.START = 2
self.END = 3
self.PAD = 0
self.MASK = 4
def tokenize(self, text):
text = [preprocess(t) for t in text]
tokens = tf.ragged.constant(self.tokenizer.tokenize(text), tf.int32)
count, _ = tokens.bounding_shape()
starts = tf.fill([count,1], self.START)
ends = tf.fill([count,1], self.END)
tokens = tf.concat([starts, tokens[:, 0: self.max_length - 2], ends], axis=1)
tokens = tokens.to_tensor(self.PAD, shape=(len(text), self.max_length))
return tokens.numpy()
def detokenize(self, tokens, human_readable=True):
words = self.tokenizer.detokenize(tokens, human_readable=human_readable)
if (human_readable==True):
return [" ".join(w) for w in words]
text = tf.strings.reduce_join(words, separator=' ', axis=-1)
return text
def top_k(self, predictions, positions, k=10):
top = []
for p, m in zip(predictions, positions):
top_k = self.detokenize([tf.argsort(p[m])[-k:][::-1]], False).numpy()[0].decode('utf8').split()
top.append(top_k)
return top
def decode_emo(self, predictions):
emo = tf.argmax(predictions, axis=-1)
return [self.emo_labels[i] for i in emo]
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