from collections import namedtuple
import numpy as np
import tensorflow as tf
import six
from tensorflow.python.training import moving_averages
HParams = namedtuple('HParams',
'batch_size, num_classes, min_lrn_rate, lrn_rate, '
'num_residual_units, use_bottleneck, weight_decay_rate, '
'relu_leakiness, optimizer,'
'RCE_train')
class ResNet(object):
"""ResNet model."""
def __init__(self, hps, images, mode, labels=None, Reuse=False):
"""ResNet constructor.
Args:
hps: Hyperparameters.
images: Batches of images. [batch_size, image_size, image_size, 3]
labels: Batches of labels. [batch_size, num_classes]
mode: One of 'train' and 'eval'.
"""
self.hps = hps
self._images = images
self.labels = labels
self.mode = mode
self.Reuse = Reuse
self._extra_train_ops = []
def build_graph(self):
"""Build a whole graph for the model."""
self.global_step = tf.contrib.framework.get_or_create_global_step()
self._build_model()
if self.mode == 'train':
self._build_train_op()
self.summaries = tf.summary.merge_all()
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):
"""Build the core model within the graph."""
with tf.variable_scope('init',reuse=self.Reuse):
x = self._images
x = self._conv('init_conv', x, 3, 3, 16, self._stride_arr(1))
strides = [1, 2, 2]
activate_before_residual = [True, False, False]
if self.hps.use_bottleneck:
res_func = self._bottleneck_residual
filters = [16, 64, 128, 256]
else:
res_func = self._residual
filters = [16, 16, 32, 64]
# Uncomment the following codes to use w28-10 wide residual network.
# It is more memory efficient than very deep residual network and has
# comparably good performance.
# https://arxiv.org/pdf/1605.07146v1.pdf
# filters = [16, 160, 320, 640]
# Update hps.num_residual_units to 4
with tf.variable_scope('unit_1_0',reuse=self.Reuse):
x = res_func(x, filters[0], filters[1], self._stride_arr(strides[0]),
activate_before_residual[0])
for i in six.moves.range(1, self.hps.num_residual_units):
with tf.variable_scope('unit_1_%d' % i,reuse=self.Reuse):
x = res_func(x, filters[1], filters[1], self._stride_arr(1), False)
with tf.variable_scope('unit_2_0',reuse=self.Reuse):
x = res_func(x, filters[1], filters[2], self._stride_arr(strides[1]),
activate_before_residual[1])
for i in six.moves.range(1, self.hps.num_residual_units):
with tf.variable_scope('unit_2_%d' % i,reuse=self.Reuse):
x = res_func(x, filters[2], filters[2], self._stride_arr(1), False)
with tf.variable_scope('unit_3_0',reuse=self.Reuse):
x = res_func(x, filters[2], filters[3], self._stride_arr(strides[2]),
activate_before_residual[2])
for i in six.moves.range(1, self.hps.num_residual_units):
with tf.variable_scope('unit_3_%d' % i,reuse=self.Reuse):
x = res_func(x, filters[3], filters[3], self._stride_arr(1), False)
with tf.variable_scope('unit_last',reuse=self.Reuse):
x = self._batch_norm('final_bn', x)
x = self._relu(x, self.hps.relu_leakiness)
x = self._global_avg_pool(x)
self.t_SNE_logits = x
with tf.variable_scope('logit',reuse=self.Reuse):
logits = self._fully_connected(x, self.hps.num_classes)
self.predictions = tf.nn.softmax(logits)
if self.mode=='train':
with tf.variable_scope('costs',reuse=self.Reuse):
if self.hps.RCE_train:
print('Using RCE as objective function')
xent = (tf.reduce_sum(self.labels * tf.log(tf.nn.softmax(logits)), axis=1) - tf.reduce_sum(
tf.log(tf.nn.softmax(logits)), axis=1)) / (self.hps.num_classes - 1)
else:
print('Using CE as objective function')
xent = tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=self.labels)
self.cost = tf.reduce_mean(xent, name='xent')
self.cost += self._decay()
tf.summary.scalar('cost', self.cost)
def _build_train_op(self):
"""Build training specific ops for the graph."""
self.lrn_rate = tf.constant(self.hps.lrn_rate, tf.float32)
tf.summary.scalar('learning_rate', self.lrn_rate)
trainable_variables = tf.trainable_variables()
grads = tf.gradients(self.cost, trainable_variables)
if self.hps.optimizer == 'sgd':
print('Optimizer is sgd')
optimizer = tf.train.GradientDescentOptimizer(self.lrn_rate)
elif self.hps.optimizer == 'mom':
print('Optimizer is mom')
optimizer = tf.train.MomentumOptimizer(self.lrn_rate, 0.9)
elif self.hps.optimizer == 'adam':
print('Optimizer is adam')
optimizer = tf.train.AdamOptimizer(
self.lrn_rate,
beta1=0.9,
beta2=0.999,
epsilon=1.0)
elif self.hps.optimizer == 'rmsprop':
print('Optimizer is rmsprop')
optimizer = tf.train.RMSPropOptimizer(
self.lrn_rate,
decay=0.9,
momentum=0.9,
epsilon=1.0)
else:
print('Wrong optimizer')
optimizer = None
apply_op = optimizer.apply_gradients(
zip(grads, trainable_variables),
global_step=self.global_step, name='train_step')
train_ops = [apply_op] + self._extra_train_ops
self.train_op = tf.group(*train_ops)
def _batch_norm(self, name, x):
"""Batch normalization."""
with tf.variable_scope(name,reuse=self.Reuse):
params_shape = [x.get_shape()[-1]]
beta = tf.get_variable(
'beta', params_shape, tf.float32,
initializer=tf.constant_initializer(0.0, tf.float32))
gamma = tf.get_variable(
'gamma', params_shape, tf.float32,
initializer=tf.constant_initializer(1.0, tf.float32))
if self.mode == 'train':
mean, variance = tf.nn.moments(x, [0, 1, 2], name='moments')
moving_mean = tf.get_variable(
'moving_mean', params_shape, tf.float32,
initializer=tf.constant_initializer(0.0, tf.float32),
trainable=False)
moving_variance = tf.get_variable(
'moving_variance', params_shape, tf.float32,
initializer=tf.constant_initializer(1.0, tf.float32),
trainable=False)
self._extra_train_ops.append(moving_averages.assign_moving_average(
moving_mean, mean, 0.9))
self._extra_train_ops.append(moving_averages.assign_moving_average(
moving_variance, variance, 0.9))
else:
mean = tf.get_variable(
'moving_mean', params_shape, tf.float32,
initializer=tf.constant_initializer(0.0, tf.float32),
trainable=False)
variance = tf.get_variable(
'moving_variance', params_shape, tf.float32,
initializer=tf.constant_initializer(1.0, tf.float32),
trainable=False)
tf.summary.histogram(mean.op.name, mean)
tf.summary.histogram(variance.op.name, variance)
# epsilon used to be 1e-5. Maybe 0.001 solves NaN problem in deeper net.
y = tf.nn.batch_normalization(
x, mean, variance, beta, gamma, 0.001)
y.set_shape(x.get_shape())
return y
def _residual(self, x, in_filter, out_filter, stride,
activate_before_residual=False):
"""Residual unit with 2 sub layers."""
if activate_before_residual:
with tf.variable_scope('shared_activation',reuse=self.Reuse):
x = self._batch_norm('init_bn', x)
x = self._relu(x, self.hps.relu_leakiness)
orig_x = x
else:
with tf.variable_scope('residual_only_activation',reuse=self.Reuse):
orig_x = x
x = self._batch_norm('init_bn', x)
x = self._relu(x, self.hps.relu_leakiness)
with tf.variable_scope('sub1',reuse=self.Reuse):
x = self._conv('conv1', x, 3, in_filter, out_filter, stride)
with tf.variable_scope('sub2',reuse=self.Reuse):
x = self._batch_norm('bn2', x)
x = self._relu(x, self.hps.relu_leakiness)
x = self._conv('conv2', x, 3, out_filter, out_filter, [1, 1, 1, 1])
with tf.variable_scope('sub_add',reuse=self.Reuse):
if in_filter != out_filter:
orig_x = tf.nn.avg_pool(orig_x, stride, stride, 'VALID')
orig_x = tf.pad(
orig_x, [[0, 0], [0, 0], [0, 0],
[(out_filter-in_filter)//2, (out_filter-in_filter)//2]])
x += orig_x
tf.logging.debug('image after unit %s', x.get_shape())
return x
def _bottleneck_residual(self, x, in_filter, out_filter, stride,
activate_before_residual=False):
"""Bottleneck residual unit with 3 sub layers."""
if activate_before_residual:
with tf.variable_scope('common_bn_relu',reuse=self.Reuse):
x = self._batch_norm('init_bn', x)
x = self._relu(x, self.hps.relu_leakiness)
orig_x = x
else:
with tf.variable_scope('residual_bn_relu',reuse=self.Reuse):
orig_x = x
x = self._batch_norm('init_bn', x)
x = self._relu(x, self.hps.relu_leakiness)
with tf.variable_scope('sub1',reuse=self.Reuse):
x = self._conv('conv1', x, 1, in_filter, out_filter/4, stride)
with tf.variable_scope('sub2',reuse=self.Reuse):
x = self._batch_norm('bn2', x)
x = self._relu(x, self.hps.relu_leakiness)
x = self._conv('conv2', x, 3, out_filter/4, out_filter/4, [1, 1, 1, 1])
with tf.variable_scope('sub3',reuse=self.Reuse):
x = self._batch_norm('bn3', x)
x = self._relu(x, self.hps.relu_leakiness)
x = self._conv('conv3', x, 1, out_filter/4, out_filter, [1, 1, 1, 1])
with tf.variable_scope('sub_add',reuse=self.Reuse):
if in_filter != out_filter:
orig_x = self._conv('project', orig_x, 1, in_filter, out_filter, stride)
x += orig_x
tf.logging.info('image after unit %s', x.get_shape())
return x
def _decay(self):
"""L2 weight decay loss."""
costs = []
for var in tf.trainable_variables():
if var.op.name.find(r'DW') > 0:
costs.append(tf.nn.l2_loss(var))
# tf.summary.histogram(var.op.name, var)
return tf.multiply(self.hps.weight_decay_rate, tf.add_n(costs))
def _conv(self, name, x, filter_size, in_filters, out_filters, strides):
"""Convolution."""
with tf.variable_scope(name,reuse=self.Reuse):
n = filter_size * filter_size * out_filters
kernel = tf.get_variable(
'DW', [filter_size, filter_size, in_filters, out_filters],
tf.float32, initializer=tf.random_normal_initializer(
stddev=np.sqrt(2.0/n)))
return tf.nn.conv2d(x, kernel, strides, padding='SAME')
def _relu(self, x, leakiness=0.0):
"""Relu, with optional leaky support."""
return tf.where(tf.less(x, 0.0), leakiness * x, x, name='leaky_relu')
def _fully_connected(self, x, out_dim):
"""FullyConnected layer for final output."""
x = tf.reshape(x, [self.hps.batch_size, -1])
w = tf.get_variable(
'DW', [x.get_shape()[1], out_dim],
initializer=tf.uniform_unit_scaling_initializer(factor=1.0))
b = tf.get_variable('biases', [out_dim],
initializer=tf.constant_initializer())
return tf.nn.xw_plus_b(x, w, b)
def _global_avg_pool(self, x):
assert x.get_shape().ndims == 4
return tf.reduce_mean(x, [1, 2])
