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vgg19_model.py
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import numpy as np
import time
import tensorflow as tf
from god_config import *
class VGG19:
def __init__(self, model_path=MODEL_PATH):
print ("Load vgg19 model from {}".format(model_path))
start_time = time.time()
self.params_dict = np.load(model_path, encoding='latin1').item()
print ("Model loaded, takes %ds." % (time.time()-start_time))
def build(self, rgb, target=None, name=None, use_prior=False, use_all_layers=False):
start_time = time.time()
print ("Start to build model....")
rgb_rescaled = rgb * 255.0
r, g, b = tf.split(value=rgb_rescaled, num_or_size_splits=3, axis=3)
# transform rgb into bgr
assert r.get_shape().as_list()[1:] == [224, 224, 1]
assert b.get_shape().as_list()[1:] == [224, 224, 1]
assert g.get_shape().as_list()[1:] == [224, 224, 1]
#print ("shape of single channel is {}".format(b.get_shape()))
bgr = tf.concat(
values=[
b - VGG_MEAN[0],
g - VGG_MEAN[1],
r - VGG_MEAN[2]
],
axis=3
)
assert bgr.get_shape().as_list()[1:] == [224,224,3]
self.conv1_1 = self.conv_layer(bgr, "conv1_1")
self.conv1_2 = self.conv_layer(self.conv1_1, "conv1_2")
self.pool1 = self.max_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.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.conv3_4 = self.conv_layer(self.conv3_3, "conv3_4")
self.pool3 = self.max_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.max_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.pool5 = self.max_pool(self.conv5_4, '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)
self.fc7 = self.fc_layer(self.relu6, "fc7")
self.relu7 = tf.nn.relu(self.fc7)
self.fc8 = self.fc_layer(self.relu7, "fc8")
self.loss = tf.convert_to_tensor(0.0, tf.float32)
if target is not None:
if use_all_layers:
print ("using all layers")
for i in range(len(LAYER_TO_BE_SAVED_LESS)):
target_pred = self.get_layer_by_name(LAYER_TO_BE_SAVED_LESS[i])
#self.loss += tf.divide(tf.reduce_mean(tf.losses.mean_squared_error(target[i], target_pred)),
# tf.reduce_sum(target[i]))
self.loss += tf.reduce_mean(tf.losses.mean_squared_error(target[i], target_pred))
else:
target_pred = self.get_layer_by_name(name)
self.loss += tf.reduce_mean(tf.losses.mean_squared_error(target, target_pred))
if use_prior:
im = tf.get_default_graph().get_tensor_by_name('recons_image:0')
tv_image = tf.image.total_variation(im)
self.loss += tv_image
self.prob = tf.nn.softmax(self.fc8, name="prob")
self.params_dict = None
print(("build model finished: %ds" % (time.time() - start_time)))
def get_layer_by_name(self, name):
if name == 'conv1_2':
return self.conv1_2
elif name == 'conv2_2':
return self.conv2_2
elif name == 'conv3_4':
return self.conv3_4
elif name == 'conv4_4':
return self.conv4_4
elif name == 'conv5_4':
return self.conv5_4
elif name == 'fc6':
return self.fc6
elif name == 'fc7':
return self.fc7
elif name == 'fc8':
return self.fc8
else:
raise Exception("{} is not supported in this version.".format(name))
def conv_layer(self, prev, name):
with tf.variable_scope(name):
filt = self.get_conv_filter(name)
conv = tf.nn.conv2d(prev, 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 max_pool(self, prev, name):
return tf.nn.max_pool(prev, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME', name=name)
def avg_pool(self, prev, name):
return tf.nn.avg_pool(prev, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
def fc_layer(self, prev, name):
with tf.variable_scope(name):
shape = prev.get_shape().as_list()
dim = 1
for d in shape[1:]:
dim = dim * d
x = tf.reshape(prev, [-1, dim])
weights = self.get_fc_weight(name)
bias = self.get_bias(name)
fc = tf.nn.bias_add(tf.matmul(x, weights), bias)
return fc
def get_conv_filter(self, name):
return tf.constant(self.params_dict[name][0], name="filter")
def get_bias(self, name):
return tf.constant(self.params_dict[name][1], name="biases")
def get_fc_weight(self, name):
return tf.constant(self.params_dict[name][0], name="weights")