File size: 10,995 Bytes
f9426e6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 |
import sys
import os
import torch
from transformers import AutoTokenizer, EncoderDecoderModel, T5Tokenizer, T5ForConditionalGeneration, AutoModelForSeq2SeqLM, MarianMTModel, MarianTokenizer
import pickle
import numpy as np
from tensorflow.keras.models import load_model, Model
from tensorflow.keras.layers import Input
from tensorflow.keras.preprocessing.sequence import pad_sequences
import os
from GRU_with_attention_ver4.load_GRU_model import translate_GRU
import tensorflow as tf
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def load_model_BERT_BARTPho():
encoder_model_name = "bert-base-uncased"
decoder_model_name = "vinai/bartpho-word"
model_path = "./EncoderDecoder_6"
encoder = AutoTokenizer.from_pretrained(encoder_model_name)
decoder = AutoTokenizer.from_pretrained(decoder_model_name)
model = EncoderDecoderModel.from_pretrained(model_path).to(device)
return encoder, decoder, model
def translate_BERT_BARTPho(input_text, encoder=None, decoder=None, model=None):
if encoder is None or decoder is None or model is None:
encoder, decoder, model = load_model_BERT_BARTPho()
inputs = encoder(
input_text,
return_tensors="pt",
padding=True,
truncation=True
).to(device)
inputs = {key: value.to(model.device) for key, value in inputs.items()}
outputs = model.generate(inputs["input_ids"], max_length=64, num_beams=4)
return decoder.decode(outputs[0], skip_special_tokens=True)
def load_model_T5():
model_folder = "./T5_ver3"
decoder_path = model_folder + "/vi_tokenizer_32128.model"
encoder = T5Tokenizer.from_pretrained("t5-small", skip_special_tokens=True)
decoder = T5Tokenizer.from_pretrained(pretrained_model_name_or_path = decoder_path, skip_special_tokens=True)
model = T5ForConditionalGeneration.from_pretrained(model_folder, max_length = 64).to(device)
return encoder, decoder, model
def translate_T5(input_text, encoder=None, decoder=None, model=None):
if encoder is None or decoder is None or model is None:
encoder, decoder, model = load_model_T5()
# Tiến hành dịch
inputs = encoder(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs['input_ids'])
output_text = decoder.decode(outputs[0].tolist(), skip_special_tokens=True)
return output_text
def load_model_BiLSTM():
model_folder = f"./BiLSTM_2"
encoder_path = model_folder + "/english_tokenizer.pkl"
decoder_path = model_folder + "/vietnamese_tokenizer.pkl"
model_path = model_folder + "/my_model_1.keras"
with open(encoder_path, "rb") as f:
encoder = pickle.load(f)
with open(decoder_path, "rb") as f:
decoder = pickle.load(f)
model = load_model(model_path)
return encoder, decoder, model
def translate_BiLSTM(input_text, encoder=None, decoder=None, model=None):
if encoder is None or decoder is None or model is None:
encoder, decoder, model = load_model_BiLSTM()
# Extract components from the model
encoder_input = model.input[0] # Input tensor for the encoder
encoder_output = model.get_layer("bidirectional").output[0]
encoder_state_h = model.get_layer("state_h_concat").output
encoder_state_c = model.get_layer("state_c_concat").output
# Build encoder model
encoder_model = Model(encoder_input, [encoder_output, encoder_state_h, encoder_state_c])
# Extract decoder components
decoder_embedding = model.get_layer("decoder_embedding")
decoder_lstm = model.get_layer("decoder_lstm")
decoder_dense = model.get_layer("decoder_dense")
# Define decoder inference inputs
units = 128 # LSTM units
decoder_state_input_h = Input(shape=(units * 2,), name="decoder_state_input_h")
decoder_state_input_c = Input(shape=(units * 2,), name="decoder_state_input_c")
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
# Reuse the embedding and LSTM layers
decoder_input = Input(shape=(1,), name="decoder_input") # Decoder input for one time step
decoder_embedding_inf = decoder_embedding(decoder_input)
decoder_lstm_inf = decoder_lstm(decoder_embedding_inf, initial_state=decoder_states_inputs)
decoder_output_inf, state_h_inf, state_c_inf = decoder_lstm_inf
decoder_states_inf = [state_h_inf, state_c_inf]
# Dense layer for probabilities
decoder_output_inf = decoder_dense(decoder_output_inf)
# Build decoder inference model
decoder_model = Model(
[decoder_input] + decoder_states_inputs, # Inputs
[decoder_output_inf] + decoder_states_inf # Outputs
)
# Helper functions
def preprocess_sentence(sentence, tokenizer, max_length):
"""Preprocess and tokenize an input sentence."""
sequence = tokenizer.texts_to_sequences([sentence])
return pad_sequences(sequence, maxlen=max_length, padding='post')
def decode_sequence(input_seq):
"""Generate a Vietnamese sentence from an English input sequence."""
# Encode the input sequence to get initial states
encoder_output, state_h, state_c = encoder_model.predict(input_seq)
# Initialize the decoder input with the <start> token
target_seq = np.zeros((1, 1)) # Shape: (batch_size, 1)
target_seq[0, 0] = decoder.texts_to_sequences(["<SOS>"])[0][0]
# Initialize states
states = [state_h, state_c]
# Generate the output sequence token by token
decoded_sentence = []
for _ in range(232):
output_tokens, h, c = decoder_model.predict([target_seq] + states)
# Sample the next token
sampled_token_index = np.argmax(output_tokens[0, -1, :])
sampled_token = decoder.index_word.get(sampled_token_index, '<unk>')
if sampled_token == '<eos>':
break
decoded_sentence.append(sampled_token)
# Update the target sequence (input to the decoder)
target_seq[0, 0] = sampled_token_index
# Update states
states = [h, c]
return ' '.join(decoded_sentence)
max_input_length = 193 # Adjust based on your tokenizer setup
input_sequence = preprocess_sentence(input_text, encoder, max_input_length)
# Generate translation
translation = decode_sequence(input_sequence)
return translation
# def load_model_GRU():
# model_folder = f"./GRU_with_attention_ver3"
# return encoder, decoder, model
# def translate_GRU(input_text, encoder=None, decoder =None, model=None):
# translation = translate_GRU(input_text)
# return translation
def load_model_LSTM():
encoder_model_name = "bert-base-uncased"
decoder_model_name = "vinai/phobert-base"
model_path = r'LSTM_Attention_2\best_model.keras'
encoder = AutoTokenizer.from_pretrained(encoder_model_name)
decoder = AutoTokenizer.from_pretrained(decoder_model_name)
model = load_model(model_path)
return encoder, decoder, model
def translate_LSTM(input_text, encoder=None, decoder=None, model=None):
max_length = 50
if encoder is None or decoder is None or model is None:
encoder, decoder, model = load_model_LSTM()
def greedy_decode(input_sequence, model, decoder, max_length=50):
input_sequence = tf.constant([input_sequence], dtype=tf.int64)
# Start with the target sequence containing only the start token
start_token = decoder.cls_token_id
end_token = decoder.sep_token_id
target_sequence = [start_token]
for _ in range(max_length):
# Prepare input for the decoder
decoder_input = tf.constant([target_sequence], dtype=tf.int64)
# Predict next token probabilities
predictions = model.predict([input_sequence, decoder_input], verbose=0)
# Take the last time-step and find the highest probability token
next_token = tf.argmax(predictions[:, -1, :], axis=-1).numpy()[0]
# Append the predicted token to the target sequence
target_sequence.append(next_token)
# Stop if the end token is predicted
if next_token == end_token:
break
# Decode the target sequence to text
translated_sentence = decoder.decode(target_sequence[1:], skip_special_tokens=True)
return translated_sentence
input_tokens = encoder.encode(input_text, add_special_tokens=True)
translated_text = greedy_decode(input_tokens, model, decoder)
return translated_text
def load_model_MarianMT():
tokenizer_model_name = "Helsinki-NLP/opus-mt-en-vi"
model_path = "./MarianMT_ver2"
tokenizer = MarianTokenizer.from_pretrained(tokenizer_model_name)
model = MarianMTModel.from_pretrained(model_path).to(device)
return tokenizer, model
def translate_MarianMT(input_text, model=None, tokenizer=None):
if model is None or tokenizer is None:
tokenizer, model = load_model_MarianMT()
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
inputs = {key: value.to(device) for key, value in inputs.items()}
outputs = model.generate(**inputs, max_length=64, num_beams=4)
translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return translated_text
if __name__ == "__main__":
input = """
I go to school
"""
translated_text = translate_LSTM(input)
print(translated_text)
|