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Decoder.py
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Decoder.py
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import tensorflow as tf
import numpy as np
from functools import reduce
VGG_MEAN = [103.939, 116.779, 123.68]
class Decoder:
"""
A trainable version VGG19.
"""
def __init__(self, vgg19_npy_path=None, trainable=True, dropout=0.5):
if vgg19_npy_path is not None:
self.data_dict = np.load(vgg19_npy_path, encoding='latin1').item()
else:
self.data_dict = None
self.var_dict = {}
self.trainable = trainable
self.dropout = dropout
def decoder(self,encode,target_layer) :
layer_num = dict(zip(['relu1','relu2','relu3','relu4','relu5'],range(1,6)))[target_layer]
var_list=[]
decode_arg={
'5':[('upsample',14,28),
('dconv5_1',512,512),
('dconv5_2',512,512),
('dconv5_3',512,512),
('dconv5_4',512,512)],
'4':[('upsample',28,56),
('dconv4_1',512,256),
('dconv4_2',256,256),
('dconv4_3',256,256),
('dconv4_4',256,256)],
'3':[('upsample',56,112),
('dconv3_1',256,128),
('dconv3_2',128,128),
('dconv3_3',128,128),
('dconv3_4',128,128)],
'2':[('upsample',112,224),
('dconv2_1',128,64),
('dconv2_2',64,64)],
'1':[('dconv1_1',64,64),
('output',64,3)]}
decode = encode
for d in reversed(range(1,layer_num+1)):
for layer in decode_arg[str(d)]:
if 'up' in layer[0]:
decode = self.upsample(decode,layer[1])
if 'dconv' in layer[0] :
decode ,var_list= self.conv_layer(decode,layer[1],layer[2],layer[0]+'_'+target_layer,var_list)
if 'out' in layer[0] :
decode, var_list = self.output_layer(decode,layer[1],layer[2],layer[0]+'_'+target_layer,var_list)
return decode , var_list
#self.data_dict = None
def upsample(self,bottom,height):
height=height
width=height
new_height=height*2
new_width = width*2
return tf.image.resize_images(bottom, [new_height, new_width], tf.image.ResizeMethod.NEAREST_NEIGHBOR)
def output_layer(self, bottom, in_channels, out_channels, name,var_list):
with tf.variable_scope(name):
filt_size = 9
filt, conv_biases = self.get_conv_var(filt_size, in_channels, out_channels, name)
bottom = tf.pad(bottom,[[0,0],[int(filt_size/2),int(filt_size/2)],[int(filt_size/2),int(filt_size/2)],[0,0]],mode= 'REFLECT')
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='VALID')
bias = tf.nn.bias_add(conv, conv_biases)
var_list.append(filt)
var_list.append(conv_biases)
return bias,var_list
def avg_pool(self, bottom, name):
return tf.nn.avg_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
def max_pool(self, bottom, name):
return tf.nn.max_pool(bottom, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
def conv_layer(self, bottom, in_channels, out_channels, name,var_list,trainable=True):
filt_size = 3
with tf.variable_scope(name):
filt, conv_biases = self.get_conv_var(filt_size, in_channels, out_channels, name)
bottom = tf.pad(bottom,[[0,0],[int(filt_size/2),int(filt_size/2)],[int(filt_size/2),int(filt_size/2)],[0,0]],mode= 'REFLECT')
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='VALID')
bias = tf.nn.bias_add(conv, conv_biases)
relu = tf.nn.relu(bias)
var_list.append(filt)
var_list.append(conv_biases)
return relu,var_list
def fc_layer(self, bottom, in_size, out_size, name):
with tf.variable_scope(name):
weights, biases = self.get_fc_var(in_size, out_size, name)
x = tf.reshape(bottom, [-1, in_size])
fc = tf.nn.bias_add(tf.matmul(x, weights), biases)
return fc
def get_conv_var(self, filter_size, in_channels, out_channels, name):
initial_value = tf.truncated_normal([filter_size, filter_size, in_channels, out_channels], 0.0, 0.001)
filters = self.get_var(initial_value, name, 0, name + "_filters")
initial_value = tf.truncated_normal([out_channels], .0, .001)
biases = self.get_var(initial_value, name, 1, name + "_biases")
return filters, biases
def get_fc_var(self, in_size, out_size, name):
initial_value = tf.truncated_normal([in_size, out_size], 0.0, 0.001)
weights = self.get_var(initial_value, name, 0, name + "_weights")
initial_value = tf.truncated_normal([out_size], .0, .001)
biases = self.get_var(initial_value, name, 1, name + "_biases")
return weights, biases
def get_var(self, initial_value, name, idx, var_name):
if self.data_dict is not None and name in self.data_dict:
value = self.data_dict[name][idx]
print ('resore %s weight'%(name))
else:
value = initial_value
if self.trainable:
var = tf.Variable(value, name=var_name)
else:
var = tf.constant(value, dtype=tf.float32, name=var_name)
self.var_dict[(name, idx)] = var
# print var_name, var.get_shape().as_list()
assert var.get_shape() == initial_value.get_shape()
return var
def save_npy(self, sess, npy_path="./vgg19-save.npy"):
assert isinstance(sess, tf.Session)
data_dict = {}
for (name, idx), var in list(self.var_dict.items()):
var_out = sess.run(var)
if name not in data_dict:
data_dict[name] = {}
data_dict[name][idx] = var_out
np.save(npy_path, data_dict)
print(("file saved", npy_path))
return npy_path
def get_var_count(self):
count = 0
for v in list(self.var_dict.values()):
count += reduce(lambda x, y: x * y, v.get_shape().as_list())
return count