File size: 8,345 Bytes
bf91a4a | 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 | import os
import sys
import numpy as np
import config
from PIL import Image
from tensorflow.keras import Input, layers
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Model
from tensorflow.keras.applications.inception_v3 import InceptionV3
import tensorflow.keras.applications.inception_v3
from tensorflow.keras.applications import VGG16
import tensorflow.keras.applications.vgg16
from tensorflow.keras.applications import MobileNet
import tensorflow.keras.applications.mobilenet
from tensorflow.keras.layers import (LSTM, GRU, Embedding,
Dense, Dropout, add)
class CNNModel:
def __init__(self,
model_name=None):
self.model_name = model_name
if self.model_name == "inception":
self.model = InceptionV3(weights='imagenet')
self.model = Model(inputs=self.model.input,
outputs=self.model.layers[-2].output)
self.width = config.INCEPTION_WIDTH
self.height = config.INCEPTION_HEIGHT
self.output_dim = config.INCEPTION_OUTPUT_DIM
self.preprocess_input = tensorflow.keras.applications.inception_v3.preprocess_input
elif self.model_name == "vgg16":
self.model = VGG16(weights='imagenet')
self.model = Model(inputs=self.model.input,
outputs=self.model.layers[-2].output)
self.width = config.VGG16_WIDTH
self.height = config.VGG16_HEIGHT
self.output_dim = config.VGG16_OUTPUT_DIM
self.preprocess_input = tensorflow.keras.applications.vgg16.preprocess_input
elif self.model_name == "mobilenet":
self.model = MobileNet(weights='imagenet', include_top=False)
self.width = config.MOBILENET_WIDTH
self.height = config.MOBILENET_HEIGHT
self.output_dim = config.MOBILENET_OUTPUT_DIM
self.preprocess_input = tensorflow.keras.applications.mobilenet.preprocess_input
else:
print("Model name not found")
sys.exit(1)
def get_model(self):
return self.model
def get_width(self):
return self.width
def get_height(self):
return self.height
def get_output_dim(self):
return self.output_dim
def get_preprocess_input(self):
return self.preprocess_input
def encode_image(self, image):
image = image.resize((self.width, self.height), Image.LANCZOS)
encoded_image = tensorflow.keras.preprocessing.image.img_to_array(
image)
encoded_image = np.expand_dims(encoded_image, axis=0)
encoded_image = self.preprocess_input(encoded_image)
encoded_image = self.model.predict(encoded_image)
encoded_image = np.reshape(encoded_image, self.output_dim)
return encoded_image
class RNNModel:
def __init__(self,
model_name=None,
num_neurons=None,
vocab_size=None,
max_length=None,
output_dim=None,
optimizer=None,
embedding_matrix=None):
self.model_name = model_name
self.vocab_size = vocab_size
self.output_dim = output_dim
self.num_neurons = num_neurons
self.max_length = max_length
self.embedding_matrix = embedding_matrix
self.optimizer = optimizer
self.loss = config.LOSS
self.metrics = config.METRICS
def build_model(self):
if self.model_name == "lstm":
self.model = self.build_lstm_model()
elif self.model_name == "gru":
self.model = self.build_gru_model()
else:
print("Invalid model name")
sys.exit(1)
def get_model_name(self):
return self.model_name
def get_model(self):
return self.model
def get_vocab_size(self):
return self.vocab_size
def get_output_dim(self):
return self.output_dim
def get_num_neurons(self):
return self.num_neurons
def get_max_length(self):
return self.max_length
def get_embedding_matrix(self):
return self.embedding_matrix
def get_loss(self):
return self.loss
def get_optimizer(self):
return self.optimizer
def set_model(self, model):
self.model = model
def build_lstm_model(self):
inputs1 = Input(shape=(self.output_dim,))
inputs2 = Input(shape=(self.max_length,))
fe1 = Dropout(0.5)(inputs1)
fe2 = Dense(self.num_neurons, activation='relu')(fe1)
se1 = Embedding(self.vocab_size, config.EMBEDDING_SIZE,
mask_zero=True)(inputs2)
se2 = Dropout(0.5)(se1)
se3 = LSTM(self.num_neurons)(se2)
decoder1 = add([fe2, se3])
decoder2 = Dense(self.num_neurons, activation='relu')(decoder1)
outputs = Dense(self.vocab_size, activation='softmax')(decoder2)
model = Model(inputs=[inputs1, inputs2], outputs=outputs)
return model
def build_gru_model(self):
inputs1 = Input(shape=(self.output_dim,))
inputs2 = Input(shape=(self.max_length,))
fe1 = Dropout(0.5)(inputs1)
fe2 = Dense(self.num_neurons, activation='relu')(fe1)
se1 = Embedding(self.vocab_size, config.EMBEDDING_SIZE,
mask_zero=True)(inputs2)
se2 = Dropout(0.5)(se1)
se3 = GRU(self.num_neurons)(se2)
decoder1 = add([fe2, se3])
decoder2 = Dense(self.num_neurons, activation='relu')(decoder1)
outputs = Dense(self.vocab_size, activation='softmax')(decoder2)
model = Model(inputs=[inputs1, inputs2], outputs=outputs)
return model
def compile_model(self):
if self.embedding_matrix is not None:
self.model.layers[2].set_weights([self.embedding_matrix])
self.model.layers[2].trainable = True
self.model.compile(optimizer=self.optimizer,
loss=self.loss, metrics=self.metrics)
self.model.optimizer.lr = config.LEARNING_RATE
def create_generator(self, images, token_captions, batch_size):
n_samples = len(token_captions)
while True:
# Embaralha a cada época
indices = np.random.permutation(n_samples)
images_shuf = [images[i] for i in indices]
captions_shuf = [token_captions[i] for i in indices]
x1, x2, y = [], [], []
n = 0
for i in range(n_samples):
n += 1
image = images_shuf[i]
seq = captions_shuf[i]
for j in range(1, len(seq)):
in_seq, out_seq = seq[:j], seq[j]
in_seq = pad_sequences(
[in_seq], maxlen=self.max_length)[0]
out_seq = to_categorical(
[out_seq], num_classes=self.vocab_size)[0]
x1.append(image)
x2.append(in_seq)
y.append(out_seq)
if n == batch_size:
yield ([np.array(x1), np.array(x2)], np.array(y))
x1, x2, y = [], [], []
n = 0
def generate_caption(self, image, wordtoidx, idxtoword):
x1 = []
x1.append(image)
in_text = config.START
for i in range(self.max_length):
sequence = [wordtoidx[w]
for w in in_text.split() if w in wordtoidx]
sequence = pad_sequences([sequence], maxlen=self.max_length)
yhat = self.model.predict([np.array(x1), sequence], verbose=0)
yhat = np.argmax(yhat)
word = idxtoword[yhat]
in_text += ' ' + word
if word == config.STOP:
break
final = in_text.split()
final = final[1:-1]
final = ' '.join(final)
return final
|