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vgg16.py
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vgg16.py
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import numpy as np
import os
import tensorflow as tf
import time
VGG_MEAN = [103.939, 116.779, 123.68]
class Vgg16:
def __init__(self, vgg16_npy_path):
self.initialized = False
self.vgg16_npy_path = vgg16_npy_path
print('npy file loaded')
def build(self, rgb):
"""
load variable from npy to build the VGG
:param rgb: rgb image [batch, height, width, 3] values scaled [-1, 1]
"""
start_time = time.time()
print('build model started')
rgb_scaled = (rgb + 1) * 255.0 / 2.
# Convert RGB to BGR
red, green, blue = tf.split(axis=3, num_or_size_splits=3,
value=rgb_scaled)
bgr = tf.concat(axis=3, values=[
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
])
self.data_dict = np.load(self.vgg16_npy_path, encoding='latin1').item()
layer_dict = dict()
with tf.variable_scope('vgg16', reuse=self.initialized):
layer_dict['conv1_1'] = self.conv_layer(bgr, 'conv1_1')
layer_dict['conv1_2'] = self.conv_layer(
layer_dict['conv1_1'], 'conv1_2')
layer_dict['pool1'] = self.max_pool(layer_dict['conv1_2'], 'pool1')
layer_dict['conv2_1'] = self.conv_layer(
layer_dict['pool1'], 'conv2_1')
layer_dict['conv2_2'] = self.conv_layer(
layer_dict['conv2_1'], 'conv2_2')
layer_dict['pool2'] = self.max_pool(
layer_dict['conv2_2'], 'pool2')
layer_dict['conv3_1'] = self.conv_layer(
layer_dict['pool2'], 'conv3_1')
layer_dict['conv3_2'] = self.conv_layer(
layer_dict['conv3_1'], 'conv3_2')
layer_dict['conv3_3'] = self.conv_layer(
layer_dict['conv3_2'], 'conv3_3')
layer_dict['pool3'] = self.max_pool(
layer_dict['conv3_3'], 'pool3')
layer_dict['conv4_1'] = self.conv_layer(
layer_dict['pool3'], 'conv4_1')
layer_dict['conv4_2'] = self.conv_layer(
layer_dict['conv4_1'], 'conv4_2')
layer_dict['conv4_3'] = self.conv_layer(
layer_dict['conv4_2'], 'conv4_3')
layer_dict['pool4'] = self.max_pool(layer_dict['conv4_3'], 'pool4')
layer_dict['conv5_1'] = self.conv_layer(
layer_dict['pool4'], 'conv5_1')
layer_dict['conv5_2'] = self.conv_layer(
layer_dict['conv5_1'], 'conv5_2')
layer_dict['conv5_3'] = self.conv_layer(
layer_dict['conv5_2'], 'conv5_3')
layer_dict['pool5'] = self.max_pool(layer_dict['conv5_3'], 'pool5')
self.data_dict = None
self.initialized = True
return layer_dict
def get_vgg_activations(self, rgb, layer_names):
layer_dict = self.build(rgb)
validate_names = reduce(lambda f1, f2: f1 & f2,
[layer_dict.has_key(x) for x in layer_names])
assert validate_names, 'invalid vgg16 layer name(s): %s' % str(layer_names)
activations = [layer_dict[k] for k in layer_names]
return activations
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)
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
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')