/
custom_vgg19.py
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/
custom_vgg19.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 Vgg19:
"""
A trainable version VGG19.
"""
def __init__(self, vgg19_npy_path=None, trainable=True):
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
def build(self, rgb, train_mode=None):
"""
load variable from npy to build the VGG
:param rgb: rgb image [batch, height, width, 3] values scaled [0, 1]
:param train_mode: a bool tensor, usually a placeholder: if True, dropout will be turned on
"""
rgb_scaled = rgb * 255.0
# 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.conv1_1 = self.conv_layer(bgr, 3, 64, "conv1_1")
self.conv1_2 = self.conv_layer(self.conv1_1, 64, 64, "conv1_2")
self.pool1 = self.max_pool(self.conv1_2, 'pool1')
self.conv2_1 = self.conv_layer(self.pool1, 64, 128, "conv2_1")
self.conv2_2 = self.conv_layer(self.conv2_1, 128, 128, "conv2_2")
self.pool2 = self.max_pool(self.conv2_2, 'pool2')
self.conv3_1 = self.conv_layer(self.pool2, 128, 256, "conv3_1")
self.conv3_2 = self.conv_layer(self.conv3_1, 256, 256, "conv3_2")
self.conv3_3 = self.conv_layer(self.conv3_2, 256, 256, "conv3_3")
self.conv3_4 = self.conv_layer(self.conv3_3, 256, 256, "conv3_4")
self.pool3 = self.max_pool(self.conv3_4, 'pool3')
self.conv4_1 = self.conv_layer(self.pool3, 256, 512, "conv4_1")
self.conv4_2 = self.conv_layer(self.conv4_1, 512, 512, "conv4_2")
self.conv4_3 = self.conv_layer(self.conv4_2, 512, 512, "conv4_3")
self.conv4_4 = self.conv_layer(self.conv4_3, 512, 512, "conv4_4")
self.pool4 = self.max_pool(self.conv4_4, 'pool4')
self.conv5_1 = self.conv_layer(self.pool4, 512, 512, "conv5_1")
self.conv5_2 = self.conv_layer(self.conv5_1, 512, 512, "conv5_2")
self.conv5_3 = self.conv_layer(self.conv5_2, 512, 512, "conv5_3")
self.conv5_4 = self.conv_layer(self.conv5_3, 512, 512, "conv5_4")
self.data_dict = None
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):
with tf.variable_scope(name):
filt, conv_biases = self.get_conv_var(3, in_channels, out_channels, name)
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
bias = tf.nn.bias_add(conv, conv_biases)
relu = tf.nn.relu(bias)
return relu
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_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]
else:
value = initial_value
if self.trainable:
var = tf.get_variable(name=var_name, initializer=value)
# var = tf.get_variable(value, name=var_name)
else:
# var = tf.constant(value, dtype=tf.float32, name=var_name)
var = tf.get_variable(name=var_name, dtype=tf.float32,
initializer=value, trainable=False)
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