def __build(self):
self.__init_global_epoch()
self.__init_global_step()
self.__init_input()
with tf.name_scope('Preprocessing'):
red, green, blue = tf.split(self.X, num_or_size_splits=3, axis=3)
preprocessed_input = tf.concat([
tf.subtract(blue, ShuffleNet.MEAN[0]) * ShuffleNet.NORMALIZER,
tf.subtract(green, ShuffleNet.MEAN[1]) * ShuffleNet.NORMALIZER,
tf.subtract(red, ShuffleNet.MEAN[2]) * ShuffleNet.NORMALIZER,
], 3)
x_padded = tf.pad(preprocessed_input, [[0, 0], [1, 1], [1, 1], [0, 0]], "CONSTANT")
conv1 = conv2d('conv1', x=x_padded, w=None, num_filters=self.output_channels['conv1'], kernel_size=(3, 3),
stride=(2, 2), l2_strength=self.args.l2_strength, bias=self.args.bias,
batchnorm_enabled=self.args.batchnorm_enabled, is_training=self.is_training,
activation=tf.nn.relu, padding='VALID')
padded = tf.pad(conv1, [[0, 0], [0, 1], [0, 1], [0, 0]], "CONSTANT")
max_pool = max_pool_2d(padded, size=(3, 3), stride=(2, 2), name='max_pool')
stage2 = self.__stage(max_pool, stage=2, repeat=3)
stage3 = self.__stage(stage2, stage=3, repeat=7)
stage4 = self.__stage(stage3, stage=4, repeat=3)
global_pool = avg_pool_2d(stage4, size=(7, 7), stride=(1, 1), name='global_pool', padding='VALID')
logits_unflattened = conv2d('fc', global_pool, w=None, num_filters=self.args.num_classes,
kernel_size=(1, 1),
l2_strength=self.args.l2_strength,
bias=self.args.bias,
is_training=self.is_training)
self.logits = flatten(logits_unflattened)
self.__init_output()
def __build(self):
self.__init_global_epoch()
self.__init_global_step()
self.__init_input()
with tf.name_scope('Preprocessing'):
red, green, blue = tf.split(self.X, num_or_size_splits=3, axis=3)
preprocessed_input = tf.concat([
tf.subtract(blue, ShuffleNet.MEAN[0]) * ShuffleNet.NORMALIZER,
tf.subtract(green, ShuffleNet.MEAN[1]) * ShuffleNet.NORMALIZER,
tf.subtract(red, ShuffleNet.MEAN[2]) * ShuffleNet.NORMALIZER,
], 3)
x_padded = tf.pad(preprocessed_input, [[0, 0], [1, 1], [1, 1], [0, 0]],
"CONSTANT")
conv1 = conv2d('conv1',
x=x_padded,
w=None,
num_filters=self.output_channels['conv1'],
kernel_size=(3, 3),
stride=(2, 2),
l2_strength=self.args.l2_strength,
bias=self.args.bias,
batchnorm_enabled=self.args.batchnorm_enabled,
is_training=self.is_training,
activation=tf.nn.relu,
padding='VALID')
padded = tf.pad(conv1, [[0, 0], [0, 1], [0, 1], [0, 0]], "CONSTANT")
max_pool = max_pool_2d(padded,
size=(3, 3),
stride=(2, 2),
name='max_pool')
stage2 = self.__stage(max_pool, stage=2, repeat=3)
stage3 = self.__stage(stage2, stage=3, repeat=7)
stage4 = self.__stage(stage3, stage=4, repeat=3)
global_pool = avg_pool_2d(stage4,
size=(7, 7),
stride=(1, 1),
name='global_pool',
padding='VALID')
logits_unflattened = conv2d('fc',
global_pool,
w=None,
num_filters=self.args.num_classes,
kernel_size=(1, 1),
l2_strength=self.args.l2_strength,
bias=self.args.bias,
is_training=self.is_training)
self.logits = flatten(logits_unflattened)
self.__init_output()
def __build(self):
self.__init_global_epoch()
self.__init_global_step()
self.__init_input()
# 0. 图像预处理 减去均值 乘以归一化系数##################################
with tf.name_scope('Preprocessing'):
# 分割成三通道
red, green, blue = tf.split(self.X, num_or_size_splits=3, axis=3)
# 每个通道 减去均值 乘以归一化系数 后再concat/merge 通道扩展合并
preprocessed_input = tf.concat([
tf.subtract(blue, ShuffleNet.MEAN[0]) * ShuffleNet.NORMALIZER,
tf.subtract(green, ShuffleNet.MEAN[1]) * ShuffleNet.NORMALIZER,
tf.subtract(red, ShuffleNet.MEAN[2]) * ShuffleNet.NORMALIZER,
], 3)
# 1. conv1 3*3*3*24 卷积 步长 2 BN RELU #########################################################
######## 周围填充
x_padded = tf.pad(preprocessed_input, [[0, 0], [1, 1], [1, 1], [0, 0]], "CONSTANT")
######## conv
conv1 = conv2d('conv1', x=x_padded, w=None, num_filters=self.output_channels['conv1'], kernel_size=(3, 3),
stride=(2, 2), l2_strength=self.args.l2_strength, bias=self.args.bias,
batchnorm_enabled=self.args.batchnorm_enabled, is_training=self.is_training,
activation=tf.nn.relu, padding='VALID')
# 2. 最大值池化 3*3 步长2 ##################################################
padded = tf.pad(conv1, [[0, 0], [0, 1], [0, 1], [0, 0]], "CONSTANT")
max_pool = max_pool_2d(padded, size=(3, 3), stride=(2, 2), name='max_pool')
# 3. 一次 步长为2 非分组点卷积 concate通道扩展合并模块， 再进行3次步长为1的 add通道叠加模块
stage2 = self.__stage(max_pool, stage=2, repeat=3)
# 4. 一次 步长为2 分组点卷积 concate通道扩展合并模块， 再进行7次步长为1的 add通道叠加模块
stage3 = self.__stage(stage2, stage=3, repeat=7)
# 5. 一次 步长为2 分组点卷积 concate通道扩展合并模块， 再进行3次步长为1的 add通道叠加模块
stage4 = self.__stage(stage3, stage=4, repeat=3)
# 6. 全局均值池化层 7*7 池化核 步长1
global_pool = avg_pool_2d(stage4, size=(7, 7), stride=(1, 1), name='global_pool', padding='VALID')
# 7. 1*1点卷积 输出 类别数量个 卷积特征图
logits_unflattened = conv2d('fc', global_pool, w=None, num_filters=self.args.num_classes,
kernel_size=(1, 1),# 1*1点卷积
l2_strength=self.args.l2_strength,
bias=self.args.bias,
is_training=self.is_training)
# 8. 摊平 到 一维
self.logits = flatten(logits_unflattened)
# 9. 计算误差
self.__init_output()
def __build(self):
self.__init_global_epoch()
self.__init_global_step()
self.__init_input()
x_resized = self.__resize(self.X)
conv1 = conv2d('conv1', x=x_resized, w=None, num_filters=self.output_channels['conv1'], kernel_size=(3, 3),
stride=(2, 2), l2_strength=self.args.l2_strength, bias=self.args.bias,
batchnorm_enabled=self.args.batchnorm_enabled, is_training=self.is_training)
conv1_padded = tf.pad(conv1, [[0, 0], [1, 1], [1, 1], [0, 0]], "CONSTANT")
max_pool = max_pool_2d(conv1_padded, size=(3, 3), stride=(2, 2), name='max_pool')
stage2 = self.__stage(max_pool, stage=2, repeat=3)
stage3 = self.__stage(stage2, stage=3, repeat=7)
stage4 = self.__stage(stage3, stage=4, repeat=3)
global_pool = avg_pool_2d(stage4, size=(7, 7), stride=(1, 1), name='global_pool')
flattened = flatten(global_pool)
self.logits = dense('fc', flattened, w=None, output_dim=self.args.num_classes,
l2_strength=self.args.l2_strength,
bias=self.args.bias,
is_training=self.is_training)
self.__init_output()