U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection
1、U2Net网络模型结构
网络的主体类似于U-Net的网络结构，在大的U-Net中，每一个小的block都是一个小型的类似于U-Net的结构，因此作者取名U2Net
仔细观察，可以将网络中的block分成两类：
第一类：En_1 ~ En_4 与 De_1 ~ De_4这8个block采用的block其实是一样的，只不过模块的深度不同。
第二类：En_5、En_6、De_5
- 在整个U2Net网络中，在Encoder阶段，每通过一个block都会进行一次下采样操作（下采样2倍，maxpool）
- 在Decoder阶段，在每个block之间，都会进行一次上采样（2倍，bilinear）
2、block模块结构解析
在 En_1 与 De_1 模块中，采用的 block 是RSU-7；
En_2 与 De_2采用的 block 是RSU-6（RSU-6相对于RSU-7 就是少了一个下采样卷积以及上采样卷积的部分，RSU-6 block只会下采样16倍，RSU-7 block下采样的32倍）；
En_3 与 De_3采用的 block 是RSU-5
En_4 与 De_4采用的 block 是RSU-4
En_5、En_6、De_5采用的block是RSU-4F
（使用RSU-4F的原因：因为数据经过En_1 ~ En4 下采样处理后对应特征图的高与宽就已经相对比较小了，如果再继续下采样就会丢失很多上下文信息，作者为了保留上下文信息，就对En_5、En_6、De_5不再进行下采样了而是在RSU-4F的模块中，将下采样、上采样结构换成了膨胀卷积）
RSU-7模块
详细结构图解
RSU-4F
saliency map fusion module
saliency map fusion module模块是将每个阶段的特征图进行融合，得到最终的预测概率图，即下图中，红色框标注的模块
其会收集De_1、De_2、De_3、De_4、De_5、En_6模块的输出，将这些输出分别通过一个3x3的卷积层（这些卷积层的kerner的个数都是为1）输出的featuremap的channel是为1的，在经过双线性插值算法将得到的特征图还原回输入图像的大小；再将得到的6个特征图进行concant拼接；在经过一个1x1的卷积层以及sigmoid激活函数，最终得到融合之后的预测概率图。
U2Net网络结构详细参数配置
u2net_full大小为176.3M、u2net_lite大小为4.7M
RSU模块代码实现
class RSU(nn.Module):
def __init__(self, height: int, in_ch: int, mid_ch: int, out_ch: int):
super().__init__()
assert height >= 2
self.conv_in = ConvBNReLU(in_ch, out_ch)
encode_list = [DownConvBNReLU(out_ch, mid_ch, flag=False)]
decode_list = [UpConvBNReLU(mid_ch * 2, mid_ch, flag=False)]
for i in range(height - 2):
encode_list.append(DownConvBNReLU(mid_ch, mid_ch))
decode_list.append(UpConvBNReLU(mid_ch * 2, mid_ch if i < height - 3 else out_ch))
encode_list.append(ConvBNReLU(mid_ch, mid_ch, dilation=2))
self.encode_modules = nn.ModuleList(encode_list)
self.decode_modules = nn.ModuleList(decode_list)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x_in = self.conv_in(x)
x = x_in
encode_outputs = []
for m in self.encode_modules:
x = m(x)
encode_outputs.append(x)
x = encode_outputs.pop()
for m in self.decode_modules:
x2 = encode_outputs.pop()
x = m(x, x2)
return x + x_in
RSU4F模块代码实现
class RSU4F(nn.Module):
def __init__(self, in_ch: int, mid_ch: int, out_ch: int):
super().__init__()
self.conv_in = ConvBNReLU(in_ch, out_ch)
self.encode_modules = nn.ModuleList([ConvBNReLU(out_ch, mid_ch),
ConvBNReLU(mid_ch, mid_ch, dilation=2),
ConvBNReLU(mid_ch, mid_ch, dilation=4),
ConvBNReLU(mid_ch, mid_ch, dilation=8)])
self.decode_modules = nn.ModuleList([ConvBNReLU(mid_ch * 2, mid_ch, dilation=4),
ConvBNReLU(mid_ch * 2, mid_ch, dilation=2),
ConvBNReLU(mid_ch * 2, out_ch)])
def forward(self, x: torch.Tensor) -> torch.Tensor:
x_in = self.conv_in(x)
x = x_in
encode_outputs = []
for m in self.encode_modules:
x = m(x)
encode_outputs.append(x)
x = encode_outputs.pop()
for m in self.decode_modules:
x2 = encode_outputs.pop()
x = m(torch.cat([x, x2], dim=1))
return x + x_in
u2net_full与u2net_lite模型配置函数
def u2net_full(out_ch: int = 1):
cfg = {
# height, in_ch, mid_ch, out_ch, RSU4F, side side：表示是否要收集当前block的输出
"encode": [[7, 3, 32, 64, False, False], # En1
[6, 64, 32, 128, False, False], # En2
[5, 128, 64, 256, False, False], # En3
[4, 256, 128, 512, False, False], # En4
[4, 512, 256, 512, True, False], # En5
[4, 512, 256, 512, True, True]], # En6
# height, in_ch, mid_ch, out_ch, RSU4F, side
"decode": [[4, 1024, 256, 512, True, True], # De5
[4, 1024, 128, 256, False, True], # De4
[5, 512, 64, 128, False, True], # De3
[6, 256, 32, 64, False, True], # De2
[7, 128, 16, 64, False, True]] # De1
}
return U2Net(cfg, out_ch)
def u2net_lite(out_ch: int = 1):
cfg = {
# height, in_ch, mid_ch, out_ch, RSU4F, side
"encode": [[7, 3, 16, 64, False, False], # En1
[6, 64, 16, 64, False, False], # En2
[5, 64, 16, 64, False, False], # En3
[4, 64, 16, 64, False, False], # En4
[4, 64, 16, 64, True, False], # En5
[4, 64, 16, 64, True, True]], # En6
# height, in_ch, mid_ch, out_ch, RSU4F, side
"decode": [[4, 128, 16, 64, True, True], # De5
[4, 128, 16, 64, False, True], # De4
[5, 128, 16, 64, False, True], # De3
[6, 128, 16, 64, False, True], # De2
[7, 128, 16, 64, False, True]] # De1
}
U2Net网络整体定义类
class U2Net(nn.Module):
def __init__(self, cfg: dict, out_ch: int = 1):
super().__init__()
assert "encode" in cfg
assert "decode" in cfg
self.encode_num = len(cfg["encode"])
encode_list = []
side_list = []
for c in cfg["encode"]:
# c: [height, in_ch, mid_ch, out_ch, RSU4F, side]
assert len(c) == 6
encode_list.append(RSU(*c[:4]) if c[4] is False else RSU4F(*c[1:4])) # 判断当前是构建RSU模块，还是构建RSU4F模块
if c[5] is True:
side_list.append(nn.Conv2d(c[3], out_ch, kernel_size=3, padding=1))
self.encode_modules = nn.ModuleList(encode_list)
decode_list = []
for c in cfg["decode"]:
# c: [height, in_ch, mid_ch, out_ch, RSU4F, side]
assert len(c) == 6
decode_list.append(RSU(*c[:4]) if c[4] is False else RSU4F(*c[1:4]))
if c[5] is True:
side_list.append(nn.Conv2d(c[3], out_ch, kernel_size=3, padding=1)) # 收集当前block的输出
self.decode_modules = nn.ModuleList(decode_list)
self.side_modules = nn.ModuleList(side_list)
self.out_conv = nn.Conv2d(self.encode_num * out_ch, out_ch, kernel_size=1) # 构建一个1x1的卷积层，去融合来自不同尺度的信息
def forward(self, x: torch.Tensor) -> Union[torch.Tensor, List[torch.Tensor]]:
_, _, h, w = x.shape
# collect encode outputs
encode_outputs = []
for i, m in enumerate(self.encode_modules):
x = m(x)
encode_outputs.append(x)
if i != self.encode_num - 1: # 此处需要进行判断，因为在没通过一个encoder模块后，都需要进行下采样的，但最后一个模块后，是不需要下采样的
x = F.max_pool2d(x, kernel_size=2, stride=2, ceil_mode=True)
# collect decode outputs
x = encode_outputs.pop()
decode_outputs = [x]
for m in self.decode_modules:
x2 = encode_outputs.pop()
x = F.interpolate(x, size=x2.shape[2:], mode='bilinear', align_corners=False)
x = m(torch.concat([x, x2], dim=1))
decode_outputs.insert(0, x)
# collect side outputs
side_outputs = []
for m in self.side_modules:
x = decode_outputs.pop()
x = F.interpolate(m(x), size=[h, w], mode='bilinear', align_corners=False)
side_outputs.insert(0, x)
x = self.out_conv(torch.concat(side_outputs, dim=1))
if self.training:
# do not use torch.sigmoid for amp safe
return [x] + side_outputs # 用于计算损失
else:
return torch.sigmoid(x)
损失函数计算
如上图所示，红色框部分为每个分量与真实标签的交叉熵损失函数求和；黄色框标部分为将各个分量经双线性插值恢复至原始尺寸、进行concant处理、经过1x1的卷积核与sigmoid处理后的结果与真实标签的交叉熵损失函数。
损失函数代码实现：
import math
import torch
from torch.nn import functional as F
import train_utils.distributed_utils as utils
def criterion(inputs, target):
losses = [F.binary_cross_entropy_with_logits(inputs[i], target) for i in range(len(inputs))]
total_loss = sum(losses)
return total_loss
评价指标
其中F-measure是在0~1之间的，数值越大，代表的网络分割效果越好；
MAE是Mean Absolute Error的缩写，其值是在0~1之间的，越趋近于0，代表网络性能越好。
数据集
pytorch训练U2Net图像分割模型
项目目录结构：
├── src: 搭建网络相关代码
├── train_utils: 训练以及验证相关代码
├── my_dataset.py: 自定义数据集读取相关代码
├── predict.py: 简易的预测代码
├── train.py: 单GPU或CPU训练代码
├── train_multi_GPU.py: 多GPU并行训练代码
├── validation.py: 单独验证模型相关代码
├── transforms.py: 数据预处理相关代码
└── requirements.txt: 项目依赖
项目目录：
项目中u2net_full大小为176.3M、u2net_lite大小为4.7M，演示过程中，训练的为u2net_lite版本
多GPU训练指令：
pytorch版本为1.7
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --use_env train_multi_GPU.py --data-path ./data_root
训练过程损失函数，评估指标变化
[epoch: 0] train_loss: 3.0948 lr: 0.000500 MAE: 0.263 maxF1: 0.539
[epoch: 10] train_loss: 1.1108 lr: 0.000998 MAE: 0.111 maxF1: 0.729
[epoch: 20] train_loss: 0.8480 lr: 0.000993 MAE: 0.093 maxF1: 0.764
[epoch: 30] train_loss: 0.7438 lr: 0.000984 MAE: 0.086 maxF1: 0.776
[epoch: 40] train_loss: 0.6625 lr: 0.000971 MAE: 0.082 maxF1: 0.790
[epoch: 50] train_loss: 0.5897 lr: 0.000954 MAE: 0.077 maxF1: 0.801
[epoch: 60] train_loss: 0.5273 lr: 0.000934 MAE: 0.071 maxF1: 0.808
[epoch: 70] train_loss: 0.5139 lr: 0.000911 MAE: 0.079 maxF1: 0.787
[epoch: 80] train_loss: 0.4775 lr: 0.000885 MAE: 0.073 maxF1: 0.801
[epoch: 90] train_loss: 0.4601 lr: 0.000855 MAE: 0.069 maxF1: 0.809
[epoch: 100] train_loss: 0.4529 lr: 0.000823 MAE: 0.065 maxF1: 0.805
[epoch: 110] train_loss: 0.4441 lr: 0.000788 MAE: 0.068 maxF1: 0.810
[epoch: 120] train_loss: 0.3991 lr: 0.000751 MAE: 0.066 maxF1: 0.806
[epoch: 130] train_loss: 0.3903 lr: 0.000712 MAE: 0.065 maxF1: 0.824
[epoch: 140] train_loss: 0.3770 lr: 0.000672 MAE: 0.060 maxF1: 0.823
[epoch: 150] train_loss: 0.3666 lr: 0.000630 MAE: 0.064 maxF1: 0.825
[epoch: 160] train_loss: 0.3530 lr: 0.000587 MAE: 0.060 maxF1: 0.829
[epoch: 170] train_loss: 0.3557 lr: 0.000544 MAE: 0.063 maxF1: 0.820
[epoch: 180] train_loss: 0.3430 lr: 0.000500 MAE: 0.065 maxF1: 0.816
[epoch: 190] train_loss: 0.3366 lr: 0.000456 MAE: 0.059 maxF1: 0.832
[epoch: 200] train_loss: 0.3285 lr: 0.000413 MAE: 0.062 maxF1: 0.822
[epoch: 210] train_loss: 0.3197 lr: 0.000370 MAE: 0.058 maxF1: 0.829
[epoch: 220] train_loss: 0.3093 lr: 0.000328 MAE: 0.058 maxF1: 0.828
[epoch: 230] train_loss: 0.3071 lr: 0.000288 MAE: 0.058 maxF1: 0.827
[epoch: 240] train_loss: 0.2983 lr: 0.000249 MAE: 0.056 maxF1: 0.830
[epoch: 250] train_loss: 0.2932 lr: 0.000212 MAE: 0.060 maxF1: 0.825
[epoch: 260] train_loss: 0.2908 lr: 0.000177 MAE: 0.060 maxF1: 0.828
[epoch: 270] train_loss: 0.2895 lr: 0.000145 MAE: 0.057 maxF1: 0.832
[epoch: 280] train_loss: 0.2834 lr: 0.000115 MAE: 0.057 maxF1: 0.832
[epoch: 290] train_loss: 0.2762 lr: 0.000089 MAE: 0.056 maxF1: 0.833
[epoch: 300] train_loss: 0.2760 lr: 0.000066 MAE: 0.056 maxF1: 0.832
[epoch: 310] train_loss: 0.2752 lr: 0.000046 MAE: 0.057 maxF1: 0.832
[epoch: 320] train_loss: 0.2782 lr: 0.000029 MAE: 0.056 maxF1: 0.834
[epoch: 330] train_loss: 0.2744 lr: 0.000016 MAE: 0.056 maxF1: 0.832
[epoch: 340] train_loss: 0.2752 lr: 0.000007 MAE: 0.056 maxF1: 0.832
[epoch: 350] train_loss: 0.2739 lr: 0.000002 MAE: 0.057 maxF1: 0.831
[epoch: 359] train_loss: 0.2770 lr: 0.000000 MAE: 0.056 maxF1: 0.833
模型测试
import os
import time
import cv2
import numpy as np
import matplotlib.pyplot as plt
import torch
from torchvision.transforms import transforms
from src import u2net_full,u2net_lite
def time_synchronized():
torch.cuda.synchronize() if torch.cuda.is_available() else None
return time.time()
def main():
weights_path = "./multi_train/model_best.pth"
img_path = "./test_image.PNG"
threshold = 0.5
assert os.path.exists(img_path), f"image file {img_path} dose not exists."
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
data_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Resize(320),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
])
origin_img = cv2.cvtColor(cv2.imread(img_path, flags=cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB)
h, w = origin_img.shape[:2]
img = data_transform(origin_img)
img = torch.unsqueeze(img, 0).to(device) # [C, H, W] -> [1, C, H, W]
# model = u2net_full()
model =u2net_lite()
weights = torch.load(weights_path, map_location='cpu')
if "model" in weights:
model.load_state_dict(weights["model"])
else:
model.load_state_dict(weights)
model.to(device)
model.eval()
with torch.no_grad():
# init model
img_height, img_width = img.shape[-2:]
init_img = torch.zeros((1, 3, img_height, img_width), device=device)
model(init_img)
t_start = time_synchronized()
pred = model(img)
t_end = time_synchronized()
print("inference time: {}".format(t_end - t_start))
pred = torch.squeeze(pred).to("cpu").numpy() # [1, 1, H, W] -> [H, W]
pred = cv2.resize(pred, dsize=(w, h), interpolation=cv2.INTER_LINEAR)
pred_mask = np.where(pred > threshold, 1, 0)
origin_img = np.array(origin_img, dtype=np.uint8)
seg_img = origin_img * pred_mask[..., None]
plt.imshow(seg_img)
plt.show()
cv2.imwrite("pred_result.png", cv2.cvtColor(seg_img.astype(np.uint8), cv2.COLOR_RGB2BGR))
if __name__ == '__main__':
main()
训练的为u2net_full版本
训练指标如下：文章来源：https://www.toymoban.com/news/detail-813553.html
[epoch: 0] train_loss: 2.7158 lr: 0.000500 MAE: 0.216 maxF1: 0.583
[epoch: 10] train_loss: 1.0359 lr: 0.000998 MAE: 0.105 maxF1: 0.745
[epoch: 20] train_loss: 0.7130 lr: 0.000993 MAE: 0.087 maxF1: 0.778
[epoch: 30] train_loss: 0.5375 lr: 0.000984 MAE: 0.077 maxF1: 0.810
[epoch: 40] train_loss: 0.4661 lr: 0.000971 MAE: 0.069 maxF1: 0.826
[epoch: 50] train_loss: 0.4181 lr: 0.000954 MAE: 0.065 maxF1: 0.823
[epoch: 60] train_loss: 0.3914 lr: 0.000934 MAE: 0.065 maxF1: 0.826
[epoch: 70] train_loss: 0.3353 lr: 0.000911 MAE: 0.059 maxF1: 0.840
[epoch: 80] train_loss: 0.2847 lr: 0.000885 MAE: 0.058 maxF1: 0.835
[epoch: 90] train_loss: 0.2977 lr: 0.000855 MAE: 0.056 maxF1: 0.843
[epoch: 100] train_loss: 0.2538 lr: 0.000823 MAE: 0.054 maxF1: 0.848
[epoch: 110] train_loss: 0.2653 lr: 0.000788 MAE: 0.052 maxF1: 0.848
[epoch: 120] train_loss: 0.2365 lr: 0.000751 MAE: 0.052 maxF1: 0.841
[epoch: 130] train_loss: 0.2397 lr: 0.000712 MAE: 0.056 maxF1: 0.843
[epoch: 140] train_loss: 0.2180 lr: 0.000672 MAE: 0.051 maxF1: 0.854
[epoch: 150] train_loss: 0.2060 lr: 0.000630 MAE: 0.051 maxF1: 0.853
[epoch: 160] train_loss: 0.2002 lr: 0.000587 MAE: 0.052 maxF1: 0.853
[epoch: 170] train_loss: 0.1952 lr: 0.000544 MAE: 0.050 maxF1: 0.859
[epoch: 180] train_loss: 0.1893 lr: 0.000500 MAE: 0.053 maxF1: 0.851
[epoch: 190] train_loss: 0.1838 lr: 0.000456 MAE: 0.050 maxF1: 0.852
[epoch: 200] train_loss: 0.1779 lr: 0.000413 MAE: 0.049 maxF1: 0.858
[epoch: 210] train_loss: 0.1745 lr: 0.000370 MAE: 0.052 maxF1: 0.851
[epoch: 220] train_loss: 0.1703 lr: 0.000328 MAE: 0.050 maxF1: 0.854
[epoch: 230] train_loss: 0.1667 lr: 0.000288 MAE: 0.049 maxF1: 0.855
[epoch: 240] train_loss: 0.1640 lr: 0.000249 MAE: 0.049 maxF1: 0.855
[epoch: 250] train_loss: 0.1618 lr: 0.000212 MAE: 0.049 maxF1: 0.855
[epoch: 260] train_loss: 0.1598 lr: 0.000177 MAE: 0.048 maxF1: 0.856
[epoch: 270] train_loss: 0.1580 lr: 0.000145 MAE: 0.049 maxF1: 0.856
[epoch: 280] train_loss: 0.1572 lr: 0.000115 MAE: 0.049 maxF1: 0.853
[epoch: 290] train_loss: 0.1561 lr: 0.000089 MAE: 0.047 maxF1: 0.857
[epoch: 300] train_loss: 0.1550 lr: 0.000066 MAE: 0.047 maxF1: 0.858
[epoch: 310] train_loss: 0.1543 lr: 0.000046 MAE: 0.048 maxF1: 0.854
[epoch: 320] train_loss: 0.1539 lr: 0.000029 MAE: 0.048 maxF1: 0.854
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