def pspnet(n_classes,
inputs_size,
downsample_factor=8,
backbone='mobilenet',
aux_branch=True):
if backbone == "mobilenet":
#----------------------------------#
# 获得两个特征层
# f4为辅助分支 [30,30,96]
# o为主干部分 [30,30,320]
#----------------------------------#
img_input, f4, o = get_mobilenet_encoder(
inputs_size, downsample_factor=downsample_factor)
out_channel = 320
elif backbone == "resnet50":
img_input, f4, o = get_resnet50_encoder(
inputs_size, downsample_factor=downsample_factor)
out_channel = 2048
else:
raise ValueError(
'Unsupported backbone - `{}`, Use mobilenet, resnet50.'.format(
backbone))
#--------------------------------------------------------------#
# PSP模块，分区域进行池化
# 分别分割成1x1的区域，2x2的区域，3x3的区域，6x6的区域
#--------------------------------------------------------------#
pool_factors = [1, 2, 3, 6]
pool_outs = [o]
for p in pool_factors:
pooled = pool_block(o, p, out_channel)
pool_outs.append(pooled)
#--------------------------------------------------------------------------------#
# 利用获取到的特征层进行堆叠
# 30, 30, 320 + 30, 30, 80 + 30, 30, 80 + 30, 30, 80 + 30, 30, 80 = 30, 30, 640
#--------------------------------------------------------------------------------#
o = Concatenate(axis=MERGE_AXIS)(pool_outs)
# 30, 30, 640 -> 30, 30, 80
o = Conv2D(out_channel // 4, (3, 3),
data_format=IMAGE_ORDERING,
padding='same',
use_bias=False)(o)
o = BatchNormalization()(o)
o = Activation('relu')(o)
# 防止过拟合
o = Dropout(0.1)(o)
#---------------------------------------------------#
# 利用特征获得预测结果
# 30, 30, 80 -> 30, 30, 21 -> 473, 473, 21
#---------------------------------------------------#
o = Conv2D(n_classes, (1, 1), data_format=IMAGE_ORDERING,
padding='same')(o)
o = Lambda(resize_images)([o, img_input])
#---------------------------------------------------#
# 获得每一个像素点属于每一个类的概率
#---------------------------------------------------#
o = Activation("softmax", name="main")(o)
if aux_branch:
# 30, 30, 96 -> 30, 30, 40
f4 = Conv2D(out_channel // 8, (3, 3),
data_format=IMAGE_ORDERING,
padding='same',
use_bias=False,
name="branch_conv1")(f4)
f4 = BatchNormalization(name="branch_batchnor1")(f4)
f4 = Activation('relu', name="branch_relu1")(f4)
f4 = Dropout(0.1)(f4)
#---------------------------------------------------#
# 利用特征获得预测结果
# 30, 30, 40 -> 30, 30, 21 -> 473, 473, 21
#---------------------------------------------------#
f4 = Conv2D(n_classes, (1, 1),
data_format=IMAGE_ORDERING,
padding='same',
name="branch_conv2")(f4)
f4 = Lambda(resize_images, name="branch_resize")([f4, img_input])
f4 = Activation("softmax", name="aux")(f4)
model = Model(img_input, [f4, o])
return model
else:
model = Model(img_input, [o])
return model
def pspnet(n_classes,
inputs_size,
downsample_factor=8,
backbone='mobilenet',
aux_branch=True):
if backbone == "mobilenet":
img_input, f4, o = get_mobilenet_encoder(
inputs_size, downsample_factor=downsample_factor)
out_channel = 320
elif backbone == "resnet50":
img_input, f4, o = get_resnet50_encoder(
inputs_size, downsample_factor=downsample_factor)
out_channel = 2048
else:
raise ValueError(
'Unsupported backbone - `{}`, Use mobilenet, resnet50.'.format(
backbone))
#-------------------------------------#
# PSP模块
# 分区域进行池化
#-------------------------------------#
pool_factors = [1, 2, 3, 6]
pool_outs = [o]
for p in pool_factors:
pooled = pool_block(o, p, out_channel)
pool_outs.append(pooled)
# 连接
# 60x60xout_channel*2
o = Concatenate(axis=MERGE_AXIS)(pool_outs)
#-------------------------------------#
# 利用特征获得预测结果
#-------------------------------------#
# 卷积
# 60x60xout_channel//4
o = Conv2D(out_channel // 4, (3, 3),
data_format=IMAGE_ORDERING,
padding='same',
use_bias=False)(o)
o = BatchNormalization()(o)
o = Activation('relu')(o)
# 正则化，防止过拟合
o = Dropout(0.1)(o)
# 60x60x21
o = Conv2D(n_classes, (1, 1), data_format=IMAGE_ORDERING,
padding='same')(o)
# [473,473,nclasses]
o = Lambda(lambda x: tf.image.resize_images(
x, (inputs_size[1], inputs_size[0]), align_corners=True))(o)
# 获得每一个像素点属于每一个类的概率了
o = Activation("softmax", name="main")(o)
if aux_branch:
f4 = Conv2D(out_channel // 8, (3, 3),
data_format=IMAGE_ORDERING,
padding='same',
use_bias=False)(f4)
f4 = BatchNormalization()(f4)
f4 = Activation('relu')(f4)
# 防止过拟合
f4 = Dropout(0.1)(f4)
# 60x60x21
f4 = Conv2D(n_classes, (1, 1),
data_format=IMAGE_ORDERING,
padding='same')(f4)
# [473,473,nclasses]
f4 = Lambda(lambda x: tf.image.resize_images(
x, (inputs_size[1], inputs_size[0]), align_corners=True))(f4)
# 获得每一个像素点属于每一个类的概率了
f4 = Activation("softmax", name="aux")(f4)
model = Model(img_input, [f4, o])
return model
else:
model = Model(img_input, [o])
return model