| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135 |
- # 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 import utils
- from paddlex.paddleseg.cvlibs import manager, param_init
- from paddlex.paddleseg.models import layers
- @manager.MODELS.add_component
- class FCN(nn.Layer):
- """
- A simple implementation for FCN based on PaddlePaddle.
- The original article refers to
- Evan Shelhamer, et, al. "Fully Convolutional Networks for Semantic Segmentation"
- (https://arxiv.org/abs/1411.4038).
- Args:
- num_classes (int): The unique number of target classes.
- backbone (paddle.nn.Layer): Backbone networks.
- backbone_indices (tuple, optional): The values in the tuple indicate the indices of output of backbone.
- Default: (-1, ).
- channels (int, optional): The channels between conv layer and the last layer of FCNHead.
- If None, it will be the number of channels of input features. Default: None.
- 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,
- backbone,
- backbone_indices=(-1, ),
- channels=None,
- align_corners=False,
- pretrained=None):
- super(FCN, self).__init__()
- self.backbone = backbone
- backbone_channels = [
- backbone.feat_channels[i] for i in backbone_indices
- ]
- self.head = FCNHead(num_classes, backbone_indices, backbone_channels,
- channels)
- self.align_corners = align_corners
- self.pretrained = pretrained
- self.init_weight()
- def forward(self, x):
- feat_list = self.backbone(x)
- logit_list = self.head(feat_list)
- return [
- F.interpolate(
- logit,
- paddle.shape(x)[2:],
- mode='bilinear',
- align_corners=self.align_corners) for logit in logit_list
- ]
- def init_weight(self):
- if self.pretrained is not None:
- utils.load_entire_model(self, self.pretrained)
- class FCNHead(nn.Layer):
- """
- A simple implementation for FCNHead based on PaddlePaddle
- Args:
- num_classes (int): The unique number of target classes.
- backbone_indices (tuple, optional): The values in the tuple indicate the indices of output of backbone.
- Default: (-1, ).
- channels (int, optional): The channels between conv layer and the last layer of FCNHead.
- If None, it will be the number of channels of input features. Default: None.
- pretrained (str, optional): The path of pretrained model. Default: None
- """
- def __init__(self,
- num_classes,
- backbone_indices=(-1, ),
- backbone_channels=(270, ),
- channels=None):
- super(FCNHead, self).__init__()
- self.num_classes = num_classes
- self.backbone_indices = backbone_indices
- if channels is None:
- channels = backbone_channels[0]
- self.conv_1 = layers.ConvBNReLU(
- in_channels=backbone_channels[0],
- out_channels=channels,
- kernel_size=1,
- padding='same',
- stride=1)
- self.cls = nn.Conv2D(
- in_channels=channels,
- out_channels=self.num_classes,
- kernel_size=1,
- stride=1,
- padding=0)
- self.init_weight()
- def forward(self, feat_list):
- logit_list = []
- x = feat_list[self.backbone_indices[0]]
- x = self.conv_1(x)
- logit = self.cls(x)
- logit_list.append(logit)
- return logit_list
- 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)
|