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TSRIRgan.py
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TSRIRgan.py
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import tensorflow as tf
def conv1d_transpose(
inputs,
filters,
kernel_width,
stride=4,
padding='same',
upsample='zeros'):
if upsample == 'zeros':
return tf.layers.conv2d_transpose(
tf.expand_dims(inputs, axis=1),
filters,
(1, kernel_width),
strides=(1, stride),
padding='same'
)[:, 0]
elif upsample == 'nn':
batch_size = tf.shape(inputs)[0]
_, w, nch = inputs.get_shape().as_list()
x = inputs
x = tf.expand_dims(x, axis=1)
x = tf.image.resize_nearest_neighbor(x, [1, w * stride])
x = x[:, 0]
return tf.layers.conv1d(
x,
filters,
kernel_width,
1,
padding='same')
else:
raise NotImplementedError
"""
Input: [None, 100]
Output: [None, slice_len, 1]
"""
def TSRIRGANGenerator_real(
z,
slice_len=16384,
nch=1,
kernel_len=25,
dim=64,
use_batchnorm=False,
upsample='zeros',
train=False):
assert slice_len in [16384, 32768, 65536]
batch_size = tf.shape(z)[0]
if use_batchnorm:
batchnorm = lambda x: tf.layers.batch_normalization(x, training=train)
else:
batchnorm = lambda x: x
# FC and reshape for convolution
# [100] -> [16, 1024]
dim_mul = 16 if slice_len == 16384 else 32
# output = z
# with tf.variable_scope('z_project'):
# output = tf.layers.dense(output, 4 * 4 * dim * dim_mul)
# output = tf.reshape(output, [batch_size, 16, dim * dim_mul])
# output = batchnorm(output)
# output = tf.nn.relu(output)
# dim_mul //= 2
# Layer 0
# [16384, 1] -> [4096, 64]
output = z
with tf.variable_scope('downconv_0'):
output = tf.layers.conv1d(output, dim, kernel_len, 4, padding='SAME')
output = tf.nn.relu(output)
# output = phaseshuffle(output)
# Layer 1
# [4096, 64] -> [1024, 128]
with tf.variable_scope('downconv_1'):
output = tf.layers.conv1d(output, dim * 2, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
# output = phaseshuffle(output)
# Layer 2
# [1024, 128] -> [256, 256]
with tf.variable_scope('downconv_2'):
output = tf.layers.conv1d(output, dim * 4, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
# output = phaseshuffle(output)
# Layer 3
# [256, 256] -> [64, 512]
with tf.variable_scope('downconv_3'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
# output = phaseshuffle(output)
# Layer 4
# [64, 512] -> [16, 1024] %[32,1024]
with tf.variable_scope('downconv_4'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 1, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
with tf.variable_scope('downconv_5'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 1, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
with tf.variable_scope('downconv_6'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 1, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
with tf.variable_scope('downconv_7'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 1, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
with tf.variable_scope('downconv_8'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 1, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
# # Layer 0
# # [16, 1024] -> [64, 512] %[32, 1024]
# with tf.variable_scope('upconv_0'):
# output = conv1d_transpose(output, dim * dim_mul, kernel_len, 2, upsample=upsample)
# output = batchnorm(output)
# output = tf.nn.relu(output)
# dim_mul //= 2
#Up Layer 1
# [64, 512] -> [256, 256]
with tf.variable_scope('upconv_1'):
output = conv1d_transpose(output, dim * dim_mul, kernel_len, 4, upsample=upsample)
output = batchnorm(output)
output = tf.nn.relu(output)
dim_mul //= 2
#Up Layer 2
# [256, 256] -> [1024, 128]
with tf.variable_scope('upconv_2'):
output = conv1d_transpose(output, dim * dim_mul, kernel_len, 4, upsample=upsample)
output = batchnorm(output)
output = tf.nn.relu(output)
dim_mul //= 2
#Up Layer 3
# [1024, 128] -> [4096, 64]
with tf.variable_scope('upconv_3'):
output = conv1d_transpose(output, dim * dim_mul, kernel_len, 4, upsample=upsample)
output = batchnorm(output)
output = tf.nn.relu(output)
if slice_len == 16384:
#Up Layer 4
# [4096, 64] -> [16384, nch]
with tf.variable_scope('upconv_4'):
output = conv1d_transpose(output, nch, kernel_len, 4, upsample=upsample)
output = tf.nn.tanh(output)
elif slice_len == 32768:
#Up Layer 4
# [4096, 128] -> [16384, 64]
with tf.variable_scope('upconv_4'):
output = conv1d_transpose(output, dim, kernel_len, 4, upsample=upsample)
output = batchnorm(output)
output = tf.nn.relu(output)
#Up Layer 5
# [16384, 64] -> [32768, nch]
with tf.variable_scope('upconv_5'):
output = conv1d_transpose(output, nch, kernel_len, 2, upsample=upsample)
output = tf.nn.tanh(output)
elif slice_len == 65536:
#Up Layer 4
# [4096, 128] -> [16384, 64]
with tf.variable_scope('upconv_4'):
output = conv1d_transpose(output, dim, kernel_len, 4, upsample=upsample)
output = batchnorm(output)
output = tf.nn.relu(output)
#Up Layer 5
# [16384, 64] -> [65536, nch]
with tf.variable_scope('upconv_5'):
output = conv1d_transpose(output, nch, kernel_len, 4, upsample=upsample)
output = tf.nn.tanh(output)
# Automatically update batchnorm moving averages every time G is used during training
if train and use_batchnorm:
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope=tf.get_variable_scope().name)
if slice_len == 16384:
assert len(update_ops) == 10
else:
assert len(update_ops) == 12
with tf.control_dependencies(update_ops):
output = tf.identity(output)
return output
def TSRIRGANGenerator_synthetic(
z,
slice_len=16384,
nch=1,
kernel_len=25,
dim=64,
use_batchnorm=False,
upsample='zeros',
train=False):
assert slice_len in [16384, 32768, 65536]
batch_size = tf.shape(z)[0]
if use_batchnorm:
batchnorm = lambda x: tf.layers.batch_normalization(x, training=train)
else:
batchnorm = lambda x: x
# FC and reshape for convolution
# [100] -> [16, 1024]
dim_mul = 16 if slice_len == 16384 else 32
# output = z
# with tf.variable_scope('z_project'):
# output = tf.layers.dense(output, 4 * 4 * dim * dim_mul)
# output = tf.reshape(output, [batch_size, 16, dim * dim_mul])
# output = batchnorm(output)
# output = tf.nn.relu(output)
# dim_mul //= 2
# Layer 0
# [16384, 1] -> [4096, 64]
output = z
with tf.variable_scope('downconv_0'):
output = tf.layers.conv1d(output, dim, kernel_len, 4, padding='SAME')
output = tf.nn.relu(output)
# output = phaseshuffle(output)
# Layer 1
# [4096, 64] -> [1024, 128]
with tf.variable_scope('downconv_1'):
output = tf.layers.conv1d(output, dim * 2, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
# output = phaseshuffle(output)
# Layer 2
# [1024, 128] -> [256, 256]
with tf.variable_scope('downconv_2'):
output = tf.layers.conv1d(output, dim * 4, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
# output = phaseshuffle(output)
# Layer 3
# [256, 256] -> [64, 512]
with tf.variable_scope('downconv_3'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
# output = phaseshuffle(output)
# Layer 4
# [64, 512] -> [16, 1024] %[32,1024]
with tf.variable_scope('downconv_4'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 1, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
with tf.variable_scope('downconv_5'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 1, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
with tf.variable_scope('downconv_6'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 1, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
with tf.variable_scope('downconv_7'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 1, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
with tf.variable_scope('downconv_8'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 1, padding='SAME')
output = batchnorm(output)
output = tf.nn.relu(output)
# # Layer 0
# # [16, 1024] -> [64, 512] %[32, 1024]
# with tf.variable_scope('upconv_0'):
# output = conv1d_transpose(output, dim * dim_mul, kernel_len, 2, upsample=upsample)
# output = batchnorm(output)
# output = tf.nn.relu(output)
# dim_mul //= 2
#Up Layer 1
# [64, 512] -> [256, 256]
with tf.variable_scope('upconv_1'):
output = conv1d_transpose(output, dim * dim_mul, kernel_len, 4, upsample=upsample)
output = batchnorm(output)
output = tf.nn.relu(output)
dim_mul //= 2
#Up Layer 2
# [256, 256] -> [1024, 128]
with tf.variable_scope('upconv_2'):
output = conv1d_transpose(output, dim * dim_mul, kernel_len, 4, upsample=upsample)
output = batchnorm(output)
output = tf.nn.relu(output)
dim_mul //= 2
#Up Layer 3
# [1024, 128] -> [4096, 64]
with tf.variable_scope('upconv_3'):
output = conv1d_transpose(output, dim * dim_mul, kernel_len, 4, upsample=upsample)
output = batchnorm(output)
output = tf.nn.relu(output)
if slice_len == 16384:
#Up Layer 4
# [4096, 64] -> [16384, nch]
with tf.variable_scope('upconv_4'):
output = conv1d_transpose(output, nch, kernel_len, 4, upsample=upsample)
output = tf.nn.tanh(output)
elif slice_len == 32768:
#Up Layer 4
# [4096, 128] -> [16384, 64]
with tf.variable_scope('upconv_4'):
output = conv1d_transpose(output, dim, kernel_len, 4, upsample=upsample)
output = batchnorm(output)
output = tf.nn.relu(output)
#Up Layer 5
# [16384, 64] -> [32768, nch]
with tf.variable_scope('upconv_5'):
output = conv1d_transpose(output, nch, kernel_len, 2, upsample=upsample)
output = tf.nn.tanh(output)
elif slice_len == 65536:
#Up Layer 4
# [4096, 128] -> [16384, 64]
with tf.variable_scope('upconv_4'):
output = conv1d_transpose(output, dim, kernel_len, 4, upsample=upsample)
output = batchnorm(output)
output = tf.nn.relu(output)
#Up Layer 5
# [16384, 64] -> [65536, nch]
with tf.variable_scope('upconv_5'):
output = conv1d_transpose(output, nch, kernel_len, 4, upsample=upsample)
output = tf.nn.tanh(output)
# Automatically update batchnorm moving averages every time G is used during training
if train and use_batchnorm:
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope=tf.get_variable_scope().name)
if slice_len == 16384:
assert len(update_ops) == 10
else:
assert len(update_ops) == 12
with tf.control_dependencies(update_ops):
output = tf.identity(output)
return output
def lrelu(inputs, alpha=0.2):
return tf.maximum(alpha * inputs, inputs)
def apply_phaseshuffle(x, rad, pad_type='reflect'):
b, x_len, nch = x.get_shape().as_list()
phase = tf.random_uniform([], minval=-rad, maxval=rad + 1, dtype=tf.int32)
pad_l = tf.maximum(phase, 0)
pad_r = tf.maximum(-phase, 0)
phase_start = pad_r
x = tf.pad(x, [[0, 0], [pad_l, pad_r], [0, 0]], mode=pad_type)
x = x[:, phase_start:phase_start+x_len]
x.set_shape([b, x_len, nch])
return x
"""
Input: [None, slice_len, nch]
Output: [None] (linear output)
"""
def TSRIRGANDiscriminator_synthetic(
x,
kernel_len=25,
dim=64,
use_batchnorm=False,
phaseshuffle_rad=0):
batch_size = tf.shape(x)[0]
slice_len = int(x.get_shape()[1])
if use_batchnorm:
batchnorm = lambda x: tf.layers.batch_normalization(x, training=True)
else:
batchnorm = lambda x: x
# if phaseshuffle_rad > 0:
# phaseshuffle = lambda x: apply_phaseshuffle(x, phaseshuffle_rad)
# else:
# phaseshuffle = lambda x: x
phaseshuffle = lambda x: x
# Layer 0
# [16384, 1] -> [4096, 64]
output = x
with tf.variable_scope('downconv_0'):
output = tf.layers.conv1d(output, dim, kernel_len, 4, padding='SAME')
output = lrelu(output)
output = phaseshuffle(output)
# Layer 1
# [4096, 64] -> [1024, 128]
with tf.variable_scope('downconv_1'):
output = tf.layers.conv1d(output, dim * 2, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
output = phaseshuffle(output)
# Layer 2
# [1024, 128] -> [256, 256]
with tf.variable_scope('downconv_2'):
output = tf.layers.conv1d(output, dim * 4, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
output = phaseshuffle(output)
# Layer 3
# [256, 256] -> [64, 512]
with tf.variable_scope('downconv_3'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
output = phaseshuffle(output)
# Layer 4
# [64, 512] -> [16, 1024]
with tf.variable_scope('downconv_4'):
output = tf.layers.conv1d(output, dim * 16, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
if slice_len == 32768:
# Layer 5
# [32, 1024] -> [16, 2048]
with tf.variable_scope('downconv_5'):
output = tf.layers.conv1d(output, dim * 32, kernel_len, 2, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
elif slice_len == 65536:
# Layer 5
# [64, 1024] -> [16, 2048]
with tf.variable_scope('downconv_5'):
output = tf.layers.conv1d(output, dim * 32, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
# Flatten
output = tf.reshape(output, [batch_size, -1])
# Connect to single logit
with tf.variable_scope('output'):
output = tf.layers.dense(output, 1)[:, 0]
# Don't need to aggregate batchnorm update ops like we do for the generator because we only use the discriminator for training
return output
def TSRIRGANDiscriminator_real(
x,
kernel_len=25,
dim=64,
use_batchnorm=False,
phaseshuffle_rad=0):
batch_size = tf.shape(x)[0]
slice_len = int(x.get_shape()[1])
if use_batchnorm:
batchnorm = lambda x: tf.layers.batch_normalization(x, training=True)
else:
batchnorm = lambda x: x
# if phaseshuffle_rad > 0:
# phaseshuffle = lambda x: apply_phaseshuffle(x, phaseshuffle_rad)
# else:
# phaseshuffle = lambda x: x
phaseshuffle = lambda x: x
# Layer 0
# [16384, 1] -> [4096, 64]
output = x
with tf.variable_scope('downconv_0'):
output = tf.layers.conv1d(output, dim, kernel_len, 4, padding='SAME')
output = lrelu(output)
output = phaseshuffle(output)
# Layer 1
# [4096, 64] -> [1024, 128]
with tf.variable_scope('downconv_1'):
output = tf.layers.conv1d(output, dim * 2, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
output = phaseshuffle(output)
# Layer 2
# [1024, 128] -> [256, 256]
with tf.variable_scope('downconv_2'):
output = tf.layers.conv1d(output, dim * 4, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
output = phaseshuffle(output)
# Layer 3
# [256, 256] -> [64, 512]
with tf.variable_scope('downconv_3'):
output = tf.layers.conv1d(output, dim * 8, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
output = phaseshuffle(output)
# Layer 4
# [64, 512] -> [16, 1024]
with tf.variable_scope('downconv_4'):
output = tf.layers.conv1d(output, dim * 16, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
if slice_len == 32768:
# Layer 5
# [32, 1024] -> [16, 2048]
with tf.variable_scope('downconv_5'):
output = tf.layers.conv1d(output, dim * 32, kernel_len, 2, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
elif slice_len == 65536:
# Layer 5
# [64, 1024] -> [16, 2048]
with tf.variable_scope('downconv_5'):
output = tf.layers.conv1d(output, dim * 32, kernel_len, 4, padding='SAME')
output = batchnorm(output)
output = lrelu(output)
# Flatten
output = tf.reshape(output, [batch_size, -1])
# Connect to single logit
with tf.variable_scope('output'):
output = tf.layers.dense(output, 1)[:, 0]
# Don't need to aggregate batchnorm update ops like we do for the generator because we only use the discriminator for training
return output