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()
def __init_network(self):
with tf.variable_scope('mobilenet_encoder'):
# Preprocessing as done in the paper
with tf.name_scope('pre_processing'):
preprocessed_input = (self.X - self.mean_img) / 255.0
# Model is here!
conv1_1 = conv2d('conv_1', preprocessed_input, num_filters=int(round(32 * self.args.width_multiplier)),
kernel_size=(3, 3),
padding='SAME', stride=(2, 2), activation=tf.nn.relu6,
batchnorm_enabled=self.args.batchnorm_enabled,
is_training=self.is_training, l2_strength=self.args.l2_strength, bias=self.args.bias)
self.__add_to_nodes([conv1_1])
############################################################################################
conv2_1_dw, conv2_1_pw = depthwise_separable_conv2d('conv_ds_2', conv1_1,
width_multiplier=self.args.width_multiplier,
num_filters=64, kernel_size=(3, 3), padding='SAME',
stride=(1, 1),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv2_1_dw, conv2_1_pw])
conv2_2_dw, conv2_2_pw = depthwise_separable_conv2d('conv_ds_3', conv2_1_pw,
width_multiplier=self.args.width_multiplier,
num_filters=128, kernel_size=(3, 3), padding='SAME',
stride=(2, 2),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv2_2_dw, conv2_2_pw])
############################################################################################
conv3_1_dw, conv3_1_pw = depthwise_separable_conv2d('conv_ds_4', conv2_2_pw,
width_multiplier=self.args.width_multiplier,
num_filters=128, kernel_size=(3, 3), padding='SAME',
stride=(1, 1),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv3_1_dw, conv3_1_pw])
conv3_2_dw, conv3_2_pw = depthwise_separable_conv2d('conv_ds_5', conv3_1_pw,
width_multiplier=self.args.width_multiplier,
num_filters=256, kernel_size=(3, 3), padding='SAME',
stride=(2, 2),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv3_2_dw, conv3_2_pw])
############################################################################################
conv4_1_dw, conv4_1_pw = depthwise_separable_conv2d('conv_ds_6', conv3_2_pw,
width_multiplier=self.args.width_multiplier,
num_filters=256, kernel_size=(3, 3), padding='SAME',
stride=(1, 1),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv4_1_dw, conv4_1_pw])
conv4_2_dw, conv4_2_pw = depthwise_separable_conv2d('conv_ds_7', conv4_1_pw,
width_multiplier=self.args.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(2, 2),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv4_2_dw, conv4_2_pw])
############################################################################################
conv5_1_dw, conv5_1_pw = depthwise_separable_conv2d('conv_ds_8', conv4_2_pw,
width_multiplier=self.args.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(1, 1),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv5_1_dw, conv5_1_pw])
conv5_2_dw, conv5_2_pw = depthwise_separable_conv2d('conv_ds_9', conv5_1_pw,
width_multiplier=self.args.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(1, 1),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv5_2_dw, conv5_2_pw])
conv5_3_dw, conv5_3_pw = depthwise_separable_conv2d('conv_ds_10', conv5_2_pw,
width_multiplier=self.args.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(1, 1),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv5_3_dw, conv5_3_pw])
conv5_4_dw, conv5_4_pw = depthwise_separable_conv2d('conv_ds_11', conv5_3_pw,
width_multiplier=self.args.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(1, 1),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv5_4_dw, conv5_4_pw])
conv5_5_dw, conv5_5_pw = depthwise_separable_conv2d('conv_ds_12', conv5_4_pw,
width_multiplier=self.args.width_multiplier,
num_filters=512, kernel_size=(3, 3), padding='SAME',
stride=(1, 1),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv5_5_dw, conv5_5_pw])
conv5_6_dw, conv5_6_pw = depthwise_separable_conv2d('conv_ds_13', conv5_5_pw,
width_multiplier=self.args.width_multiplier,
num_filters=1024, kernel_size=(3, 3), padding='SAME',
stride=(2, 2),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv5_6_dw, conv5_6_pw])
############################################################################################
conv6_1_dw, conv6_1_pw = depthwise_separable_conv2d('conv_ds_14', conv5_6_pw,
width_multiplier=self.args.width_multiplier,
num_filters=1024, kernel_size=(3, 3), padding='SAME',
stride=(1, 1),
batchnorm_enabled=self.args.batchnorm_enabled,
activation=tf.nn.relu6,
is_training=self.is_training,
l2_strength=self.args.l2_strength,
biases=(self.args.bias, self.args.bias))
self.__add_to_nodes([conv6_1_dw, conv6_1_pw])
############################################################################################
avg_pool = avg_pool_2d(conv6_1_pw, size=(7, 7), stride=(1, 1))
dropped = dropout(avg_pool, self.args.dropout_keep_prob, self.is_training)
self.logits = flatten(conv2d('fc', dropped, kernel_size=(1, 1), num_filters=self.args.num_classes,
l2_strength=self.args.l2_strength,
bias=self.args.bias))
self.__add_to_nodes([avg_pool, dropped, self.logits])
def shufflenet_unit(name,
x,
w=None,
num_groups=1,
group_conv_bottleneck=True,
num_filters=16,
stride=(1, 1),
l2_strength=0.0,
bias=0.0,
batchnorm_enabled=True,
is_training=True,
fusion='add'):
# refer to ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
## 1 * 1 GConv (BN ReLU) -> Channel Shuffle -> 3*3 DWConv(stride=2)(BN) -> 1*1GConv(BN)
## 3*3 AVG Pool(stride=2)
## concat above-> ReLU
activation = tf.nn.relu
with tf.variable_scope(name) as scope:
residual = x
bottleneck_filters = (num_filters // 4) if fusion == 'add' else (
num_filters - residual.get_shape()[3].value) // 4
if group_conv_bottleneck:
bottleneck = grouped_conv2d('Gbottleneck',
x=x,
w=None,
num_filters=bottleneck_filters,
kernel_size=(1, 1),
padding='VALID',
num_groups=num_groups,
l2_strength=l2_strength,
bias=bias,
activation=activation,
batchnorm_enabled=batchnorm_enabled,
is_training=is_training)
shuffled = channel_shuffle('channel_shuffle', bottleneck,
num_groups)
else:
bottleneck = conv2d('bottleneck',
x=x,
w=None,
num_filters=bottleneck_filters,
kernel_size=(1, 1),
padding='VALID',
l2_strength=l2_strength,
bias=bias,
activation=activation,
batchnorm_enabled=batchnorm_enabled,
is_training=is_training)
shuffled = bottleneck
padded = tf.pad(shuffled, [[0, 0], [1, 1], [1, 1], [0, 0]], "CONSTANT")
depthwise = depthwise_conv2d('depthwise',
x=padded,
w=None,
stride=stride,
l2_strength=l2_strength,
padding='VALID',
bias=bias,
activation=None,
batchnorm_enabled=batchnorm_enabled,
is_training=is_training)
if stride == (2, 2):
residual_pooled = avg_pool_2d(residual,
size=(3, 3),
stride=stride,
padding='SAME')
else:
residual_pooled = residual
if fusion == 'concat':
group_conv1x1 = grouped_conv2d('Gconv1x1',
x=depthwise,
w=None,
num_filters=num_filters -
residual.get_shape()[3].value,
kernel_size=(1, 1),
padding='VALID',
num_groups=num_groups,
l2_strength=l2_strength,
bias=bias,
activation=None,
batchnorm_enabled=batchnorm_enabled,
is_training=is_training)
return activation(
tf.concat([residual_pooled, group_conv1x1], axis=-1))
elif fusion == 'add':
group_conv1x1 = grouped_conv2d('Gconv1x1',
x=depthwise,
w=None,
num_filters=num_filters,
kernel_size=(1, 1),
padding='VALID',
num_groups=num_groups,
l2_strength=l2_strength,
bias=bias,
activation=None,
batchnorm_enabled=batchnorm_enabled,
is_training=is_training)
residual_match = residual_pooled
# This is used if the number of filters of the residual block is different from that
# of the group convolution.
if num_filters != residual_pooled.get_shape()[3].value:
residual_match = conv2d('residual_match',
x=residual_pooled,
w=None,
num_filters=num_filters,
kernel_size=(1, 1),
padding='VALID',
l2_strength=l2_strength,
bias=bias,
activation=None,
batchnorm_enabled=batchnorm_enabled,
is_training=is_training)
return activation(group_conv1x1 + residual_match)
else:
raise ValueError(
"Specify whether the fusion is \'concat\' or \'add\'")
def mobilenet_conv_layers(input, batch_size, num_unrolls):
input = tf.to_float(input) - IMAGENET_MEAN
conv1_1 = conv2d('conv_1',
input,
num_filters=int(round(32 * 1)),
kernel_size=(3, 3),
padding='SAME',
stride=(2, 2),
activation=tf.nn.relu6,
batchnorm_enabled=False,
is_training=True,
l2_strength=0.0,
bias=0.0)
#self.__add_to_nodes([conv1_1])
############################################################################################
conv2_1_dw, conv2_1_pw = depthwise_separable_conv2d(
'conv_ds_2',
conv1_1,
width_multiplier=1,
num_filters=64,
kernel_size=(3, 3),
padding='SAME',
stride=(1, 1),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv2_1_dw, conv2_1_pw])
conv2_2_dw, conv2_2_pw = depthwise_separable_conv2d(
'conv_ds_3',
conv2_1_pw,
width_multiplier=1,
num_filters=128,
kernel_size=(3, 3),
padding='SAME',
stride=(2, 2),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv2_2_dw, conv2_2_pw])
############################################################################################
#with tf.variable_scope('conv1_skip'):
if 0:
pool2 = tf.nn.max_pool(conv2_2_pw,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool2')
lrn2 = tf.nn.local_response_normalization(pool2,
depth_radius=2,
alpha=2e-5,
beta=0.75,
bias=1.0,
name='norm2')
prelu_skip = tf_util.get_variable('prelu',
shape=[16],
dtype=tf.float32,
initializer=prelu_initializer)
conv1_skip = tf_util.prelu(
tf_util.conv_layer(lrn2, 16, 1, activation=None), prelu_skip)
conv1_skip = tf.transpose(conv1_skip, perm=[0, 3, 1, 2])
conv1_skip_flat = tf_util.remove_axis(conv1_skip, [2, 3])
conv3_1_dw, conv3_1_pw = depthwise_separable_conv2d(
'conv_ds_4',
conv2_2_pw,
width_multiplier=1,
num_filters=128,
kernel_size=(3, 3),
padding='SAME',
stride=(1, 1),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv3_1_dw, conv3_1_pw])
conv3_2_dw, conv3_2_pw = depthwise_separable_conv2d(
'conv_ds_5',
conv3_1_pw,
width_multiplier=1,
num_filters=256,
kernel_size=(3, 3),
padding='SAME',
stride=(2, 2),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv3_2_dw, conv3_2_pw])
############################################################################################
conv4_1_dw, conv4_1_pw = depthwise_separable_conv2d(
'conv_ds_6',
conv3_2_pw,
width_multiplier=1,
num_filters=256,
kernel_size=(3, 3),
padding='SAME',
stride=(1, 1),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv4_1_dw, conv4_1_pw])
conv4_2_dw, conv4_2_pw = depthwise_separable_conv2d(
'conv_ds_7',
conv4_1_pw,
width_multiplier=1,
num_filters=512,
kernel_size=(3, 3),
padding='SAME',
stride=(2, 2),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv4_2_dw, conv4_2_pw])
############################################################################################
if 0:
pool3 = tf.nn.max_pool(conv4_2_pw,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='VALID',
name='pool2')
lrn3 = tf.nn.local_response_normalization(pool2,
depth_radius=2,
alpha=2e-5,
beta=0.75,
bias=1.0,
name='norm2')
with tf.variable_scope('conv2_skip'):
prelu_skip = tf_util.get_variable('prelu',
shape=[16],
dtype=tf.float32,
initializer=prelu_initializer)
conv2_skip = tf_util.prelu(
tf_util.conv_layer(lrn3, 16, 1, activation=None), prelu_skip)
conv2_skip = tf.transpose(conv2_skip, perm=[0, 3, 1, 2])
conv2_skip_flat = tf_util.remove_axis(conv2_skip, [2, 3])
conv5_1_dw, conv5_1_pw = depthwise_separable_conv2d(
'conv_ds_8',
conv4_2_pw,
width_multiplier=1,
num_filters=512,
kernel_size=(3, 3),
padding='SAME',
stride=(1, 1),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv5_1_dw, conv5_1_pw])
conv5_2_dw, conv5_2_pw = depthwise_separable_conv2d(
'conv_ds_9',
conv5_1_pw,
width_multiplier=1,
num_filters=512,
kernel_size=(3, 3),
padding='SAME',
stride=(1, 1),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv5_2_dw, conv5_2_pw])
conv5_3_dw, conv5_3_pw = depthwise_separable_conv2d(
'conv_ds_10',
conv5_2_pw,
width_multiplier=1,
num_filters=512,
kernel_size=(3, 3),
padding='SAME',
stride=(1, 1),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv5_3_dw, conv5_3_pw])
conv5_4_dw, conv5_4_pw = depthwise_separable_conv2d(
'conv_ds_11',
conv5_3_pw,
width_multiplier=1,
num_filters=512,
kernel_size=(3, 3),
padding='SAME',
stride=(1, 1),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv5_4_dw, conv5_4_pw])
conv5_5_dw, conv5_5_pw = depthwise_separable_conv2d(
'conv_ds_12',
conv5_4_pw,
width_multiplier=1,
num_filters=512,
kernel_size=(3, 3),
padding='SAME',
stride=(1, 1),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv5_5_dw, conv5_5_pw])
conv5_6_dw, conv5_6_pw = depthwise_separable_conv2d(
'conv_ds_13',
conv5_5_pw,
width_multiplier=1,
num_filters=1024,
kernel_size=(3, 3),
padding='SAME',
stride=(2, 2),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv5_6_dw, conv5_6_pw])
############################################################################################
conv6_1_dw, conv6_1_pw = depthwise_separable_conv2d(
'conv_ds_14',
conv5_6_pw,
width_multiplier=1,
num_filters=1024,
kernel_size=(3, 3),
padding='SAME',
stride=(1, 1),
batchnorm_enabled=False,
activation=tf.nn.relu6,
is_training=True,
l2_strength=0.0,
biases=(0.0, 0.0))
#self.__add_to_nodes([conv6_1_dw, conv6_1_pw])
############################################################################################
avg_pool = avg_pool_2d(conv6_1_pw, size=(7, 7), stride=(1, 1))
dropped = dropout(avg_pool, -1, True)
#print("dropout:shape:")
#print(dropped.get_shape())
if 1:
logits = flatten(
conv2d('fc',
dropped,
kernel_size=(1, 1),
num_filters=32,
l2_strength=0.0,
bias=0.0,
padding='SAME'))
else:
logits = (conv2d('fc',
dropped,
kernel_size=(1, 1),
num_filters=32,
l2_strength=0.0,
bias=0.0,
padding='SAME'))
logits = tf_util.remove_axis(logits, [2, 3])
if 1:
logits_shape = logits.get_shape().as_list()
pool5_reshape = tf.reshape(
logits, [batch_size, num_unrolls, 2, logits_shape[-1]])
else:
skip_concat = tf.concat([conv1_skip_flat, logits], 1)
#skip_concat = tf.concat([conv1_skip_flat, conv2_skip_flat, logits], 1)
#
#print("logitss:shape:")
#print(logits_shape)
#
skip_concat_shape = skip_concat.get_shape().as_list()
print("Ship_concat shape")
print(skip_concat_shape)
# Split and merge image pairs
# (BxTx2)xHxWxC
pool5_reshape = tf.reshape(
skip_concat, [batch_size, num_unrolls, 2, skip_concat_shape[-1]])
# (BxT)x(2xHxWxC)
reshaped = tf_util.remove_axis(pool5_reshape, [1, 3])
return reshaped
