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base_vgg16.py
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base_vgg16.py
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import inspect
import os
import numpy as np
import tensorflow as tf
import time
from network_util import *
VGG_MEAN = [103.939, 116.779, 123.68]
class Vgg16:
def __init__(self, vgg16_npy_path=None):
if vgg16_npy_path is None:
path = inspect.getfile(Vgg16)
path = os.path.abspath(os.path.join(path, os.pardir))
path = os.path.join(path, "vgg16.npy")
vgg16_npy_path = path
print(path)
self.data_dict = np.load(vgg16_npy_path, encoding='latin1').item()
print("npy file loaded")
def build_model(self, bgr, from_npy=True):
"""
load variable from npy to build the VGG
:param rgb: rgb image [batch, height, width, 3] values scaled [0, 1]
"""
start_time = time.time()
print("build model started")
if from_npy:
self.conv1_1 = self.conv_layer(bgr, "conv1_1", training=False)
self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2", training=False)
self.pool1 = self.max_pool(self.conv1_2, 'pool1')
self.conv2_1 = self.conv_layer(self.pool1, "conv2_1", training=False)
self.conv2_2 = self.conv_layer(self.conv2_1, "conv2_2", training=False)
self.pool2 = self.max_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.pool3 = self.max_pool(self.conv3_3, '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.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.data_dict = None
else:
self.conv1_1 = convLayer(bgr, 3, 64, 3, 1, activation=tf.nn.relu, name="conv1_1")
self.conv1_2 = convLayer(self.conv1_1, 64, 64, 3, 1, activation=tf.nn.relu, name="conv1_2")
self.pool1 = maxpool2d(self.conv1_2, kernel=2, stride=2, name="pool1")
self.conv2_1 = convLayer(self.pool1, 64, 128, 3, 1, activation=tf.nn.relu, name="conv2_1")
self.conv2_2 = convLayer(self.conv2_1, 128, 128, 3, 1, activation=tf.nn.relu, name="conv2_2")
self.pool2 = maxpool2d(self.conv2_2, kernel=2, stride=2, name="pool2")
self.conv3_1 = convLayer(self.pool2, 128, 256, 3, 1, activation=tf.nn.relu, name="conv3_1")
self.conv3_2 = convLayer(self.conv3_1, 256, 256, 3, 1, activation=tf.nn.relu, name="conv3_2")
self.conv3_3 = convLayer(self.conv3_2, 256, 256, 3, 1, activation=tf.nn.relu, name="conv3_3")
self.pool3 = maxpool2d(self.conv3_3, kernel=2, stride=2, name="pool3")
self.conv4_1 = convLayer(self.pool2, 256, 512, 3, 1, activation=tf.nn.relu, name="conv4_1")
self.conv4_2 = convLayer(self.conv4_1, 512, 512, 3, 1, activation=tf.nn.relu, name="conv4_2")
self.conv4_3 = convLayer(self.conv4_2, 512, 512, 3, 1, activation=tf.nn.relu, name="conv4_3")
self.pool4 = maxpool2d(self.conv4_3, kernel=2, stride=2, name="pool4")
self.conv5_1 = convLayer(self.pool2, 512, 512, 3, 1, activation=tf.nn.relu, name="conv5_1")
self.conv5_2 = convLayer(self.conv5_1, 512, 512, 3, 1, activation=tf.nn.relu, name="conv5_2")
self.conv5_3 = convLayer(self.conv5_2, 512, 512, 3, 1, activation=tf.nn.relu, name="conv5_3")
print(("build model finished: %ds" % (time.time() - start_time)))
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, training=True):
with tf.variable_scope(name):
filt = self.get_conv_filter(name, training=training)
conv = tf.nn.conv2d(bottom, filt, [1, 1, 1, 1], padding='SAME')
conv_biases = self.get_bias(name, training=training)
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, training=True):
return tf.Variable(self.data_dict[name][0], name="filter", trainable=training)
def get_bias(self, name, training=True):
return tf.Variable(self.data_dict[name][1], name="biases", trainable=training)
def get_fc_weight(self, name):
return tf.Variable(self.data_dict[name][0], name="weights")