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- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
-
- import paddle
- import paddle.nn as nn
- import paddle.nn.functional as F
-
- from paddleseg.cvlibs import manager, param_init
- from paddleseg.models import layers
- from paddleseg.utils import utils
-
- __all__ = ['PPHumanSegLite']
-
-
- @manager.MODELS.add_component
- class PPHumanSegLite(nn.Layer):
- "A self-developed ultra lightweight model from PaddleSeg, is suitable for real-time scene segmentation on web or mobile terminals."
-
- def __init__(self,
- num_classes,
- in_channels=3,
- pretrained=None,
- align_corners=False):
- super().__init__()
- self.pretrained = pretrained
- self.num_classes = num_classes
- self.align_corners = align_corners
-
- self.conv_bn0 = _ConvBNReLU(in_channels, 36, 3, 2, 1)
- self.conv_bn1 = _ConvBNReLU(36, 18, 1, 1, 0)
-
- self.block1 = nn.Sequential(
- InvertedResidual(
- 36, stride=2, out_channels=72),
- InvertedResidual(
- 72, stride=1),
- InvertedResidual(
- 72, stride=1),
- InvertedResidual(
- 72, stride=1))
-
- self.block2 = nn.Sequential(
- InvertedResidual(
- 72, stride=2),
- InvertedResidual(
- 144, stride=1),
- InvertedResidual(
- 144, stride=1),
- InvertedResidual(
- 144, stride=1),
- InvertedResidual(
- 144, stride=1),
- InvertedResidual(
- 144, stride=1),
- InvertedResidual(
- 144, stride=1),
- InvertedResidual(
- 144, stride=1))
-
- self.depthwise_separable0 = _SeparableConvBNReLU(144, 64, 3, stride=1)
- self.depthwise_separable1 = _SeparableConvBNReLU(82, 64, 3, stride=1)
- self.depthwise_separable2 = _SeparableConvBNReLU(
- 64, self.num_classes, 3, stride=1)
-
- self.init_weight()
-
- def forward(self, x):
- # Encoder
- input_shape = paddle.shape(x)[2:]
-
- x = self.conv_bn0(x) # 1/2
- shortcut = self.conv_bn1(x) # shortcut
- x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1) # 1/4
- x = self.block1(x) # 1/8
- x = self.block2(x) # 1/16
-
- # Decoder
- x = self.depthwise_separable0(x)
- shortcut_shape = paddle.shape(shortcut)[2:]
- x = F.interpolate(
- x,
- shortcut_shape,
- mode='bilinear',
- align_corners=self.align_corners)
- x = paddle.concat(x=[shortcut, x], axis=1)
- x = self.depthwise_separable1(x)
-
- logit = self.depthwise_separable2(x)
- logit = F.interpolate(
- logit,
- input_shape,
- mode='bilinear',
- align_corners=self.align_corners)
-
- return [logit]
-
- def init_weight(self):
- for layer in self.sublayers():
- if isinstance(layer, nn.Conv2D):
- param_init.normal_init(layer.weight, std=0.001)
- elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)):
- param_init.constant_init(layer.weight, value=1.0)
- param_init.constant_init(layer.bias, value=0.0)
- if self.pretrained is not None:
- utils.load_pretrained_model(self, self.pretrained)
-
-
- class _ConvBNReLU(nn.Layer):
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- groups=1,
- **kwargs):
- super().__init__()
- weight_attr = paddle.ParamAttr(
- learning_rate=1, initializer=nn.initializer.KaimingUniform())
- self._conv = nn.Conv2D(
- in_channels,
- out_channels,
- kernel_size,
- padding=padding,
- stride=stride,
- groups=groups,
- weight_attr=weight_attr,
- bias_attr=False,
- **kwargs)
-
- self._batch_norm = layers.SyncBatchNorm(out_channels)
-
- def forward(self, x):
- x = self._conv(x)
- x = self._batch_norm(x)
- x = F.relu(x)
- return x
-
-
- class _ConvBN(nn.Layer):
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- groups=1,
- **kwargs):
- super().__init__()
- weight_attr = paddle.ParamAttr(
- learning_rate=1, initializer=nn.initializer.KaimingUniform())
- self._conv = nn.Conv2D(
- in_channels,
- out_channels,
- kernel_size,
- padding=padding,
- stride=stride,
- groups=groups,
- weight_attr=weight_attr,
- bias_attr=False,
- **kwargs)
-
- self._batch_norm = layers.SyncBatchNorm(out_channels)
-
- def forward(self, x):
- x = self._conv(x)
- x = self._batch_norm(x)
- return x
-
-
- class _SeparableConvBNReLU(nn.Layer):
- def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
- super().__init__()
- self.depthwise_conv = _ConvBN(
- in_channels,
- out_channels=in_channels,
- kernel_size=kernel_size,
- padding=int(kernel_size / 2),
- groups=in_channels,
- **kwargs)
- self.piontwise_conv = _ConvBNReLU(
- in_channels,
- out_channels,
- kernel_size=1,
- groups=1,
- stride=1,
- padding=0)
-
- def forward(self, x):
- x = self.depthwise_conv(x)
- x = self.piontwise_conv(x)
- return x
-
-
- class InvertedResidual(nn.Layer):
- def __init__(self, input_channels, stride, out_channels=None):
- super().__init__()
- if stride == 1:
- branch_channel = int(input_channels / 2)
- else:
- branch_channel = input_channels
-
- if out_channels is None:
- self.in_channels = int(branch_channel)
- else:
- self.in_channels = int(out_channels / 2)
-
- self._depthwise_separable_0 = _SeparableConvBNReLU(
- input_channels, self.in_channels, 3, stride=stride)
- self._conv = _ConvBNReLU(
- branch_channel, self.in_channels, 1, stride=1, padding=0)
- self._depthwise_separable_1 = _SeparableConvBNReLU(
- self.in_channels, self.in_channels, 3, stride=stride)
-
- self.stride = stride
-
- def forward(self, input):
-
- if self.stride == 1:
- shortcut, branch = paddle.split(x=input, num_or_sections=2, axis=1)
- else:
- branch = input
- shortcut = self._depthwise_separable_0(input)
-
- branch_1x1 = self._conv(branch)
- branch_dw1x1 = self._depthwise_separable_1(branch_1x1)
- output = paddle.concat(x=[shortcut, branch_dw1x1], axis=1)
-
- # channel shuffle
- out_shape = paddle.shape(output)
- h, w = out_shape[2], out_shape[3]
- output = paddle.reshape(x=output, shape=[0, 2, self.in_channels, h, w])
- output = paddle.transpose(x=output, perm=[0, 2, 1, 3, 4])
- output = paddle.reshape(x=output, shape=[0, 2 * self.in_channels, h, w])
- return output
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