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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 cv2
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg.cvlibs import manager
from paddleseg.models import layers
from paddleseg.models.backbones import resnet_vd
from paddleseg.models import deeplab
from paddleseg.utils import utils
@manager.MODELS.add_component
class DecoupledSegNet(nn.Layer):
"""
The DecoupledSegNet implementation based on PaddlePaddle.
The original article refers to
Xiangtai Li, et, al. "Improving Semantic Segmentation via Decoupled Body and Edge Supervision"
(https://arxiv.org/pdf/2007.10035.pdf)
Args:
num_classes (int): The unique number of target classes.
backbone (paddle.nn.Layer): Backbone network, currently support Resnet50_vd/Resnet101_vd.
backbone_indices (tuple, optional): Two values in the tuple indicate the indices of output of backbone.
Default: (0, 3).
aspp_ratios (tuple, optional): The dilation rate using in ASSP module.
If output_stride=16, aspp_ratios should be set as (1, 6, 12, 18).
If output_stride=8, aspp_ratios is (1, 12, 24, 36).
Default: (1, 6, 12, 18).
aspp_out_channels (int, optional): The output channels of ASPP module. Default: 256.
align_corners (bool, optional): An argument of F.interpolate. It should be set to False when the feature size 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=(0, 3),
aspp_ratios=(1, 6, 12, 18),
aspp_out_channels=256,
align_corners=False,
pretrained=None):
super().__init__()
self.backbone = backbone
backbone_channels = self.backbone.feat_channels
self.head = DecoupledSegNetHead(num_classes, backbone_indices,
backbone_channels, aspp_ratios,
aspp_out_channels, align_corners)
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)
seg_logit, body_logit, edge_logit = [
F.interpolate(
logit,
paddle.shape(x)[2:],
mode='bilinear',
align_corners=self.align_corners) for logit in logit_list
]
if self.training:
return [seg_logit, body_logit, edge_logit, (seg_logit, edge_logit)]
return [seg_logit]
def init_weight(self):
if self.pretrained is not None:
utils.load_entire_model(self, self.pretrained)
class DecoupledSegNetHead(nn.Layer):
"""
The DecoupledSegNetHead implementation based on PaddlePaddle.
Args:
num_classes (int): The unique number of target classes.
backbone_indices (tuple): Two values in the tuple indicate the indices of output of backbone.
the first index will be taken as a low-level feature in Edge presevation component;
the second one will be taken as input of ASPP component.
backbone_channels (tuple): The channels of output of backbone.
aspp_ratios (tuple): The dilation rates using in ASSP module.
aspp_out_channels (int): The output channels of ASPP 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, num_classes, backbone_indices, backbone_channels,
aspp_ratios, aspp_out_channels, align_corners):
super().__init__()
self.backbone_indices = backbone_indices
self.align_corners = align_corners
self.aspp = layers.ASPPModule(
aspp_ratios=aspp_ratios,
in_channels=backbone_channels[backbone_indices[1]],
out_channels=aspp_out_channels,
align_corners=align_corners,
image_pooling=True)
self.bot_fine = nn.Conv2D(
backbone_channels[backbone_indices[0]], 48, 1, bias_attr=False)
# decoupled
self.squeeze_body_edge = SqueezeBodyEdge(
256, align_corners=self.align_corners)
self.edge_fusion = nn.Conv2D(256 + 48, 256, 1, bias_attr=False)
self.sigmoid_edge = nn.Sigmoid()
self.edge_out = nn.Sequential(
layers.ConvBNReLU(
in_channels=256,
out_channels=48,
kernel_size=3,
bias_attr=False),
nn.Conv2D(
48, 1, 1, bias_attr=False))
self.dsn_seg_body = nn.Sequential(
layers.ConvBNReLU(
in_channels=256,
out_channels=256,
kernel_size=3,
bias_attr=False),
nn.Conv2D(
256, num_classes, 1, bias_attr=False))
self.final_seg = nn.Sequential(
layers.ConvBNReLU(
in_channels=512,
out_channels=256,
kernel_size=3,
bias_attr=False),
layers.ConvBNReLU(
in_channels=256,
out_channels=256,
kernel_size=3,
bias_attr=False),
nn.Conv2D(
256, num_classes, kernel_size=1, bias_attr=False))
def forward(self, feat_list):
fine_fea = feat_list[self.backbone_indices[0]]
fine_size = paddle.shape(fine_fea)
x = feat_list[self.backbone_indices[1]]
aspp = self.aspp(x)
# decoupled
seg_body, seg_edge = self.squeeze_body_edge(aspp)
# Edge presevation and edge out
fine_fea = self.bot_fine(fine_fea)
seg_edge = F.interpolate(
seg_edge,
fine_size[2:],
mode='bilinear',
align_corners=self.align_corners)
seg_edge = self.edge_fusion(paddle.concat([seg_edge, fine_fea], axis=1))
seg_edge_out = self.edge_out(seg_edge)
seg_edge_out = self.sigmoid_edge(seg_edge_out) # seg_edge output
seg_body_out = self.dsn_seg_body(seg_body) # body out
# seg_final out
seg_out = seg_edge + F.interpolate(
seg_body,
fine_size[2:],
mode='bilinear',
align_corners=self.align_corners)
aspp = F.interpolate(
aspp,
fine_size[2:],
mode='bilinear',
align_corners=self.align_corners)
seg_out = paddle.concat([aspp, seg_out], axis=1)
seg_final_out = self.final_seg(seg_out)
return [seg_final_out, seg_body_out, seg_edge_out]
class SqueezeBodyEdge(nn.Layer):
def __init__(self, inplane, align_corners=False):
super().__init__()
self.align_corners = align_corners
self.down = nn.Sequential(
layers.ConvBNReLU(
inplane, inplane, kernel_size=3, groups=inplane, stride=2),
layers.ConvBNReLU(
inplane, inplane, kernel_size=3, groups=inplane, stride=2))
self.flow_make = nn.Conv2D(
inplane * 2, 2, kernel_size=3, padding='same', bias_attr=False)
def forward(self, x):
size = paddle.shape(x)[2:]
seg_down = self.down(x)
seg_down = F.interpolate(
seg_down,
size=size,
mode='bilinear',
align_corners=self.align_corners)
flow = self.flow_make(paddle.concat([x, seg_down], axis=1))
seg_flow_warp = self.flow_warp(x, flow, size)
seg_edge = x - seg_flow_warp
return seg_flow_warp, seg_edge
def flow_warp(self, input, flow, size):
input_shape = paddle.shape(input)
norm = size[::-1].reshape([1, 1, 1, -1])
norm.stop_gradient = True
h_grid = paddle.linspace(-1.0, 1.0, size[0]).reshape([-1, 1])
h_grid = h_grid.tile([size[1]])
w_grid = paddle.linspace(-1.0, 1.0, size[1]).reshape([-1, 1])
w_grid = w_grid.tile([size[0]]).transpose([1, 0])
grid = paddle.concat([w_grid.unsqueeze(2), h_grid.unsqueeze(2)], axis=2)
grid.unsqueeze(0).tile([input_shape[0], 1, 1, 1])
grid = grid + paddle.transpose(flow, (0, 2, 3, 1)) / norm
output = F.grid_sample(input, grid)
return output
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