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- # Copyright (c) 2020 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.nn as nn
- import paddle.nn.functional as F
- import paddle
- from paddlex.paddleseg.cvlibs import manager
- from paddlex.paddleseg.models import layers
- from paddlex.paddleseg.utils import utils
- __all__ = ['FastSCNN']
- @manager.MODELS.add_component
- class FastSCNN(nn.Layer):
- """
- The FastSCNN implementation based on PaddlePaddle.
- As mentioned in the original paper, FastSCNN is a real-time segmentation algorithm (123.5fps)
- even for high resolution images (1024x2048).
- The original article refers to
- Poudel, Rudra PK, et al. "Fast-scnn: Fast semantic segmentation network"
- (https://arxiv.org/pdf/1902.04502.pdf).
- Args:
- num_classes (int): The unique number of target classes.
- enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss.
- If true, auxiliary loss will be added after LearningToDownsample module. Default: False.
- align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
- is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.. Default: False.
- pretrained (str, optional): The path or url of pretrained model. Default: None.
- """
- def __init__(self,
- num_classes,
- enable_auxiliary_loss=True,
- align_corners=False,
- pretrained=None):
- super().__init__()
- self.learning_to_downsample = LearningToDownsample(32, 48, 64)
- self.global_feature_extractor = GlobalFeatureExtractor(
- in_channels=64,
- block_channels=[64, 96, 128],
- out_channels=128,
- expansion=6,
- num_blocks=[3, 3, 3],
- align_corners=True)
- self.feature_fusion = FeatureFusionModule(64, 128, 128, align_corners)
- self.classifier = Classifier(128, num_classes)
- if enable_auxiliary_loss:
- self.auxlayer = layers.AuxLayer(64, 32, num_classes)
- self.enable_auxiliary_loss = enable_auxiliary_loss
- self.align_corners = align_corners
- self.pretrained = pretrained
- self.init_weight()
- def forward(self, x):
- logit_list = []
- input_size = paddle.shape(x)[2:]
- higher_res_features = self.learning_to_downsample(x)
- x = self.global_feature_extractor(higher_res_features)
- x = self.feature_fusion(higher_res_features, x)
- logit = self.classifier(x)
- logit = F.interpolate(
- logit,
- input_size,
- mode='bilinear',
- align_corners=self.align_corners)
- logit_list.append(logit)
- if self.enable_auxiliary_loss:
- auxiliary_logit = self.auxlayer(higher_res_features)
- auxiliary_logit = F.interpolate(
- auxiliary_logit,
- input_size,
- mode='bilinear',
- align_corners=self.align_corners)
- logit_list.append(auxiliary_logit)
- return logit_list
- def init_weight(self):
- if self.pretrained is not None:
- utils.load_entire_model(self, self.pretrained)
- class LearningToDownsample(nn.Layer):
- """
- Learning to downsample module.
- This module consists of three downsampling blocks (one conv and two separable conv)
- Args:
- dw_channels1 (int, optional): The input channels of the first sep conv. Default: 32.
- dw_channels2 (int, optional): The input channels of the second sep conv. Default: 48.
- out_channels (int, optional): The output channels of LearningToDownsample module. Default: 64.
- """
- def __init__(self, dw_channels1=32, dw_channels2=48, out_channels=64):
- super(LearningToDownsample, self).__init__()
- self.conv_bn_relu = layers.ConvBNReLU(
- in_channels=3, out_channels=dw_channels1, kernel_size=3, stride=2)
- self.dsconv_bn_relu1 = layers.SeparableConvBNReLU(
- in_channels=dw_channels1,
- out_channels=dw_channels2,
- kernel_size=3,
- stride=2,
- padding=1)
- self.dsconv_bn_relu2 = layers.SeparableConvBNReLU(
- in_channels=dw_channels2,
- out_channels=out_channels,
- kernel_size=3,
- stride=2,
- padding=1)
- def forward(self, x):
- x = self.conv_bn_relu(x)
- x = self.dsconv_bn_relu1(x)
- x = self.dsconv_bn_relu2(x)
- return x
- class GlobalFeatureExtractor(nn.Layer):
- """
- Global feature extractor module.
- This module consists of three InvertedBottleneck blocks (like inverted residual introduced by MobileNetV2) and
- a PPModule (introduced by PSPNet).
- Args:
- in_channels (int): The number of input channels to the module.
- block_channels (tuple): A tuple represents output channels of each bottleneck block.
- out_channels (int): The number of output channels of the module. Default:
- expansion (int): The expansion factor in bottleneck.
- num_blocks (tuple): It indicates the repeat time of each bottleneck.
- align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
- is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.
- """
- def __init__(self, in_channels, block_channels, out_channels, expansion,
- num_blocks, align_corners):
- super(GlobalFeatureExtractor, self).__init__()
- self.bottleneck1 = self._make_layer(InvertedBottleneck, in_channels,
- block_channels[0], num_blocks[0],
- expansion, 2)
- self.bottleneck2 = self._make_layer(
- InvertedBottleneck, block_channels[0], block_channels[1],
- num_blocks[1], expansion, 2)
- self.bottleneck3 = self._make_layer(
- InvertedBottleneck, block_channels[1], block_channels[2],
- num_blocks[2], expansion, 1)
- self.ppm = layers.PPModule(
- block_channels[2],
- out_channels,
- bin_sizes=(1, 2, 3, 6),
- dim_reduction=True,
- align_corners=align_corners)
- def _make_layer(self,
- block,
- in_channels,
- out_channels,
- blocks,
- expansion=6,
- stride=1):
- layers = []
- layers.append(block(in_channels, out_channels, expansion, stride))
- for _ in range(1, blocks):
- layers.append(block(out_channels, out_channels, expansion, 1))
- return nn.Sequential(*layers)
- def forward(self, x):
- x = self.bottleneck1(x)
- x = self.bottleneck2(x)
- x = self.bottleneck3(x)
- x = self.ppm(x)
- return x
- class InvertedBottleneck(nn.Layer):
- """
- Single Inverted bottleneck implementation.
- Args:
- in_channels (int): The number of input channels to bottleneck block.
- out_channels (int): The number of output channels of bottleneck block.
- expansion (int, optional). The expansion factor in bottleneck. Default: 6.
- stride (int, optional). The stride used in depth-wise conv. Defalt: 2.
- """
- def __init__(self, in_channels, out_channels, expansion=6, stride=2):
- super().__init__()
- self.use_shortcut = stride == 1 and in_channels == out_channels
- expand_channels = in_channels * expansion
- self.block = nn.Sequential(
- # pw
- layers.ConvBNReLU(
- in_channels=in_channels,
- out_channels=expand_channels,
- kernel_size=1,
- bias_attr=False),
- # dw
- layers.ConvBNReLU(
- in_channels=expand_channels,
- out_channels=expand_channels,
- kernel_size=3,
- stride=stride,
- padding=1,
- groups=expand_channels,
- bias_attr=False),
- # pw-linear
- layers.ConvBN(
- in_channels=expand_channels,
- out_channels=out_channels,
- kernel_size=1,
- bias_attr=False))
- def forward(self, x):
- out = self.block(x)
- if self.use_shortcut:
- out = x + out
- return out
- class FeatureFusionModule(nn.Layer):
- """
- Feature Fusion Module Implementation.
- This module fuses high-resolution feature and low-resolution feature.
- Args:
- high_in_channels (int): The channels of high-resolution feature (output of LearningToDownsample).
- low_in_channels (int): The channels of low-resolution feature (output of GlobalFeatureExtractor).
- out_channels (int): The output channels of this module.
- align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
- is even, e.g. 1024x512, otherwise it is True, e.g. 769x769.
- """
- def __init__(self, high_in_channels, low_in_channels, out_channels,
- align_corners):
- super().__init__()
- # Only depth-wise conv
- self.dwconv = layers.ConvBNReLU(
- in_channels=low_in_channels,
- out_channels=out_channels,
- kernel_size=3,
- padding=1,
- groups=128,
- bias_attr=False)
- self.conv_low_res = layers.ConvBN(out_channels, out_channels, 1)
- self.conv_high_res = layers.ConvBN(high_in_channels, out_channels, 1)
- self.align_corners = align_corners
- def forward(self, high_res_input, low_res_input):
- low_res_input = F.interpolate(
- low_res_input,
- paddle.shape(high_res_input)[2:],
- mode='bilinear',
- align_corners=self.align_corners)
- low_res_input = self.dwconv(low_res_input)
- low_res_input = self.conv_low_res(low_res_input)
- high_res_input = self.conv_high_res(high_res_input)
- x = high_res_input + low_res_input
- return F.relu(x)
- class Classifier(nn.Layer):
- """
- The Classifier module implementation.
- This module consists of two depth-wise conv and one conv.
- Args:
- input_channels (int): The input channels to this module.
- num_classes (int): The unique number of target classes.
- """
- def __init__(self, input_channels, num_classes):
- super().__init__()
- self.dsconv1 = layers.SeparableConvBNReLU(
- in_channels=input_channels,
- out_channels=input_channels,
- kernel_size=3,
- padding=1)
- self.dsconv2 = layers.SeparableConvBNReLU(
- in_channels=input_channels,
- out_channels=input_channels,
- kernel_size=3,
- padding=1)
- self.conv = nn.Conv2D(
- in_channels=input_channels,
- out_channels=num_classes,
- kernel_size=1)
- self.dropout = nn.Dropout(p=0.1) # dropout_prob
- def forward(self, x):
- x = self.dsconv1(x)
- x = self.dsconv2(x)
- x = self.dropout(x)
- x = self.conv(x)
- return x
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