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modules.py
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modules.py
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import numpy as np
import tensorflow as tf
from tensorflow.keras import Model, layers, Input
from collections import namedtuple
def abs_criterion(pred, target):
return tf.reduce_mean(tf.abs(pred - target))
def mae_criterion(pred, target):
return tf.reduce_mean((pred - target) ** 2)
def sce_criterion(logits, labels):
return tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels)
)
def softmax_criterion(logits, labels):
return tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels)
)
def padding(x, p=3):
return tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]], "REFLECT")
class InstanceNorm(layers.Layer):
def __init__(self, input_shape):
super(InstanceNorm, self).__init__()
self.scale = tf.Variable(
initial_value=np.random.normal(1.0, 0.02, input_shape),
trainable=True,
name="SCALE",
dtype=tf.float32,
)
self.offset = tf.Variable(
initial_value=np.zeros(input_shape),
trainable=True,
name="OFFSET",
dtype=tf.float32,
)
def call(self, x, epsilon=1e-5):
mean, variance = tf.nn.moments(x, axes=[1, 2], keepdims=True)
inv = tf.math.rsqrt(variance + epsilon)
normalized = (x - mean) * inv
return self.scale * normalized + self.offset
class Padding(layers.Layer):
def __init__(self):
super(Padding, self).__init__()
def call(self, x, p=3):
return tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]], "REFLECT")
class ResNetBlock(layers.Layer):
def __init__(self):
super(ResNetBlock, self).__init__()
def call(self, x, dim, k_init, ks=3, s=1):
p = (ks - 1) // 2
# For ks = 3, p = 1
y = layers.Lambda(padding, arguments={"p": p}, name="PADDING_1")(x)
# After first padding, (batch * 130 * 130 * 3)
y = layers.Conv2D(
filters=dim,
kernel_size=ks,
strides=s,
padding="valid",
kernel_initializer=k_init,
use_bias=False,
)(y)
y = InstanceNorm(y.shape[-1:])(y)
y = layers.ReLU()(y)
# After first conv2d, (batch * 128 * 128 * 3)
y = layers.Lambda(padding, arguments={"p": p}, name="PADDING_2")(y)
# After second padding, (batch * 130 * 130 * 3)
y = layers.Conv2D(
filters=dim,
kernel_size=ks,
strides=s,
padding="valid",
kernel_initializer=k_init,
use_bias=False,
)(y)
y = InstanceNorm(y.shape[-1:])(y)
y = layers.ReLU()(y + x)
# After second conv2d, (batch * 128 * 128 * 3)
return y
# def instance_norm(x, epsilon=1e-5):
# scale = tf.Variable(
# initial_value=np.random.normal(1.0, 0.02, x.shape[-1:]),
# trainable=True,
# name="SCALE",
# dtype=tf.float32,
# )
# offset = tf.Variable(
# initial_value=np.zeros(x.shape[-1:]),
# trainable=True,
# name="OFFSET",
# dtype=tf.float32,
# )
# mean, variance = tf.nn.moments(x, axes=[1, 2], keepdims=True)
# inv = tf.math.rsqrt(variance + epsilon)
# normalized = (x - mean) * inv
# return scale * normalized + offset
def build_discriminator(options, name="Discriminator"):
initializer = tf.random_normal_initializer(0.0, 0.02)
inputs = Input(shape=(options.time_step, options.pitch_range, options.output_nc))
x = inputs
x = layers.Conv2D(
filters=options.df_dim,
kernel_size=7,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_1",
)(x)
x = layers.LeakyReLU(alpha=0.2)(x)
# (batch * 32 * 42 * 64)
x = layers.Conv2D(
filters=options.df_dim * 4,
kernel_size=7,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_2",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.LeakyReLU(alpha=0.2)(x)
# (batch * 16 * 21 * 256)
x = layers.Conv2D(
filters=1,
kernel_size=7,
strides=1,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_3",
)(x)
# (batch * 16 * 21 * 1)
outputs = x
return Model(inputs=inputs, outputs=outputs, name=name)
def build_generator(options, name="Generator"):
initializer = tf.random_normal_initializer(0.0, 0.02)
inputs = Input(shape=(options.time_step, options.pitch_range, options.output_nc))
x = inputs
# (batch * 64 * 84 * 1)
x = layers.Lambda(padding, name="PADDING_1")(x)
# (batch * 70 * 90 * 1)
x = layers.Conv2D(
filters=options.gf_dim,
kernel_size=7,
strides=1,
padding="valid",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_1",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.ReLU()(x)
# (batch * 64 * 84 * 64)
x = layers.Conv2D(
filters=options.gf_dim * 2,
kernel_size=3,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_2",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.ReLU()(x)
# (batch * 32 * 42 * 128)
x = layers.Conv2D(
filters=options.gf_dim * 4,
kernel_size=3,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_3",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.ReLU()(x)
# (batch * 16 * 21 * 256)
for i in range(10):
# x = resnet_block(x, options.gf_dim * 4)
x = ResNetBlock()(x, options.gf_dim * 4, initializer)
# (batch * 16 * 21 * 256)
x = layers.Conv2DTranspose(
filters=options.gf_dim * 2,
kernel_size=3,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="DECONV2D_1",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.ReLU()(x)
# (batch * 32 * 42 * 128)
x = layers.Conv2DTranspose(
filters=options.gf_dim,
kernel_size=3,
strides=2,
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="DECONV2D_2",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.ReLU()(x)
# (batch * 64 * 84 * 64)
x = layers.Lambda(padding, name="PADDING_2")(x)
# After padding, (batch * 70 * 90 * 64)
x = layers.Conv2D(
filters=options.output_nc,
kernel_size=7,
strides=1,
padding="valid",
kernel_initializer=initializer,
activation="sigmoid",
use_bias=False,
name="CONV2D_4",
)(x)
# (batch * 64 * 84 * 1)
outputs = x
return Model(inputs=inputs, outputs=outputs, name=name)
def build_discriminator_classifier(options, name="Discriminator_Classifier"):
initializer = tf.random_normal_initializer(0.0, 0.02)
inputs = Input(shape=(options.time_step, options.pitch_range, options.output_nc))
x = inputs
# (batch * 64, 84, 1)
x = layers.Conv2D(
filters=options.df_dim,
kernel_size=[1, 12],
strides=[1, 12],
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_1",
)(x)
x = layers.LeakyReLU(alpha=0.2)(x)
# (batch * 64 * 7 * 64)
x = layers.Conv2D(
filters=options.df_dim * 2,
kernel_size=[4, 1],
strides=[4, 1],
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_2",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.LeakyReLU(alpha=0.2)(x)
# (batch * 16 * 7 * 128)
x = layers.Conv2D(
filters=options.df_dim * 4,
kernel_size=[2, 1],
strides=[2, 1],
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_3",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.LeakyReLU(alpha=0.2)(x)
# (batch * 8 * 7 * 256)
x = layers.Conv2D(
filters=options.df_dim * 8,
kernel_size=[8, 1],
strides=[8, 1],
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_4",
)(x)
x = InstanceNorm(x.shape[-1:])(x)
x = layers.LeakyReLU(alpha=0.2)(x)
# (batch * 1 * 7 * 512)
x = layers.Conv2D(
filters=2,
kernel_size=[1, 7],
strides=[1, 7],
padding="same",
kernel_initializer=initializer,
use_bias=False,
name="CONV2D_5",
)(x)
# (batch * 1 * 1 * 2)
x = tf.reshape(x, [-1, 2])
# (batch * 2)
outputs = x
return Model(inputs=inputs, outputs=outputs, name=name)
if __name__ == "__main__":
OPTIONS = namedtuple(
"OPTIONS",
"batch_size "
"time_step "
"input_nc "
"output_nc "
"pitch_range "
"gf_dim "
"df_dim ",
)
options = OPTIONS._make((128, 64, 1, 1, 84, 64, 64))
model = build_generator(options)
print(model.summary())