语义分割系列18-FPN（pytorch实现）
FPN：《Panoptic Feature Pyramid Networks》
发布于2019CVPR
FPN在实例分割中取得巨大成功（Mask R-Cnn）后，进军语义分割，结果发现FPN在语义分割中能够提供轻巧的网络结构、快速的分割速度、精确的分割结果。于是，作者提出了Panoptic FPN来完成语义分割任务。
论文
FPN在实例分割网络中扮演了及其重要的角色，作者思考后将其引入语义分割领域，同时也对原始的FPN做了对应的修改。
FPN-Semantic segmentation branch
为了实现从FPN中输出语义结果的功能，作者将FPN金字塔每一层的输出合并为单个输出，已最深层为例，1/32的特征图，经过3次卷积和2倍上采样后，输出为1/4大小的特征图。其余层也经过类似的方法生成类似的结果，然后相加成一个输出。经过卷积和上采样后生成语义结果。
与Unet直接将各对应层的结果在通道上相加不同，FPN在每一层的连接中间加了卷积和上采样，这样主干网络的下采样层也可以获得更自由的结果，更加灵活。
模型复现
主干网络Resnet50
import torch
import torch.nn as nn
class BasicBlock(nn.Module):
expansion: int = 4
def __init__(self, inplanes, planes, stride = 1, downsample = None, groups = 1,
base_width = 64, dilation = 1, norm_layer = None):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError("BasicBlock only supports groups=1 and base_width=64")
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = nn.Conv2d(inplanes, planes ,kernel_size=3, stride=stride,
padding=dilation,groups=groups, bias=False,dilation=dilation)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(planes, planes ,kernel_size=3, stride=stride,
padding=dilation,groups=groups, bias=False,dilation=dilation)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample= None,
groups = 1, base_width = 64, dilation = 1, norm_layer = None,):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.0)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, stride=1, bias=False)
self.bn1 = norm_layer(width)
self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, bias=False, padding=dilation, dilation=dilation)
self.bn2 = norm_layer(width)
self.conv3 = nn.Conv2d(width, planes * self.expansion, kernel_size=1, stride=1, bias=False)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(
self,block, layers,num_classes = 1000, zero_init_residual = False, groups = 1,
width_per_group = 64, replace_stride_with_dilation = None, norm_layer = None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 2
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError(
"replace_stride_with_dilation should be None "
f"or a 3-element tuple, got {replace_stride_with_dilation}"
)
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
def _make_layer(
self,
block,
planes,
blocks,
stride = 1,
dilate = False,
):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = stride
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
norm_layer(planes * block.expansion))
layers = []
layers.append(
block(
self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer
)
)
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(
self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation,
norm_layer=norm_layer,
)
)
return nn.Sequential(*layers)
def _forward_impl(self, x):
out = []
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
out.append(x)
x = self.layer2(x)
out.append(x)
x = self.layer3(x)
out.append(x)
x = self.layer4(x)
out.append(x)
return out
def forward(self, x) :
return self._forward_impl(x)
def _resnet(block, layers, pretrained_path = None, **kwargs,):
model = ResNet(block, layers, **kwargs)
if pretrained_path is not None:
model.load_state_dict(torch.load(pretrained_path), strict=False)
return model
def resnet50(pretrained_path=None, **kwargs):
return ResNet._resnet(Bottleneck, [3, 4, 6, 3],pretrained_path,**kwargs)
def resnet101(pretrained_path=None, **kwargs):
return ResNet._resnet(Bottleneck, [3, 4, 23, 3],pretrained_path,**kwargs)FPN
import numpy as np
import torch.nn as nn
class FPNHead(nn.Module):
def __init__(self, feature_strides=[4, 8, 16, 32], in_channels=[256, 512, 1024, 2048], channels=256, align_corners=True):
super(FPNHead, self).__init__()
self.in_channels = in_channels
self.channels = channels
self.align_corners = align_corners
assert len(feature_strides) == len(self.in_channels)
assert min(feature_strides) == feature_strides[0]
self.feature_strides = feature_strides
self.scale_heads = nn.ModuleList()
for i in range(len(feature_strides)):
head_length = max(
1,
int(np.log2(feature_strides[i]) - np.log2(feature_strides[0])))
scale_head = []
for k in range(head_length):
scale_head.append(
nn.Conv2d(
self.in_channels[i] if k == 0 else self.channels,
self.channels,
3,
padding=1))
if feature_strides[i] != feature_strides[0]:
scale_head.append(
nn.Upsample(
scale_factor=2,
mode='bilinear',
align_corners=self.align_corners))
self.scale_heads.append(nn.Sequential(*scale_head))
def forward(self, inputs):
x = inputs[-len(inputs):]
output = self.scale_heads[0](x[0])
for i in range(1, len(self.feature_strides)):
# non inplace
output = output + nn.functional.interpolate(
self.scale_heads[i](x[i]),
size=output.shape[2:],
mode='bilinear',
align_corners=self.align_corners)
return output
FPNNet
import torch
import torch.nn as nn
class FPNNet(nn.Module):
def __init__(self, num_classes):
super(FPNNet, self).__init__()
self.num_classes = num_classes
self.backbone = ResNet.resnet50(replace_stride_with_dilation=[1,2,4])
self.Head = FPNHead()
self.cls_seg = nn.Sequential(
nn.Upsample(scale_factor=4,
mode='bilinear',
align_corners=True),
nn.Conv2d(256, 256, 3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Conv2d(256, num_classes, 3, padding=1)
)
def forward(self, x):
x = self.backbone(x)
x = self.Head(x)
x = self.cls_seg(x)
return x
Dataset-Camvid
# 导入库
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch import optim
from torch.utils.data import Dataset, DataLoader, random_split
from tqdm import tqdm
import warnings
warnings.filterwarnings("ignore")
import os.path as osp
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
torch.manual_seed(17)
# 自定义数据集CamVidDataset
class CamVidDataset(torch.utils.data.Dataset):
"""CamVid Dataset. Read images, apply augmentation and preprocessing transformations.
Args:
images_dir (str): path to images folder
masks_dir (str): path to segmentation masks folder
class_values (list): values of classes to extract from segmentation mask
augmentation (albumentations.Compose): data transfromation pipeline
(e.g. flip, scale, etc.)
preprocessing (albumentations.Compose): data preprocessing
(e.g. noralization, shape manipulation, etc.)
"""
def __init__(self, images_dir, masks_dir):
self.transform = A.Compose([
A.Resize(224, 224),
A.HorizontalFlip(),
A.VerticalFlip(),
A.Normalize(),
ToTensorV2(),
])
self.ids = os.listdir(images_dir)
self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]
self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]
def __getitem__(self, i):
# read data
image = np.array(Image.open(self.images_fps[i]).convert('RGB'))
mask = np.array( Image.open(self.masks_fps[i]).convert('RGB'))
image = self.transform(image=image,mask=mask)
return image['image'], image['mask'][:,:,0]
def __len__(self):
return len(self.ids)
# 设置数据集路径
DATA_DIR = r'database/camvid/camvid/' # 根据自己的路径来设置
x_train_dir = os.path.join(DATA_DIR, 'train_images')
y_train_dir = os.path.join(DATA_DIR, 'train_labels')
x_valid_dir = os.path.join(DATA_DIR, 'valid_images')
y_valid_dir = os.path.join(DATA_DIR, 'valid_labels')
train_dataset = CamVidDataset(
x_train_dir,
y_train_dir,
)
val_dataset = CamVidDataset(
x_valid_dir,
y_valid_dir,
)
train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True,drop_last=True)
val_loader = DataLoader(val_dataset, batch_size=8, shuffle=True,drop_last=True)Train
model = FPNNet(num_classes=33).cuda()
#载入预训练模型
#model.load_state_dict(torch.load(r"checkpoints/Unet++_25.pth"),strict=False)
from d2l import torch as d2l
from tqdm import tqdm
import pandas as pd
#损失函数选用多分类交叉熵损失函数
lossf = nn.CrossEntropyLoss(ignore_index=255)
#选用adam优化器来训练
optimizer = optim.SGD(model.parameters(), lr=0.1)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.5, last_epoch=-1)
#训练50轮
epochs_num = 100
def train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs,scheduler,
devices=d2l.try_all_gpus()):
timer, num_batches = d2l.Timer(), len(train_iter)
animator = d2l.Animator(xlabel='epoch', xlim=[1, num_epochs], ylim=[0, 1],
legend=['train loss', 'train acc', 'test acc'])
net = nn.DataParallel(net, device_ids=devices).to(devices[0])
loss_list = []
train_acc_list = []
test_acc_list = []
epochs_list = []
time_list = []
for epoch in range(num_epochs):
# Sum of training loss, sum of training accuracy, no. of examples,
# no. of predictions
metric = d2l.Accumulator(4)
for i, (features, labels) in enumerate(train_iter):
timer.start()
l, acc = d2l.train_batch_ch13(
net, features, labels.long(), loss, trainer, devices)
metric.add(l, acc, labels.shape[0], labels.numel())
timer.stop()
if (i + 1) % (num_batches // 5) == 0 or i == num_batches - 1:
animator.add(epoch + (i + 1) / num_batches,
(metric[0] / metric[2], metric[1] / metric[3],
None))
test_acc = d2l.evaluate_accuracy_gpu(net, test_iter)
animator.add(epoch + 1, (None, None, test_acc))
scheduler.step()
print(f"epoch {epoch+1} --- loss {metric[0] / metric[2]:.3f} --- train acc {metric[1] / metric[3]:.3f} --- test acc {test_acc:.3f} --- cost time {timer.sum()}")
#---------保存训练数据---------------
df = pd.DataFrame()
loss_list.append(metric[0] / metric[2])
train_acc_list.append(metric[1] / metric[3])
test_acc_list.append(test_acc)
epochs_list.append(epoch+1)
time_list.append(timer.sum())
df['epoch'] = epochs_list
df['loss'] = loss_list
df['train_acc'] = train_acc_list
df['test_acc'] = test_acc_list
df['time'] = time_list
df.to_excel("savefile/FPNNet_camvid.xlsx")
#----------------保存模型-------------------
if np.mod(epoch+1, 5) == 0:
torch.save(model.state_dict(), f'checkpoints/FPNNet_{epoch+1}.pth')
train_ch13(model, train_loader, val_loader, lossf, optimizer, epochs_num,scheduler)Result
