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model.py
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model.py
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# based on https://github.com/tensorflow/models/tree/master/resnet
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
class Model(object):
"""ResNet model."""
def __init__(self, mode):
"""ResNet constructor.
Args:
mode: One of 'train' and 'eval'.
"""
self.mode = mode
self._build_model()
def add_internal_summaries(self):
pass
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):
assert self.mode == 'train' or self.mode == 'eval'
"""Build the core model within the graph."""
with tf.variable_scope('input'):
self.x_input = tf.placeholder(
tf.float32,
shape=[None, 32, 32, 3])
self.y_input = tf.placeholder(tf.int64, shape=None)
input_standardized = tf.map_fn(lambda img: tf.image.per_image_standardization(img),
self.x_input)
x = self._conv('init_conv', input_standardized, 3, 3, 16, self._stride_arr(1))
strides = [1, 2, 2]
activate_before_residual = [True, False, False]
res_func = self._residual
# wide residual network (https://arxiv.org/abs/1605.07146v1)
# use filters = [16, 16, 32, 64] for a non-wide version
filters = [16, 160, 320, 640]
# Update hps.num_residual_units to 9
with tf.variable_scope('unit_1_0'):
x = res_func(x, filters[0], filters[1], self._stride_arr(strides[0]),
activate_before_residual[0])
for i in range(1, 5):
with tf.variable_scope('unit_1_%d' % i):
x = res_func(x, filters[1], filters[1], self._stride_arr(1), False)
with tf.variable_scope('unit_2_0'):
x = res_func(x, filters[1], filters[2], self._stride_arr(strides[1]),
activate_before_residual[1])
for i in range(1, 5):
with tf.variable_scope('unit_2_%d' % i):
x = res_func(x, filters[2], filters[2], self._stride_arr(1), False)
with tf.variable_scope('unit_3_0'):
x = res_func(x, filters[2], filters[3], self._stride_arr(strides[2]),
activate_before_residual[2])
for i in range(1, 5):
with tf.variable_scope('unit_3_%d' % i):
x = res_func(x, filters[3], filters[3], self._stride_arr(1), False)
with tf.variable_scope('unit_last'):
x = self._batch_norm('final_bn', x)
x = self._relu(x, 0.1)
x = self._global_avg_pool(x)
with tf.variable_scope('logit'):
self.pre_softmax = self._fully_connected(x, 10)
self.predictions = tf.argmax(self.pre_softmax, 1)
self.correct_prediction = tf.equal(self.predictions, self.y_input)
self.num_correct = tf.reduce_sum(
tf.cast(self.correct_prediction, tf.int64))
self.accuracy = tf.reduce_mean(
tf.cast(self.correct_prediction, tf.float32))
with tf.variable_scope('costs'):
self.y_xent = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.pre_softmax, labels=self.y_input)
self.xent = tf.reduce_sum(self.y_xent, name='y_xent')
self.mean_xent = tf.reduce_mean(self.y_xent)
self.weight_decay_loss = self._decay()
def _batch_norm(self, name, x):
"""Batch normalization."""
with tf.name_scope(name):
return tf.contrib.layers.batch_norm(
inputs=x,
decay=.9,
center=True,
scale=True,
activation_fn=None,
updates_collections=None,
is_training=(self.mode == 'train'))
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'):
x = self._batch_norm('init_bn', x)
x = self._relu(x, 0.1)
orig_x = x
else:
with tf.variable_scope('residual_only_activation'):
orig_x = x
x = self._batch_norm('init_bn', x)
x = self._relu(x, 0.1)
with tf.variable_scope('sub1'):
x = self._conv('conv1', x, 3, in_filter, out_filter, stride)
with tf.variable_scope('sub2'):
x = self._batch_norm('bn2', x)
x = self._relu(x, 0.1)
x = self._conv('conv2', x, 3, out_filter, out_filter, [1, 1, 1, 1])
with tf.variable_scope('sub_add'):
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 _decay(self):
"""L2 weight decay loss."""
costs = []
for var in tf.trainable_variables():
if var.op.name.find('DW') > 0:
costs.append(tf.nn.l2_loss(var))
return tf.add_n(costs)
def _conv(self, name, x, filter_size, in_filters, out_filters, strides):
"""Convolution."""
with tf.variable_scope(name):
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."""
num_non_batch_dimensions = len(x.shape)
prod_non_batch_dimensions = 1
for ii in range(num_non_batch_dimensions - 1):
prod_non_batch_dimensions *= int(x.shape[ii + 1])
x = tf.reshape(x, [tf.shape(x)[0], -1])
w = tf.get_variable(
'DW', [prod_non_batch_dimensions, 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])