File size: 8,262 Bytes
d1d6816 |
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 |
import tensorflow as tf
from keras import backend as K
from keras.layers import Input, Dense, Conv2D, Conv2DTranspose, Flatten, Reshape, Lambda, BatchNormalization
from keras.models import Model
import numpy as np
import threading
KL = tf.keras.layers
def cbam_layer(inputs_tensor=None, ratio=None):
"""Source: https://blog.csdn.net/ZXF_1991/article/details/104615942
The channel attention
"""
channels = K.int_shape(inputs_tensor)[-1]
def share_layer(inputs=None):
x_ = KL.Conv2D(channels // ratio, (1, 1), strides=1, padding="valid")(inputs)
x_ = KL.Activation('relu')(x_)
output_share = KL.Conv2D(channels, (1, 1), strides=1, padding="valid")(x_)
return output_share
x_global_avg_pool = KL.GlobalAveragePooling2D()(inputs_tensor)
x_global_avg_pool = KL.Reshape((1, 1, channels))(x_global_avg_pool)
x_global_max_pool = KL.GlobalMaxPool2D()(inputs_tensor)
x_global_max_pool = KL.Reshape((1, 1, channels))(x_global_max_pool)
x_global_avg_pool = share_layer(x_global_avg_pool)
x_global_max_pool = share_layer(x_global_max_pool)
x = KL.Add()([x_global_avg_pool, x_global_max_pool])
x = KL.Activation('sigmoid')(x)
CAM = KL.multiply([inputs_tensor, x])
output = CAM
return output
def res_cell(x, n_channel=64, stride=1):
"""The basic unit in the VAE, cell."""
if stride == -1:
# upsample cell
skip = tf.keras.layers.UpSampling2D(size=(2, 2), interpolation='bilinear')(x)
skip = Conv2D(filters=n_channel, kernel_size=(1, 1), strides=1, padding='same')(skip)
x = Conv2DTranspose(filters=n_channel, kernel_size=(5, 5), strides=2, padding='same')(x)
x = BatchNormalization()(x)
x = tf.keras.activations.elu(x)
x = Conv2DTranspose(filters=n_channel, kernel_size=(5, 5), padding='same')(x)
elif stride == 2:
# downsample cell
skip = Conv2D(filters=n_channel, kernel_size=(1, 1), strides=2, padding='same')(x)
x = Conv2D(filters=n_channel, kernel_size=(5, 5), strides=stride, padding='same')(x)
x = BatchNormalization()(x)
x = tf.keras.activations.elu(x)
x = Conv2D(filters=n_channel, kernel_size=(5, 5), padding='same')(x)
else:
# preserving cell
skip = tf.identity(x)
x = Conv2D(filters=n_channel, kernel_size=(5, 5), strides=stride, padding='same')(x)
x = BatchNormalization()(x)
x = tf.keras.activations.elu(x)
x = Conv2D(filters=n_channel, kernel_size=(5, 5), padding='same')(x)
x = BatchNormalization()(x)
x = cbam_layer(inputs_tensor=x, ratio=8)
x = x + skip
x = tf.keras.activations.elu(x)
return x
def res_block(x, n_channel=64, upsample=False, n_cells=2):
"""The block is a stack of cells."""
if upsample:
x = res_cell(x, n_channel=n_channel, stride=-1)
else:
x = res_cell(x, n_channel=n_channel, stride=2)
for _ in range(n_cells - 1):
x = res_cell(x, n_channel=n_channel, stride=1)
return x
def l1_distance(x1, x2):
return tf.reduce_mean(tf.math.abs(x1 - x2))
def l1_log_distance(x1, x2):
return tf.reduce_mean(tf.math.abs(tf.math.log(tf.maximum(1e-6, x1)) - tf.math.log(tf.maximum(1e-6, x2))))
img_height = 512
img_width = 256
num_channels = 1
input_shape = (img_height, img_width, num_channels)
timbre_dim = 20
n_filters = 64
act = 'elu'
def compute_latent(x):
"""Re-parameterizing."""
mu, sigma = x
batch = K.shape(mu)[0]
dim = K.int_shape(mu)[1]
eps = K.random_normal(shape=(batch, dim))
return mu + K.exp(sigma / 2) * eps
def get_encoder(N2=0, channel_sizes=None):
"""Assemble and return the VAE encoder."""
if channel_sizes is None:
channel_sizes = [32, 64, 64, 96, 96, 128, 160, 216]
encoder_input = Input(shape=input_shape)
encoder_conv = res_block(encoder_input, channel_sizes[0], upsample=False, n_cells=1)
for c in channel_sizes[1:]:
encoder_conv = res_block(encoder_conv, c, upsample=False, n_cells=1 + N2)
encoder = Flatten()(encoder_conv)
mu_timbre = Dense(timbre_dim)(encoder)
sigma_timbre = Dense(timbre_dim)(encoder)
latent_vector = Lambda(compute_latent, output_shape=(timbre_dim,))([mu_timbre, sigma_timbre])
kl_loss = -0.5 * (1 + sigma_timbre - tf.square(mu_timbre) - tf.exp(sigma_timbre))
kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))
encoder = Model(encoder_input, [latent_vector, kl_loss])
return encoder
def get_decoder(N2=0, N3=8, channel_sizes=None):
"""Assemble and return the VAE decoder."""
if channel_sizes is None:
channel_sizes = [32, 64, 64, 96, 96, 128, 160, 216]
conv_shape = [-1, 2 ** (9 - N3), 2 ** (8 - N3), channel_sizes[-1]]
decoder_input = Input(shape=(timbre_dim,))
decoder = Dense(conv_shape[1] * conv_shape[2] * conv_shape[3], activation=act)(decoder_input)
decoder_conv = Reshape((conv_shape[1], conv_shape[2], conv_shape[3]))(decoder)
for c in list(reversed(channel_sizes))[1:]:
decoder_conv = res_block(decoder_conv, c, upsample=True, n_cells=1 + N2)
decoder_conv = Conv2DTranspose(filters=num_channels, kernel_size=5, strides=2,
padding='same', activation='sigmoid')(decoder_conv)
decoder = Model(decoder_input, decoder_conv)
return decoder
def VAE(N2=0, N3=8, channel_sizes=None):
"""Assemble and return the VAE."""
if channel_sizes is None:
channel_sizes = [32, 64, 64, 96, 96, 128, 160, 216]
print("Creating model...")
assert N2 >= 0, "Please set N2 >= 0"
assert N3 >= 1, "Please set 1 <= N3 <= 8"
assert N3 <= 8, "Please set 1 <= N3 <= 8"
assert N3 == len(channel_sizes), "Please set N3 = len(channel_sizes)"
encoder = get_encoder(N2, channel_sizes)
decoder = get_decoder(N2, N3, channel_sizes)
# encoder = tf.keras.models.load_model(f"encoder_thesis_record_1.h5")
# decoder = tf.keras.models.load_model(f"decoder_thesis_record_1.h5")
encoder_input1 = Input(shape=input_shape)
scalar_input1 = Input(shape=(1,))
embedding_1_timbre, kl_loss = encoder(encoder_input1)
reconstruction_1 = decoder(embedding_1_timbre)
VAE = Model([encoder_input1, scalar_input1], [reconstruction_1, kl_loss])
# decoder.summary()
VAE.summary()
return encoder, decoder, VAE
def my_thread(data_cache):
data_cache.refresh()
def train_VAE(vae, encoder, decoder, data_cache, stages, batch_size):
"""Train the VAE.
Parameters
----------
vae: keras.engine.functional.Functional
The VAE.
encoder: keras.engine.functional.Functional
The VAE encoder.
decoder: keras.engine.functional.Functional
The VAE decoder.
data_cache: Data_cache
A Data_cache entity that provides training data.
stages: Dict
Defines the training stages. In each stage, the synthetic data will be refreshed and
models will be stored once.
Returns
-------
"""
threshold = 1e-0
kl_weight = 100.0
def weighted_binary_cross_entropy_loss(true, pred):
b_n = true * tf.math.log(tf.maximum(1e-20, pred)) + (1 - true) * tf.math.log(tf.maximum(1e-20, 1 - pred))
w = tf.maximum(threshold, true)
return -tf.reduce_sum(b_n / w) / batch_size
def reconstruction_loss(true, pred):
reconstruction_loss = weighted_binary_cross_entropy_loss(K.flatten(true), K.flatten(pred))
return K.mean(reconstruction_loss)
def kl_loss(true, pred):
return pred * kl_weight
for stage in stages:
threshold = stage["threshold"]
kl_weight = stage["kl_weight"]
vae.compile(tf.keras.optimizers.Adam(learning_rate=stage["learning_rate"]), loss=[reconstruction_loss, kl_loss])
Input_all = data_cache.get_all_data()
n_total = np.shape(Input_all)[0]
t = threading.Thread(target=my_thread, args=(data_cache,))
t.start()
history = vae.fit([Input_all, np.ones(n_total)], [Input_all, np.ones(n_total)], epochs=stage["n_epoch"],
batch_size=batch_size)
t.join()
encoder.save(f"./models/new_trained_models/encoder.h5")
decoder.save(f"./models/new_trained_models/decoder.h5")
|