推荐一个可视化工具：TensorBoard

注：
本次所使用的为AlexNet与ResNet34俩个网络，关于这俩个网络的详细信息可以在我另外俩篇blog查看

ResNet——CNN经典网络模型详解(pytorch实现)
AlexNet–CNN经典网络模型详解（pytorch实现）

查看中间层特征矩阵

AlexNet

alexnet_model.py

import torch.nn as nn
import torchclass AlexNet(nn.Module):def __init__(self, num_classes=1000, init_weights=False):super(AlexNet, self).__init__()self.features = nn.Sequential(nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2),  # input[3, 224, 224]  output[48, 55, 55]nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=3, stride=2),                  # output[48, 27, 27]nn.Conv2d(48, 128, kernel_size=5, padding=2),           # output[128, 27, 27]nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 13, 13]nn.Conv2d(128, 192, kernel_size=3, padding=1),          # output[192, 13, 13]nn.ReLU(inplace=True),nn.Conv2d(192, 192, kernel_size=3, padding=1),          # output[192, 13, 13]nn.ReLU(inplace=True),nn.Conv2d(192, 128, kernel_size=3, padding=1),          # output[128, 13, 13]nn.ReLU(inplace=True),nn.MaxPool2d(kernel_size=3, stride=2),                  # output[128, 6, 6])self.classifier = nn.Sequential(nn.Dropout(p=0.5),nn.Linear(128 * 6 * 6, 2048),nn.ReLU(inplace=True),nn.Dropout(p=0.5),nn.Linear(2048, 2048),nn.ReLU(inplace=True),nn.Linear(2048, num_classes),)if init_weights:self._initialize_weights()def forward(self, x):outputs = []for name, module in self.features.named_children():x = module(x)if name in ["0", "3", "6"]:outputs.append(x)return outputsdef _initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')if m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.normal_(m.weight, 0, 0.01)nn.init.constant_(m.bias, 0)

在 for name, module in self.features.named_children():设置一个断点来确认name是否为conv

analyze_feature_map.py

import torch
from alexnet_model import AlexNet
from resnet_model import resnet34
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from torchvision import transforms#图像预处理，要与生成alexnet.pth文件的train预处理一致
data_transform = transforms.Compose([transforms.Resize((224, 224)),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])# 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 = AlexNet(num_classes=5)
# model = resnet34(num_classes=5)
# load model weights
model_weight_path = "./AlexNet.pth"  # "./resNet34.pth"
model.load_state_dict(torch.load(model_weight_path))
print(model)# load image
img = Image.open("roses.jpg")
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)# forward
out_put = model(img)
for feature_map in out_put:# [N, C, H, W] -> [C, H, W]im = np.squeeze(feature_map.detach().numpy())# [C, H, W] -> [H, W, C]print(model)im = np.transpose(im, [1, 2, 0])# show top 12 feature mapsplt.figure()for i in range(12):ax = plt.subplot(3, 4, i+1)#行，列，索引# [H, W, C]plt.imshow(im[:, :, i], cmap='gray')#cmap默认为蓝绿图plt.show()

图中的俩个.pth文件为训练模型所生成，所以文件目录应该为

在out_put = model(img)设置一个断点，来查看print(model)的信息
在for feature_map in out_put:设置断点out_put = model(img)
原图

其输出为：
conv1：
conv2：
conv3

若将plt.imshow(im[:, :, i], cmap=‘gray’)中 cmap='gray’去掉

ResnetNet34

resnet_model.py

import torch.nn as nn
import torchclass BasicBlock(nn.Module):expansion = 1def __init__(self, in_channel, out_channel, stride=1, downsample=None):super(BasicBlock, self).__init__()self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,kernel_size=3, stride=stride, padding=1, bias=False)self.bn1 = nn.BatchNorm2d(out_channel)self.relu = nn.ReLU()self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,kernel_size=3, stride=1, padding=1, bias=False)self.bn2 = nn.BatchNorm2d(out_channel)self.downsample = downsampledef forward(self, x):identity = xif self.downsample is not None:identity = self.downsample(x)out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out += identityout = self.relu(out)return outclass Bottleneck(nn.Module):expansion = 4def __init__(self, in_channel, out_channel, stride=1, downsample=None):super(Bottleneck, self).__init__()self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,kernel_size=1, stride=1, bias=False)  # squeeze channelsself.bn1 = nn.BatchNorm2d(out_channel)# -----------------------------------------self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,kernel_size=3, stride=stride, bias=False, padding=1)self.bn2 = nn.BatchNorm2d(out_channel)# -----------------------------------------self.conv3 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel*self.expansion,kernel_size=1, stride=1, bias=False)  # unsqueeze channelsself.bn3 = nn.BatchNorm2d(out_channel*self.expansion)self.relu = nn.ReLU(inplace=True)self.downsample = downsampledef forward(self, x):identity = xif self.downsample is not None:identity = self.downsample(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)out += identityout = self.relu(out)return outclass ResNet(nn.Module):def __init__(self, block, blocks_num, num_classes=1000, include_top=True):super(ResNet, self).__init__()self.include_top = include_topself.in_channel = 64self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,padding=3, bias=False)self.bn1 = nn.BatchNorm2d(self.in_channel)self.relu = nn.ReLU(inplace=True)self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.layer1 = self._make_layer(block, 64, blocks_num[0])self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)if self.include_top:self.avgpool = nn.AdaptiveAvgPool2d((1, 1))  # output size = (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')def _make_layer(self, block, channel, block_num, stride=1):downsample = Noneif stride != 1 or self.in_channel != channel * block.expansion:downsample = nn.Sequential(nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),nn.BatchNorm2d(channel * block.expansion))layers = []layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride))self.in_channel = channel * block.expansionfor _ in range(1, block_num):layers.append(block(self.in_channel, channel))return nn.Sequential(*layers)def forward(self, x):outputs = []x = self.conv1(x)outputs.append(x)x = self.bn1(x)x = self.relu(x)x = self.maxpool(x)x = self.layer1(x)#仅查看layer1outputs.append(x)# x = self.layer2(x)# x = self.layer3(x)# x = self.layer4(x)## if self.include_top:#     x = self.avgpool(x)#     x = torch.flatten(x, 1)#     x = self.fc(x)return outputsdef resnet34(num_classes=1000, include_top=True):return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)def resnet101(num_classes=1000, include_top=True):return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)

analyze_feature_map.py

import torch
from alexnet_model import AlexNet
from resnet_model import resnet34
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from torchvision import transforms#图像预处理，要与生成alexnet.pth文件的train预处理一致
# data_transform = transforms.Compose(
#     [transforms.Resize((224, 224)),
#      transforms.ToTensor(),
#      transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])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 = AlexNet(num_classes=5)
model = resnet34(num_classes=5)
# load model weights
model_weight_path = "./resNet34.pth"  # "./resNet34.pth"
model.load_state_dict(torch.load(model_weight_path))
print(model)# load image
img = Image.open("roses.jpg")
# [N, C, H, W]
img = data_transform(img)
# expand batch dimension
img = torch.unsqueeze(img, dim=0)# forward
out_put = model(img)
for feature_map in out_put:# [N, C, H, W] -> [C, H, W]im = np.squeeze(feature_map.detach().numpy())# [C, H, W] -> [H, W, C]print(model)im = np.transpose(im, [1, 2, 0])# show top 12 feature mapsplt.figure()for i in range(12):ax = plt.subplot(3, 4, i+1)#行，列，索引# [H, W, C]plt.imshow(im[:, :, i])#cmap默认为蓝绿图plt.show()

在img = Image.open(“roses.jpg”)设置断点打印model层结构为
最终的输出为

对比可以发现resnet比alexnet更加好

查看卷积核参数

AlnexNet

analyze_kernel_weight.py

import torch
from alexnet_model import AlexNet
from resnet_model import resnet34
import matplotlib.pyplot as plt
import numpy as np# create model
model = AlexNet(num_classes=5)
# model = resnet34(num_classes=5)
# load model weights
model_weight_path = "./AlexNet.pth"  # "resNet34.pth"
model.load_state_dict(torch.load(model_weight_path))
print(model)weights_keys = model.state_dict().keys()
for key in weights_keys:# remove num_batches_tracked para(in bn)if "num_batches_tracked" in key:continue# [kernel_number, kernel_channel, kernel_height, kernel_width]weight_t = model.state_dict()[key].numpy()# read a kernel information# k = weight_t[0, :, :, :]# calculate mean, std, min, maxweight_mean = weight_t.mean()weight_std = weight_t.std(ddof=1)weight_min = weight_t.min()weight_max = weight_t.max()print("mean is {}, std is {}, min is {}, max is {}".format(weight_mean,weight_std,weight_max,weight_min))# plot hist imageplt.close()weight_vec = np.reshape(weight_t, [-1])plt.hist(weight_vec, bins=50)plt.title(key)plt.show()

在weights_keys = model.state_dict().keys()设置断点来单步运行查看weights_keys
卷积核1kernel值分布
卷积核1偏置分布
下面也都一样

ResNet34

analyze_kernel_weight.py

import torch
from alexnet_model import AlexNet
from resnet_model import resnet34
import matplotlib.pyplot as plt
import numpy as np# create model
# model = AlexNet(num_classes=5)
model = resnet34(num_classes=5)
# load model weights
model_weight_path = "./resNet34.pth"  # "resNet34.pth"
model.load_state_dict(torch.load(model_weight_path))
print(model)weights_keys = model.state_dict().keys()
for key in weights_keys:# remove num_batches_tracked para(in bn)if "num_batches_tracked" in key:continue# [kernel_number, kernel_channel, kernel_height, kernel_width]weight_t = model.state_dict()[key].numpy()# read a kernel information# k = weight_t[0, :, :, :]# calculate mean, std, min, maxweight_mean = weight_t.mean()weight_std = weight_t.std(ddof=1)weight_min = weight_t.min()weight_max = weight_t.max()print("mean is {}, std is {}, min is {}, max is {}".format(weight_mean,weight_std,weight_max,weight_min))# plot hist imageplt.close()weight_vec = np.reshape(weight_t, [-1])plt.hist(weight_vec, bins=50)plt.title(key)plt.show()

卷积层
BN层
对BN忘记同学可以查看Batch Normalization（BN）超详细解析
weight为γ\gammaγ参数
bias为β\betaβ参数
mean就是μ\muμ参数
var就是σ2\sigma^{2}σ2

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