1. 可视化网络结构
在复杂的网络结构中确定每一层的输入结构，方便我们在短时间内完成debug
1.1 使用print函数打印模型基础信息
使用ResNet18的结构进行展示
-
import torchvision.models
as models
-
model = models.resnet18()
-
print(model)
-
-
#打印结果
-
ResNet(
-
(conv1): Conv2d(
3,
64, kernel_size=(
7,
7), stride=(
2,
2), padding=(
3,
3), bias=
False)
-
(bn1): BatchNorm2d(
64, eps=
1e-05, momentum=
0.1, affine=
True, track_running_stats=
True)
-
(relu): ReLU(inplace=
True)
-
(maxpool): MaxPool2d(kernel_size=
3, stride=
2, padding=
1, dilation=
1, ceil_mode=
False)
-
(layer1): Sequential(
-
(
0): Bottleneck(
-
(conv1): Conv2d(
64,
64, kernel_size=(
1,
1), stride=(
1,
1), bias=
False)
-
(bn1): BatchNorm2d(
64, eps=
1e-05, momentum=
0.1, affine=
True, track_running_stats=
True)
-
(conv2): Conv2d(
64,
64, kernel_size=(
3,
3), stride=(
1,
1), padding=(
1,
1), bias=
False)
-
(bn2): BatchNorm2d(
64, eps=
1e-05, momentum=
0.1, affine=
True, track_running_stats=
True)
-
(conv3): Conv2d(
64,
256, kernel_size=(
1,
1), stride=(
1,
1), bias=
False)
-
(bn3): BatchNorm2d(
256, eps=
1e-05, momentum=
0.1, affine=
True, track_running_stats=
True)
-
(relu): ReLU(inplace=
True)
-
(downsample): Sequential(
-
(
0): Conv2d(
64,
256, kernel_size=(
1,
1), stride=(
1,
1), bias=
False)
-
(
1): BatchNorm2d(
256, eps=
1e-05, momentum=
0.1, affine=
True, track_running_stats=
True)
-
)
-
)
-
... ...
-
)
-
(avgpool): AdaptiveAvgPool2d(output_size=(
1,
1))
-
(fc): Linear(in_features=
2048, out_features=
1000, bias=
True)
-
)
1.2 使用torchinfo可视化网络结构
1.2.1 torchinfo的安装
-
# 安装方法一
-
pip install torchinfo
-
# 安装方法二
-
conda install -c conda-forge torchinfo
1.2.2 torchinfo的使用
（1）方法：torchinfo.summary()
（2）参数：（这里展示的是函数定义时传入的参数），具体请看参数详解）
-
def
summary(
-
model: nn.Module,
-
input_size: Optional[INPUT_SIZE_TYPE] = None,
-
input_data: Optional[INPUT_DATA_TYPE] = None,
-
batch_dim: Optional[int] = None,
-
cache_forward_pass: Optional[bool] = None,
-
col_names: Optional[Iterable[str]] = None,
-
col_width: int = 25,
-
depth: int = 3,
-
device: Optional[torch.device] = None,
-
dtypes: Optional[List[torch.dtype]] = None,
-
mode: str | None = None,
-
row_settings: Optional[Iterable[str]] = None,
-
verbose: int = 1,
-
**kwargs: Any,
-
) -> ModelStatistics
（3）实例以ResNet18为例：
-
import torchvision.models
as models
-
from torchinfo
import summary
-
resnet18 = models.resnet18()
# 实例化模型
-
summary(resnet18, (
1,
3,
224,
224))
# 1：batch_size 3:图片的通道数 224: 图片的高宽
-
-
-
# 结果输出
-
=========================================================================================
-
Layer (
type:depth-idx) Output Shape Param
#
-
=========================================================================================
-
ResNet -- --
-
├─Conv2d:
1-
1 [
1,
64,
112,
112]
9,
408
-
├─BatchNorm2d:
1-
2 [
1,
64,
112,
112]
128
-
├─ReLU:
1-
3 [
1,
64,
112,
112] --
-
├─MaxPool2d:
1-
4 [
1,
64,
56,
56] --
-
├─Sequential:
1-
5 [
1,
64,
56,
56] --
-
│ └─BasicBlock:
2-
1 [
1,
64,
56,
56] --
-
│ │ └─Conv2d:
3-
1 [
1,
64,
56,
56]
36,
864
-
│ │ └─BatchNorm2d:
3-
2 [
1,
64,
56,
56]
128
-
│ │ └─ReLU:
3-
3 [
1,
64,
56,
56] --
-
│ │ └─Conv2d:
3-
4 [
1,
64,
56,
56]
36,
864
-
│ │ └─BatchNorm2d:
3-
5 [
1,
64,
56,
56]
128
-
│ │ └─ReLU:
3-
6 [
1,
64,
56,
56] --
-
│ └─BasicBlock:
2-
2 [
1,
64,
56,
56] --
-
│ │ └─Conv2d:
3-
7 [
1,
64,
56,
56]
36,
864
-
│ │ └─BatchNorm2d:
3-
8 [
1,
64,
56,
56]
128
-
│ │ └─ReLU:
3-
9 [
1,
64,
56,
56] --
-
│ │ └─Conv2d:
3-
10 [
1,
64,
56,
56]
36,
864
-
│ │ └─BatchNorm2d:
3-
11 [
1,
64,
56,
56]
128
-
│ │ └─ReLU:
3-
12 [
1,
64,
56,
56] --
-
├─Sequential:
1-
6 [
1,
128,
28,
28] --
-
│ └─BasicBlock:
2-
3 [
1,
128,
28,
28] --
-
│ │ └─Conv2d:
3-
13 [
1,
128,
28,
28]
73,
728
-
│ │ └─BatchNorm2d:
3-
14 [
1,
128,
28,
28]
256
-
│ │ └─ReLU:
3-
15 [
1,
128,
28,
28] --
-
│ │ └─Conv2d:
3-
16 [
1,
128,
28,
28]
147,
456
-
│ │ └─BatchNorm2d:
3-
17 [
1,
128,
28,
28]
256
-
│ │ └─Sequential:
3-
18 [
1,
128,
28,
28]
8,
448
-
│ │ └─ReLU:
3-
19 [
1,
128,
28,
28] --
-
│ └─BasicBlock:
2-
4 [
1,
128,
28,
28] --
-
│ │ └─Conv2d:
3-
20 [
1,
128,
28,
28]
147,
456
-
│ │ └─BatchNorm2d:
3-
21 [
1,
128,
28,
28]
256
-
│ │ └─ReLU:
3-
22 [
1,
128,
28,
28] --
-
│ │ └─Conv2d:
3-
23 [
1,
128,
28,
28]
147,
456
-
│ │ └─BatchNorm2d:
3-
24 [
1,
128,
28,
28]
256
-
│ │ └─ReLU:
3-
25 [
1,
128,
28,
28] --
-
├─Sequential:
1-
7 [
1,
256,
14,
14] --
-
│ └─BasicBlock:
2-
5 [
1,
256,
14,
14] --
-
│ │ └─Conv2d:
3-
26 [
1,
256,
14,
14]
294,
912
-
│ │ └─BatchNorm2d:
3-
27 [
1,
256,
14,
14]
512
-
│ │ └─ReLU:
3-
28 [
1,
256,
14,
14] --
-
│ │ └─Conv2d:
3-
29 [
1,
256,
14,
14]
589,
824
-
│ │ └─BatchNorm2d:
3-
30 [
1,
256,
14,
14]
512
-
│ │ └─Sequential:
3-
31 [
1,
256,
14,
14]
33,
280
-
│ │ └─ReLU:
3-
32 [
1,
256,
14,
14] --
-
│ └─BasicBlock:
2-
6 [
1,
256,
14,
14] --
-
│ │ └─Conv2d:
3-
33 [
1,
256,
14,
14]
589,
824
-
│ │ └─BatchNorm2d:
3-
34 [
1,
256,
14,
14]
512
-
│ │ └─ReLU:
3-
35 [
1,
256,
14,
14] --
-
│ │ └─Conv2d:
3-
36 [
1,
256,
14,
14]
589,
824
-
│ │ └─BatchNorm2d:
3-
37 [
1,
256,
14,
14]
512
-
│ │ └─ReLU:
3-
38 [
1,
256,
14,
14] --
-
├─Sequential:
1-
8 [
1,
512,
7,
7] --
-
│ └─BasicBlock:
2-
7 [
1,
512,
7,
7] --
-
│ │ └─Conv2d:
3-
39 [
1,
512,
7,
7]
1,
179,
648
-
│ │ └─BatchNorm2d:
3-
40 [
1,
512,
7,
7]
1,024
-
│ │ └─ReLU:
3-
41 [
1,
512,
7,
7] --
-
│ │ └─Conv2d:
3-
42 [
1,
512,
7,
7]
2,
359,
296
-
│ │ └─BatchNorm2d:
3-
43 [
1,
512,
7,
7]
1,024
-
│ │ └─Sequential:
3-
44 [
1,
512,
7,
7]
132,096
-
│ │ └─ReLU:
3-
45 [
1,
512,
7,
7] --
-
│ └─BasicBlock:
2-
8 [
1,
512,
7,
7] --
-
│ │ └─Conv2d:
3-
46 [
1,
512,
7,
7]
2,
359,
296
-
│ │ └─BatchNorm2d:
3-
47 [
1,
512,
7,
7]
1,024
-
│ │ └─ReLU:
3-
48 [
1,
512,
7,
7] --
-
│ │ └─Conv2d:
3-
49 [
1,
512,
7,
7]
2,
359,
296
-
│ │ └─BatchNorm2d:
3-
50 [
1,
512,
7,
7]
1,024
-
│ │ └─ReLU:
3-
51 [
1,
512,
7,
7] --
-
├─AdaptiveAvgPool2d:
1-
9 [
1,
512,
1,
1] --
-
├─Linear:
1-
10 [
1,
1000]
513,
000
-
=========================================================================================
-
Total params:
11,
689,
512
-
Trainable params:
11,
689,
512
-
Non-trainable params:
0
-
Total mult-adds (G):
1.81
-
=========================================================================================
-
Input size (MB):
0.60
-
Forward/backward
pass size (MB):
39.75
-
Params size (MB):
46.76
-
Estimated Total Size (MB):
87.11
-
=========================================================================================
2. CNN可视化
卷积神经网路——CNN
2.1 CNN卷积核可视化
以torchvision自带的VGG11模型为例。
-
import torch
-
from torchvision.models
import vgg11
-
-
model = vgg11(pretrained=
True)
-
print(
dict(model.features.named_children()))
-
-
-
# 输出
-
{
'0': Conv2d(
3,
64, kernel_size=(
3,
3), stride=(
1,
1), padding=(
1,
1)),
-
'1': ReLU(inplace=
True),
-
'2': MaxPool2d(kernel_size=
2, stride=
2, padding=
0, dilation=
1, ceil_mode=
False),
-
'3': Conv2d(
64,
128, kernel_size=(
3,
3), stride=(
1,
1), padding=(
1,
1)),
-
'4': ReLU(inplace=
True),
-
'5': MaxPool2d(kernel_size=
2, stride=
2, padding=
0, dilation=
1, ceil_mode=
False),
-
'6': Conv2d(
128,
256, kernel_size=(
3,
3), stride=(
1,
1), padding=(
1,
1)),
-
'7': ReLU(inplace=
True),
-
'8': Conv2d(
256,
256, kernel_size=(
3,
3), stride=(
1,
1), padding=(
1,
1)),
-
'9': ReLU(inplace=
True),
-
'10': MaxPool2d(kernel_size=
2, stride=
2, padding=
0, dilation=
1, ceil_mode=
False),
-
'11': Conv2d(
256,
512, kernel_size=(
3,
3), stride=(
1,
1), padding=(
1,
1)),
-
'12': ReLU(inplace=
True),
-
'13': Conv2d(
512,
512, kernel_size=(
3,
3), stride=(
1,
1), padding=(
1,
1)),
-
'14': ReLU(inplace=
True),
-
'15': MaxPool2d(kernel_size=
2, stride=
2, padding=
0, dilation=
1, ceil_mode=
False),
-
'16': Conv2d(
512,
512, kernel_size=(
3,
3), stride=(
1,
1), padding=(
1,
1)),
-
'17': ReLU(inplace=
True),
-
'18': Conv2d(
512,
512, kernel_size=(
3,
3), stride=(
1,
1), padding=(
1,
1)),
-
'19': ReLU(inplace=
True),
-
'20': MaxPool2d(kernel_size=
2, stride=
2, padding=
0, dilation=
1, ceil_mode=
False)}
2.2 CNN特征图可视化方法
在PyTorch中，提供了一个专用的接口使得网络在前向传播过程中能够获取到特征图，这个接口的名称非常形象，叫做hook.
首先实现了一个hook类，之后在plot_feature函数中，将该hook类的对象注册到要进行可视化的网络的某层中。model在进行前向传播的时候会调用hook的__call__函数，我们也就是在那里存储了当前层的输入和输出。
-
class
Hook(
object):
-
def
__init__(
self):
-
self.module_name = []
-
self.features_in_hook = []
-
self.features_out_hook = []
-
-
def
__call__(
self,module, fea_in, fea_out):
-
print(
"hooker working", self)
-
self.module_name.append(module.__class__)
-
self.features_in_hook.append(fea_in)
-
self.features_out_hook.append(fea_out)
-
return
None
-
-
-
def
plot_feature(
model, idx, inputs):
-
hh = Hook()
-
model.features[idx].register_forward_hook(hh)
-
-
# forward_model(model,False)
-
model.
eval()
-
_ = model(inputs)
-
print(hh.module_name)
-
print((hh.features_in_hook[
0][
0].shape))
-
print((hh.features_out_hook[
0].shape))
-
-
out1 = hh.features_out_hook[
0]
-
-
total_ft = out1.shape[
1]
-
first_item = out1[
0].cpu().clone()
-
-
plt.figure(figsize=(
20,
17))
-
-
-
for ftidx
in
range(total_ft):
-
if ftidx >
99:
-
break
-
ft = first_item[ftidx]
-
plt.subplot(
10,
10, ftidx+
1)
-
-
plt.axis(
'off')
-
#plt.imshow(ft[ :, :].detach(),cmap='gray')
-
plt.imshow(ft[ :, :].detach())
2.3 CNN class activation map可视化方法
2.3.1 实现方法
CAM系列操作的实现可以通过开源工具包pytorch-grad-cam来实现。
2.3.2 安装
pip install grad-cam2.3.3 例子
-
import torch
-
from torchvision.models
import vgg11,resnet18,resnet101,resnext101_32x8d
-
import matplotlib.pyplot
as plt
-
from PIL
import Image
-
import numpy
as np
-
-
model = vgg11(pretrained=
True)
-
img_path =
'./dog.png'
-
# resize操作是为了和传入神经网络训练图片大小一致
-
img = Image.
open(img_path).resize((
224,
224))
-
# 需要将原始图片转为np.float32格式并且在0-1之间
-
rgb_img = np.float32(img)/
255
-
plt.imshow(img)
-
-
##########################################################################
-
from pytorch_grad_cam
import GradCAM,ScoreCAM,GradCAMPlusPlus,AblationCAM,XGradCAM,EigenCAM,FullGrad
-
from pytorch_grad_cam.utils.model_targets
import ClassifierOutputTarget
-
from pytorch_grad_cam.utils.image
import show_cam_on_image
-
-
target_layers = [model.features[-
1]]
-
# 选取合适的类激活图，但是ScoreCAM和AblationCAM需要batch_size
-
cam = GradCAM(model=model,target_layers=target_layers)
-
targets = [ClassifierOutputTarget(preds)]
-
# 上方preds需要设定，比如ImageNet有1000类，这里可以设为200
-
grayscale_cam = cam(input_tensor=img_tensor, targets=targets)
-
grayscale_cam = grayscale_cam[
0, :]
-
cam_img = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=
True)
-
print(
type(cam_img))
-
Image.fromarray(cam_img)
2.4 使用FlashTorch快速实现CNN可视化
2.4.1 安装
pip install flashtorch2.4.2 可视化梯度
-
# Download example images
-
# !mkdir -p images
-
# !wget -nv \
-
# https://github.com/MisaOgura/flashtorch/raw/master/examples/images/great_grey_owl.jpg \
-
# https://github.com/MisaOgura/flashtorch/raw/master/examples/images/peacock.jpg \
-
# https://github.com/MisaOgura/flashtorch/raw/master/examples/images/toucan.jpg \
-
# -P /content/images
-
-
import matplotlib.pyplot
as plt
-
import torchvision.models
as models
-
from flashtorch.utils
import apply_transforms, load_image
-
from flashtorch.saliency
import Backprop
-
-
model = models.alexnet(pretrained=
True)
-
backprop = Backprop(model)
-
-
image = load_image(
'/content/images/great_grey_owl.jpg')
-
owl = apply_transforms(image)
-
-
target_class =
24
-
backprop.visualize(owl, target_class, guided=
True, use_gpu=
True)
2.4.3 可视化卷积核
-
import torchvision.models
as models
-
from flashtorch.activmax
import GradientAscent
-
-
model = models.vgg16(pretrained=
True)
-
g_ascent = GradientAscent(model.features)
-
-
# specify layer and filter info
-
conv5_1 = model.features[
24]
-
conv5_1_filters = [
45,
271,
363,
489]
-
-
g_ascent.visualize(conv5_1, conv5_1_filters, title=
"VGG16: conv5_1")
3. 使用TensorBoard可视化训练过程
3.1 TensorBoard安装
pip install tensorboardX3.2 TensorBoard可视化的基本逻辑
（1）TensorBoard是一个记录员；
（2）记录我们指定的数据，包括模型每一层的feature map，权重，以及训练loss等等；
（3）保存在指定的文件夹里；
（4）程序不断运行TensorBoard会不断记录；
（5）可以通过网页的形式加以可视化。
3.3 TensorBoard的配置与启动
-
# 方法一、
-
from tensorboardX
import SummaryWriter
-
writer = SummaryWriter(
'./runs')
-
-
# 方法二
-
from torch.utils.tensorboard
import SummaryWriter
3.4 TensorBoard模型结构可视化
-
import torch.nn
as nn
-
-
class
Net(nn.Module):
-
def
__init__(
self):
-
super(Net, self).__init__()
-
self.conv1 = nn.Conv2d(in_channels=
3,out_channels=
32,kernel_size =
3)
-
self.pool = nn.MaxPool2d(kernel_size =
2,stride =
2)
-
self.conv2 = nn.Conv2d(in_channels=
32,out_channels=
64,kernel_size =
5)
-
self.adaptive_pool = nn.AdaptiveMaxPool2d((
1,
1))
-
self.flatten = nn.Flatten()
-
self.linear1 = nn.Linear(
64,
32)
-
self.relu = nn.ReLU()
-
self.linear2 = nn.Linear(
32,
1)
-
self.sigmoid = nn.Sigmoid()
-
-
def
forward(
self,x):
-
x = self.conv1(x)
-
x = self.pool(x)
-
x = self.conv2(x)
-
x = self.pool(x)
-
x = self.adaptive_pool(x)
-
x = self.flatten(x)
-
x = self.linear1(x)
-
x = self.relu(x)
-
x = self.linear2(x)
-
y = self.sigmoid(x)
-
return y
-
-
model = Net()
-
# 保存模型信息
-
writer.add_graph(model, input_to_model = torch.rand(
1,
3,
224,
224))
-
writer.close()
3.5 TensorBoard图像可视化
（1）对于单张图片的显示使用add_image；
（2）对于多张图片的显示使用add_images；
（3）有时需要使用torchvision.utils.make_grid将多张图片拼成一张图片后，用writer.add_image显示。
使用torchvision的CIFAR10数据集为例：
-
import torchvision
-
from torchvision
import datasets, transforms
-
from torch.utils.data
import DataLoader
-
-
transform_train = transforms.Compose(
-
[transforms.ToTensor()])
-
transform_test = transforms.Compose(
-
[transforms.ToTensor()])
-
-
train_data = datasets.CIFAR10(
".", train=
True, download=
True, transform=transform_train)
-
test_data = datasets.CIFAR10(
".", train=
False, download=
True, transform=transform_test)
-
train_loader = DataLoader(train_data, batch_size=
64, shuffle=
True)
-
test_loader = DataLoader(test_data, batch_size=
64)
-
-
images, labels =
next(
iter(train_loader))
-
-
# 仅查看一张图片
-
writer = SummaryWriter(
'./pytorch_tb')
-
writer.add_image(
'images[0]', images[
0])
-
writer.close()
-
-
# 将多张图片拼接成一张图片，中间用黑色网格分割
-
# create grid of images
-
writer = SummaryWriter(
'./pytorch_tb')
-
img_grid = torchvision.utils.make_grid(images)
-
writer.add_image(
'image_grid', img_grid)
-
writer.close()
-
-
# 将多张图片直接写入
-
writer = SummaryWriter(
'./pytorch_tb')
-
writer.add_images(
"images",images,global_step =
0)
-
writer.close()
3.6 TensorBoard连续变量可视化
通过add_scalar实现
-
writer = SummaryWriter(
'./pytorch_tb')
-
for i
in
range(
500):
-
x = i
-
y = x**
2
-
writer.add_scalar(
"x", x, i)
#日志中记录x在第step i 的值
-
writer.add_scalar(
"y", y, i)
#日志中记录y在第step i 的值
-
writer.close()
3.7 TensorBoard参数分布可视化
通过add_histogram实现
-
import torch
-
import numpy
as np
-
-
# 创建正态分布的张量模拟参数矩阵
-
def
norm(
mean, std):
-
t = std * torch.randn((
100,
20)) + mean
-
return t
-
-
writer = SummaryWriter(
'./pytorch_tb/')
-
for step, mean
in
enumerate(
range(-
10,
10,
1)):
-
w = norm(mean,
1)
-
writer.add_histogram(
"w", w, step)
-
writer.flush()
-
writer.close()
参考：PyTorch可视化
转载：https://blog.csdn.net/qq_51167531/article/details/128043195
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