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# Copyright (c) 2021 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 numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddleseg.models import layers
from paddleseg.cvlibs import manager
from paddleseg.utils import utils
@manager.MODELS.add_component
class PFPNNet(nn.Layer):
"""
The Panoptic Feature Pyramid Networks implementation based on PaddlePaddle.
The original article refers to
Alexander Kirillov, Ross Girshick, Kaiming He, Piotr Dollár, et al. "Panoptic Feature Pyramid Networks"
(https://arxiv.org/abs/1901.02446)
Args:
num_classes (int): The unique number of target classes.
backbone (Paddle.nn.Layer): Backbone network, currently support Resnet50/101.
backbone_indices (tuple): Four values in the tuple indicate the indices of output of backbone.
enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: False.
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,
channels,
enable_auxiliary_loss=False,
align_corners=False,
dropout_ratio=0.1,
fpn_inplanes=[256, 512, 1024, 2048],
pretrained=None):
super(PFPNNet, self).__init__()
self.backbone = backbone
self.backbone_indices = backbone_indices
self.in_channels = [
self.backbone.feat_channels[i] for i in backbone_indices
]
self.align_corners = align_corners
self.pretrained = pretrained
self.enable_auxiliary_loss = enable_auxiliary_loss
self.head = PFPNHead(
num_class=num_classes,
fpn_inplanes=fpn_inplanes,
dropout_ratio=dropout_ratio,
channels=channels,
fpn_dim=channels,
enable_auxiliary_loss=self.enable_auxiliary_loss)
self.init_weight()
def forward(self, x):
feats = self.backbone(x)
feats = [feats[i] for i in self.backbone_indices]
logit_list = self.head(feats)
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 PFPNHead(nn.Layer):
"""
The PFPNHead implementation.
Args:
inplane (int): Input channels of PPM module.
num_class (int): The unique number of target classes.
fpn_inplanes (list): The feature channels from backbone.
fpn_dim (int, optional): The input channels of FPN module. Default: 512.
enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: False.
"""
def __init__(self,
num_class,
fpn_inplanes,
channels,
dropout_ratio=0.1,
fpn_dim=256,
enable_auxiliary_loss=False,
align_corners=False):
super(PFPNHead, self).__init__()
self.enable_auxiliary_loss = enable_auxiliary_loss
self.align_corners = align_corners
self.lateral_convs = nn.LayerList()
self.fpn_out = nn.LayerList()
for fpn_inplane in fpn_inplanes:
self.lateral_convs.append(
nn.Sequential(
nn.Conv2D(fpn_inplane, fpn_dim, 1),
layers.SyncBatchNorm(fpn_dim), nn.ReLU()))
self.fpn_out.append(
nn.Sequential(
layers.ConvBNReLU(
fpn_dim, fpn_dim, 3, bias_attr=False)))
self.scale_heads = nn.LayerList()
for index in range(len(fpn_inplanes)):
head_length = max(
1,
int(np.log2(fpn_inplanes[index]) - np.log2(fpn_inplanes[0])))
scale_head = nn.LayerList()
for head_index in range(head_length):
scale_head.append(
layers.ConvBNReLU(
fpn_dim,
channels,
3,
padding=1, ))
if fpn_inplanes[index] != fpn_inplanes[0]:
scale_head.append(
nn.Upsample(
scale_factor=2,
mode='bilinear',
align_corners=align_corners))
self.scale_heads.append(nn.Sequential(*scale_head))
if dropout_ratio:
self.dropout = nn.Dropout2D(dropout_ratio)
if self.enable_auxiliary_loss:
self.dsn = nn.Sequential(
layers.ConvBNReLU(
fpn_inplanes[2], fpn_inplanes[2], 3, padding=1),
nn.Dropout2D(dropout_ratio),
nn.Conv2D(
fpn_inplanes[2], num_class, kernel_size=1))
else:
self.dropout = None
if self.enable_auxiliary_loss:
self.dsn = nn.Sequential(
layers.ConvBNReLU(
fpn_inplanes[2], fpn_inplanes[2], 3, padding=1),
nn.Conv2D(
fpn_inplanes[2], num_class, kernel_size=1))
self.conv_last = nn.Sequential(
layers.ConvBNReLU(
len(fpn_inplanes) * fpn_dim, fpn_dim, 3, bias_attr=False),
nn.Conv2D(
fpn_dim, num_class, kernel_size=1))
self.conv_seg = nn.Conv2D(channels, num_class, kernel_size=1)
def cls_seg(self, feat):
if self.dropout is not None:
feat = self.dropout(feat)
output = self.conv_seg(feat)
return output
def forward(self, conv_out):
last_out = self.lateral_convs[-1](conv_out[-1])
f = last_out
fpn_feature_list = [last_out]
for i in reversed(range(len(conv_out) - 1)):
conv_x = conv_out[i]
conv_x = self.lateral_convs[i](conv_x)
prev_shape = paddle.shape(conv_x)[2:]
f = conv_x + F.interpolate(
f, prev_shape, mode='bilinear', align_corners=True)
fpn_feature_list.append(self.fpn_out[i](f))
output_size = paddle.shape(fpn_feature_list[-1])[2:]
x = self.scale_heads[0](fpn_feature_list[-1])
for index in range(len(self.scale_heads) - 2, 0, -1):
x = x + F.interpolate(
self.scale_heads[index](fpn_feature_list[index]),
size=output_size,
mode='bilinear',
align_corners=self.align_corners)
x = self.cls_seg(x)
if self.enable_auxiliary_loss:
dsn = self.dsn(conv_out[2])
return [x, dsn]
else:
return [x]
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