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model.py
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model.py
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from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from ops import conv2d, deconv2d, huber_loss
from util import log
class Model(object):
def __init__(self, config,
debug_information=False,
is_train=True):
self.debug = debug_information
self.config = config
self.batch_size = self.config.batch_size
self.input_height = self.config.data_info[0]
self.input_width = self.config.data_info[1]
self.num_class = self.config.data_info[2]
self.c_dim = self.config.data_info[3]
self.deconv_info = self.config.deconv_info
self.conv_info = self.config.conv_info
self.AUs = ["1","2","4","6","12","15","20","25"]
# create placeholders for the input
self.image = tf.placeholder(
name='image', dtype=tf.float32,
shape=[self.batch_size, self.input_height, self.input_width, self.c_dim],
)
self.label = tf.placeholder(
name='label', dtype=tf.float32, shape=[self.batch_size, self.num_class],
)
self.is_training = tf.placeholder_with_default(bool(is_train), [], name='is_training')
self.recon_weight = tf.placeholder_with_default(
tf.cast(1.0, tf.float32), [])
tf.summary.scalar("loss/recon_weight", self.recon_weight)
self.build(is_train=is_train)
def get_feed_dict(self, batch_chunk, step=None, is_training=None):
fd = {
self.image: batch_chunk['image'], # [B, h, w, c]
self.label: batch_chunk['label'], # [B, n]
}
if is_training is not None:
fd[self.is_training] = is_training
# Weight annealing
if step is not None:
fd[self.recon_weight] = min(max(0, (1500 - step) / 1500), 1.0)*10
return fd
def build(self, is_train=True):
n_VA = 2
n_AU = 8
n = self.num_class
deconv_info = self.deconv_info
conv_info = self.conv_info
n_z = 100
# Build loss {{{
# =========
def build_loss(d_real, d_real_logits, d_fake, d_fake_logits, label, real_image, fake_image):
#alpha = 0.9
real_label = tf.concat([label, tf.zeros([self.batch_size, 1])], axis=1)
#fake_label = tf.concat([(1-alpha)*tf.ones([self.batch_size, n])/n, alpha*tf.ones([self.batch_size, 1])], axis=1)
fake_label = tf.concat([tf.zeros([self.batch_size, n]), tf.ones([self.batch_size, 1])], axis=1)
# Supervised loss
s_loss = tf.reduce_mean(huber_loss(label[:,:n], d_real[:, :n]))
# Discriminator/classifier loss {{
d_loss_real_rf = tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real_logits[:,-1], labels=real_label[:,-1])
d_loss_fake_rf = tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits[:,-1], labels=fake_label[:,-1])
if self.config.model in ('VA', 'BOTH'):
d_loss_real_va = tf.losses.mean_squared_error(real_label[:,:n_VA], d_real_logits[:,:n_VA])
d_loss_fake_va = tf.losses.mean_squared_error(fake_label[:,:n_VA], d_fake_logits[:,:n_VA])
#d_loss_real_va = concordance_cc2(d_real_logits[:,:n_VA], real_label[:,:n_VA])
#d_loss_fake_va = concordance_cc2(d_fake_logits[:,:n_VA], fake_label[:,:n_VA])
if self.config.model in ('AU', 'BOTH'):
d_loss_real_au = tf.nn.sigmoid_cross_entropy_with_logits(logits=d_real_logits[:,n-n_AU:n], labels=real_label[:,n-n_AU:n])
d_loss_fake_au = tf.nn.sigmoid_cross_entropy_with_logits(logits=d_fake_logits[:,n-n_AU:n], labels=fake_label[:,n-n_AU:n])
if self.config.model == 'BOTH':
d_loss_real = tf.reduce_mean(tf.reduce_mean(d_loss_real_va) + \
tf.reduce_mean(d_loss_real_au) + tf.reshape(d_loss_real_rf, [self.batch_size, 1]))
d_loss_fake = tf.reduce_mean(tf.reduce_mean(d_loss_fake_va) + \
tf.reduce_mean(d_loss_fake_au) + tf.reshape(d_loss_fake_rf, [self.batch_size, 1]))
elif self.config.model == 'AU':
d_loss_real = tf.reduce_mean(d_loss_real_au + tf.reshape(d_loss_real_rf, [self.batch_size, 1]))
d_loss_fake = tf.reduce_mean(d_loss_fake_au + tf.reshape(d_loss_fake_rf, [self.batch_size, 1]))
elif self.config.model == 'VA':
d_loss_real = tf.reduce_mean(tf.reduce_mean(d_loss_real_va) + \
tf.reshape(d_loss_real_rf, [self.batch_size, 1]))
d_loss_fake = tf.reduce_mean(tf.reduce_mean(d_loss_fake_va) + \
tf.reshape(d_loss_fake_rf, [self.batch_size, 1]))
d_loss = tf.reduce_mean(d_loss_real + d_loss_fake)
# }}
# Generator loss {{
g_loss = tf.reduce_mean(tf.log(d_fake[:, -1]))
# Weight annealing
g_loss += tf.reduce_mean(huber_loss(real_image, fake_image)) * self.recon_weight
# }}
GAN_loss = tf.reduce_mean(d_loss + g_loss)
return s_loss, d_loss_real, d_loss_fake, d_loss, g_loss, GAN_loss
# G takes ramdon noise and tries to generate images [B, h, w, c]
def G(z, scope='Generator'):
with tf.variable_scope(scope) as scope:
log.warn(scope.name)
z = tf.reshape(z, [self.batch_size, 1, 1, -1])
g_1 = deconv2d(z, deconv_info[0], is_train, name='g_1_deconv')
log.info('{} {}'.format(scope.name, g_1))
g_2 = deconv2d(g_1, deconv_info[1], is_train, name='g_2_deconv')
log.info('{} {}'.format(scope.name, g_2))
g_3 = deconv2d(g_2, deconv_info[2], is_train, name='g_3_deconv')
log.info('{} {}'.format(scope.name, g_3))
g_4 = deconv2d(g_3, deconv_info[3], is_train, name='g_4_deconv', activation_fn=tf.tanh)
log.info('{} {}'.format(scope.name, g_4))
#g_5 = deconv2d(g_4, deconv_info[4], is_train, name='g_5_deconv')
#log.info('{} {}'.format(scope.name, g_5))
#g_6 = deconv2d(g_5, deconv_info[5], is_train, name='g_6_deconv', activation_fn=tf.tanh)
#log.info('{} {}'.format(scope.name, g_6))
output = g_4
assert output.get_shape().as_list() == self.image.get_shape().as_list(), output.get_shape().as_list()
return output
# D takes images as input and tries to output class label [B, n+1]
def D(img, scope='Discriminator', reuse=True):
with tf.variable_scope(scope, reuse=reuse) as scope:
if not reuse: log.warn(scope.name)
d_1 = conv2d(img, conv_info[0], is_train, name='d_1_conv')
d_1 = slim.dropout(d_1, keep_prob=0.5, is_training=is_train, scope='d_1_conv/')
if not reuse: log.info('{} {}'.format(scope.name, d_1))
d_2 = conv2d(d_1, conv_info[1], is_train, name='d_2_conv')
d_2 = slim.dropout(d_2, keep_prob=0.5, is_training=is_train, scope='d_2_conv/')
if not reuse: log.info('{} {}'.format(scope.name, d_2))
d_3 = conv2d(d_2, conv_info[2], is_train, name='d_3_conv')
d_3 = slim.dropout(d_3, keep_prob=0.5, is_training=is_train, scope='d_3_conv/')
if not reuse: log.info('{} {}'.format(scope.name, d_3))
d_4 = slim.fully_connected(
tf.reshape(d_3, [self.batch_size, -1]), n+1, scope='d_4_fc', activation_fn=None)
if not reuse: log.info('{} {}'.format(scope.name, d_4))
output = d_4
assert output.get_shape().as_list() == [self.batch_size, n+1]
pred_rf = tf.reshape(tf.sigmoid(output[:,-1]), [self.batch_size,1])
if self.config.model in ('VA', 'BOTH'):
pred_va = tf.nn.tanh(output[:, :n_VA])
if self.config.model in ('AU', 'BOTH'):
pred_au = tf.sigmoid(output[:, n-n_AU:n])
if self.config.model == 'BOTH':
pred = tf.concat([pred_va, pred_au, pred_rf], axis=1)
elif self.config.model == 'AU':
pred = tf.concat([pred_au, pred_rf], axis=1)
elif self.config.model == 'VA':
pred = tf.concat([pred_va, pred_rf], axis=1)
return pred, output
# Generator {{{
# =========
# input is a normal noise between -1 and 1
z = tf.random_uniform([self.batch_size, n_z], minval=-1, maxval=1, dtype=tf.float32)
fake_image = G(z)
self.fake_img = fake_image
# }}}
# Discriminator {{{
# =========
d_real, d_real_logits = D(self.image, scope='Discriminator', reuse=False)
d_fake, d_fake_logits = D(fake_image, scope='Discriminator', reuse=True)
self.all_preds_real = d_real
self.all_preds_fake = d_fake
self.all_targets = self.label
# }}}
self.S_loss, d_loss_real, d_loss_fake, self.d_loss, self.g_loss, self.GAN_loss = \
build_loss(d_real, d_real_logits, d_fake, d_fake_logits, self.label, self.image, self.fake_img)
# Metrics {{{
# =========
# Metrics for Valence Arousal
def concordance_cc2(predictions, labels):
pred_mean, pred_var = tf.nn.moments(predictions, (0,))
gt_mean, gt_var = tf.nn.moments(labels, (0,))
mean_cent_prod = tf.reduce_mean((predictions - pred_mean) * (labels - gt_mean))
return 1 - (2 * mean_cent_prod) / (pred_var + gt_var + tf.square(pred_mean - gt_mean))
def build_metrics_va(label, d_real_logits):
label_v = label[:,0]
label_a = label[:,1]
d_real_logits_v = d_real_logits[:,0]
d_real_logits_a = d_real_logits[:,1]
ccc_v = 1-concordance_cc2(d_real_logits_v, label_v)
ccc_a = 1-concordance_cc2(d_real_logits_a, label_a)
return ccc_v, ccc_a
# Metrics for Action Units
def build_metrics_au(label, d_real):
label_au = label[:, n-n_AU:n]
label_au = tf.cast(label_au, tf.float32)
threshold = 0.5
d_real_au = d_real[:, n-n_AU:n]
pred = tf.cast(tf.greater(d_real_au, threshold*tf.ones([self.batch_size, 8])), tf.float32)
ones = tf.cast(tf.ones([self.batch_size, 8]), tf.float32)
tp = tf.count_nonzero(tf.multiply(label_au,pred), axis=0)
fp = tf.count_nonzero(tf.multiply(tf.subtract(ones,label_au),pred), axis=0)
tn = tf.count_nonzero(tf.multiply(tf.subtract(ones,label_au), tf.subtract(ones,pred)), axis=0)
fn = tf.count_nonzero(tf.multiply(label_au, tf.subtract(ones,pred)), axis=0)
au_precision = tp / (tp + fp)
au_recall = tp / (tp + fn)
au_f1 = 2*(au_precision*au_recall) / (au_precision+au_recall)
au_acc = (tp + tn) / (tp + tn + fp + fn)
return au_precision, au_recall, au_f1, au_acc
def build_metrics_mean(au_precision, au_recall, au_f1, au_acc):
au_precision_mean = tf.reduce_mean(au_precision)
au_recall_mean = tf.reduce_mean(au_recall)
au_f1_mean = tf.reduce_mean(au_f1)
au_acc_mean = tf.reduce_mean(au_acc)
return au_precision_mean, au_recall_mean, au_f1_mean, au_acc_mean
# Metrics for distinguishing real and fake images
def build_metrics_rf(d_real, d_fake):
threshold = 0.5
d_real_rf = d_real[:,-1]
pred_real = tf.cast(tf.greater(d_real_rf, threshold*tf.ones([self.batch_size,1])), tf.float32)
d_fake_rf = d_fake[:,-1]
pred_fake = tf.cast(tf.greater(d_fake_rf, threshold*tf.ones([self.batch_size,1])), tf.float32)
ones = tf.cast(tf.ones([self.batch_size, 1]), tf.float32)
tp = tf.count_nonzero(tf.subtract(ones, pred_real))
fn = tf.count_nonzero(pred_real)
tn = tf.count_nonzero(pred_fake)
fp = tf.count_nonzero(tf.subtract(ones, pred_fake))
rf_precision = tp / (tp + fp)
rf_recall = tp / (tp + fn)
rf_f1 = 2*(rf_precision*rf_recall) / (rf_precision + rf_recall)
rf_acc = (tp + tn) / (tp + tn + fp + fn)
return rf_precision, rf_recall, rf_f1, rf_acc
def build_metrics():
tf.summary.scalar("loss/GAN_loss", self.GAN_loss)
tf.summary.scalar("loss/S_loss", self.S_loss)
tf.summary.scalar("loss/d_loss", tf.reduce_mean(self.d_loss))
tf.summary.scalar("loss/d_loss_real", tf.reduce_mean(d_loss_real))
tf.summary.scalar("loss/d_loss_fake", tf.reduce_mean(d_loss_fake))
tf.summary.scalar("loss/g_loss", tf.reduce_mean(self.g_loss))
tf.summary.image("img/fake", self.fake_img)
tf.summary.image("img/real", self.image, max_outputs=1)
#tf.summary.image("label/target_real", tf.reshape(self.label, [1, self.batch_size, n, 1]))
#tf.summary.image("label/pred_real", tf.reshape(tf.cast(tf.greater(d_real[:, n-n_AU:n], 0.5*tf.ones([self.batch_size, 8])), dtype=np.float32), [1, self.batch_size, n_AU, 1]))
log.warn('\033[93mSuccessfully loaded the model.\033[0m')
self.rf_precision, self.rf_recall, self.rf_f1, self.rf_acc = build_metrics_rf(d_real, d_fake)
tf.summary.scalar("real_or_fake/precision", self.rf_precision)
tf.summary.scalar("real_or_fake/recall", self.rf_recall)
tf.summary.scalar("real_or_fake/f1", self.rf_f1)
tf.summary.scalar("real_or_fake/accuracy", self.rf_acc)
if self.config.model in ('VA', 'BOTH'):
self.ccc_v, self.ccc_a = build_metrics_va(self.label, d_real_logits)
tf.summary.scalar("concordance_correlation_coefficient/valence", self.ccc_v)
tf.summary.scalar("concordance_correlation_coefficient/arousal", self.ccc_a)
if self.config.model in ('AU', 'BOTH'):
self.au_precision, self.au_recall, self.au_f1, self.au_acc = build_metrics_au(self.label, d_real)
for i in range(n_AU):
au = "AU_" + self.AUs[i]
tf.summary.scalar("precision/" + au, self.au_precision[i])
tf.summary.scalar("recall/" + au, self.au_recall[i])
tf.summary.scalar("f1/" + au, self.au_f1[i])
tf.summary.scalar("accuracy/" + au, self.au_acc[i])
self.au_precision_mean, self.au_recall_mean, self.au_f1_mean, self.au_acc_mean = build_metrics_mean(self.au_precision, self.au_recall, self.au_f1, self.au_acc)
tf.summary.scalar("precision/au_mean", self.au_precision_mean)
tf.summary.scalar("recall/au_mean", self.au_recall_mean)
tf.summary.scalar("f1/au_mean", self.au_f1_mean)
tf.summary.scalar("accuracy/au_mean", self.au_acc_mean)
# }}}
build_metrics()