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custom_vgg19.py
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custom_vgg19.py
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import os
import tensorflow as tf
import numpy as np
import inspect
import urllib.request
VGG_MEAN = [103.939, 116.779, 123.68]
data = None
dir_path = os.path.dirname(os.path.realpath(__file__))
weights_name = os.path.abspath(dir_path + "/../lib/weights/vgg19.npy")
weights_url = "https://mega.nz/#!xZ8glS6J!MAnE91ND_WyfZ_8mvkuSa2YcA7q-1ehfSm-Q1fxOvvs"
class Vgg19:
def __init__(self, vgg19_npy_path=None):
global data
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, weights_name)
if os.path.exists(path):
vgg19_npy_path = path
else:
print("VGG19 weights were not found in the project directory!")
print("Please download the .npy file from: %s" % weights_url)
print("The file should be placed as '%s'" % weights_name)
exit(0)
if data is None:
data = np.load(vgg19_npy_path, encoding='latin1')
self.data_dict = data.item()
print("VGG19 weights loaded")
else:
self.data_dict = data.item()
def build(self, rgb, shape):
rgb_scaled = rgb * 255.0
num_channels = shape[2]
channel_shape = shape
channel_shape[2] = 1
# Convert RGB to BGR
red, green, blue = tf.split(axis=3, num_or_size_splits=3, value=rgb_scaled)
assert red.get_shape().as_list()[1:] == channel_shape
assert green.get_shape().as_list()[1:] == channel_shape
assert blue.get_shape().as_list()[1:] == channel_shape
bgr = tf.concat(axis=3, values=[
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
])
shape[2] = num_channels
assert bgr.get_shape().as_list()[1:] == shape
self.conv1_1 = self.conv_layer(bgr, "conv1_1")
self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
self.pool1 = self.avg_pool(self.conv1_2, 'pool1')
self.conv2_1 = self.conv_layer(self.pool1, "conv2_1")
self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2")
self.pool2 = self.avg_pool(self.conv2_2, 'pool2')
self.conv3_1 = self.conv_layer(self.pool2, "conv3_1")
self.conv3_2 = self.conv_layer(self.conv3_1, "conv3_2")
self.conv3_3 = self.conv_layer(self.conv3_2, "conv3_3")
self.conv3_4 = self.conv_layer(self.conv3_3, "conv3_4")
self.pool3 = self.avg_pool(self.conv3_4, 'pool3')
self.conv4_1 = self.conv_layer(self.pool3, "conv4_1")
self.conv4_2 = self.conv_layer(self.conv4_1, "conv4_2")
self.conv4_3 = self.conv_layer(self.conv4_2, "conv4_3")
self.conv4_4 = self.conv_layer(self.conv4_3, "conv4_4")
self.pool4 = self.avg_pool(self.conv4_4, 'pool4')
self.conv5_1 = self.conv_layer(self.pool4, "conv5_1")
self.conv5_2 = self.conv_layer(self.conv5_1, "conv5_2")
self.conv5_3 = self.conv_layer(self.conv5_2, "conv5_3")
self.conv5_4 = self.conv_layer(self.conv5_3, "conv5_4")
self.data_dict = None
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")