346 lines
11 KiB
Python
346 lines
11 KiB
Python
import paddle
|
|
from paddle import ParamAttr
|
|
import paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
|
|
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
|
|
from paddle.nn.initializer import Uniform
|
|
import math
|
|
import sys
|
|
|
|
__all__ = ['Xception41', 'Xception65', 'Xception71']
|
|
|
|
|
|
class ConvBNLayer(nn.Layer):
|
|
def __init__(self,
|
|
num_channels,
|
|
num_filters,
|
|
filter_size,
|
|
stride=1,
|
|
groups=1,
|
|
act=None,
|
|
name=None):
|
|
super(ConvBNLayer, self).__init__()
|
|
|
|
self._conv = Conv2D(
|
|
in_channels=num_channels,
|
|
out_channels=num_filters,
|
|
kernel_size=filter_size,
|
|
stride=stride,
|
|
padding=(filter_size - 1) // 2,
|
|
groups=groups,
|
|
weight_attr=ParamAttr(name=name + "_weights"),
|
|
bias_attr=False)
|
|
bn_name = "bn_" + name
|
|
self._batch_norm = BatchNorm(
|
|
num_filters,
|
|
act=act,
|
|
param_attr=ParamAttr(name=bn_name + "_scale"),
|
|
bias_attr=ParamAttr(name=bn_name + "_offset"),
|
|
moving_mean_name=bn_name + '_mean',
|
|
moving_variance_name=bn_name + '_variance')
|
|
|
|
def forward(self, inputs):
|
|
y = self._conv(inputs)
|
|
y = self._batch_norm(y)
|
|
return y
|
|
|
|
|
|
class SeparableConv(nn.Layer):
|
|
def __init__(self, input_channels, output_channels, stride=1, name=None):
|
|
super(SeparableConv, self).__init__()
|
|
|
|
self._pointwise_conv = ConvBNLayer(
|
|
input_channels, output_channels, 1, name=name + "_sep")
|
|
self._depthwise_conv = ConvBNLayer(
|
|
output_channels,
|
|
output_channels,
|
|
3,
|
|
stride=stride,
|
|
groups=output_channels,
|
|
name=name + "_dw")
|
|
|
|
def forward(self, inputs):
|
|
x = self._pointwise_conv(inputs)
|
|
x = self._depthwise_conv(x)
|
|
return x
|
|
|
|
|
|
class EntryFlowBottleneckBlock(nn.Layer):
|
|
def __init__(self,
|
|
input_channels,
|
|
output_channels,
|
|
stride=2,
|
|
name=None,
|
|
relu_first=False):
|
|
super(EntryFlowBottleneckBlock, self).__init__()
|
|
self.relu_first = relu_first
|
|
|
|
self._short = Conv2D(
|
|
in_channels=input_channels,
|
|
out_channels=output_channels,
|
|
kernel_size=1,
|
|
stride=stride,
|
|
padding=0,
|
|
weight_attr=ParamAttr(name + "_branch1_weights"),
|
|
bias_attr=False)
|
|
self._conv1 = SeparableConv(
|
|
input_channels,
|
|
output_channels,
|
|
stride=1,
|
|
name=name + "_branch2a_weights")
|
|
self._conv2 = SeparableConv(
|
|
output_channels,
|
|
output_channels,
|
|
stride=1,
|
|
name=name + "_branch2b_weights")
|
|
self._pool = MaxPool2D(kernel_size=3, stride=stride, padding=1)
|
|
|
|
def forward(self, inputs):
|
|
conv0 = inputs
|
|
short = self._short(inputs)
|
|
if self.relu_first:
|
|
conv0 = F.relu(conv0)
|
|
conv1 = self._conv1(conv0)
|
|
conv2 = F.relu(conv1)
|
|
conv2 = self._conv2(conv2)
|
|
pool = self._pool(conv2)
|
|
return paddle.add(x=short, y=pool)
|
|
|
|
|
|
class EntryFlow(nn.Layer):
|
|
def __init__(self, block_num=3):
|
|
super(EntryFlow, self).__init__()
|
|
|
|
name = "entry_flow"
|
|
self.block_num = block_num
|
|
self._conv1 = ConvBNLayer(
|
|
3, 32, 3, stride=2, act="relu", name=name + "_conv1")
|
|
self._conv2 = ConvBNLayer(32, 64, 3, act="relu", name=name + "_conv2")
|
|
if block_num == 3:
|
|
self._conv_0 = EntryFlowBottleneckBlock(
|
|
64, 128, stride=2, name=name + "_0", relu_first=False)
|
|
self._conv_1 = EntryFlowBottleneckBlock(
|
|
128, 256, stride=2, name=name + "_1", relu_first=True)
|
|
self._conv_2 = EntryFlowBottleneckBlock(
|
|
256, 728, stride=2, name=name + "_2", relu_first=True)
|
|
elif block_num == 5:
|
|
self._conv_0 = EntryFlowBottleneckBlock(
|
|
64, 128, stride=2, name=name + "_0", relu_first=False)
|
|
self._conv_1 = EntryFlowBottleneckBlock(
|
|
128, 256, stride=1, name=name + "_1", relu_first=True)
|
|
self._conv_2 = EntryFlowBottleneckBlock(
|
|
256, 256, stride=2, name=name + "_2", relu_first=True)
|
|
self._conv_3 = EntryFlowBottleneckBlock(
|
|
256, 728, stride=1, name=name + "_3", relu_first=True)
|
|
self._conv_4 = EntryFlowBottleneckBlock(
|
|
728, 728, stride=2, name=name + "_4", relu_first=True)
|
|
else:
|
|
sys.exit(-1)
|
|
|
|
def forward(self, inputs):
|
|
x = self._conv1(inputs)
|
|
x = self._conv2(x)
|
|
|
|
if self.block_num == 3:
|
|
x = self._conv_0(x)
|
|
x = self._conv_1(x)
|
|
x = self._conv_2(x)
|
|
elif self.block_num == 5:
|
|
x = self._conv_0(x)
|
|
x = self._conv_1(x)
|
|
x = self._conv_2(x)
|
|
x = self._conv_3(x)
|
|
x = self._conv_4(x)
|
|
return x
|
|
|
|
|
|
class MiddleFlowBottleneckBlock(nn.Layer):
|
|
def __init__(self, input_channels, output_channels, name):
|
|
super(MiddleFlowBottleneckBlock, self).__init__()
|
|
|
|
self._conv_0 = SeparableConv(
|
|
input_channels,
|
|
output_channels,
|
|
stride=1,
|
|
name=name + "_branch2a_weights")
|
|
self._conv_1 = SeparableConv(
|
|
output_channels,
|
|
output_channels,
|
|
stride=1,
|
|
name=name + "_branch2b_weights")
|
|
self._conv_2 = SeparableConv(
|
|
output_channels,
|
|
output_channels,
|
|
stride=1,
|
|
name=name + "_branch2c_weights")
|
|
|
|
def forward(self, inputs):
|
|
conv0 = F.relu(inputs)
|
|
conv0 = self._conv_0(conv0)
|
|
conv1 = F.relu(conv0)
|
|
conv1 = self._conv_1(conv1)
|
|
conv2 = F.relu(conv1)
|
|
conv2 = self._conv_2(conv2)
|
|
return paddle.add(x=inputs, y=conv2)
|
|
|
|
|
|
class MiddleFlow(nn.Layer):
|
|
def __init__(self, block_num=8):
|
|
super(MiddleFlow, self).__init__()
|
|
|
|
self.block_num = block_num
|
|
self._conv_0 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_0")
|
|
self._conv_1 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_1")
|
|
self._conv_2 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_2")
|
|
self._conv_3 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_3")
|
|
self._conv_4 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_4")
|
|
self._conv_5 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_5")
|
|
self._conv_6 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_6")
|
|
self._conv_7 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_7")
|
|
if block_num == 16:
|
|
self._conv_8 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_8")
|
|
self._conv_9 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_9")
|
|
self._conv_10 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_10")
|
|
self._conv_11 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_11")
|
|
self._conv_12 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_12")
|
|
self._conv_13 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_13")
|
|
self._conv_14 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_14")
|
|
self._conv_15 = MiddleFlowBottleneckBlock(
|
|
728, 728, name="middle_flow_15")
|
|
|
|
def forward(self, inputs):
|
|
x = self._conv_0(inputs)
|
|
x = self._conv_1(x)
|
|
x = self._conv_2(x)
|
|
x = self._conv_3(x)
|
|
x = self._conv_4(x)
|
|
x = self._conv_5(x)
|
|
x = self._conv_6(x)
|
|
x = self._conv_7(x)
|
|
if self.block_num == 16:
|
|
x = self._conv_8(x)
|
|
x = self._conv_9(x)
|
|
x = self._conv_10(x)
|
|
x = self._conv_11(x)
|
|
x = self._conv_12(x)
|
|
x = self._conv_13(x)
|
|
x = self._conv_14(x)
|
|
x = self._conv_15(x)
|
|
return x
|
|
|
|
|
|
class ExitFlowBottleneckBlock(nn.Layer):
|
|
def __init__(self, input_channels, output_channels1, output_channels2,
|
|
name):
|
|
super(ExitFlowBottleneckBlock, self).__init__()
|
|
|
|
self._short = Conv2D(
|
|
in_channels=input_channels,
|
|
out_channels=output_channels2,
|
|
kernel_size=1,
|
|
stride=2,
|
|
padding=0,
|
|
weight_attr=ParamAttr(name + "_branch1_weights"),
|
|
bias_attr=False)
|
|
self._conv_1 = SeparableConv(
|
|
input_channels,
|
|
output_channels1,
|
|
stride=1,
|
|
name=name + "_branch2a_weights")
|
|
self._conv_2 = SeparableConv(
|
|
output_channels1,
|
|
output_channels2,
|
|
stride=1,
|
|
name=name + "_branch2b_weights")
|
|
self._pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
|
|
|
|
def forward(self, inputs):
|
|
short = self._short(inputs)
|
|
conv0 = F.relu(inputs)
|
|
conv1 = self._conv_1(conv0)
|
|
conv2 = F.relu(conv1)
|
|
conv2 = self._conv_2(conv2)
|
|
pool = self._pool(conv2)
|
|
return paddle.add(x=short, y=pool)
|
|
|
|
|
|
class ExitFlow(nn.Layer):
|
|
def __init__(self, class_dim):
|
|
super(ExitFlow, self).__init__()
|
|
|
|
name = "exit_flow"
|
|
|
|
self._conv_0 = ExitFlowBottleneckBlock(
|
|
728, 728, 1024, name=name + "_1")
|
|
self._conv_1 = SeparableConv(1024, 1536, stride=1, name=name + "_2")
|
|
self._conv_2 = SeparableConv(1536, 2048, stride=1, name=name + "_3")
|
|
self._pool = AdaptiveAvgPool2D(1)
|
|
stdv = 1.0 / math.sqrt(2048 * 1.0)
|
|
self._out = Linear(
|
|
2048,
|
|
class_dim,
|
|
weight_attr=ParamAttr(
|
|
name="fc_weights", initializer=Uniform(-stdv, stdv)),
|
|
bias_attr=ParamAttr(name="fc_offset"))
|
|
|
|
def forward(self, inputs):
|
|
conv0 = self._conv_0(inputs)
|
|
conv1 = self._conv_1(conv0)
|
|
conv1 = F.relu(conv1)
|
|
conv2 = self._conv_2(conv1)
|
|
conv2 = F.relu(conv2)
|
|
pool = self._pool(conv2)
|
|
pool = paddle.flatten(pool, start_axis=1, stop_axis=-1)
|
|
out = self._out(pool)
|
|
return out
|
|
|
|
|
|
class Xception(nn.Layer):
|
|
def __init__(self,
|
|
entry_flow_block_num=3,
|
|
middle_flow_block_num=8,
|
|
class_dim=1000):
|
|
super(Xception, self).__init__()
|
|
self.entry_flow_block_num = entry_flow_block_num
|
|
self.middle_flow_block_num = middle_flow_block_num
|
|
self._entry_flow = EntryFlow(entry_flow_block_num)
|
|
self._middle_flow = MiddleFlow(middle_flow_block_num)
|
|
self._exit_flow = ExitFlow(class_dim)
|
|
|
|
def forward(self, inputs):
|
|
x = self._entry_flow(inputs)
|
|
x = self._middle_flow(x)
|
|
x = self._exit_flow(x)
|
|
return x
|
|
|
|
|
|
def Xception41(**args):
|
|
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8, **args)
|
|
return model
|
|
|
|
|
|
def Xception65(**args):
|
|
model = Xception(entry_flow_block_num=3, middle_flow_block_num=16, **args)
|
|
return model
|
|
|
|
|
|
def Xception71(**args):
|
|
model = Xception(entry_flow_block_num=5, middle_flow_block_num=16, **args)
|
|
return model
|