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97a9e1f Jan 29, 2016
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import tensorflow as tf
VGG_MEAN = [103.939, 116.779, 123.68]
class Model():
def get_conv_filter(self, name):
raise NotImplementedError
def get_bias(self, name):
raise NotImplementedError
def get_fc_weight(self, name):
raise NotImplementedError
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) as scope:
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) as scope:
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
# Input should be an rgb image [batch, height, width, 3]
# values scaled [0, 1]
def build(self, rgb, train=False):
rgb_scaled = rgb * 255.0
# Convert RGB to BGR
red, green, blue = tf.split(3, 3, rgb_scaled)
assert red.get_shape().as_list()[1:] == [224, 224, 1]
assert green.get_shape().as_list()[1:] == [224, 224, 1]
assert blue.get_shape().as_list()[1:] == [224, 224, 1]
bgr = tf.concat(3, [
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
])
assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
self.relu1_1 = self._conv_layer(bgr, "conv1_1")
self.relu1_2 = self._conv_layer(self.relu1_1, "conv1_2")
self.pool1 = self._max_pool(self.relu1_2, 'pool1')
self.relu2_1 = self._conv_layer(self.pool1, "conv2_1")
self.relu2_2 = self._conv_layer(self.relu2_1, "conv2_2")
self.pool2 = self._max_pool(self.relu2_2, 'pool2')
self.relu3_1 = self._conv_layer(self.pool2, "conv3_1")
self.relu3_2 = self._conv_layer(self.relu3_1, "conv3_2")
self.relu3_3 = self._conv_layer(self.relu3_2, "conv3_3")
self.pool3 = self._max_pool(self.relu3_3, 'pool3')
self.relu4_1 = self._conv_layer(self.pool3, "conv4_1")
self.relu4_2 = self._conv_layer(self.relu4_1, "conv4_2")
self.relu4_3 = self._conv_layer(self.relu4_2, "conv4_3")
self.pool4 = self._max_pool(self.relu4_3, 'pool4')
self.relu5_1 = self._conv_layer(self.pool4, "conv5_1")
self.relu5_2 = self._conv_layer(self.relu5_1, "conv5_2")
self.relu5_3 = self._conv_layer(self.relu5_2, "conv5_3")
self.pool5 = self._max_pool(self.relu5_3, 'pool5')
self.fc6 = self._fc_layer(self.pool5, "fc6")
assert self.fc6.get_shape().as_list()[1:] == [4096]
self.relu6 = tf.nn.relu(self.fc6)
if train:
self.relu6 = tf.nn.dropout(self.relu6, 0.5)
self.fc7 = self._fc_layer(self.relu6, "fc7")
self.relu7 = tf.nn.relu(self.fc7)
if train:
self.relu7 = tf.nn.dropout(self.relu7, 0.5)
self.fc8 = self._fc_layer(self.relu7, "fc8")
self.prob = tf.nn.softmax(self.fc8, name="prob")