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unet.py
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unet.py
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from paddle.nn import functional as F
import paddle
import numpy as np
class SeparableConv2D(paddle.nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=None,
weight_attr=None,
bias_attr=None,
data_format="NCHW"):
super(SeparableConv2D, self).__init__()
self._padding = padding
self._stride = stride
self._dilation = dilation
self._in_channels = in_channels
self._data_format = data_format
# 第一次卷积参数，没有偏置参数
filter_shape = [in_channels, 1] + self.convert_to_list(kernel_size, 2, 'kernel_size')
self.weight_conv = self.create_parameter(shape=filter_shape, attr=weight_attr)
# 第二次卷积参数
filter_shape = [out_channels, in_channels] + self.convert_to_list(1, 2, 'kernel_size')
self.weight_pointwise = self.create_parameter(shape=filter_shape, attr=weight_attr)
self.bias_pointwise = self.create_parameter(shape=[out_channels],
attr=bias_attr,
is_bias=True)
def convert_to_list(self, value, n, name, dtype=np.int):
if isinstance(value, dtype):
return [value, ] * n
else:
try:
value_list = list(value)
except TypeError:
raise ValueError("The " + name +
"'s type must be list or tuple. Received: " + str(
value))
if len(value_list) != n:
raise ValueError("The " + name + "'s length must be " + str(n) +
". Received: " + str(value))
for single_value in value_list:
try:
dtype(single_value)
except (ValueError, TypeError):
raise ValueError(
"The " + name + "'s type must be a list or tuple of " + str(
n) + " " + str(dtype) + " . Received: " + str(
value) + " "
"including element " + str(single_value) + " of type" + " "
+ str(type(single_value)))
return value_list
def forward(self, inputs):
conv_out = F.conv2d(inputs,
self.weight_conv,
padding=self._padding,
stride=self._stride,
dilation=self._dilation,
groups=self._in_channels,
data_format=self._data_format)
out = F.conv2d(conv_out,
self.weight_pointwise,
bias=self.bias_pointwise,
padding=0,
stride=1,
dilation=1,
groups=1,
data_format=self._data_format)
return out
class Encoder(paddle.nn.Layer):
def __init__(self, in_channels, out_channels):
super(Encoder, self).__init__()
self.relus = paddle.nn.LayerList(
[paddle.nn.ReLU() for i in range(2)])
self.separable_conv_01 = SeparableConv2D(in_channels,
out_channels,
kernel_size=3,
padding='same')
self.bns = paddle.nn.LayerList(
[paddle.nn.BatchNorm2D(out_channels) for i in range(2)])
self.separable_conv_02 = SeparableConv2D(out_channels,
out_channels,
kernel_size=3,
padding='same')
self.pool = paddle.nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.residual_conv = paddle.nn.Conv2D(in_channels,
out_channels,
kernel_size=1,
stride=2,
padding='same')
def forward(self, inputs):
previous_block_activation = inputs
y = self.relus[0](inputs)
y = self.separable_conv_01(y)
y = self.bns[0](y)
y = self.relus[1](y)
y = self.separable_conv_02(y)
y = self.bns[1](y)
y = self.pool(y)
residual = self.residual_conv(previous_block_activation)
y = paddle.add(y, residual)
return y
class Decoder(paddle.nn.Layer):
def __init__(self, in_channels, out_channels):
super(Decoder, self).__init__()
self.relus = paddle.nn.LayerList(
[paddle.nn.ReLU() for i in range(2)])
self.conv_transpose_01 = paddle.nn.Conv2DTranspose(in_channels,
out_channels,
kernel_size=3,
padding='same')
self.conv_transpose_02 = paddle.nn.Conv2DTranspose(out_channels,
out_channels,
kernel_size=3,
padding='same')
self.bns = paddle.nn.LayerList(
[paddle.nn.BatchNorm2D(out_channels) for i in range(2)]
)
self.upsamples = paddle.nn.LayerList(
[paddle.nn.Upsample(scale_factor=2.0) for i in range(2)]
)
self.residual_conv = paddle.nn.Conv2D(in_channels,
out_channels,
kernel_size=1,
padding='same')
def forward(self, inputs):
previous_block_activation = inputs
y = self.relus[0](inputs)
y = self.conv_transpose_01(y)
y = self.bns[0](y)
y = self.relus[1](y)
y = self.conv_transpose_02(y)
y = self.bns[1](y)
y = self.upsamples[0](y)
residual = self.upsamples[1](previous_block_activation)
residual = self.residual_conv(residual)
y = paddle.add(y, residual)
return y
class PetNet(paddle.nn.Layer):
def __init__(self, num_classes):
super(PetNet, self).__init__()
self.conv_1 = paddle.nn.Conv2D(3, 32,
kernel_size=3,
stride=2,
padding='same')
self.bn = paddle.nn.BatchNorm2D(32)
self.relu = paddle.nn.ReLU()
in_channels = 32
self.encoders = []
self.encoder_list = [64, 128, 256]
self.decoder_list = [256, 128, 64, 32]
# 根据下采样个数和配置循环定义子Layer，避免重复写一样的程序
for out_channels in self.encoder_list:
block = self.add_sublayer('encoder_{}'.format(out_channels),
Encoder(in_channels, out_channels))
self.encoders.append(block)
in_channels = out_channels
self.decoders = []
# 根据上采样个数和配置循环定义子Layer，避免重复写一样的程序
for out_channels in self.decoder_list:
block = self.add_sublayer('decoder_{}'.format(out_channels),
Decoder(in_channels, out_channels))
self.decoders.append(block)
in_channels = out_channels
self.output_conv = paddle.nn.Conv2D(in_channels,
num_classes,
kernel_size=3,
padding='same')
def forward(self, inputs):
y = self.conv_1(inputs)
y = self.bn(y)
y = self.relu(y)
for encoder in self.encoders:
y = encoder(y)
for decoder in self.decoders:
y = decoder(y)
y = self.output_conv(y)
return y
if __name__ == '__main__':
num_classes = 4
network = PetNet(num_classes)
model = paddle.Model(network)
model.summary((-1, 3, 160, 160))