def DenseNet40(self,
inputs=None,
is_train=True,
reload_w=None,
num_classes=None):
model = Pruner(reload_file=reload_w)
# net header
x = model._add_layer(inputs,
mode="conv",
out_c=32,
k_size=3,
strides=1,
with_bn=False,
act=None)
# stage 1
x = self.densenet_block(model, x, is_train=is_train)
x = self.densenet_trans(model, x, is_train=is_train) #16
# stage 2
x = self.densenet_block(model, x, is_train=is_train)
x = self.densenet_trans(model, x, is_train=is_train) #8
# stage 3
x = self.densenet_block(model, x, is_train=is_train)
x = model.bn_act_layer(x, is_train=is_train)
x = model.gap_layer(x)
x = model._add_layer(x,
mode="fc",
out_c=num_classes,
act=None,
with_bn=False)
return x, model
def vgg19(self,
inputs=None,
is_train=True,
reload_w=None,
num_classes=None):
model = Pruner(reload_file=reload_w)
x = inputs
init_size = 64
for i in range(4):
x = model._add_layer(x,
mode="conv",
out_c=init_size * (2**i),
k_size=3,
strides=1,
with_bn=False)
x = model._add_layer(x,
mode="conv",
out_c=init_size * (2**i),
k_size=3,
strides=1,
with_bn=False)
x = model.pool_layer(x, "max", pool_size=2, strides=2)
x = model._add_layer(x,
mode="conv",
out_c=512,
k_size=3,
strides=1,
with_bn=False)
x = model._add_layer(x,
mode="conv",
out_c=512,
k_size=3,
strides=1,
with_bn=False)
x = model._add_layer(x,
mode="conv",
out_c=512,
k_size=3,
strides=1,
with_bn=False)
x = model.gmp_layer(x)
x = model._add_layer(x, mode="fc", out_c=1024, with_bn=False)
x = model._add_layer(x, mode="fc", out_c=1024, with_bn=False)
x = model._add_layer(x,
mode="fc",
out_c=num_classes,
with_bn=False,
act=None)
return x, model
def simpleNet(self,
inputs=None,
is_train=True,
reload_w=None,
num_classes=None):
model = Pruner(reload_file=reload_w)
k_size = 5
x = model._add_layer(inputs,
mode='conv',
out_c=32,
k_size=k_size,
strides=1,
is_train=is_train)
x = model._add_layer(x,
mode='conv',
out_c=64,
k_size=k_size,
strides=2,
is_train=is_train)
x1 = model._add_layer(x,
mode='conv',
out_c=64,
k_size=k_size,
strides=1,
is_train=is_train)
x2 = model._add_layer(x,
mode='conv',
out_c=64,
k_size=k_size,
strides=1,
is_train=is_train)
x = model.Add_layer(x1, x2)
x = model._add_layer(x,
mode='conv',
out_c=64,
k_size=k_size,
strides=1,
is_train=is_train)
x = model._add_layer(x,
mode='conv',
out_c=96,
k_size=k_size,
strides=2,
is_train=is_train)
x = model._add_layer(x,
mode='conv',
out_c=96,
k_size=k_size,
strides=1,
is_train=is_train)
x = model._add_layer(x,
mode='conv',
out_c=128,
k_size=k_size,
strides=2,
is_train=is_train)
x = model.gap_layer(x)
x = model._add_layer(x,
mode="fc",
out_c=num_classes,
with_bn=False,
act=None)
return x, model
def MobileNetV1(self,
inputs=None,
is_train=None,
reload_w=None,
num_classes=None):
model = Pruner(reload_file=reload_w)
x = model._add_layer(inputs,
mode="conv",
out_c=32,
k_size=3,
strides=1)
x = model._add_layer(x,
mode="dconv",
out_c=64,
k_size=3,
strides=1,
with_bn=True,
is_train=is_train)
x = model._add_layer(x,
mode="dconv",
out_c=128,
k_size=3,
strides=2,
with_bn=True,
is_train=is_train)
x = model._add_layer(x,
mode="dconv",
out_c=128,
k_size=3,
strides=1,
with_bn=True,
is_train=is_train)
x = model._add_layer(x,
mode="dconv",
out_c=256,
k_size=3,
strides=2,
with_bn=True,
is_train=is_train)
x = model._add_layer(x,
mode="dconv",
out_c=256,
k_size=3,
strides=1,
with_bn=True,
is_train=is_train)
x = model._add_layer(x,
mode="dconv",
out_c=512,
k_size=3,
strides=2,
with_bn=True,
is_train=is_train)
x = model._add_layer(x,
mode="dconv",
out_c=512,
k_size=3,
strides=1,
with_bn=True,
is_train=is_train)
x = model._add_layer(x,
mode="dconv",
out_c=512,
k_size=3,
strides=1,
with_bn=True,
is_train=is_train)
x = model._add_layer(x,
mode="dconv",
out_c=512,
k_size=3,
strides=1,
with_bn=True,
is_train=is_train)
x = model._add_layer(x,
mode="dconv",
out_c=512,
k_size=3,
strides=1,
with_bn=True,
is_train=is_train)
x = model._add_layer(x,
mode="dconv",
out_c=512,
k_size=3,
strides=1,
with_bn=True,
is_train=is_train)
x = model.gap_layer(x)
x = model._add_layer(x, mode="fc", out_c=num_classes, act=None)
return x, model
def ResNet18(self,
inputs=None,
is_train=True,
reload_w=None,
num_classes=None):
model = Pruner(reload_file=reload_w)
num_block = self.model_config["resnet18"]
block_func = self.__resnet_block_v1 if self.block_version == 1 else self.__resnet_block_v2
if self.block_version == 1:
x = model._add_layer(inputs,
mode="conv",
out_c=self.init_channels,
k_size=3,
strides=1,
with_bn=True,
is_train=is_train)
else:
x = model._add_layer(inputs,
mode="conv",
out_c=self.init_channels,
k_size=3,
strides=1,
with_bn=False,
act=None)
# stage 1 out size = 32
for _ in range(num_block[0]):
x = block_func(model,
inputs=x,
out_c=self.init_channels,
strides=1,
is_train=is_train)
# stage 2 out_size = 16
x = block_func(model,
inputs=x,
out_c=self.init_channels * 2,
strides=2,
is_train=is_train)
for _ in range(num_block[1] - 1):
x = block_func(model,
inputs=x,
out_c=self.init_channels * 2,
strides=1,
is_train=is_train)
# stage 3 out_size = 8
x = block_func(model,
inputs=x,
out_c=self.init_channels * 4,
strides=2,
is_train=is_train)
for _ in range(num_block[2] - 1):
x = block_func(model,
inputs=x,
out_c=self.init_channels * 4,
strides=1,
is_train=is_train)
# stage 4 out_size = 4
x = block_func(model,
inputs=x,
out_c=self.init_channels * 8,
strides=2,
is_train=is_train)
for _ in range(num_block[3] - 1):
x = block_func(model,
inputs=x,
out_c=self.init_channels * 8,
strides=1,
is_train=is_train)
if self.block_version == 2:
x = model.bn_act_layer(x, is_train=is_train)
x = model.gap_layer(x)
x = model._add_layer(x,
mode="fc",
out_c=num_classes,
act=None,
with_bn=False)
return x, model