def create_block_list(self):
self.clear_list()
block1 = InputDownSample()
self.add_block_list(block1.get_name(), block1, self.data_channel)
conv1 = ConvBNActivationBlock(in_channels=self.data_channel,
out_channels=32,
kernel_size=3,
padding=1,
stride=2,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(conv1.get_name(), conv1, 32)
conv2 = ConvBNActivationBlock(in_channels=32,
out_channels=32,
kernel_size=3,
padding=1,
stride=2,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(conv2.get_name(), conv2, 32)
conv3 = ConvBNActivationBlock(in_channels=32,
out_channels=64,
kernel_size=3,
padding=1,
stride=2,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(conv3.get_name(), conv3, 64)
backbone1 = self.factory.get_base_model(BackboneName.ResNet50, data_channel=self.data_channel)
base_out_channels1 = backbone1.get_outchannel_list()
self.add_block_list(BlockType.BaseNet, backbone1, base_out_channels1[-1])
layer1 = RouteLayer('8')
output_channel = sum([base_out_channels1[i] if i >= 0
else self.block_out_channels[i] for i in layer1.layers])
self.add_block_list(layer1.get_name(), layer1, output_channel)
backbone2 = self.factory.get_base_model(BackboneName.ResNet50, data_channel=self.data_channel)
base_out_channels2 = backbone2.get_outchannel_list()
self.add_block_list(BlockType.BaseNet, backbone2, base_out_channels2[-1])
layer2 = RouteLayer('17')
output_channel = sum([base_out_channels2[i] if i >= 0
else self.block_out_channels[i] for i in layer2.layers])
self.add_block_list(layer2.get_name(), layer2, output_channel)
self.create_head()
self.create_loss()
def create_block_list(self):
self.block_out_channels = []
self.index = 0
c1_channels = 24
c4_channels = 320
backbone = self.factory.get_base_model(BackboneName.MobileNetV2_1_0)
base_out_channels = backbone.get_outchannel_list()
self.add_block_list(BlockType.BaseNet, backbone, base_out_channels[-1])
aspp = ASPP(c4_channels, 256, bn_name=self.bn_name,
activation_name=self.activation_name)
out_channel = 256
self.add_block_list(aspp.get_name(), aspp, out_channel)
up1 = Upsample(scale_factor=8, mode='bilinear')
self.add_block_list(up1.get_name(), up1, self.block_out_channels[-1])
layer1 = RouteLayer('3')
output_channel = sum([base_out_channels[i] if i >= 0
else self.block_out_channels[i] for i in layer1.layers])
self.add_block_list(layer1.get_name(), layer1, output_channel)
conv1 = ConvBNActivationBlock(in_channels=c1_channels,
out_channels=48,
kernel_size=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(conv1.get_name(), conv1, 48)
layer2 = RouteLayer('-3, -1')
output_channel = sum([base_out_channels[i] if i >= 0
else self.block_out_channels[i] for i in layer2.layers])
self.add_block_list(layer2.get_name(), layer2, output_channel)
block1 = SeparableConv2dBNActivation(output_channel, 256, 3, relu_first=False,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(block1.get_name(), block1, 256)
block2 = SeparableConv2dBNActivation(256, 256, 3, relu_first=False,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(block2.get_name(), block2, 256)
conv2 = nn.Conv2d(256, self.class_number, 1)
self.add_block_list(LayerType.Convolutional, conv2, self.class_number)
layer3 = Upsample(scale_factor=4, mode='bilinear')
self.add_block_list(layer3.get_name(), layer3, self.block_out_channels[-1])
self.create_loss()
def create_block_list(self):
self.block_out_channels = []
self.index = 0
basic_model = self.factory.get_base_model("./cfg/det3d/complex-darknet19.cfg")
base_out_channels = basic_model.get_outchannel_list()
self.add_block_list(BlockType.BaseNet, basic_model, base_out_channels[-1])
input_channel = self.block_out_channels[-1]
output_channel = 1024
conv1 = ConvBNActivationBlock(input_channel, output_channel,
kernel_size=3, stride=1, padding=1,
bnName=self.bn_name, activationName=self.activation_name)
self.add_block_list(conv1.get_name(), conv1, output_channel)
input_channel = self.block_out_channels[-1]
output_channel = 1024
conv2 = ConvBNActivationBlock(input_channel, output_channel,
kernel_size=3, stride=1, padding=1,
bnName=self.bn_name, activationName=self.activation_name)
self.add_block_list(conv2.get_name(), conv2, output_channel)
layer1 = RouteLayer('12')
output_channel = sum([base_out_channels[i] if i >= 0 else self.block_out_channels[i] for i in layer1.layers])
self.add_block_list(layer1.get_name(), layer1, output_channel)
reorg = ReorgBlock()
output_channel = reorg.stride * reorg.stride * self.out_channels[-1]
self.add_block_list(reorg.get_name(), reorg, output_channel)
layer2 = RouteLayer('-3,-1')
output_channel = sum([base_out_channels[i] if i >= 0 else self.block_out_channels[i] for i in layer2.layers])
self.add_block_list(layer2.get_name(), layer2, output_channel)
input_channel = self.block_out_channels[-1]
output_channel = 1024
conv3 = ConvBNActivationBlock(input_channel, output_channel,
kernel_size=3, stride=1, padding=1,
bnName=self.bn_name, activationName=self.activation_name)
self.add_block_list(conv3.get_name(), conv3, output_channel)
input_channel = self.block_out_channels[-1]
output_channel = 75
conv4 = nn.Conv2d(in_channels=input_channel,
out_channels=output_channel,
kernel_size=1,
stride=1,
padding=0,
bias=True)
self.add_block_list(LayerType.Convolutional, conv4, output_channel)
def create_block_list(self):
self.clear_list()
backbone = self.factory.get_base_model(BackboneName.ResNet50)
base_out_channels = backbone.get_outchannel_list()
self.add_block_list(BlockType.BaseNet, backbone, base_out_channels[-1])
if self.is_jpu:
jup = JPUBlock(layers='4,8,14,17',
in_planes=(512, 1024, 2048),
width=512,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(jup.get_name(), jup, 512 + 512 + 512 + 512)
self.enc_head(2048, base_out_channels)
self.create_loss()
if self.is_aux:
route = RouteLayer('14')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in route.layers
])
self.add_block_list(route.get_name(), route, output_channel)
fcn_head = FCNHeadBlock(1024,
self.class_number,
16,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(fcn_head.get_name(), fcn_head,
self.class_number)
def dsn_head(self, base_out_channels):
route = RouteLayer('14')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in route.layers
])
self.add_block_list(route.get_name(), route, output_channel)
conv1 = ConvBNActivationBlock(in_channels=output_channel,
out_channels=512,
kernel_size=3,
stride=1,
padding=1,
bias=True,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(conv1.get_name(), conv1, 512)
dropout = nn.Dropout2d(0.1)
self.add_block_list(LayerType.Dropout, dropout,
self.block_out_channels[-1])
out_conv = nn.Conv2d(512,
self.class_number,
kernel_size=1,
stride=1,
padding=0,
bias=True)
self.add_block_list(LayerType.Convolutional, out_conv,
self.class_number)
up1 = Upsample(scale_factor=16, mode='bilinear')
self.add_block_list(up1.get_name(), up1, self.block_out_channels[-1])
def make_layer(self, base_out_channels, conv_output_channel, scale_factor, route_layer_indexs):
layer1 = Upsample(scale_factor=scale_factor, mode='bilinear')
self.add_block_list(layer1.get_name(), layer1, self.block_out_channels[-1])
layer2 = RouteLayer(route_layer_indexs)
output_channel = sum([base_out_channels[i] if i >= 0
else self.block_out_channels[i] for i in layer2.layers])
self.add_block_list(layer2.get_name(), layer2, output_channel)
conv1 = ConvBNActivationBlock(self.block_out_channels[-1], conv_output_channel,
kernel_size=1, stride=1, padding=0,
bnName=self.bn_name, activationName=self.activation_name)
self.add_block_list(conv1.get_name(), conv1, conv_output_channel)
layer3 = RouteLayer('-1,-3')
output_channel = sum([base_out_channels[i] if i >= 0 else
self.block_out_channels[i] for i in layer3.layers])
self.add_block_list(layer3.get_name(), layer3, output_channel)
def createOrderedDict(self, modelDefine, inputChannels):
self.index = 0
self.blockDict.clear()
self.outChannelList.clear()
self.filters = 0
self.input_channels = 0
for module_def in modelDefine:
if module_def['type'] == BlockType.InputData:
data_channel = int(module_def['data_channel'])
self.input_channels = data_channel
elif module_def['type'] == LayerType.RouteLayer:
block = RouteLayer(module_def['layers'])
self.filters = sum([inputChannels[i] if i >= 0 else self.outChannelList[i]
for i in block.layers])
self.addBlockList(LayerType.RouteLayer, block, self.filters)
self.input_channels = self.filters
elif module_def['type'] == LayerType.ShortRouteLayer:
block = ShortRouteLayer(module_def['from'], module_def['activation'])
self.filters = self.outChannelList[block.layer_from] + \
self.outChannelList[-1]
self.addBlockList(LayerType.ShortRouteLayer, block, self.filters)
self.input_channels = self.filters
elif module_def['type'] == LayerType.ShortcutLayer:
block = ShortcutLayer(module_def['from'], module_def['activation'])
self.filters = self.outChannelList[block.layer_from]
self.addBlockList(LayerType.ShortcutLayer, block, self.filters)
self.input_channels = self.filters
elif module_def['type'] == DarknetBlockName.ReorgBlock:
stride = int(module_def['stride'])
block = ReorgBlock(stride=stride)
self.filters = block.stride * block.stride * self.outChannelList[-1]
self.addBlockList(DarknetBlockName.ReorgBlock, block, self.filters)
self.input_channels = self.filters
else:
self.create_layer(module_def)
self.create_convolutional(module_def)
self.create_loss(module_def)
def create_block_list(self):
self.block_out_channels = []
self.index = 0
backbone = self.factory.get_base_model(BackboneName.Vgg16)
base_out_channels = backbone.get_outchannel_list()
self.add_block_list(BlockType.BaseNet, backbone, base_out_channels[-1])
layer1 = ConvBNActivationBlock(in_channels=base_out_channels[-1],
out_channels=4096,
kernel_size=7,
padding=3,
bias=True,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(layer1.get_name(), layer1, 4096)
dropout1 = nn.Dropout()
self.add_block_list(LayerType.Dropout, dropout1, 4096)
layer2 = ConvBNActivationBlock(in_channels=4096,
out_channels=4096,
kernel_size=1,
padding=0,
bias=True,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(layer2.get_name(), layer2, 4096)
dropout2 = nn.Dropout()
self.add_block_list(LayerType.Dropout, dropout2, 4096)
layer3 = nn.Conv2d(4096, self.class_number, kernel_size=1)
self.add_block_list(LayerType.Convolutional, layer3, self.class_number)
upsample1 = Upsample(scale_factor=2, mode='bilinear')
self.add_block_list(LayerType.Upsample, upsample1,
self.block_out_channels[-1])
layer4 = RouteLayer('13')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in layer4.layers
])
self.add_block_list(layer4.get_name(), layer4, output_channel)
layer5 = nn.Conv2d(512, self.class_number, kernel_size=1)
self.add_block_list(LayerType.Convolutional, layer5, self.class_number)
layer6 = AddLayer('-1,-3')
index = layer6.layers[0]
output_channel = base_out_channels[
index] if index >= 0 else self.block_out_channels[index]
self.add_block_list(layer6.get_name(), layer6, output_channel)
layer7 = RouteLayer('9')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in layer7.layers
])
self.add_block_list(layer7.get_name(), layer7, output_channel)
layer8 = nn.Conv2d(256, self.class_number, kernel_size=1)
self.add_block_list(LayerType.Convolutional, layer8, self.class_number)
layer9 = RouteLayer('-3')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in layer7.layers
])
self.add_block_list(layer9.get_name(), layer9, output_channel)
upsample2 = Upsample(scale_factor=2, mode='bilinear')
self.add_block_list(LayerType.Upsample, upsample2,
self.block_out_channels[-1])
layer10 = AddLayer('-1,-3')
index = layer10.layers[0]
output_channel = base_out_channels[
index] if index >= 0 else self.block_out_channels[index]
self.add_block_list(layer10.get_name(), layer10, output_channel)
upsample3 = Upsample(scale_factor=8, mode='bilinear')
self.add_block_list(LayerType.Upsample, upsample3,
self.block_out_channels[-1])
self.create_loss()
def create_block_list(self):
self.clear_list()
spatial_path = SpatialPath(self.data_channel,
128,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(spatial_path.get_name(), spatial_path, 128)
backbone = self.factory.get_base_model(BackboneName.ResNet18)
base_out_channels = backbone.get_outchannel_list()
self.add_block_list(BlockType.BaseNet, backbone, base_out_channels[-1])
inter_channels = 128
global_context = GlobalAvgPooling(512,
inter_channels,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(global_context.get_name(), global_context,
base_out_channels[-1])
layer1 = RouteLayer('9')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in layer1.layers
])
self.add_block_list(layer1.get_name(), layer1, output_channel)
context_path1 = ContextPath(512,
inter_channels,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(context_path1.get_name(), context_path1,
inter_channels)
layer2 = RouteLayer('7')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in layer2.layers
])
self.add_block_list(layer2.get_name(), layer2, output_channel)
context_path2 = ContextPath(256,
inter_channels,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(context_path2.get_name(), context_path2,
inter_channels)
ffm = FeatureFusionBlock(256,
256,
4,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(ffm.get_name(), ffm, 256)
conv1 = ConvBNActivationBlock(in_channels=256,
out_channels=64,
kernel_size=3,
padding=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(conv1.get_name(), conv1, 64)
dropout = nn.Dropout(0.1)
self.add_block_list(LayerType.Dropout, dropout,
self.block_out_channels[-1])
conv2 = nn.Conv2d(64, self.class_number, 1)
self.add_block_list(LayerType.Convolutional, conv2, self.class_number)
layer = Upsample(scale_factor=8, mode='bilinear')
self.add_block_list(layer.get_name(), layer,
self.block_out_channels[-1])
self.create_loss()
def entry_flow(self, entry_block3_stride):
# Entry flow
conv1 = ConvBNActivationBlock(in_channels=self.data_channel,
out_channels=32,
kernel_size=3,
stride=2,
padding=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(conv1.get_name(), conv1, 32)
conv2 = ConvBNActivationBlock(in_channels=32,
out_channels=64,
kernel_size=3,
stride=1,
padding=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(conv2.get_name(), conv2, 64)
block1 = XceptionConvBlock([64, 128, 128, 128],
stride=2,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(block1.get_name(), block1, 128)
double_sep_conv1 = DoubleSeparableConv2dBlock(
[128, 256, 256],
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(double_sep_conv1.get_name(), double_sep_conv1, 256)
sep_conv1 = SeparableConv2dBNActivation(
inplanes=256,
planes=256,
dilation=1,
stride=2,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(sep_conv1.get_name(), sep_conv1, 256)
layer1 = RouteLayer('-3')
output_channel = sum(
[self.block_out_channels[i] for i in layer1.layers])
self.add_block_list(layer1.get_name(), layer1, output_channel)
conv3 = ConvBNActivationBlock(in_channels=128,
out_channels=256,
kernel_size=1,
stride=2,
bias=False,
bnName=self.bn_name,
activationName=ActivationType.Linear)
self.add_block_list(conv3.get_name(), conv3, 256)
layer2 = AddLayer('-3,-1')
output_channel = 256
self.add_block_list(layer2.get_name(), layer2, output_channel)
double_sep_conv2 = DoubleSeparableConv2dBlock(
[256, 728, 728],
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(double_sep_conv2.get_name(), double_sep_conv2, 728)
sep_conv2 = SeparableConv2dBNActivation(
inplanes=728,
planes=728,
dilation=1,
stride=entry_block3_stride,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(sep_conv2.get_name(), sep_conv2, 728)
layer3 = RouteLayer('-3')
output_channel = sum(
[self.block_out_channels[i] for i in layer3.layers])
self.add_block_list(layer3.get_name(), layer3, output_channel)
conv4 = ConvBNActivationBlock(in_channels=256,
out_channels=728,
kernel_size=1,
stride=entry_block3_stride,
bias=False,
bnName=self.bn_name,
activationName=ActivationType.Linear)
self.add_block_list(conv4.get_name(), conv4, 728)
layer4 = AddLayer('-3,-1')
output_channel = 728
self.add_block_list(layer4.get_name(), layer4, output_channel)
def enc_head(self, in_channels, base_out_channels):
if self.is_jpu:
conv1 = ConvBNActivationBlock(in_channels=in_channels,
out_channels=512,
kernel_size=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(conv1.get_name(), conv1, 512)
else:
conv1 = ConvBNActivationBlock(in_channels=in_channels,
out_channels=512,
kernel_size=3,
padding=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(conv1.get_name(), conv1, 512)
if self.lateral:
route1 = RouteLayer('8')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in route1.layers
])
self.add_block_list(route1.get_name(), route1, output_channel)
connect1 = ConvBNActivationBlock(
in_channels=output_channel,
out_channels=512,
kernel_size=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(connect1.get_name(), connect1, 512)
route2 = RouteLayer('14')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in route2.layers
])
self.add_block_list(route2.get_name(), route2, output_channel)
connect2 = ConvBNActivationBlock(
in_channels=output_channel,
out_channels=512,
kernel_size=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(connect2.get_name(), connect2, 512)
route3 = RouteLayer('-5,-3,-1')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in route2.layers
])
self.add_block_list(route3.get_name(), route3, output_channel)
fusion = ConvBNActivationBlock(in_channels=output_channel,
out_channels=512,
kernel_size=3,
padding=1,
bias=False,
bnName=self.bn_name,
activationName=self.activation_name)
self.add_block_list(fusion.get_name(), fusion, 512)
encmodule = EncBlock(in_channels=512,
nclass=self.class_number,
se_loss=self.is_se_loss,
bn_name=self.bn_name,
activation_name=self.activation_name)
self.add_block_list(encmodule.get_name(), encmodule, 512)
dropout = nn.Dropout2d(0.1, False)
self.add_block_list(LayerType.Dropout, dropout,
self.block_out_channels[-1])
conv2 = nn.Conv2d(self.block_out_channels[-1], self.class_number, 1)
self.add_block_list(LayerType.Convolutional, conv2, self.class_number)
up = Upsample(scale_factor=8, mode='bilinear')
self.add_block_list(up.get_name(), up, self.class_number)
def create_block_list(self):
self.block_out_channels = []
self.index = 0
backbone = self.factory.get_base_model(BackboneName.ResNet101)
base_out_channels = backbone.get_outchannel_list()
self.add_block_list(BlockType.BaseNet, backbone, base_out_channels[-1])
dropout1 = nn.Dropout(p=0.5)
self.add_block_list(LayerType.Dropout, dropout1,
self.block_out_channels[-1])
layer1 = RouteLayer('31')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in layer1.layers
])
self.add_block_list(layer1.get_name(), layer1, output_channel)
dropout2 = nn.Dropout(p=0.5)
self.add_block_list(LayerType.Dropout, dropout2,
self.block_out_channels[-1])
layer2 = RouteLayer('-3')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in layer1.layers
])
self.add_block_list(layer2.get_name(), layer2, output_channel)
conv1 = nn.Conv2d(2048, 512, 1, bias=False)
self.add_block_list(LayerType.Convolutional, conv1, 512)
layer3 = RouteLayer('-3')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in layer1.layers
])
self.add_block_list(layer3.get_name(), layer3, output_channel)
conv2 = nn.Conv2d(1024, 256, 1, bias=False)
self.add_block_list(LayerType.Convolutional, conv2, 256)
refinenet1 = RefineNetBlock(512,
512,
4,
activation_name=self.activation_name)
output_channel = 256
self.add_block_list(refinenet1.get_name(), refinenet1, output_channel)
layer4 = RouteLayer('8')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in layer1.layers
])
self.add_block_list(layer4.get_name(), layer4, output_channel)
conv3 = nn.Conv2d(512, 256, 1, bias=False)
self.add_block_list(LayerType.Convolutional, conv3, 256)
refinenet2 = RefineNetBlock(256,
256,
4,
activation_name=self.activation_name)
output_channel = 256
self.add_block_list(refinenet2.get_name(), refinenet2, output_channel)
layer5 = RouteLayer('4')
output_channel = sum([
base_out_channels[i] if i >= 0 else self.block_out_channels[i]
for i in layer1.layers
])
self.add_block_list(layer5.get_name(), layer5, output_channel)
conv4 = nn.Conv2d(256, 256, 1, bias=False)
self.add_block_list(LayerType.Convolutional, conv4, 256)
refinenet3 = RefineNetBlock(256,
256,
4,
activation_name=self.activation_name)
output_channel = 256
self.add_block_list(refinenet3.get_name(), refinenet3, output_channel)
crp = CRPBlock(256, 256, 4)
output_channel = 256
self.add_block_list(crp.get_name(), crp, output_channel)
conv5 = nn.Conv2d(256,
self.class_number,
kernel_size=3,
stride=1,
padding=1,
bias=True)
self.add_block_list(LayerType.Convolutional, conv5, self.class_number)
layer6 = Upsample(scale_factor=4, mode='bilinear')
self.add_block_list(layer6.get_name(), layer6,
self.block_out_channels[-1])
self.create_loss()