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vgg19.py
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vgg19.py
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import os
import tensorflow as tf
import numpy as np
import inspect
VGG_MEAN = [103.939, 116.779, 123.68]
class Vgg19:
def __init__(self, vgg19_npy_path=None):
if vgg19_npy_path is None:
path = inspect.getfile(Vgg19)
path = os.path.abspath(os.path.join(path, os.pardir))
path = os.path.join(path, "vgg19.npy")
vgg19_npy_path = path
print(vgg19_npy_path)
self.data_dict = np.load(vgg19_npy_path, encoding='latin1').item()
print("npy file loaded")
def encoder(self,inputs,target_layer):
layer_num =dict(zip(['relu1','relu2','relu3','relu4','relu5'],range(1,6)))[target_layer]
encode = inputs
encoder_arg={
'1':[('conv1_1',64),
('conv1_2',64),
('pool1',64)],
'2':[('conv2_1',128),
('conv2_2',128),
('pool2',128)],
'3':[('conv3_1',256),
('conv3_2',256),
('conv3_3',256),
('conv3_4',256),
('pool3',256)],
'4':[('conv4_1',512),
('conv4_2',512),
('conv4_3',512),
('conv4_4',512),
('pool4',512)],
'5':[('conv5_1',512),
('conv5_2',512),
('conv5_3',512),
('conv5_4',512),]}
for d in range(1,layer_num+1):
for layer in encoder_arg[str(d)]:
if 'conv' in layer[0] :
encode =self.conv_layer(encode,layer[0])
if 'pool' in layer[0] and d <layer_num :
encode = self.max_pool(encode,layer[0])
return encode
def encoder_all_layer(self, inputs):
"""
load variable from npy to build the VGG
:param rgb: rgb image [batch, height, width, 3] values scaled [0, 1]
"""
encode = inputs
conv1_1 = self.conv_layer(encode, "conv1_1")
conv1_2 = self.conv_layer(conv1_1, "conv1_2")
pool1 = self.max_pool(conv1_2, 'pool1')
conv2_1 = self.conv_layer(pool1, "conv2_1")
conv2_2 = self.conv_layer(conv2_1, "conv2_2")
pool2 = self.max_pool(conv2_2, 'pool2')
conv3_1 = self.conv_layer(pool2, "conv3_1")
conv3_2 = self.conv_layer(conv3_1, "conv3_2")
conv3_3 = self.conv_layer(conv3_2, "conv3_3")
conv3_4 = self.conv_layer(conv3_3, "conv3_4")
pool3 = self.max_pool(conv3_4, 'pool3')
conv4_1 = self.conv_layer(pool3, "conv4_1")
conv4_2 = self.conv_layer(conv4_1, "conv4_2")
conv4_3 = self.conv_layer(conv4_2, "conv4_3")
conv4_4 = self.conv_layer(conv4_3, "conv4_4")
return conv1_2 , conv2_2, conv3_4, conv4_4
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, name):
with tf.variable_scope(name):
filt = self.get_conv_filter(name)
filt_size=3
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')
conv_biases = self.get_bias(name)
bias = tf.nn.bias_add(conv, conv_biases)
relu = tf.nn.relu(bias)
return relu
def fc_layer(self, bottom, name):
with tf.variable_scope(name):
shape = bottom.get_shape().as_list()
dim = 1
for d in shape[1:]:
dim *= d
x = tf.reshape(bottom, [-1, dim])
weights = self.get_fc_weight(name)
biases = self.get_bias(name)
# Fully connected layer. Note that the '+' operation automatically
# broadcasts the biases.
fc = tf.nn.bias_add(tf.matmul(x, weights), biases)
return fc
def get_conv_filter(self, name):
return tf.constant(self.data_dict[name][0], name="filter")
def get_bias(self, name):
return tf.constant(self.data_dict[name][1], name="biases")
def get_fc_weight(self, name):
return tf.constant(self.data_dict[name][0], name="weights")