【22】查看中间层特征矩阵并保存图像与参数
作者：互联网
网络模型backbone我使用了之前搭建过的MobileNetV3，具体的搭建过程可以查看：【20】MobileNetV3
然后由于此次任务需要的是输出其中的中间特征矩阵，所以前向传播的部分代码需要修改一下：
参考代码如下：
backbone.py
import torch
import torch.nn as nn
import torchvision
num_class = 5
# 定义h-swith激活函数
class HardSwish(nn.Module):
def __init__(self, inplace=True):
super(HardSwish, self).__init__()
self.relu6 = nn.ReLU6(inplace)
def forward(self, x):
return x*self.relu6(x+3)/6
# DW卷积
def ConvBNActivation(in_channels,out_channels,kernel_size,stride,activate):
# 通过设置padding达到当stride=2时，hw减半的效果。此时不与kernel_size有关，所实现的公式为: padding=(kernel_size-1)//2
# 当kernel_size=3,padding=1时: stride=2 hw减半, stride=1 hw不变
# 当kernel_size=5,padding=2时: stride=2 hw减半, stride=1 hw不变
# 从而达到了使用 stride 来控制hw的效果， 不用去关心kernel_size的大小，控制单一变量
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, groups=in_channels),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True) if activate == 'relu' else HardSwish()
)
# PW卷积(接全连接层)
def Conv1x1BN(in_channels,out_channels):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(out_channels)
)
# 普通的1x1卷积
def Conv1x1BNActivation(in_channels,out_channels,activate):
return nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1),
nn.BatchNorm2d(out_channels),
nn.ReLU6(inplace=True) if activate == 'relu' else HardSwish()
)
# 注意力机制(SE模块)
class SqueezeAndExcite(nn.Module):
def __init__(self, in_channels, out_channels, se_kernel_size, divide=4):
super(SqueezeAndExcite, self).__init__()
mid_channels = in_channels // divide # 维度变为原来的1/4
# 将当前的channel平均池化成1
self.pool = nn.AvgPool2d(kernel_size=se_kernel_size,stride=1)
# 两个全连接层 最后输出每层channel的权值
self.SEblock = nn.Sequential(
nn.Linear(in_features=in_channels, out_features=mid_channels),
nn.ReLU6(inplace=True),
nn.Linear(in_features=mid_channels, out_features=out_channels),
HardSwish(inplace=True),
)
def forward(self, x):
b, c, h, w = x.size()
out = self.pool(x) # 不管当前的 h,w 为多少, 全部池化为1
out = out.view(b, -1) # 打平处理，与全连接层相连
# 获取注意力机制后的权重
out = self.SEblock(out)
# out是每层channel的权重，需要扩维才能与原特征矩阵相乘
out = out.view(b, c, 1, 1) # 增维
return out * x
class SEInvertedBottleneck(nn.Module):
def __init__(self, in_channels, mid_channels, out_channels, kernel_size, stride, activate, use_se, se_kernel_size=1):
super(SEInvertedBottleneck, self).__init__()
self.stride = stride
self.use_se = use_se
self.in_channels = in_channels
self.out_channels = out_channels
# mid_channels = (in_channels * expansion_factor)
# 普通1x1卷积升维操作
self.conv = Conv1x1BNActivation(in_channels, mid_channels,activate)
# DW卷积 维度不变，但可通过stride改变尺寸 groups=in_channels
self.depth_conv = ConvBNActivation(mid_channels, mid_channels, kernel_size,stride,activate)
# 注意力机制的使用判断
if self.use_se:
self.SEblock = SqueezeAndExcite(mid_channels, mid_channels, se_kernel_size)
# PW卷积 降维操作
self.point_conv = Conv1x1BNActivation(mid_channels, out_channels,activate)
# shortcut的使用判断
if self.stride == 1:
self.shortcut = Conv1x1BN(in_channels, out_channels)
def forward(self, x):
# DW卷积
out = self.depth_conv(self.conv(x))
# 当 use_se=True 时使用注意力机制
if self.use_se:
out = self.SEblock(out)
# PW卷积
out = self.point_conv(out)
# 残差操作
# 第一种: 只看步长，步长相同shape不一样的输入输出使用1x1卷积使其相加
# out = (out + self.shortcut(x)) if self.stride == 1 else out
# 第二种: 同时满足步长与输入输出的channel, 不使用1x1卷积强行升维
out = (out + x) if self.stride == 1 and self.in_channels == self.out_channels else out
return out
class MobileNetV3(nn.Module):
def __init__(self, num_classes=num_class):
super(MobileNetV3, self).__init__()
self.type = type
# 224x224x3 conv2d 3 -> 16 SE=False HS s=2
self.first_conv = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(16),
HardSwish(inplace=True),
)
# torch.Size([1, 16, 112, 112])
# MobileNetV3_Large 网络结构
# torch.Size([1, 16, 112, 112]) 16 -> 16 -> 16 SE=False RE s=1
self.block1 = SEInvertedBottleneck(in_channels=16, mid_channels=16, out_channels=16, kernel_size=3, stride=1,activate='relu', use_se=False)
# torch.Size([1, 16, 112, 112]) 16 -> 64 -> 24 SE=False RE s=2
self.block2 = SEInvertedBottleneck(in_channels=16, mid_channels=64, out_channels=24, kernel_size=3, stride=2, activate='relu', use_se=False)
# torch.Size([1, 24, 56, 56]) 24 -> 72 -> 24 SE=False RE s=1
self.block3 = SEInvertedBottleneck(in_channels=24, mid_channels=72, out_channels=24, kernel_size=3, stride=1, activate='relu', use_se=False)
# torch.Size([1, 24, 56, 56]) 24 -> 72 -> 40 SE=True RE s=2
self.block4 = SEInvertedBottleneck(in_channels=24, mid_channels=72, out_channels=40, kernel_size=5, stride=2,activate='relu', use_se=True, se_kernel_size=28)
# torch.Size([1, 40, 28, 28]) 40 -> 120 -> 40 SE=True RE s=1
self.block5 = SEInvertedBottleneck(in_channels=40, mid_channels=120, out_channels=40, kernel_size=5, stride=1,activate='relu', use_se=True, se_kernel_size=28)
# torch.Size([1, 40, 28, 28]) 40 -> 120 -> 40 SE=True RE s=1
self.block6 = SEInvertedBottleneck(in_channels=40, mid_channels=120, out_channels=40, kernel_size=5, stride=1,activate='relu', use_se=True, se_kernel_size=28)
# torch.Size([1, 40, 28, 28]) 40 -> 240 -> 80 SE=False HS s=1
self.block7 = SEInvertedBottleneck(in_channels=40, mid_channels=240, out_channels=80, kernel_size=3, stride=1,activate='hswish', use_se=False)
# torch.Size([1, 80, 28, 28]) 80 -> 200 -> 80 SE=False HS s=1
self.block8 = SEInvertedBottleneck(in_channels=80, mid_channels=200, out_channels=80, kernel_size=3, stride=1,activate='hswish', use_se=False)
# torch.Size([1, 80, 28, 28]) 80 -> 184 -> 80 SE=False HS s=2
self.block9 = SEInvertedBottleneck(in_channels=80, mid_channels=184, out_channels=80, kernel_size=3, stride=2,activate='hswish', use_se=False)
# torch.Size([1, 80, 14, 14]) 80 -> 184 -> 80 SE=False HS s=1
self.block10 = SEInvertedBottleneck(in_channels=80, mid_channels=184, out_channels=80, kernel_size=3, stride=1,activate='hswish', use_se=False)
# torch.Size([1, 80, 14, 14]) 80 -> 480 -> 112 SE=True HS s=1
self.block11 = SEInvertedBottleneck(in_channels=80, mid_channels=480, out_channels=112, kernel_size=3, stride=1,activate='hswish', use_se=True, se_kernel_size=14)
# torch.Size([1, 112, 14, 14]) 112 -> 672 -> 112 SE=True HS s=1
self.block12 = SEInvertedBottleneck(in_channels=112, mid_channels=672, out_channels=112, kernel_size=3, stride=1,activate='hswish', use_se=True, se_kernel_size=14)
# torch.Size([1, 112, 14, 14]) 112 -> 672 -> 160 SE=True HS s=2
self.block13 = SEInvertedBottleneck(in_channels=112, mid_channels=672, out_channels=160, kernel_size=5, stride=2,activate='hswish', use_se=True,se_kernel_size=7)
# torch.Size([1, 160, 7, 7]) 160 -> 960 -> 160 SE=True HS s=1
self.block14 = SEInvertedBottleneck(in_channels=160, mid_channels=960, out_channels=160, kernel_size=5, stride=1,activate='hswish', use_se=True,se_kernel_size=7)
# torch.Size([1, 160, 7, 7]) 160 -> 960 -> 160 SE=True HS s=1
self.block15 = SEInvertedBottleneck(in_channels=160, mid_channels=960, out_channels=160, kernel_size=5, stride=1,activate='hswish', use_se=True,se_kernel_size=7)
# torch.Size([1, 160, 7, 7])
# 相比MobileNetV2，尾部结构改变，,变得更加的高效
self.large_last_stage = nn.Sequential(
nn.Conv2d(in_channels=160, out_channels=960, kernel_size=1, stride=1),
nn.BatchNorm2d(960),
HardSwish(inplace=True),
nn.AvgPool2d(kernel_size=7, stride=1),
nn.Conv2d(in_channels=960, out_channels=1280, kernel_size=1, stride=1),
HardSwish(inplace=True),
)
self.classifier = nn.Linear(in_features=1280,out_features=num_classes)
self.init_params()
# 初始化权重
def init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.Linear):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
outputs = []
x = self.first_conv(x) # torch.Size([1, 16, 112, 112])
outputs.append(x)
x = self.block1(x)
outputs.append(x)
x = self.block2(x)
outputs.append(x)
x = self.block3(x)
outputs.append(x)
x = self.block4(x)
outputs.append(x)
x = self.block5(x)
outputs.append(x)
x = self.block6(x)
outputs.append(x)
x = self.block7(x)
outputs.append(x)
x = self.block8(x)
outputs.append(x)
x = self.block9(x)
outputs.append(x)
x = self.block10(x)
outputs.append(x)
x = self.block11(x)
outputs.append(x)
x = self.block12(x)
outputs.append(x)
x = self.block13(x)
outputs.append(x)
x = self.block14(x)
outputs.append(x)
x = self.block15(x) # torch.Size([1, 160, 7, 7])
outputs.append(x)
x = self.large_last_stage(x) # torch.Size([1, 1280, 1, 1])
# outputs.append(x)
x = x.view(x.size(0), -1) # torch.Size([1, 1280])
x = self.classifier(x) # torch.Size([1, 5])
return outputs
def MobileNetV3_large():
return MobileNetV3()
if __name__ == '__main__':
model = MobileNetV3_large()
# print(model)
input = torch.randn(1, 3, 224, 224)
out = model(input)
# print(out.shape)
torch.save(model.state_dict(), 'MobileNetV3_Large.mdl')
主要思路：
主要思路就是经过卷积之后的特征矩阵保存在一个列表中，然后依次对其进行读取并利用plt进行显示与保存图像，而且具体是查看特征矩阵的每层channel的图像：plt.imshow(im[:, :, i], cmap=‘gray’)
analyze_feature_map.py
import torch
from backbone import MobileNetV3_large
import matplotlib.pyplot as plt
import numpy as np
import os
from PIL import Image
from torchvision import transforms
# 图像的格式转换
data_transform = transforms.Compose(
[transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
# create model
model = MobileNetV3_large()
# load model weights
model_weight_path = 'MobileNetV3_Large.mdl'
model.load_state_dict(torch.load(model_weight_path))
# print(model)
# load image
# img = Image.open('./Brightprint_above_1.jpg')
img = Image.open('./Brightprint_side_1.jpg')
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)
# print(img.shape) # torch.Size([1, 3, 224, 224])
# forward
out_put = model(img)
print(out_put)
# for feature_map in out_put:
for batchidx, feature_map in enumerate(out_put):
# 保存中间层的参数
with open('parameter.txt', mode='a') as fw:
fw.writelines('block{}:parameter'.format(batchidx))
fw.writelines('\n\t')
fw.writelines(str(feature_map))
fw.writelines('\n\t\n\t')
print('batchidx = ', batchidx)
# [N, C, H, W] -> [C, H, W]
im = np.squeeze(feature_map.detach().numpy())
# [C, H, W] -> [H, W, C]
# 需要改变才可以正常显示图像
im = np.transpose(im, [1, 2, 0])
# show top 16 feature maps
plt.figure()
for i in range(16):
ax = plt.subplot(4, 4, i+1)
# [H, W, C] cmap='gray' :设置为灰度图， [:, :, i]选择对channels进行切分
plt.imshow(im[:, :, i], cmap='gray')
# 保存图像的方法
plt.savefig('block{}_outputs.jpg'.format(batchidx))
# plt.imsave(batchidx, arr, format='jpg')
plt.show()
参考链接：
- https://www.bilibili.com/video/BV1z7411f7za
- Python中读取,显示,保存图片的方法
标签：kernel,22,self,矩阵,channels,stride,中间层,size,out 来源： https://blog.csdn.net/weixin_44751294/article/details/117813953