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vgg16.py
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########################################################################################
# Davi Frossard, 2016 #
# VGG16 implementation in TensorFlow #
# Details: #
# http://www.cs.toronto.edu/~frossard/post/vgg16/ #
# #
# Model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md #
# Weights from Caffe converted using https://github.com/ethereon/caffe-tensorflow #
########################################################################################
import tensorflow as tf
import numpy as np
class vgg16:
def __init__(self, imgs, keep_prob, phase_train, weights=None, sess=None):
self.imgs = imgs
self.phase_train = phase_train
self.NUM_ClASSES = 10
self.convlayers(imgs)
self.fc_layers(keep_prob)
self.weights_file = weights
def convlayers(self, images):
self.parameters = []
# conv1_1
with tf.name_scope('conv1_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv1_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
# conv1_2
with tf.name_scope('conv1_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv1_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv1_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
self.bn1 = self.batch_norm('bn1',self.conv1_2,64,self.phase_train)
# pool1
self.pool1 = tf.nn.max_pool(self.bn1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool1')
# conv2_1
with tf.name_scope('conv2_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv2_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
# conv2_2
with tf.name_scope('conv2_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv2_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv2_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
self.bn2 = self.batch_norm('bn2',self.conv2_2,128,self.phase_train)
# pool2
self.pool2 = tf.nn.max_pool(self.bn2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2')
# conv3_1
with tf.name_scope('conv3_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv3_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
# conv3_2
with tf.name_scope('conv3_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv3_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv3_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
# conv3_3
with tf.name_scope('conv3_3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv3_2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv3_3 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
self.bn3 = self.batch_norm('bn3',self.conv3_3,256,self.phase_train)
# pool3
self.pool3 = tf.nn.max_pool(self.bn3,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool3')
# conv4_1
with tf.name_scope('conv4_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.pool3, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv4_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
# conv4_2
with tf.name_scope('conv4_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv4_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv4_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
# conv4_3
with tf.name_scope('conv4_3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv4_2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv4_3 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
self.bn4 = self.batch_norm('bn4',self.conv4_3,512,self.phase_train)
# pool4
self.pool4 = tf.nn.max_pool(self.bn4,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool4')
# conv5_1
with tf.name_scope('conv5_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.pool4, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv5_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
# conv5_2
with tf.name_scope('conv5_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv5_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv5_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
# conv5_3
with tf.name_scope('conv5_3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv5_2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv5_3 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
tf.summary.histogram('kernel', kernel)
tf.summary.histogram('bias', biases)
self.bn5 = self.batch_norm('bn5',self.conv5_3,512,self.phase_train)
# pool5
self.pool5 = tf.nn.max_pool(self.bn5,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool4')
def dropout(self, name, previous_layer, keep_prob):
with tf.name_scope(name) as scope:
dropout = tf.nn.dropout(previous_layer, keep_prob = keep_prob)
tf.summary.histogram(name, dropout)
return dropout
def fc_layers(self, keep_prob):
# fc1
with tf.variable_scope('fc1') as scope:
shape = int(np.prod(self.pool5.get_shape()[1:]))
fc1w = tf.get_variable('fcweights', [shape, 4096], trainable=True)
fc1b = tf.get_variable('bias', [4096], trainable=True)
pool5_flat = tf.reshape(self.pool5, [-1, shape])
fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b)
self.fc1 = tf.nn.relu(fc1l)
self.parameters += [fc1w, fc1b]
tf.summary.histogram('fc1w', fc1w)
tf.summary.histogram('fc1b', fc1b)
# dropout1
#self.dropou1 = self.dropout('dropout1', self.fc1, keep_prob)
# fc2
with tf.variable_scope('fc2') as scope:
fc2w = tf.get_variable('fcweights', [4096, 2048], trainable=True)
fc2b = tf.get_variable('bias', [2048], trainable=True)
fc2l = tf.nn.bias_add(tf.matmul(self.fc1, fc2w), fc2b)
self.fc2 = tf.nn.relu(fc2l)
self.parameters += [fc2w, fc2b]
tf.summary.histogram('fc2w', fc2w)
tf.summary.histogram('fc2b', fc2b)
# dropout2
#self.dropou2 = self.dropout('dropout2', self.fc2, keep_prob)
# fc3
# with tf.variable_scope('fc3') as scope:
# fc3w = tf.get_variable('fcweights',
# shape=[2048, self.NUM_ClASSES],
# trainable=True)
# fc3b = tf.get_variable('bias',
# shape=[self.NUM_ClASSES],
# trainable=True)
# self.fc3 = tf.nn.relu(tf.nn.bias_add(tf.matmul(self.fc2, fc3w), fc3b))
# self.parameters += [fc3w, fc3b]
# tf.summary.histogram('fc3w', fc3w)
# tf.summary.histogram('fc3b', fc3b)
def batch_norm(self, name,x, n_out, phase_train):
"""
Batch normalization on convolutional maps.
Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow
Args:
x: Tensor, 4D BHWD input maps
n_out: integer, depth of input maps
phase_train: boolean tf.Varialbe, true indicates training phase
scope: string, variable scope
Return:
normed: batch-normalized maps
"""
with tf.variable_scope(name) as scope:
beta = tf.Variable(tf.constant(0.0, shape=[n_out]),
name='beta', trainable=True)
gamma = tf.Variable(tf.constant(1.0, shape=[n_out]),
name='gamma', trainable=True)
batch_mean, batch_var = tf.nn.moments(x, [0,1,2], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.9)
def mean_var_with_update():
ema_apply_op = ema.apply([batch_mean, batch_var])
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
mean, var = tf.cond(phase_train,
mean_var_with_update,
lambda: (ema.average(batch_mean), ema.average(batch_var)))
normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, 1e-3)
tf.summary.histogram('normed', normed)
return normed
def load_weights(self, sess):
weights = np.load(self.weights_file)
keys = sorted(weights.keys())
for i, k in enumerate(keys):
if k.find("fc") != -1:
continue
print(i, k, np.shape(weights[k]))
sess.run(self.parameters[i].assign(weights[k]))
if __name__ == '__main__':
sess = tf.Session()
imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])
vgg = np.load('vgg16_weights.npz')
keys = sorted(vgg.keys())
for i, k in enumerate(keys):
print(i, k, np.shape(vgg[k]))