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resnet网络特征提取过程可视化

news 文章来源：https://blog.csdn.net/m0_63172128/article/details/129709196 2025/5/15 20:32:04

我们在训练图片时，是不是要看看具体提取时的每个特征图提取的样子，找了很多，终于功夫不负有心人，找到了，通过修改的代码：

resnet代码：

import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
import math
model_urls = {'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth','resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth','resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth','resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth','resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}class GhostModule(nn.Module):def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True):super(GhostModule, self).__init__()self.oup = oupinit_channels = math.ceil(oup / ratio)new_channels = init_channels*(ratio-1)self.primary_conv = nn.Sequential(nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False),nn.BatchNorm2d(init_channels),nn.ReLU(inplace=True) if relu else nn.Sequential(),)self.cheap_operation = nn.Sequential(nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False),nn.BatchNorm2d(new_channels),nn.ReLU(inplace=True) if relu else nn.Sequential(),)def forward(self, x):x1 = self.primary_conv(x)x2 = self.cheap_operation(x1)out = torch.cat([x1,x2], dim=1)return out[:,:self.oup,:,:]def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,padding=dilation, groups=groups, bias=False, dilation=dilation)def conv1x1(in_planes, out_planes, stride=1):return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)class BasicBlock(nn.Module):expansion = 1def __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.BatchNorm2dif 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 != 1self.conv1 = conv3x3(inplanes, planes, stride)self.bn1 = norm_layer(planes)self.relu = nn.ReLU(inplace=True)self.conv2 = conv3x3(planes, planes)self.bn2 = norm_layer(planes)self.downsample = downsampleself.stride = stridedef forward(self, x):identity = xout = 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 += identityout = self.relu(out)return outclass Bottleneck(nn.Module):expansion = 4def __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.BatchNorm2dwidth = int(planes * (base_width / 64.)) * groups# Both self.conv2 and self.downsample layers downsample the input when stride != 1self.conv1 = conv1x1(inplanes, width)self.bn1 = norm_layer(width)self.conv2 = conv3x3(width, width, stride, groups, dilation)self.bn2 = norm_layer(width)self.conv3 = conv1x1(width, planes * self.expansion)self.bn3 = norm_layer(planes * self.expansion)self.relu = nn.ReLU(inplace=True)self.downsample = downsampleself.stride = stridedef forward(self, x):identity = xout = 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 += identityout = self.relu(out)return outclass 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.BatchNorm2dself._norm_layer = norm_layerself.inplanes = 64self.dilation = 1if replace_stride_with_dilation is None:replace_stride_with_dilation = [False, False, False]if len(replace_stride_with_dilation) != 3:raise ValueError("replace_stride_with_dilation should be None ""or a 3-element tuple, got {}".format(replace_stride_with_dilation))self.block = blockself.groups = groupsself.base_width = width_per_group# 224,224,3 -> 112,112,64self.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)# 112,112,64 -> 56,56,64self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)# 56,56,64 -> 56,56,256self.layer1 = self._make_layer(block, 64, layers[0])# 56,56,256 -> 28,28,512self.layer2 = self._make_layer(block, 128, layers[1], stride=2,dilate=replace_stride_with_dilation[0])# 28,28,512 -> 14,14,1024self.layer3 = self._make_layer(block, 256, layers[2], stride=2,dilate=replace_stride_with_dilation[1])# 14,14,1024 -> 7,7,2048self.layer4 = self._make_layer(block, 512, layers[3], stride=2,dilate=replace_stride_with_dilation[2])# 7,7,2048 -> 2048self.avgpool = nn.AdaptiveAvgPool2d((1, 1))# 2048 -> num_classesself.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)if zero_init_residual:for m in self.modules():if isinstance(m, Bottleneck):nn.init.constant_(m.bn3.weight, 0)def _make_layer(self, block, planes, blocks, stride=1, dilate=False):norm_layer = self._norm_layerdownsample = Noneprevious_dilation = self.dilationif dilate:self.dilation *= stridestride = 1if stride != 1 or self.inplanes != planes * block.expansion:downsample = nn.Sequential(conv1x1(self.inplanes, planes * block.expansion, stride),norm_layer(planes * block.expansion),)layers = []# Conv_blocklayers.append(block(self.inplanes, planes, stride, downsample, self.groups,self.base_width, previous_dilation, norm_layer))self.inplanes = planes * block.expansionfor _ in range(1, blocks):# identity_blocklayers.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(self, x):x = self.conv1(x)x = self.bn1(x)x = self.relu(x)x = self.maxpool(x)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)x = self.avgpool(x)x = torch.flatten(x, 1)x = self.fc(x)return xdef freeze_backbone(self):backbone = [self.conv1, self.bn1, self.layer1, self.layer2, self.layer3, self.layer4]for module in backbone:for param in module.parameters():param.requires_grad = Falsedef Unfreeze_backbone(self):backbone = [self.conv1, self.bn1, self.layer1, self.layer2, self.layer3, self.layer4]for module in backbone:for param in module.parameters():param.requires_grad = Truedef resnet18(pretrained=False, progress=True, num_classes=1000):model = ResNet(BasicBlock, [2, 2, 2, 2])if pretrained:state_dict = load_state_dict_from_url(model_urls['resnet18'], model_dir='./model_data',progress=progress)model.load_state_dict(state_dict)if num_classes!=1000:model.fc = nn.Linear(512 * model.block.expansion, num_classes)return modeldef resnet34(pretrained=False, progress=True, num_classes=1000):model = ResNet(BasicBlock, [3, 4, 6, 3])if pretrained:state_dict = load_state_dict_from_url(model_urls['resnet34'], model_dir='./model_data',progress=progress)model.load_state_dict(state_dict)if num_classes!=1000:model.fc = nn.Linear(512 * model.block.expansion, num_classes)return modeldef resnet50(pretrained=False, progress=True, num_classes=1000):model = ResNet(Bottleneck, [3, 4, 6, 3])if pretrained:state_dict = load_state_dict_from_url(model_urls['resnet50'], model_dir='./model_data',progress=progress)model.load_state_dict(state_dict)if num_classes!=1000:model.fc = nn.Linear(512 * model.block.expansion, num_classes)return modeldef resnet101(pretrained=False, progress=True, num_classes=1000):model = ResNet(Bottleneck, [3, 4, 23, 3])if pretrained:state_dict = load_state_dict_from_url(model_urls['resnet101'], model_dir='./model_data',progress=progress)model.load_state_dict(state_dict)if num_classes!=1000:model.fc = nn.Linear(512 * model.block.expansion, num_classes)return modeldef resnet152(pretrained=False, progress=True, num_classes=1000):model = ResNet(Bottleneck, [3, 8, 36, 3])if pretrained:state_dict = load_state_dict_from_url(model_urls['resnet152'], model_dir='./model_data',progress=progress)model.load_state_dict(state_dict)if num_classes!=1000:model.fc = nn.Linear(512 * model.block.expansion, num_classes)return model

可以查看网络的结构：

#--------------------------------------------#
#   该部分代码只用于看网络结构，并非测试代码
#--------------------------------------------#
import torch
from thop import clever_format, profile
from torchsummary import summaryfrom nets import get_model_from_name
# from nets import resnet_cbam # 使用哪个引入哪个即可
if __name__ == "__main__":input_shape = [224, 224]num_classes = 3  #写自己的分类个数，如果是训练图像分割，要多分一个背景，比如猫狗两种，num_classes=2+1# backbone    = "mobilenetv2"backbone = "resnet50"device  = torch.device("cuda" if torch.cuda.is_available() else "cpu")model   = get_model_from_name[backbone](num_classes=num_classes, pretrained=False).to(device)summary(model, (3, input_shape[0], input_shape[1]))dummy_input     = torch.randn(1, 3, input_shape[0], input_shape[1]).to(device)flops, params   = profile(model.to(device), (dummy_input, ), verbose=False)#--------------------------------------------------------##   flops * 2是因为profile没有将卷积作为两个operations#   有些论文将卷积算乘法、加法两个operations。此时乘2#   有些论文只考虑乘法的运算次数，忽略加法。此时不乘2#   本代码选择乘2，参考YOLOX。#--------------------------------------------------------#flops           = flops * 2flops, params   = clever_format([flops, params], "%.3f")print('Total GFLOPS: %s' % (flops))print('Total params: %s' % (params))

结构如下：（resnet50的）

其中两个残差模块Conv Block用于改变通道数，Identity Block用于加深网络

第一个残差块4个卷积，第二个3个

（4+3+3 ） + （ 4+3+3+3） + （ 4+3+3+3+3+3+3） + （ 4+3+3）==52

再加上一上来有个7*7卷积==53个

----------------------------------------------------------------
list   Layer (type) Output Shape Param #
================================================================
0    Conv2d-1 [-1, 64, 112, 112] 9,408
BatchNorm2d-2 [-1, 64, 112, 112] 128
ReLU-3 [-1, 64, 112, 112] 0
MaxPool2d-4 [-1, 64, 56, 56] 0
1 Conv2d-5 [-1, 64, 56, 56] 4,096
BatchNorm2d-6 [-1, 64, 56, 56] 128
ReLU-7 [-1, 64, 56, 56] 0
2    Conv2d-8 [-1, 64, 56, 56] 36,864
BatchNorm2d-9 [-1, 64, 56, 56] 128
ReLU-10 [-1, 64, 56, 56] 0
3 Conv2d-11 [-1, 256, 56, 56] 16,384
BatchNorm2d-12 [-1, 256, 56, 56] 512
4    Conv2d-13 [-1, 256, 56, 56] 16,384
BatchNorm2d-14 [-1, 256, 56, 56] 512
ReLU-15 [-1, 256, 56, 56] 0
Bottleneck-16 [-1, 256, 56, 56] 0
5     Conv2d-17 [-1, 64, 56, 56] 16,384
BatchNorm2d-18 [-1, 64, 56, 56] 128
ReLU-19 [-1, 64, 56, 56] 0
6   Conv2d-20 [-1, 64, 56, 56] 36,864
BatchNorm2d-21 [-1, 64, 56, 56] 128
ReLU-22 [-1, 64, 56, 56] 0
7 Conv2d-23 [-1, 256, 56, 56] 16,384
BatchNorm2d-24 [-1, 256, 56, 56] 512
ReLU-25 [-1, 256, 56, 56] 0
Bottleneck-26 [-1, 256, 56, 56] 0
8    Conv2d-27 [-1, 64, 56, 56] 16,384
BatchNorm2d-28 [-1, 64, 56, 56] 128
ReLU-29 [-1, 64, 56, 56] 0
9    Conv2d-30 [-1, 64, 56, 56] 36,864
BatchNorm2d-31 [-1, 64, 56, 56] 128
ReLU-32 [-1, 64, 56, 56] 0
10    Conv2d-33 [-1, 256, 56, 56] 16,384
BatchNorm2d-34 [-1, 256, 56, 56] 512
ReLU-35 [-1, 256, 56, 56] 0
Bottleneck-36 [-1, 256, 56, 56] 0
11     Conv2d-37 [-1, 128, 56, 56] 32,768
BatchNorm2d-38 [-1, 128, 56, 56] 256
ReLU-39 [-1, 128, 56, 56] 0
12    Conv2d-40 [-1, 128, 28, 28] 147,456
BatchNorm2d-41 [-1, 128, 28, 28] 256
ReLU-42 [-1, 128, 28, 28] 0
13    Conv2d-43 [-1, 512, 28, 28] 65,536
BatchNorm2d-44 [-1, 512, 28, 28] 1,024
14    Conv2d-45 [-1, 512, 28, 28] 131,072
BatchNorm2d-46 [-1, 512, 28, 28] 1,024
ReLU-47 [-1, 512, 28, 28] 0
Bottleneck-48 [-1, 512, 28, 28] 0
15    Conv2d-49 [-1, 128, 28, 28] 65,536
BatchNorm2d-50 [-1, 128, 28, 28] 256
ReLU-51 [-1, 128, 28, 28] 0
16 Conv2d-52 [-1, 128, 28, 28] 147,456
BatchNorm2d-53 [-1, 128, 28, 28] 256
ReLU-54 [-1, 128, 28, 28] 0
17 Conv2d-55 [-1, 512, 28, 28] 65,536
BatchNorm2d-56 [-1, 512, 28, 28] 1,024
ReLU-57 [-1, 512, 28, 28] 0
Bottleneck-58 [-1, 512, 28, 28] 0
18 Conv2d-59 [-1, 128, 28, 28] 65,536
BatchNorm2d-60 [-1, 128, 28, 28] 256
ReLU-61 [-1, 128, 28, 28] 0
19    Conv2d-62 [-1, 128, 28, 28] 147,456
BatchNorm2d-63 [-1, 128, 28, 28] 256
ReLU-64 [-1, 128, 28, 28] 0
20 Conv2d-65 [-1, 512, 28, 28] 65,536
BatchNorm2d-66 [-1, 512, 28, 28] 1,024
ReLU-67 [-1, 512, 28, 28] 0
Bottleneck-68 [-1, 512, 28, 28] 0
21    Conv2d-69 [-1, 128, 28, 28] 65,536
BatchNorm2d-70 [-1, 128, 28, 28] 256
ReLU-71 [-1, 128, 28, 28] 0
22     Conv2d-72 [-1, 128, 28, 28] 147,456
BatchNorm2d-73 [-1, 128, 28, 28] 256
ReLU-74 [-1, 128, 28, 28] 0
23 Conv2d-75 [-1, 512, 28, 28] 65,536
BatchNorm2d-76 [-1, 512, 28, 28] 1,024
ReLU-77 [-1, 512, 28, 28] 0
Bottleneck-78 [-1, 512, 28, 28] 0
24     Conv2d-79 [-1, 256, 28, 28] 131,072
BatchNorm2d-80 [-1, 256, 28, 28] 512
ReLU-81 [-1, 256, 28, 28] 0
25    Conv2d-82 [-1, 256, 14, 14] 589,824
BatchNorm2d-83 [-1, 256, 14, 14] 512
ReLU-84 [-1, 256, 14, 14] 0
26 Conv2d-85 [-1, 1024, 14, 14] 262,144
BatchNorm2d-86 [-1, 1024, 14, 14] 2,048
27   Conv2d-87 [-1, 1024, 14, 14] 524,288
BatchNorm2d-88 [-1, 1024, 14, 14] 2,048
ReLU-89 [-1, 1024, 14, 14] 0
Bottleneck-90 [-1, 1024, 14, 14] 0
  28 Conv2d-91 [-1, 256, 14, 14] 262,144
BatchNorm2d-92 [-1, 256, 14, 14] 512
ReLU-93 [-1, 256, 14, 14] 0
29   Conv2d-94 [-1, 256, 14, 14] 589,824
BatchNorm2d-95 [-1, 256, 14, 14] 512
ReLU-96 [-1, 256, 14, 14] 0
30 Conv2d-97 [-1, 1024, 14, 14] 262,144
BatchNorm2d-98 [-1, 1024, 14, 14] 2,048
ReLU-99 [-1, 1024, 14, 14] 0
Bottleneck-100 [-1, 1024, 14, 14] 0
31    Conv2d-101 [-1, 256, 14, 14] 262,144
BatchNorm2d-102 [-1, 256, 14, 14] 512
ReLU-103 [-1, 256, 14, 14] 0
32    Conv2d-104 [-1, 256, 14, 14] 589,824
BatchNorm2d-105 [-1, 256, 14, 14] 512
ReLU-106 [-1, 256, 14, 14] 0
33    Conv2d-107 [-1, 1024, 14, 14] 262,144
BatchNorm2d-108 [-1, 1024, 14, 14] 2,048
ReLU-109 [-1, 1024, 14, 14] 0
Bottleneck-110 [-1, 1024, 14, 14] 0
34    Conv2d-111 [-1, 256, 14, 14] 262,144
BatchNorm2d-112 [-1, 256, 14, 14] 512
ReLU-113 [-1, 256, 14, 14] 0
35    Conv2d-114 [-1, 256, 14, 14] 589,824
BatchNorm2d-115 [-1, 256, 14, 14] 512
ReLU-116 [-1, 256, 14, 14] 0
36    Conv2d-117 [-1, 1024, 14, 14] 262,144
BatchNorm2d-118 [-1, 1024, 14, 14] 2,048
ReLU-119 [-1, 1024, 14, 14] 0
Bottleneck-120 [-1, 1024, 14, 14] 0
37     Conv2d-121 [-1, 256, 14, 14] 262,144
BatchNorm2d-122 [-1, 256, 14, 14] 512
ReLU-123 [-1, 256, 14, 14] 0
38     Conv2d-124 [-1, 256, 14, 14] 589,824
BatchNorm2d-125 [-1, 256, 14, 14] 512
ReLU-126 [-1, 256, 14, 14] 0
39    Conv2d-127 [-1, 1024, 14, 14] 262,144
BatchNorm2d-128 [-1, 1024, 14, 14] 2,048
ReLU-129 [-1, 1024, 14, 14] 0
Bottleneck-130 [-1, 1024, 14, 14] 0
40 Conv2d-131 [-1, 256, 14, 14] 262,144
BatchNorm2d-132 [-1, 256, 14, 14] 512
ReLU-133 [-1, 256, 14, 14] 0
41    Conv2d-134 [-1, 256, 14, 14] 589,824
BatchNorm2d-135 [-1, 256, 14, 14] 512
ReLU-136 [-1, 256, 14, 14] 0
42    Conv2d-137 [-1, 1024, 14, 14] 262,144
BatchNorm2d-138 [-1, 1024, 14, 14] 2,048
ReLU-139 [-1, 1024, 14, 14] 0
Bottleneck-140 [-1, 1024, 14, 14] 0
43    Conv2d-141 [-1, 512, 14, 14] 524,288
BatchNorm2d-142 [-1, 512, 14, 14] 1,024
ReLU-143 [-1, 512, 14, 14] 0
44    Conv2d-144 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-145 [-1, 512, 7, 7] 1,024
ReLU-146 [-1, 512, 7, 7] 0
45    Conv2d-147 [-1, 2048, 7, 7] 1,048,576
BatchNorm2d-148 [-1, 2048, 7, 7] 4,096
46    Conv2d-149 [-1, 2048, 7, 7] 2,097,152
BatchNorm2d-150 [-1, 2048, 7, 7] 4,096
ReLU-151 [-1, 2048, 7, 7] 0
Bottleneck-152 [-1, 2048, 7, 7] 0
47    Conv2d-153 [-1, 512, 7, 7] 1,048,576
BatchNorm2d-154 [-1, 512, 7, 7] 1,024
ReLU-155 [-1, 512, 7, 7] 0
48 Conv2d-156 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-157 [-1, 512, 7, 7] 1,024
ReLU-158 [-1, 512, 7, 7] 0
49    Conv2d-159 [-1, 2048, 7, 7] 1,048,576
BatchNorm2d-160 [-1, 2048, 7, 7] 4,096
ReLU-161 [-1, 2048, 7, 7] 0
Bottleneck-162 [-1, 2048, 7, 7] 0
50 Conv2d-163 [-1, 512, 7, 7] 1,048,576
BatchNorm2d-164 [-1, 512, 7, 7] 1,024
ReLU-165 [-1, 512, 7, 7] 0
51    Conv2d-166 [-1, 512, 7, 7] 2,359,296
BatchNorm2d-167 [-1, 512, 7, 7] 1,024
ReLU-168 [-1, 512, 7, 7] 0
52    Conv2d-169 [-1, 2048, 7, 7] 1,048,576
BatchNorm2d-170 [-1, 2048, 7, 7] 4,096
ReLU-171 [-1, 2048, 7, 7] 0
Bottleneck-172 [-1, 2048, 7, 7] 0
AdaptiveAvgPool2d-173 [-1, 2048, 1, 1] 0
Linear-174 [-1, 3] 6,147
================================================================
Total params: 23,514,179
Trainable params: 23,514,179
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 286.55
Params size (MB): 89.70
Estimated Total Size (MB): 376.82
----------------------------------------------------------------
Total GFLOPS: 8.263G
Total params: 23.514M

可视化代码：

此段代码参考：https://blog.csdn.net/qq_34769162/article/details/115567093

# https://blog.csdn.net/qq_34769162/article/details/115567093
import numpy as npimport torch
import torchvision
from PIL import Image
from torchvision import transforms as Timport matplotlib.pyplot as plt
import pylabimport torch
import torchvisionfeature_extractor = torchvision.models.resnet50(pretrained=True)
if torch.cuda.is_available():feature_extractor.cuda()device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')class SaveOutput:def __init__(self):self.outputs = []def __call__(self, module, module_in, module_out):self.outputs.append(module_out)def clear(self):self.outputs = []save_output = SaveOutput()hook_handles = []for layer in feature_extractor.modules():if isinstance(layer, torch.nn.Conv2d):handle = layer.register_forward_hook(save_output)hook_handles.append(handle)from PIL import Image
from torchvision import transforms as Timage = Image.open('img/rot.jpg')
transform = T.Compose([T.Resize((224, 224)), T.ToTensor()])
X = transform(image).unsqueeze(dim=0).to(device)out = feature_extractor(X)print(len(save_output.outputs))
# 选择看的层数
# a_list = [0, 1, 6, 15, 28, 35]
a_list = [0, 1, 5, 11, 15, 24,28,43,47]# 43:512,14,14
#47:512, 7, 7
for i in a_list:print(save_output.outputs[i].cpu().detach().squeeze(0).shape)def grid_gray_image(imgs, each_row: int):'''imgs shape: batch * size (e.g., 64x32x32, 64 is the number of the gray images, and (32, 32) is the size of each gray image)'''row_num = imgs.shape[0]//each_rowfor i in range(row_num):img = imgs[i*each_row]img = (img - img.min()) / (img.max() - img.min())for j in range(1, each_row):tmp_img = imgs[i*each_row+j]tmp_img = (tmp_img - tmp_img.min()) / (tmp_img.max() - tmp_img.min())img = np.hstack((img, tmp_img))if i == 0:ans = imgelse:ans = np.vstack((ans, img))return ans# a_list = [0, 1, 5, 11, 15, 24,28,43,47]
img0 = save_output.outputs[0].cpu().detach().squeeze(0)
img0 = grid_gray_image(img0.numpy(), 8)
img1 = save_output.outputs[1].cpu().detach().squeeze(0)
img1 = grid_gray_image(img1.numpy(), 8)
img5 = save_output.outputs[5].cpu().detach().squeeze(0)
img5 = grid_gray_image(img5.numpy(), 8)
img11 = save_output.outputs[11].cpu().detach().squeeze(0)
img11 = grid_gray_image(img11.numpy(), 16)
img15 = save_output.outputs[15].cpu().detach().squeeze(0)
img15 = grid_gray_image(img15.numpy(), 16)
img24 = save_output.outputs[24].cpu().detach().squeeze(0)
img24 = grid_gray_image(img24.numpy(), 16)
img28 = save_output.outputs[28].cpu().detach().squeeze(0)
img28 = grid_gray_image(img28.numpy(), 16)
img43 = save_output.outputs[43].cpu().detach().squeeze(0)
img43 = grid_gray_image(img43.numpy(), 16)
img47 = save_output.outputs[47].cpu().detach().squeeze(0)
img47 = grid_gray_image(img47.numpy(), 16)# 64,112,112
plt.figure(figsize=(15, 15))
plt.imshow(img0, cmap='gray')#64,56,56
plt.figure(figsize=(15, 15))
plt.imshow(img1, cmap='gray')#64,56,56
plt.figure(figsize=(15, 15))
plt.imshow(img5, cmap='gray')#128，56，56
plt.figure(figsize=(30, 15))
plt.imshow(img11, cmap='gray')#128，28，28
plt.figure(figsize=(30, 15))
plt.imshow(img15, cmap='gray')#256，28，28
plt.figure(figsize=(30, 30))
plt.imshow(img24, cmap='gray')#256，14，14
plt.figure(figsize=(30, 30))
plt.imshow(img28, cmap='gray')#512，14，14
plt.figure(figsize=(45, 45))
plt.imshow(img43, cmap='gray')#512，7，7
plt.figure(figsize=(45, 45))
plt.imshow(img47, cmap='gray')pylab.show()

原图：

resnet50来说，首个卷积层的卷积核为7*7，将输入的三通道彩色图像通道增加至64，尺寸从224*224对折为112*112.

从输入图片3，224，224——>64, 112, 112

我们对首个卷积层的提取结果进行可视化：

最后一层太抽象看不清了，512，7，7

resnet网络特征提取过程可视化

resnet代码：

可以查看网络的结构：

结构如下：（resnet50的）

可视化代码：

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