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dnn_model.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
from ops import *
import re
class spec_Generator(object):
def __init__(self, name="generator"):
self.name = name
def __call__(self, x, reuse=True):
''' Can be conditioned on `y` or not '''
ngf = 32
# nc, nh, nw = self.n_shape
# cc, ch, cw = self.c_shape
layers = []
with tf.variable_scope(self.name) as vs:
if reuse:
vs.reuse_variables()
# encoder_1: [batch, 256, 256, in_channels] => [batch, 128, 128, ngf]
with tf.device('/gpu:1'):
with tf.variable_scope("encoder_1"):
output = conv2d(x, ngf, [3,3], [1,1,1,1])
layers.append(output)
layer_specs = [
ngf * 2, # encoder_2: [batch, 128, 128, ngf] => [batch, 64, 64, ngf * 2]
ngf * 4, # encoder_3: [batch, 64, 64, ngf * 2] => [batch, 32, 32, ngf * 4]
ngf * 8, # encoder_4: [batch, 32, 32, ngf * 4] => [batch, 16, 16, ngf * 8]
ngf * 8, # encoder_5: [batch, 16, 16, ngf * 8] => [batch, 8, 8, ngf * 8]
ngf * 8, # encoder_6: [batch, 8, 8, ngf * 8] => [batch, 4, 4, ngf * 8]
]
for out_channels in layer_specs:
with tf.variable_scope("encoder_%d" % (len(layers) + 1)):
rectified = activation(layers[-1], 'prelu')
# [batch, in_height, in_width, in_channels] => [batch, in_height/2, in_width/2, out_channels]
output = conv2d(rectified, out_channels, [3,3], [1,1,2,2])
# output = batchnorm(convolved, axis=[1, 2, 3], name='G_layernorm')
layers.append(output)
# ---------------------------------------------------------------------- #
layer_specs = [
(ngf * 8, 0.5), # decoder_8: [batch, 1, 1, ngf * 8] => [batch, 2, 2, ngf * 8 * 2]
(ngf * 8, 0.5), # decoder_7: [batch, 2, 2, ngf * 8 * 2] => [batch, 4, 4, ngf * 8 * 2]
(ngf * 8, 0.5), # decoder_6: [batch, 4, 4, ngf * 8 * 2] => [batch, 8, 8, ngf * 8 * 2]
(ngf * 4, 0.0), # decoder_4: [batch, 16, 16, ngf * 8 * 2] => [batch, 32, 32, ngf * 4 * 2]
(ngf * 2, 0.0), # decoder_3: [batch, 32, 32, ngf * 4 * 2] => [batch, 64, 64, ngf * 2 * 2]
]
with tf.device('/gpu:0'):
num_encoder_layers = len(layers)
for decoder_layer, (out_channels, dropout) in enumerate(layer_specs):
skip_layer = num_encoder_layers - decoder_layer - 1
with tf.variable_scope("decoder_%d" % (skip_layer + 1)):
if decoder_layer == 0:
# first decoder layer doesn't have skip connections
# since it is directly connected to the skip_layer
input = layers[-1]
else:
input = tf.concat([layers[-1], layers[skip_layer]], axis=1)
rectified = activation(input, 'prelu')
# [batch, in_height, in_width, in_channels] => [batch, in_height*2, in_width*2, out_channels]
output = deconv2D_up(rectified, out_channels, [3,3], [1,1,1,1])
# output = batchnorm(output)
# if dropout > 0.0:
# output = tf.nn.dropout(output, keep_prob=1 - dropout)
layers.append(output)
# decoder_1: [batch, 128, 128, ngf * 2] => [batch, 256, 256, generator_outputs_channels]
with tf.variable_scope("decoder_1"):
input = tf.concat([layers[-1], layers[0]], axis=1)
rectified = activation(input, 'prelu')
output = conv2d(rectified, ngf, [3,3], [1,1,1,1])
layers.append(output)
with tf.variable_scope("output_layer"):
rectified = activation(layers[-1], 'prelu')
output = conv2d(rectified, 1, [3,3], [1,1,1,1])
layers.append(output)
for l in layers:
print(l)
return layers[-1]
@property
def vars(self):
return [var for var in tf.global_variables() if self.name == var.name.split('/')[0]]
class spec_Discriminator(object):
def __init__(self, name="discriminator"):
self.name = name
def __call__(self, noisy, clean, reuse=True):
n_layers = 4
ndf = 64
layers = []
with tf.device('/gpu:0'):
with tf.variable_scope(self.name) as vs:
if reuse:
vs.reuse_variables()
# 2x [batch, in_channels, height, width] => [batch, in_channels * 2, height, width]
input = tf.concat([noisy, clean], axis=1)
with tf.variable_scope("layer_1"):
convolved = conv2d(input, ndf, [3,3], [1,1,2,2])
rectified = activation(convolved, 'lrelu')
layers.append(rectified)
# layer_2: [batch, 128, 128, ndf] => [batch, 64, 64, ndf * 2]
# layer_3: [batch, 64, 64, ndf * 2] => [batch, 32, 32, ndf * 4]
# layer_4: [batch, 32, 32, ndf * 4] => [batch, 31, 31, ndf * 8]
for i in range(n_layers):
with tf.variable_scope("layer_%d" % (len(layers) + 1)):
out_channels = ndf * min(2**(i+1), 8)
stride = 1 if i == n_layers - 1 else 2 # last layer here has stride 1
convolved = conv2d(layers[-1], out_channels, [3,3], [1,1,stride,stride])
normalized = layernorm(convolved, axis=[1, 2, 3], name='D_layernorm')
rectified = activation(normalized, 'lrelu')
layers.append(rectified)
# layer_5: [batch, 31, 31, ndf * 8] => [batch, 30, 30, 1]
with tf.variable_scope("layer_%d" % (len(layers) + 1)):
convolved = conv2d(layers[-1], 1, [3,3], [1,1,1,1])
normalized = layernorm(convolved, axis=[1, 2, 3], name='D_layernorm')
rectified = activation(normalized, 'lrelu')
layers.append(convolved)
with tf.variable_scope("fully_connected"):
flatten = tf.contrib.layers.flatten(layers[-1])
fc1 = tf.contrib.layers.fully_connected(flatten, 256, activation_fn=None)
normalized = layernorm(fc1, axis=[1], name='D_layernorm')
rectified = activation(normalized, 'lrelu')
final = tf.contrib.layers.fully_connected(rectified, 1, activation_fn=None)
layers.append(final)
for l in layers:
print(l)
return layers[-1]
@property
def vars(self):
return [var for var in tf.global_variables() if self.name == var.name.split('/')[0]]