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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 paddleseg import utils
from paddleseg.cvlibs import manager, param_init
from 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,
bias=True,
data_format="NCHW"):
super(FCN, self).__init__()
if data_format != 'NCHW':
raise ('fcn only support NCHW data format')
self.backbone = backbone
backbone_channels = [
backbone.feat_channels[i] for i in backbone_indices
]
self.head = FCNHead(
num_classes,
backbone_indices,
backbone_channels,
channels,
bias=bias)
self.align_corners = align_corners
self.pretrained = pretrained
self.data_format = data_format
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,
bias=True):
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,
stride=1,
bias_attr=bias)
self.cls = nn.Conv2D(
in_channels=channels,
out_channels=self.num_classes,
kernel_size=1,
stride=1,
bias_attr=bias)
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)
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