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ResNetv2_oct.py
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ResNetv2_oct.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
class Resnetv2:
def __init__(self, config, input_shape, num_classes, weight_decay, data_format):
self.input_shape = input_shape
self.num_classes = num_classes
self.weight_decay = weight_decay
assert data_format in ['channels_last', 'channels_first']
self.data_format = data_format
self.config = config
self.is_bottleneck = config['is_bottleneck']
self.block_list = config['residual_block_list']
self.block_list[-1] -= 1
self.filters_list = [config['init_conv_filters']*(2**i) for i in range(len(config['residual_block_list']))]
self.alpha = config['alpha']
self.global_step = tf.train.get_or_create_global_step()
self.is_training = True
self._define_inputs()
self._build_graph()
self._init_session()
def _define_inputs(self):
shape = [None]
shape.extend(self.input_shape)
self.images = tf.placeholder(dtype=tf.float32, shape=shape, name='images')
self.labels = tf.placeholder(dtype=tf.int32, shape=[None, self.num_classes], name='labels')
self.lr = tf.placeholder(dtype=tf.float32, shape=[], name='lr')
def _build_graph(self):
with tf.variable_scope('before_split'):
conv1_1 = self._octconv_first(
bottom=self.images,
filters=self.config['init_conv_filters'],
kernel_size=self.config['init_conv_kernel_size'],
strides=self.config['init_conv_strides'],
)
high, low = conv1_1
high = tf.nn.relu(self._bn(high))
low = tf.nn.relu(self._bn(low))
high = self._max_pooling(
bottom=high,
pool_size=self.config['init_pooling_pool_size'],
strides=self.config['init_pooling_strides'],
)
low = self._max_pooling(
bottom=low,
pool_size=self.config['init_pooling_pool_size'],
strides=self.config['init_pooling_strides'],
)
if self.is_bottleneck:
stack_residual_unit_fn = self._residual_bottleneck
else:
stack_residual_unit_fn = self._residual_block
with tf.variable_scope('split'):
residual_block = [high, low]
for i in range(self.block_list[0]):
residual_block = stack_residual_unit_fn(residual_block, self.filters_list[0], 1, last_layer=False, scope='block1_unit'+str(i+1))
for i in range(1, len(self.block_list)):
residual_block = stack_residual_unit_fn(residual_block, self.filters_list[i], 2, last_layer=False, scope='block'+str(i+1)+'_unit'+str(1))
for j in range(1, self.block_list[i]):
residual_block = stack_residual_unit_fn(residual_block, self.filters_list[i], 1, last_layer=False, scope='block'+str(i+1)+'_unit'+str(j+1))
residual_block = stack_residual_unit_fn(residual_block, self.filters_list[-1], 1, last_layer=True, scope='final_conv')
with tf.variable_scope('after_spliting'):
bn = self._bn(residual_block)
relu = tf.nn.relu(bn)
with tf.variable_scope('final_dense'):
axes = [1, 2] if self.data_format == 'channels_last' else [2, 3]
global_pool = tf.reduce_mean(relu, axis=axes, keepdims=False, name='global_pool')
final_dense = tf.layers.dense(global_pool, self.num_classes, name='final_dense')
with tf.variable_scope('optimizer'):
self.logit = tf.nn.softmax(final_dense, name='logit')
self.classifer_loss = tf.losses.softmax_cross_entropy(self.labels, final_dense, label_smoothing=0.1, reduction=tf.losses.Reduction.MEAN)
self.l2_loss = self.weight_decay * tf.add_n(
[tf.nn.l2_loss(var) for var in tf.trainable_variables()]
)
total_loss = self.classifer_loss + self.l2_loss
lossavg = tf.train.ExponentialMovingAverage(0.9, name='loss_moveavg')
lossavg_op = lossavg.apply([total_loss])
with tf.control_dependencies([lossavg_op]):
self.total_loss = tf.identity(total_loss)
var_list = tf.trainable_variables()
varavg = tf.train.ExponentialMovingAverage(0.9, name='var_moveavg')
varavg_op = varavg.apply(var_list)
optimizer = tf.train.MomentumOptimizer(self.lr, momentum=0.9)
train_op = optimizer.minimize(self.total_loss, global_step=self.global_step)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
self.train_op = tf.group([update_ops, lossavg_op, varavg_op, train_op])
self.accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(final_dense, 1), tf.argmax(self.labels, 1)), tf.float32), name='accuracy'
)
def _init_session(self):
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver()
self.best_saver = tf.train.Saver()
def train_one_batch(self, images, labels, lr, sess=None):
self.is_training = True
if sess is None:
sess_ = self.sess
else:
sess_ = sess
_, loss, acc = sess_.run([self.train_op, self.total_loss, self.accuracy],
feed_dict={
self.images: images,
self.labels: labels,
self.lr: lr
})
return loss, acc
def validate_one_batch(self, images, labels, sess=None):
self.is_training = False
if sess is None:
sess_ = self.sess
else:
sess_ = sess
logit, acc = sess_.run([self.logit, self.accuracy], feed_dict={
self.images: images,
self.labels: labels,
self.lr: 0.
})
return logit, acc
def test_one_batch(self, images, sess=None):
self.is_training = False
if sess is None:
sess_ = self.sess
else:
sess_ = sess
logit = sess_.run([self.logit], feed_dict={
self.images: images,
self.lr: 0.
})
return logit
def save_weight(self, mode, path, sess=None):
assert(mode in ['latest', 'best'])
if sess is None:
sess_ = self.sess
else:
sess_ = sess
saver = self.saver if mode == 'latest' else self.best_saver
saver.save(sess_, path, global_step=self.global_step)
print('save', mode, 'model in', path, 'successfully')
def load_weight(self, mode, path, sess=None):
assert(mode in ['latest', 'best'])
if sess is None:
sess_ = self.sess
else:
sess_ = sess
saver = self.saver if mode == 'latest' else self.best_saver
ckpt = tf.train.get_checkpoint_state(path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess_, path)
print('load', mode, 'model in', path, 'successfully')
else:
raise FileNotFoundError('Not Found Model File!')
def _bn(self, bottom):
bn = tf.layers.batch_normalization(
inputs=bottom,
axis=3 if self.data_format == 'channels_last' else 1,
training=self.is_training
)
return bn
def _octconv_first(self, bottom, filters, kernel_size, strides, is_bn=False, activation=None):
high_filters = int(self.alpha * filters)
low_filters = filters - high_filters
if is_bn:
bottom = self._bn(bottom)
if activation is not None:
bottom = activation(bottom)
high = tf.layers.conv2d(
inputs=bottom,
filters=high_filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
data_format=self.data_format,
kernel_initializer=tf.contrib.layers.variance_scaling_initializer()
)
high_downsampling = self._avg_pooling(bottom, 2, 2)
low = tf.layers.conv2d(
inputs=high_downsampling,
filters=low_filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
data_format=self.data_format,
kernel_initializer=tf.contrib.layers.variance_scaling_initializer()
)
return high, low
def _octconv(self, bottom, filters, kernel_size, strides, is_bn=True,activation=tf.nn.relu):
high_filters = int(self.alpha * filters)
low_filters = filters - high_filters
high, low = bottom
if is_bn:
high = self._bn(high)
low = self._bn(low)
if activation is not None:
high = activation(high)
low = activation(low)
high_high = tf.layers.conv2d(
inputs=high,
filters=high_filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
data_format=self.data_format,
kernel_initializer=tf.contrib.layers.variance_scaling_initializer()
)
high_downsampling = self._avg_pooling(high, 2, 2)
high_low = tf.layers.conv2d(
inputs=high_downsampling,
filters=low_filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
data_format=self.data_format,
kernel_initializer=tf.contrib.layers.variance_scaling_initializer()
)
low_low = tf.layers.conv2d(
inputs=low,
filters=low_filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
data_format=self.data_format,
kernel_initializer=tf.contrib.layers.variance_scaling_initializer()
)
axes = [1, 2] if self.data_format == 'channels_last' else [2, 3]
low_upsampling = tf.keras.backend.repeat_elements(low, 2, axis=axes[0])
low_upsampling = tf.keras.backend.repeat_elements(low_upsampling, 2, axis=axes[1])
low_high = tf.layers.conv2d(
inputs=low_upsampling,
filters=high_filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
data_format=self.data_format,
kernel_initializer=tf.contrib.layers.variance_scaling_initializer()
)
high = high_high + low_high
low = high_low + low_low
return high, low
def _octconv_last(self, bottom, filters, kernel_size, strides, is_bn=True, activation=tf.nn.relu):
high, low = bottom
if is_bn:
high = self._bn(high)
low = self._bn(low)
if activation is not None:
high = activation(high)
low = activation(low)
high_high = tf.layers.conv2d(
inputs=high,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
data_format=self.data_format,
kernel_initializer=tf.contrib.layers.variance_scaling_initializer()
)
axes = [1, 2] if self.data_format == 'channels_last' else [2, 3]
low_upsampling = tf.keras.backend.repeat_elements(low, 2, axis=axes[0])
low_upsampling = tf.keras.backend.repeat_elements(low_upsampling, 2, axis=axes[1])
low_high = tf.layers.conv2d(
inputs=low_upsampling,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
data_format=self.data_format,
kernel_initializer=tf.contrib.layers.variance_scaling_initializer()
)
high = high_high + low_high
return high
def _residual_block(self, bottom, filters, strides, last_layer, scope):
octconv = self._octconv if not last_layer else self._octconv_last
with tf.variable_scope(scope):
with tf.variable_scope('conv_branch'):
conv = self._octconv(bottom, filters, 3, strides)
conv = octconv(conv, filters, 3, 1)
with tf.variable_scope('identity_branch'):
if strides != 1:
shutcut = [self._avg_pooling(bottom[i], strides, strides) for i in range(len(bottom))]
shutcut = octconv(shutcut, filters, 1, 1)
else:
if not last_layer:
shutcut = bottom
else:
shutcut = octconv(bottom, filters, 1, 1)
if not last_layer:
conv = [conv[i]+shutcut[i] for i in range(len(conv))]
return conv
else:
return conv + shutcut
def _residual_bottleneck(self, bottom, filters, strides, last_layer, scope):
octconv = self._octconv if not last_layer else self._octconv_last
with tf.variable_scope(scope):
with tf.variable_scope('conv_branch'):
conv = self._octconv(bottom, filters, 1, 1)
conv = self._octconv(conv, filters, 3, strides)
conv = octconv(conv, filters*4, 1, 1)
with tf.variable_scope('identity_branch'):
shutcut = [self._avg_pooling(bottom[i], strides, strides) for i in range(len(bottom))]
shutcut = octconv(shutcut, filters*4, 1, 1)
if not last_layer:
conv = [conv[i] + shutcut[i] for i in range(len(conv))]
return conv
else:
return conv + shutcut
def _max_pooling(self, bottom, pool_size, strides, name=None):
return tf.layers.max_pooling2d(
inputs=bottom,
pool_size=pool_size,
strides=strides,
padding='same',
data_format=self.data_format,
name=name
)
def _avg_pooling(self, bottom, pool_size, strides, name=None):
return tf.layers.average_pooling2d(
inputs=bottom,
pool_size=pool_size,
strides=strides,
padding='same',
data_format=self.data_format,
name=name
)
def _dropout(self, bottom, name=None):
return tf.layers.dropout(
inputs=bottom,
rate=self.prob,
training=self.is_training,
name=name
)