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118 lines
4.1 KiB
118 lines
4.1 KiB
3 years ago
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle.nn as nn
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import paddle.nn.functional as F
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from .layers import ConvBNReLU
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class FCN(nn.Layer):
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"""
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A simple implementation for FCN based on PaddlePaddle.
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The original article refers to
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Evan Shelhamer, et, al. "Fully Convolutional Networks for Semantic Segmentation"
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(https://arxiv.org/abs/1411.4038).
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Args:
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num_classes (int): The unique number of target classes.
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backbone (paddle.nn.Layer): Backbone networks.
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backbone_indices (tuple, optional): The values in the tuple indicate the indices of output of backbone.
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Default: (-1, ).
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channels (int, optional): The channels between conv layer and the last layer of FCNHead.
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If None, it will be the number of channels of input features. Default: None.
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align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
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is even, e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
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pretrained (str, optional): The path or url of pretrained model. Default: None
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"""
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def __init__(self,
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num_classes,
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backbone,
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backbone_indices=(-1, ),
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channels=None,
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align_corners=False,
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pretrained=None):
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super(FCN, self).__init__()
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self.backbone = backbone
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backbone_channels = [
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backbone.feat_channels[i] for i in backbone_indices
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]
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self.head = FCNHead(num_classes, backbone_indices, backbone_channels,
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channels)
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self.align_corners = align_corners
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self.pretrained = pretrained
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def forward(self, x):
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feat_list = self.backbone(x)
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logit_list = self.head(feat_list)
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return [
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F.interpolate(
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logit,
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x.shape[2:],
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mode='bilinear',
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align_corners=self.align_corners) for logit in logit_list
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]
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class FCNHead(nn.Layer):
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"""
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A simple implementation for FCNHead based on PaddlePaddle
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Args:
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num_classes (int): The unique number of target classes.
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backbone_indices (tuple, optional): The values in the tuple indicate the indices of output of backbone.
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Default: (-1, ).
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channels (int, optional): The channels between conv layer and the last layer of FCNHead.
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If None, it will be the number of channels of input features. Default: None.
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pretrained (str, optional): The path of pretrained model. Default: None
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"""
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def __init__(self,
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num_classes,
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backbone_indices=(-1, ),
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backbone_channels=(270, ),
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channels=None):
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super(FCNHead, self).__init__()
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self.num_classes = num_classes
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self.backbone_indices = backbone_indices
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if channels is None:
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channels = backbone_channels[0]
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self.conv_1 = ConvBNReLU(
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in_channels=backbone_channels[0],
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out_channels=channels,
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kernel_size=1,
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padding='same',
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stride=1)
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self.cls = nn.Conv2D(
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in_channels=channels,
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out_channels=self.num_classes,
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kernel_size=1,
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stride=1,
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padding=0)
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def forward(self, feat_list):
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logit_list = []
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x = feat_list[self.backbone_indices[0]]
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x = self.conv_1(x)
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logit = self.cls(x)
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logit_list.append(logit)
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return logit_list
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