File size: 8,054 Bytes
3be620b |
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 |
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model, Sequential
from tensorflow.keras.layers import (
LSTM,
Activation,
BatchNormalization,
Conv2D,
Conv2DTranspose,
Conv3D,
Conv3DTranspose,
Dense,
Flatten,
Input,
Layer,
LeakyReLU,
MaxPooling2D,
Reshape,
TimeDistributed,
UpSampling2D,
)
SEQ_LEN = 20
class Sampling(Layer):
"""Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""
def call(self, inputs):
z_mean, z_log_var = inputs
batch = tf.shape(z_mean)[0]
dim = tf.shape(z_mean)[1]
epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
def compute_output_shape(self, input_shape):
return input_shape[0]
class P2P(Model):
def __init__(
self,
channels: int = 1,
g_dim: int = 128,
z_dim: int = 10,
rnn_size: int = 256,
prior_rnn_layers: int = 1,
posterior_rnn_layers: int = 1,
predictor_rnn_layers: float = 1,
skip_prob: float = 0.1,
n_past: int = 1,
last_frame_skip: bool = False,
beta: float = 0.0001,
weight_align: float = 0.1,
weight_cpc: float = 100,
):
super().__init__()
# Models parameters
self.channels = channels
self.g_dim = g_dim
self.z_dim = z_dim
self.rnn_size = rnn_size
self.prior_rnn_layers = prior_rnn_layers
self.posterior_rnn_layers = posterior_rnn_layers
self.predictor_rnn_layers = predictor_rnn_layers
# Training parameters
self.skip_prob = skip_prob
self.n_past = n_past
self.last_frame_skip = last_frame_skip
self.beta = beta
self.weight_align = weight_align
self.weight_cpc = weight_cpc
self.frame_predictor = self.build_lstm()
self.prior = self.build_gaussian_lstm()
self.posterior = self.build_gaussian_lstm()
self.encoder = self.build_encoder()
self.decoder = self.build_decoder()
self.total_loss_tracker = tf.keras.metrics.Mean(name="total_loss")
self.reconstruction_loss_tracker = tf.keras.metrics.Mean(
name="reconstruction_loss"
)
self.kl_loss_tracker = tf.keras.metrics.Mean(name="kl_loss")
# region Model building
def build_lstm(self):
input = Input(shape=(20, self.g_dim + self.z_dim + 1))
embed = TimeDistributed(Dense(self.rnn_size))(input)
lstm = LSTM(self.rnn_size, return_sequences=True)(embed)
output = TimeDistributed(Dense(self.g_dim))(lstm)
return Model(inputs=input, outputs=output, name="frame_predictor")
def build_gaussian_lstm(self):
input = Input(shape=(20, self.g_dim))
embed = TimeDistributed(Dense(self.rnn_size))(input)
lstm = LSTM(self.rnn_size, return_sequences=True)(embed)
mu = TimeDistributed(Dense(self.z_dim))(lstm)
logvar = TimeDistributed(Dense(self.z_dim))(lstm)
z = TimeDistributed(Sampling())([mu, logvar])
return Model(inputs=input, outputs=[mu, logvar, z])
def build_encoder(self):
input = Input(shape=(2, 64, 64, 1))
h = TimeDistributed(Conv2D(64, kernel_size=4, strides=2, padding="same"))(input)
h = BatchNormalization()(h)
h = LeakyReLU(alpha=0.2)(h)
# h = TimeDistributed(MaxPooling2D(pool_size=2, strides=2, padding="same"))(h)
h = TimeDistributed(Conv2D(128, kernel_size=4, strides=2, padding="same"))(h)
h = BatchNormalization()(h)
h = LeakyReLU(alpha=0.2)(h)
# h = TimeDistributed(MaxPooling2D(pool_size=2, strides=2, padding="same"))(h)
h = TimeDistributed(Conv2D(256, kernel_size=4, strides=2, padding="same"))(h)
h = BatchNormalization()(h)
h = LeakyReLU(alpha=0.2)(h)
# h = TimeDistributed(MaxPooling2D(pool_size=2, strides=2, padding="same"))(h)
h = TimeDistributed(Conv2D(512, kernel_size=4, strides=2, padding="same"))(h)
h = BatchNormalization()(h)
h = LeakyReLU(alpha=0.2)(h)
# h = TimeDistributed(MaxPooling2D(pool_size=2, strides=2, padding="same"))(h)
h = Flatten()(h)
# mu = Dense(self.g_dim)(h)
# logvar = Dense(self.g_dim)(h)
# z = Sampling()([mu, logvar])
lstm_input = Dense(self.g_dim * SEQ_LEN)(h)
lstm_input = Reshape((SEQ_LEN, self.g_dim))(lstm_input)
mu, logvar, z = self.posterior(lstm_input)
return Model(inputs=input, outputs=[mu, logvar, z], name="encoder")
def build_decoder(self):
latent_inputs = Input(shape=(SEQ_LEN, self.z_dim))
x = Dense(1 * 1 * 1 * 512, activation="relu")(latent_inputs)
x = Reshape((SEQ_LEN, 1, 1, 512))(x)
x = TimeDistributed(
Conv2DTranspose(512, kernel_size=4, strides=1, padding="valid")
)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = TimeDistributed(
Conv2DTranspose(256, kernel_size=4, strides=2, padding="same")
)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = TimeDistributed(
Conv2DTranspose(128, kernel_size=4, strides=2, padding="same")
)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = TimeDistributed(
Conv2DTranspose(64, kernel_size=4, strides=2, padding="same")
)(x)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
x = TimeDistributed(
Conv2DTranspose(1, kernel_size=4, strides=2, padding="same")
)(x)
x = Activation("sigmoid")(x)
return Model(inputs=latent_inputs, outputs=x, name="decoder")
# endregion
@property
def metrics(self):
return [
self.total_loss_tracker,
self.reconstruction_loss_tracker,
self.kl_loss_tracker,
]
def call(self, inputs, training=None, mask=None):
z_mean, z_log_var, z = self.encoder(inputs)
pred = self.decoder(z)
return pred
def train_step(self, data):
x, y = data
with tf.GradientTape() as tape:
z_mean, z_log_var, z = self.encoder(x)
reconstruction = self.decoder(z)
reconstruction_loss = tf.reduce_mean(
tf.reduce_sum(
tf.keras.losses.binary_crossentropy(y, reconstruction),
axis=(1, 2),
)
)
kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))
kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))
total_loss = reconstruction_loss + self.beta * kl_loss
grads = tape.gradient(total_loss, self.trainable_weights)
self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
self.total_loss_tracker.update_state(total_loss)
self.reconstruction_loss_tracker.update_state(reconstruction_loss)
self.kl_loss_tracker.update_state(kl_loss)
return {
"loss": self.total_loss_tracker.result(),
"reconstruction_loss": self.reconstruction_loss_tracker.result(),
"kl_loss": self.kl_loss_tracker.result(),
}
def test_step(self, data):
if isinstance(data, tuple):
data = data[0]
z_mean, z_log_var, z = self.encoder(data)
reconstruction = self.decoder(z)
reconstruction_loss = tf.reduce_mean(
tf.keras.losses.binary_crossentropy(data, reconstruction)
)
reconstruction_loss *= 28 * 28
kl_loss = 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)
kl_loss = tf.reduce_mean(kl_loss)
kl_loss *= -0.5
total_loss = reconstruction_loss + kl_loss
return {
"loss": total_loss,
"reconstruction_loss": reconstruction_loss,
"kl_loss": kl_loss,
}
|