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ST_model_nonTrainable_AlexNetOnFaces.py
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ST_model_nonTrainable_AlexNetOnFaces.py
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import numpy as np
import tensorflow as tf
from tensorflow.python.training import moving_averages
class Pose_Estimation(object):
def __init__(self, images, labels, mode, ifdropout, keep_rate_fc6, keep_rate_fc7, lr_rate_fac, net_data, batch_size, mean_labels, std_labels):
self.batch_size = batch_size
self._images = images
self.labels = labels
self.mode = mode
self.ifdropout = ifdropout
self.keep_rate_fc6 = keep_rate_fc6
self.keep_rate_fc7 = keep_rate_fc7
self.ifadd_weight_decay = 0 #ifadd_weight_decay
self.net_data = net_data
self.lr_rate_fac = lr_rate_fac
self._extra_train_ops = []
self.optimizer = 'Adam'
self.mean_labels = mean_labels
self.std_labels = std_labels
#self.train_mean_vec = train_mean_vec
def _build_graph(self, reuse):
"""Build a whole graph for the model."""
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self._build_model(reuse=reuse)
if self.mode == 'train':
self._build_train_op()
#self.summaries = tf.merge_all_summaries()
def _stride_arr(self, stride):
"""Map a stride scalar to the stride array for tf.nn.conv2d."""
return [1, stride, stride, 1]
def _build_model(self, reuse):
"""Build the core model within the graph."""
with tf.variable_scope('Spatial_Transformer', reuse=reuse):
x = self._images
x = tf.image.resize_bilinear(x, tf.constant([227,227], dtype=tf.int32)) # the image should be 227 x 227 x 3
self.resized_img = x
theta = self._ST('ST2', x, 3, (16,16), 3, 16, self._stride_arr(1),reuse=reuse)
#print "*** ", x.get_shape()
with tf.variable_scope('costs', reuse=reuse):
self.predictions = theta
self.preds_unNormalized = theta * (self.std_labels + 0.000000000000000001) + self.mean_labels
pred_dim1 = theta.get_shape()[0]
pred_dim2 = theta.get_shape()[1]
del theta
pow_res = tf.pow(self.predictions-self.labels, 2)
xent = tf.reduce_sum(pow_res,1)
self.cost = tf.reduce_mean(xent, name='xent')
if self.ifadd_weight_decay == 1:
self.cost += self._decay()
def conv(self, input, kernel, biases, k_h, k_w, c_o, s_h, s_w, padding="VALID", group=1):
'''From https://github.com/ethereon/caffe-tensorflow
'''
c_i = input.get_shape()[-1]
assert c_i%group==0
assert c_o%group==0
convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
if group==1:
conv = convolve(input, kernel)
else:
input_groups = tf.split(input, group, 3)
kernel_groups = tf.split(kernel, group, 3)
output_groups = [convolve(i, k) for i,k in zip(input_groups, kernel_groups)]
conv = tf.concat(output_groups, 3)
return tf.reshape(tf.nn.bias_add(conv, biases), [-1]+conv.get_shape().as_list()[1:])
def _ST(self, name, x, channel_x, out_size, filter_size, out_filters, strides,reuse):
""" Spatial Transformer. """
with tf.variable_scope(name, reuse=reuse):
# zero-mean input [B,G,R]: [93.5940, 104.7624, 129.1863] --> provided by vgg-face
"""
with tf.name_scope('preprocess') as scope:
mean = tf.constant(tf.reshape(self.train_mean_vec*255.0, [3]), dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
x = x - mean
"""
# conv1
with tf.variable_scope('conv1', reuse=reuse) as scope:
#conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')
k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4
conv1W = tf.get_variable(initializer=self.net_data["conv1"]["weights"], trainable=False, name='W')
conv1b = tf.get_variable(initializer=self.net_data["conv1"]["biases"], trainable=False, name='baises')
conv1_in = self.conv(x, conv1W, conv1b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=1)
conv1 = tf.nn.relu(conv1_in, name='conv1')
#print x.get_shape(), conv1.get_shape()
self.conv1 = conv1
k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
maxpool1 = tf.nn.max_pool(conv1, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding, name='pool1')
radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0
lrn1 = tf.nn.local_response_normalization(maxpool1,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias, name='norm1')
# conv2
with tf.variable_scope('conv2', reuse=reuse) as scope:
#conv(5, 5, 256, 1, 1, group=2, name='conv2')
k_h = 5; k_w = 5; c_o = 256; s_h = 1; s_w = 1; group = 2
conv2W = tf.get_variable(initializer=self.net_data["conv2"]["weights"], trainable=False, name='W')
conv2b = tf.get_variable(initializer=self.net_data["conv2"]["biases"], trainable=False, name='baises')
conv2_in = self.conv(lrn1, conv2W, conv2b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv2 = tf.nn.relu(conv2_in, name='conv2')
#print conv2.get_shape(), self.net_data["conv2"]["weights"].shape, self.net_data["conv2"]["biases"].shape
self.conv2 = conv2
#maxpool2
#max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
maxpool2 = tf.nn.max_pool(conv2, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding, name='pool2')
#print maxpool2.get_shape()
#lrn2
#lrn(2, 2e-05, 0.75, name='norm2')
radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0
lrn2 = tf.nn.local_response_normalization(maxpool2,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias, name='norm2')
# conv3
with tf.variable_scope('conv3', reuse=reuse) as scope:
#conv(3, 3, 384, 1, 1, name='conv3')
k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 1
conv3W = tf.get_variable(initializer=self.net_data["conv3"]["weights"], trainable=False, name='W')
conv3b = tf.get_variable(initializer=self.net_data["conv3"]["biases"], trainable=False, name='baises')
conv3_in = self.conv(lrn2, conv3W, conv3b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv3 = tf.nn.relu(conv3_in, name='conv3')
#print conv3.get_shape(), self.net_data["conv3"]["weights"].shape, self.net_data["conv3"]["biases"].shape
# conv4
with tf.variable_scope('conv4', reuse=reuse) as scope:
#conv(3, 3, 384, 1, 1, group=2, name='conv4')
k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 2
conv4W = tf.get_variable(initializer=self.net_data["conv4"]["weights"], trainable=False, name='W')
conv4b = tf.get_variable(initializer=self.net_data["conv4"]["biases"], trainable=False, name='baises')
conv4_in = self.conv(conv3, conv4W, conv4b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv4 = tf.nn.relu(conv4_in, name='conv4')
#print conv4.get_shape()
self.conv4 = conv4
# conv5
with tf.variable_scope('conv5', reuse=reuse) as scope:
#conv(3, 3, 256, 1, 1, group=2, name='conv5')
k_h = 3; k_w = 3; c_o = 256; s_h = 1; s_w = 1; group = 2
conv5W = tf.get_variable(initializer=self.net_data["conv5"]["weights"], trainable=False, name='W')
conv5b = tf.get_variable(initializer=self.net_data["conv5"]["biases"], trainable=False, name='baises')
self.conv5b = conv5b
conv5_in = self.conv(conv4, conv5W, conv5b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
conv5 = tf.nn.relu(conv5_in, name='conv5')
#print conv5.get_shape()
self.conv5 = conv5
#maxpool5
#max_pool(3, 3, 2, 2, padding='VALID', name='pool5')
k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
maxpool5 = tf.nn.max_pool(conv5, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding, name='pool5')
#print maxpool5.get_shape(), maxpool5.get_shape()[1:], int(np.prod(maxpool5.get_shape()[1:]))
# fc6
with tf.variable_scope('fc6', reuse=reuse) as scope:
#fc(4096, name='fc6')
fc6W = tf.get_variable(initializer=self.net_data["fc6"]["weights"], trainable=False, name='W')
fc6b = tf.get_variable(initializer=self.net_data["fc6"]["biases"], trainable=False, name='baises')
self.fc6W = fc6W
self.fc6b = fc6b
fc6 = tf.nn.relu_layer(tf.reshape(maxpool5, [-1, int(np.prod(maxpool5.get_shape()[1:]))]), fc6W, fc6b, name='fc6')
#print fc6.get_shape()
if self.ifdropout == 1:
fc6 = tf.nn.dropout(fc6, self.keep_rate_fc6, name='fc6_dropout')
# fc7
with tf.variable_scope('fc7', reuse=reuse) as scope:
#fc(4096, name='fc7')
fc7W = tf.get_variable(initializer=self.net_data["fc7"]["weights"], trainable=False, name='W')
fc7b = tf.get_variable(initializer=self.net_data["fc7"]["biases"], trainable=False, name='baises')
self.fc7b = fc7b
fc7 = tf.nn.relu_layer(fc6, fc7W, fc7b, name='fc7')
#print fc7.get_shape()
if self.ifdropout == 1:
fc7 = tf.nn.dropout(fc7, self.keep_rate_fc7, name='fc7_dropout')
# fc8
with tf.variable_scope('fc8', reuse=reuse) as scope:
# Move everything into depth so we can perform a single matrix multiplication.
fc7 = tf.reshape(fc7, [self.batch_size, -1])
dim = fc7.get_shape()[1].value
#print "fc7 dim:\n"
#print fc7.get_shape(), dim
fc8W = tf.get_variable(initializer=tf.random_normal(tf.stack([dim, self.labels.shape[1]]), mean=0.0, stddev=0.01), trainable=False, name='W')
fc8b = tf.get_variable(initializer=tf.zeros([self.labels.shape[1]]), trainable=False, name='baises')
self.fc8b = fc8b
theta = tf.nn.xw_plus_b(fc7, fc8W, fc8b)
self.theta = theta
self.fc8W = fc8W
self.fc8b = fc8b
# %% We'll create a spatial transformer module to identify discriminative
# %% patches
#h_trans = self._transform(theta, x, out_size, channel_x)
#print h_trans.get_shape()
return theta
def _build_train_op(self):
"""Build training specific ops for the graph."""
#self.lrn_rate = tf.constant(self.hps.lrn_rate, tf.float32)
#tf.scalar_summary('learning rate', self.lrn_rate)
"""
trainable_variables = tf.trainable_variables()
grads = tf.gradients(self.cost, trainable_variables)
"""
if self.optimizer == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(self.lrn_rate)
elif self.optimizer == 'Adam':
optimizer = tf.train.AdamOptimizer(0.001 * self.lr_rate_fac)
elif self.optimizer == 'mom':
optimizer = tf.train.MomentumOptimizer(self.lrn_rate, 0.9)
self.train_op = optimizer.minimize(self.cost)