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dcgan.py
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dcgan.py
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import numpy as np
import tensorflow as tf
_DTYPE = tf.float32
"""
DCGAN 64x64 generator
z shape during training: [b * k, d_i + d_o]
z shape during inference: [None, d_i + d_o]
"""
def DCGANGenerator64x64(z, nch, dim=128, batchnorm=True, train=False):
_G_DIM_MUL = [8, 4, 2, 1]
_G_IMG_DIM_INIT = 4
batch_size = tf.shape(z)[0]
weight_limit = 0.02 * np.sqrt(3)
weight_init = tf.random_uniform_initializer(-weight_limit, weight_limit, dtype=_DTYPE)
outputs = z
# Project each z to [4, 4, dim * 8]
with tf.variable_scope('z_project'):
outputs = tf.layers.dense(
outputs,
dim * _G_DIM_MUL[0] * _G_IMG_DIM_INIT * _G_IMG_DIM_INIT,
kernel_initializer=weight_init)
outputs = tf.reshape(outputs, [batch_size, _G_IMG_DIM_INIT, _G_IMG_DIM_INIT, dim * _G_DIM_MUL[0]])
if batchnorm:
outputs = tf.layers.batch_normalization(outputs, training=train)
outputs = tf.nn.relu(outputs)
# Upscale to [32, 32, dim]
for i, dim_mul in enumerate(_G_DIM_MUL[1:]):
with tf.variable_scope('upconv_2d_{}'.format(i)):
outputs = tf.layers.conv2d_transpose(
outputs,
dim * dim_mul,
[5, 5],
strides=(2, 2),
padding='SAME',
kernel_initializer=weight_init)
if batchnorm:
outputs = tf.layers.batch_normalization(outputs, training=train)
outputs = tf.nn.relu(outputs)
# Upscale to [64, 64, nch] on [-1, 1]
with tf.variable_scope('upconv_2d_3'):
outputs = tf.layers.conv2d_transpose(
outputs,
nch,
[5, 5],
strides=(2, 2),
padding='SAME',
kernel_initializer=weight_init)
outputs = tf.nn.tanh(outputs)
# Update batchnorm moving statistics
# NOTE: Only one generator in graph should have train=True
# NOTE: Incompatible with graphs containing other UPDATE_OPS
if train:
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if len(update_ops) != 8:
raise Exception('Other update ops found in graph')
with tf.control_dependencies(update_ops):
outputs = tf.identity(outputs)
return outputs
"""
SD-DCGAN 64x64 discriminator
x (real or fake) is [b, k, 64, 64, 3]
"""
def SDDCGANDiscriminator64x64(x, dim=128, batchnorm=True, siamese=True, collate='conv_1'):
_D_DIM_MUL = [1, 2, 4, 8]
def lrelu(x, alpha=0.2):
return tf.maximum(alpha * x, x)
batch_size = int(x.get_shape()[0])
weight_limit = 0.02 * np.sqrt(3)
weight_init = tf.random_uniform_initializer(-weight_limit, weight_limit, dtype=_DTYPE)
# Split along tuple dimension k
inputs_instances = tf.split(x, int(x.get_shape()[1]), axis=1)
inputs_instances = [tf.squeeze(x, axis=1) for x in inputs_instances]
# Merge channels if not using Siamese architecture
if not siamese:
inputs_instances = [tf.concat(inputs_instances, axis=3)]
# Separately encode k images in tuple
outputs_instances = []
reuse = False
for outputs in inputs_instances:
# Downscale to [32, 32, dim]
with tf.variable_scope('downconv_2d_0', reuse=reuse):
outputs = tf.layers.conv2d(
outputs,
dim * _D_DIM_MUL[0],
[5, 5],
strides=(2, 2),
padding='SAME',
kernel_initializer=weight_init)
outputs = lrelu(outputs)
# Downscale to [4, 4, dim * 8]
for i, dim_mul in enumerate(_D_DIM_MUL[1:]):
with tf.variable_scope('downconv_2d_{}'.format(i + 1), reuse=reuse):
outputs = tf.layers.conv2d(
outputs,
dim * dim_mul,
[5, 5],
strides=(2, 2),
padding='SAME',
kernel_initializer=weight_init)
# TODO: Layer norm instead? (iwgan)
if batchnorm:
outputs = tf.layers.batch_normalization(outputs, training=True)
outputs = lrelu(outputs)
outputs_instances.append(outputs)
reuse = True
# Collate encoder outputs (using either dense or conv layers)
if collate.startswith('dense_'):
nlayers = int(collate.split('_')[1])
outputs_flattened = []
for outputs in outputs_instances:
outputs = tf.reshape(outputs, [batch_size, -1])
outputs_flattened.append(outputs)
outputs = tf.concat(outputs_flattened, axis=1)
for i in range(nlayers):
with tf.variable_scope('collate_dense_{}'.format(i)):
outputs = tf.layers.dense(
outputs,
int(outputs.get_shape()[1]) // 2,
kernel_initializer=weight_init)
if batchnorm:
outputs = tf.layers.batch_normalization(outputs, training=True)
outputs = lrelu(outputs)
elif collate.startswith('conv_'):
nlayers = int(collate.split('_')[1])
assert nlayers <= 2
outputs = tf.concat(outputs_instances, axis=3)
dim_mul = _D_DIM_MUL[-1]
for i in range(nlayers):
with tf.variable_scope('collate_conv_{}'.format(i)):
outputs = tf.layers.conv2d(
outputs,
dim * dim_mul,
[3, 3],
strides=(2, 2),
padding='SAME',
kernel_initializer=weight_init)
if batchnorm:
outputs = tf.layers.batch_normalization(outputs, training=True)
outputs = lrelu(outputs)
dim_mul /= 2
outputs = tf.reshape(outputs, [batch_size, -1])
else:
raise NotImplementedError()
# Concatenate one-hot labels
with tf.variable_scope('classify'):
outputs = tf.layers.dense(outputs, 1, kernel_initializer=weight_init)[:, 0]
return outputs