网站建设中怎么写,房产交易网官网,公司网站开发设计,怎么做网站源码我们在训练图片时#xff0c;是不是要看看具体提取时的每个特征图提取的样子#xff0c;找了很多#xff0c;终于功夫不负有心人#xff0c;找到了#xff0c;通过修改的代码#xff1a;
resnet代码#xff1a;
import torch
import torch.nn as nn
from torchvision…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_size1, ratio2, dw_size3, stride1, reluTrue):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, biasFalse),nn.BatchNorm2d(init_channels),nn.ReLU(inplaceTrue) if relu else nn.Sequential(),)self.cheap_operation nn.Sequential(nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groupsinit_channels, biasFalse),nn.BatchNorm2d(new_channels),nn.ReLU(inplaceTrue) if relu else nn.Sequential(),)def forward(self, x):x1 self.primary_conv(x)x2 self.cheap_operation(x1)out torch.cat([x1,x2], dim1)return out[:,:self.oup,:,:]def conv3x3(in_planes, out_planes, stride1, groups1, dilation1):return nn.Conv2d(in_planes, out_planes, kernel_size3, stridestride,paddingdilation, groupsgroups, biasFalse, dilationdilation)def conv1x1(in_planes, out_planes, stride1):return nn.Conv2d(in_planes, out_planes, kernel_size1, stridestride, biasFalse)class BasicBlock(nn.Module):expansion 1def __init__(self, inplanes, planes, stride1, downsampleNone, groups1,base_width64, dilation1, norm_layerNone):super(BasicBlock, self).__init__()if norm_layer is None:norm_layer nn.BatchNorm2dif groups ! 1 or base_width ! 64:raise ValueError(BasicBlock only supports groups1 and base_width64)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(inplaceTrue)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, stride1, downsampleNone, groups1,base_width64, dilation1, norm_layerNone):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(inplaceTrue)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_classes1000, zero_init_residualFalse,groups1, width_per_group64, replace_stride_with_dilationNone,norm_layerNone):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_size7, stride2, padding3,biasFalse)self.bn1 norm_layer(self.inplanes)self.relu nn.ReLU(inplaceTrue)# 112,112,64 - 56,56,64self.maxpool nn.MaxPool2d(kernel_size3, stride2, padding1)# 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], stride2,dilatereplace_stride_with_dilation[0])# 28,28,512 - 14,14,1024self.layer3 self._make_layer(block, 256, layers[2], stride2,dilatereplace_stride_with_dilation[1])# 14,14,1024 - 7,7,2048self.layer4 self._make_layer(block, 512, layers[3], stride2,dilatereplace_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, modefan_out, nonlinearityrelu)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, stride1, dilateFalse):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, groupsself.groups,base_widthself.base_width, dilationself.dilation,norm_layernorm_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(pretrainedFalse, progressTrue, num_classes1000):model ResNet(BasicBlock, [2, 2, 2, 2])if pretrained:state_dict load_state_dict_from_url(model_urls[resnet18], model_dir./model_data,progressprogress)model.load_state_dict(state_dict)if num_classes!1000:model.fc nn.Linear(512 * model.block.expansion, num_classes)return modeldef resnet34(pretrainedFalse, progressTrue, num_classes1000):model ResNet(BasicBlock, [3, 4, 6, 3])if pretrained:state_dict load_state_dict_from_url(model_urls[resnet34], model_dir./model_data,progressprogress)model.load_state_dict(state_dict)if num_classes!1000:model.fc nn.Linear(512 * model.block.expansion, num_classes)return modeldef resnet50(pretrainedFalse, progressTrue, num_classes1000):model ResNet(Bottleneck, [3, 4, 6, 3])if pretrained:state_dict load_state_dict_from_url(model_urls[resnet50], model_dir./model_data,progressprogress)model.load_state_dict(state_dict)if num_classes!1000:model.fc nn.Linear(512 * model.block.expansion, num_classes)return modeldef resnet101(pretrainedFalse, progressTrue, num_classes1000):model ResNet(Bottleneck, [3, 4, 23, 3])if pretrained:state_dict load_state_dict_from_url(model_urls[resnet101], model_dir./model_data,progressprogress)model.load_state_dict(state_dict)if num_classes!1000:model.fc nn.Linear(512 * model.block.expansion, num_classes)return modeldef resnet152(pretrainedFalse, progressTrue, num_classes1000):model ResNet(Bottleneck, [3, 8, 36, 3])if pretrained:state_dict load_state_dict_from_url(model_urls[resnet152], model_dir./model_data,progressprogress)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_classes21# backbone mobilenetv2backbone resnet50device torch.device(cuda if torch.cuda.is_available() else cpu)model get_model_from_name[backbone](num_classesnum_classes, pretrainedFalse).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, ), verboseFalse)#--------------------------------------------------------## 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
433 4333 4333333 43352
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] 01 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] 03 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] 05 Conv2d-17 [-1, 64, 56, 56] 16,384 BatchNorm2d-18 [-1, 64, 56, 56] 128 ReLU-19 [-1, 64, 56, 56] 06 Conv2d-20 [-1, 64, 56, 56] 36,864 BatchNorm2d-21 [-1, 64, 56, 56] 128 ReLU-22 [-1, 64, 56, 56] 07 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] 08 Conv2d-27 [-1, 64, 56, 56] 16,384 BatchNorm2d-28 [-1, 64, 56, 56] 128 ReLU-29 [-1, 64, 56, 56] 09 Conv2d-30 [-1, 64, 56, 56] 36,864 BatchNorm2d-31 [-1, 64, 56, 56] 128 ReLU-32 [-1, 64, 56, 56] 010 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] 011 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] 013 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] 016 Conv2d-52 [-1, 128, 28, 28] 147,456 BatchNorm2d-53 [-1, 128, 28, 28] 256 ReLU-54 [-1, 128, 28, 28] 017 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] 018 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] 020 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] 022 Conv2d-72 [-1, 128, 28, 28] 147,456 BatchNorm2d-73 [-1, 128, 28, 28] 256 ReLU-74 [-1, 128, 28, 28] 023 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] 024 Conv2d-79 [-1, 256, 28, 28] 131,072 BatchNorm2d-80 [-1, 256, 28, 28] 512 ReLU-81 [-1, 256, 28, 28] 025 Conv2d-82 [-1, 256, 14, 14] 589,824 BatchNorm2d-83 [-1, 256, 14, 14] 512 ReLU-84 [-1, 256, 14, 14] 026 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] 030 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] 037 Conv2d-121 [-1, 256, 14, 14] 262,144 BatchNorm2d-122 [-1, 256, 14, 14] 512 ReLU-123 [-1, 256, 14, 14] 038 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] 040 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] 050 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] 0AdaptiveAvgPool2d-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(pretrainedTrue)
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(dim0).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_rowj]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, cmapgray)#64,56,56
plt.figure(figsize(15, 15))
plt.imshow(img1, cmapgray)#64,56,56
plt.figure(figsize(15, 15))
plt.imshow(img5, cmapgray)#1285656
plt.figure(figsize(30, 15))
plt.imshow(img11, cmapgray)#1282828
plt.figure(figsize(30, 15))
plt.imshow(img15, cmapgray)#2562828
plt.figure(figsize(30, 30))
plt.imshow(img24, cmapgray)#2561414
plt.figure(figsize(30, 30))
plt.imshow(img28, cmapgray)#5121414
plt.figure(figsize(45, 45))
plt.imshow(img43, cmapgray)#51277
plt.figure(figsize(45, 45))
plt.imshow(img47, cmapgray)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3224224——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51277
