1 爬取奥特曼

get_data.py

import requests
import urllib.parse as up
import json
import time
import osmajor_url = 'https://image.baidu.com/search/index?'
headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.135 Safari/537.36'}
def pic_spider(kw, path, page = 10):path = os.path.join(path, kw)if not os.path.exists(path):os.mkdir(path)if kw != '':for num in range(page):data = {"tn": "resultjson_com","logid": "11587207680030063767","ipn": "rj","ct": "201326592","is": "","fp": "result","queryWord": kw,"cl": "2","lm": "-1","ie": "utf-8","oe": "utf-8","adpicid": "","st": "-1","z": "","ic": "0","hd": "","latest": "","copyright": "","word": kw,"s": "","se": "","tab": "","width": "","height": "","face": "0","istype": "2","qc": "","nc": "1","fr": "","expermode": "","force": "","pn": num*30,"rn": "30","gsm": oct(num*30),"1602481599433": ""}url = major_url + up.urlencode(data)i = 0pic_list = []while i < 5:try:pic_list = requests.get(url=url, headers=headers).json().get('data')breakexcept:print('网络不好，正在重试...')i += 1time.sleep(1.3)for pic in pic_list:url = pic.get('thumbURL', '') # 有的没有图片链接，就设置成空if url == '':continuename = pic.get('fromPageTitleEnc')for char in ['?', '\\', '/', '*', '"', '|', ':', '<', '>']:name = name.replace(char, '') # 将所有不能出现在文件名中的字符去除掉type = pic.get('type', 'jpg') # 找到图片的类型，若没有找到，默认为 jpgpic_path = (os.path.join(path, '%s.%s') % (name, type))print(name, '已完成下载')if not os.path.exists(pic_path):with open(pic_path, 'wb') as f:f.write(requests.get(url = url, headers = headers).content)
cwd = os.getcwd() # 当前路径
file1 = 'flower_data/flower_photos'
file2 = '数据/下载数据'
save_path = os.path.join(cwd,file2)
#flower_class = [cla for cla in os.listdir(file1) if ".txt" not in cla]
#lists = ['猫','哈士奇','燕子','恐龙','鹦鹉','老鹰','柴犬','田园犬','咖啡猫','老虎','狮子','哥斯拉','奥特曼']
lists = ['佐菲','初代','赛文','杰克','艾斯','泰罗','奥特之父','奥特之母','爱迪','尤莉安','雷欧','阿斯特拉','奥特之王','葛雷','帕瓦特','奈克斯特','奈克瑟斯','哉阿斯','迪加','戴拿','盖亚（大地）','阿古茹（海洋）','高斯（慈爱）','杰斯提斯（正义）','雷杰多（高斯与杰斯提斯的合体）','诺亚（奈克斯特的最终形态）','撒加','奈欧斯','赛文21','麦克斯','杰诺','梦比优斯','希卡利','赛罗','赛文X']for list in lists:if not os.path.exists(save_path):os.mkdir(save_path)pic_spider(list+'奥特曼',save_path, page = 10)print("lists_len: ",len(lists))

2 划分数据集

训练集 train ：80%
验证集 val ：10%
测试集 predict ：10%

spile_data.py

import os
from shutil import copy
import random
import cv2def mkfile(file):if not os.path.exists(file):os.makedirs(file)#file = 'flower_data/flower_photos'
file = '数据/下载数据'
flower_class = [cla for cla in os.listdir(file) if ".txt" not in cla]
#mkfile('flower_data/train')
mkfile('数据/train')
for cla in flower_class:#mkfile('flower_data/train/'+cla)mkfile('数据/train/'+cla)#mkfile('flower_data/val')
mkfile('数据/val')
for cla in flower_class:#mkfile('flower_data/val/'+cla)mkfile('数据/val/'+cla)mkfile('数据/predict')
for cla in flower_class:#mkfile('flower_data/predict/'+cla)mkfile('数据/predict/'+cla)split_rate = 0.1for cla in flower_class:images = []cla_path = file + '/' + cla + '/'# 过滤jpg和pngimages1 = [cla1 for cla1 in os.listdir(cla_path) if ".jpg" in cla1]images2 = [cla1 for cla1 in os.listdir(cla_path) if ".png" in cla1]+images1# 去掉小于256的图for image in images2:img = cv2.imread(cla_path+image)if img.shape[0]>255 and img.shape[1]>255:images.append(image)#images = os.listdir(cla_path) num = len(images)#eval_index = random.sample(images, k=int(num*split_rate))for index, image in enumerate(images):if index<0.1*num:image_path = cla_path +'/'+ imagenew_path = '数据/val/' + clacopy(image_path, new_path)elif index<0.2*num:image_path = cla_path + imagenew_path = '数据/predict/' + clacopy(image_path, new_path)else:image_path = cla_path + imagenew_path = '数据/train/' + clacopy(image_path, new_path)print("\r[{}] processing [{}/{}]".format(cla, index+1, num), end="")  # processing barprint()print("processing done!")

3 GoogLeNet model

https://github.com/pytorch/vision/blob/master/torchvision/models/googlenet.py

model.py

import warnings
from collections import namedtuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
#from .utils import load_state_dict_from_url
from typing import Optional, Tuple, List, Callable, Any__all__ = ['GoogLeNet', 'googlenet', "GoogLeNetOutputs", "_GoogLeNetOutputs"]model_urls = {# GoogLeNet ported from TensorFlow'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
}GoogLeNetOutputs = namedtuple('GoogLeNetOutputs', ['logits', 'aux_logits2', 'aux_logits1'])
GoogLeNetOutputs.__annotations__ = {'logits': Tensor, 'aux_logits2': Optional[Tensor],'aux_logits1': Optional[Tensor]}# Script annotations failed with _GoogleNetOutputs = namedtuple ...
# _GoogLeNetOutputs set here for backwards compat
_GoogLeNetOutputs = GoogLeNetOutputsdef googlenet(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> "GoogLeNet":r"""GoogLeNet (Inception v1) model architecture from`"Going Deeper with Convolutions" <http://arxiv.org/abs/1409.4842>`_.Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderraux_logits (bool): If True, adds two auxiliary branches that can improve training.Default: *False* when pretrained is True otherwise *True*transform_input (bool): If True, preprocesses the input according to the method with which itwas trained on ImageNet. Default: *False*"""if pretrained:if 'transform_input' not in kwargs:kwargs['transform_input'] = Trueif 'aux_logits' not in kwargs:kwargs['aux_logits'] = Falseif kwargs['aux_logits']:warnings.warn('auxiliary heads in the pretrained googlenet model are NOT pretrained, ''so make sure to train them')original_aux_logits = kwargs['aux_logits']kwargs['aux_logits'] = Truekwargs['init_weights'] = Falsemodel = GoogLeNet(**kwargs)#state_dict = load_state_dict_from_url(model_urls['googlenet'],progress=progress)#model.load_state_dict(state_dict)model_weight_path = "./googlenet-1378be20.pth"model.load_state_dict(torch.load(model_weight_path))if not original_aux_logits:model.aux_logits = Falsemodel.aux1 = None  # type: ignore[assignment]model.aux2 = None  # type: ignore[assignment]return modelreturn GoogLeNet(**kwargs)class GoogLeNet(nn.Module):__constants__ = ['aux_logits', 'transform_input']def __init__(self,num_classes: int = 1000,aux_logits: bool = True,transform_input: bool = False,init_weights: Optional[bool] = None,blocks: Optional[List[Callable[..., nn.Module]]] = None) -> None:super(GoogLeNet, self).__init__()if blocks is None:blocks = [BasicConv2d, Inception, InceptionAux]if init_weights is None:warnings.warn('The default weight initialization of GoogleNet will be changed in future releases of ''torchvision. If you wish to keep the old behavior (which leads to long initialization times'' due to scipy/scipy#11299), please set init_weights=True.', FutureWarning)init_weights = Trueassert len(blocks) == 3conv_block = blocks[0]inception_block = blocks[1]inception_aux_block = blocks[2]self.aux_logits = aux_logitsself.transform_input = transform_inputself.conv1 = conv_block(3, 64, kernel_size=7, stride=2, padding=3)self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)self.conv2 = conv_block(64, 64, kernel_size=1)self.conv3 = conv_block(64, 192, kernel_size=3, padding=1)self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)self.inception3a = inception_block(192, 64, 96, 128, 16, 32, 32)self.inception3b = inception_block(256, 128, 128, 192, 32, 96, 64)self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)self.inception4a = inception_block(480, 192, 96, 208, 16, 48, 64)self.inception4b = inception_block(512, 160, 112, 224, 24, 64, 64)self.inception4c = inception_block(512, 128, 128, 256, 24, 64, 64)self.inception4d = inception_block(512, 112, 144, 288, 32, 64, 64)self.inception4e = inception_block(528, 256, 160, 320, 32, 128, 128)self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)self.inception5a = inception_block(832, 256, 160, 320, 32, 128, 128)self.inception5b = inception_block(832, 384, 192, 384, 48, 128, 128)if aux_logits:self.aux1 = inception_aux_block(512, num_classes)self.aux2 = inception_aux_block(528, num_classes)else:self.aux1 = None  # type: ignore[assignment]self.aux2 = None  # type: ignore[assignment]self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.dropout = nn.Dropout(0.2)self.fc = nn.Linear(1024, num_classes)if init_weights:self._initialize_weights()def _initialize_weights(self) -> None:for m in self.modules():if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):import scipy.stats as statsX = stats.truncnorm(-2, 2, scale=0.01)values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype)values = values.view(m.weight.size())with torch.no_grad():m.weight.copy_(values)elif isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)def _transform_input(self, x: Tensor) -> Tensor:if self.transform_input:x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5x = torch.cat((x_ch0, x_ch1, x_ch2), 1)return xdef _forward(self, x: Tensor) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:# N x 3 x 224 x 224x = self.conv1(x)# N x 64 x 112 x 112x = self.maxpool1(x)# N x 64 x 56 x 56x = self.conv2(x)# N x 64 x 56 x 56x = self.conv3(x)# N x 192 x 56 x 56x = self.maxpool2(x)# N x 192 x 28 x 28x = self.inception3a(x)# N x 256 x 28 x 28x = self.inception3b(x)# N x 480 x 28 x 28x = self.maxpool3(x)# N x 480 x 14 x 14x = self.inception4a(x)# N x 512 x 14 x 14aux1: Optional[Tensor] = Noneif self.aux1 is not None:if self.training:aux1 = self.aux1(x)x = self.inception4b(x)# N x 512 x 14 x 14x = self.inception4c(x)# N x 512 x 14 x 14x = self.inception4d(x)# N x 528 x 14 x 14aux2: Optional[Tensor] = Noneif self.aux2 is not None:if self.training:aux2 = self.aux2(x)x = self.inception4e(x)# N x 832 x 14 x 14x = self.maxpool4(x)# N x 832 x 7 x 7x = self.inception5a(x)# N x 832 x 7 x 7x = self.inception5b(x)# N x 1024 x 7 x 7x = self.avgpool(x)# N x 1024 x 1 x 1x = torch.flatten(x, 1)# N x 1024x = self.dropout(x)x = self.fc(x)# N x 1000 (num_classes)return x, aux2, aux1@torch.jit.unuseddef eager_outputs(self, x: Tensor, aux2: Tensor, aux1: Optional[Tensor]) -> GoogLeNetOutputs:if self.training and self.aux_logits:return _GoogLeNetOutputs(x, aux2, aux1)else:return x   # type: ignore[return-value]def forward(self, x: Tensor) -> GoogLeNetOutputs:x = self._transform_input(x)x, aux1, aux2 = self._forward(x)aux_defined = self.training and self.aux_logitsif torch.jit.is_scripting():if not aux_defined:warnings.warn("Scripted GoogleNet always returns GoogleNetOutputs Tuple")return GoogLeNetOutputs(x, aux2, aux1)else:return self.eager_outputs(x, aux2, aux1)class Inception(nn.Module):def __init__(self,in_channels: int,ch1x1: int,ch3x3red: int,ch3x3: int,ch5x5red: int,ch5x5: int,pool_proj: int,conv_block: Optional[Callable[..., nn.Module]] = None) -> None:super(Inception, self).__init__()if conv_block is None:conv_block = BasicConv2dself.branch1 = conv_block(in_channels, ch1x1, kernel_size=1)self.branch2 = nn.Sequential(conv_block(in_channels, ch3x3red, kernel_size=1),conv_block(ch3x3red, ch3x3, kernel_size=3, padding=1))self.branch3 = nn.Sequential(conv_block(in_channels, ch5x5red, kernel_size=1),# Here, kernel_size=3 instead of kernel_size=5 is a known bug.# Please see https://github.com/pytorch/vision/issues/906 for details.conv_block(ch5x5red, ch5x5, kernel_size=3, padding=1))self.branch4 = nn.Sequential(nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),conv_block(in_channels, pool_proj, kernel_size=1))def _forward(self, x: Tensor) -> List[Tensor]:branch1 = self.branch1(x)branch2 = self.branch2(x)branch3 = self.branch3(x)branch4 = self.branch4(x)outputs = [branch1, branch2, branch3, branch4]return outputsdef forward(self, x: Tensor) -> Tensor:outputs = self._forward(x)return torch.cat(outputs, 1)class InceptionAux(nn.Module):def __init__(self,in_channels: int,num_classes: int,conv_block: Optional[Callable[..., nn.Module]] = None) -> None:super(InceptionAux, self).__init__()if conv_block is None:conv_block = BasicConv2dself.conv = conv_block(in_channels, 128, kernel_size=1)self.fc1 = nn.Linear(2048, 1024)self.fc2 = nn.Linear(1024, num_classes)def forward(self, x: Tensor) -> Tensor:# aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14x = F.adaptive_avg_pool2d(x, (4, 4))# aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4x = self.conv(x)# N x 128 x 4 x 4x = torch.flatten(x, 1)# N x 2048x = F.relu(self.fc1(x), inplace=True)# N x 1024x = F.dropout(x, 0.7, training=self.training)# N x 1024x = self.fc2(x)# N x 1000 (num_classes)return xclass BasicConv2d(nn.Module):def __init__(self,in_channels: int,out_channels: int,**kwargs: Any) -> None:super(BasicConv2d, self).__init__()self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)self.bn = nn.BatchNorm2d(out_channels, eps=0.001)def forward(self, x: Tensor) -> Tensor:x = self.conv(x)x = self.bn(x)return F.relu(x, inplace=True)

4 训练及验证

train.py

import torch
import torch.nn as nn
from torchvision import transforms, datasets, utils
import matplotlib.pyplot as plt
import numpy as np
import torch.optim as optim
from model import googlenet
import os
import json
import time
import torchvision#device : GPU 或 CPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)#数据预处理
data_transform = {"train": transforms.Compose([transforms.RandomResizedCrop(224), # 随机裁剪为224x224transforms.RandomHorizontalFlip(), # 水平翻转transforms.ToTensor(), # 转为张量transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]),# 均值和方差为0.5"val": transforms.Compose([transforms.Resize((224, 224)), # 重置大小transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])}batch_size = 4 # 批次大小
data_root = os.getcwd() # 获取当前路径
image_path = data_root + "/数据"  # 数据路径
#image_path = data_root + "/数据/flower_data"  # 数据路径
train_dataset = datasets.ImageFolder(root=image_path + "/train",transform=data_transform["train"]) # 加载训练数据集并预处理
train_num = len(train_dataset) # 训练数据集大小train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size, shuffle=True,num_workers=2) # 训练加载器validate_dataset = datasets.ImageFolder(root=image_path + "/val",transform=data_transform["val"]) # 验证数据集
val_num = len(validate_dataset) # 验证数据集大小
validate_loader = torch.utils.data.DataLoader(validate_dataset,batch_size=batch_size, shuffle=True,num_workers=2) # 验证加载器print("训练数据集大小: ",train_num,"\n") # 28218
print("验证数据集大小: ",val_num,"\n") # 308def imshow(img):img = img / 2 + 0.5     # unnormalizenpimg = img.numpy()plt.imshow(np.transpose(npimg, (1, 2, 0)))plt.show()num_classes=int(input("输入分类数目："))
net = googlenet(num_classes, init_weights=True) # 调用模型net.to(device)loss_function = nn.CrossEntropyLoss() # 损失函数:交叉熵
optimizer = optim.Adam(net.parameters(), lr=0.0002) #优化器 Adam
save_path = './googlenet.pth' # 训练参数保存路径
best_acc = 0.0 # 训练过程中最高准确率#开始进行训练和测试，训练一轮，测试一轮
for epoch in range(10):# 训练部分print(">>开始训练: ",epoch+1)net.train()    #训练dropoutrunning_loss = 0.0t1 = time.perf_counter()for step, data in enumerate(train_loader, start=0):images, labels = data#print("\nlabels: ",labels)#imshow(torchvision.utils.make_grid(images))optimizer.zero_grad() # 梯度置0outputs = net(images.to(device)) loss = loss_function(outputs, labels.to(device))loss.backward() # 反向传播optimizer.step()running_loss += loss.item() # 累加损失rate = (step + 1) / len(train_loader) # 训练进度a = "*" * int(rate * 50) # *数b = "." * int((1 - rate) * 50) # .数print("\rtrain loss: {:^3.0f}%[{}->{}]{:.3f}".format(int(rate * 100), a, b, loss), end="")print()print(time.perf_counter()-t1) # 一个epoch花费的时间# 验证部分print(">>开始验证： ",epoch+1)net.eval()    #验证不需要dropoutacc = 0.0  # 一个批次中分类正确个数with torch.no_grad():for val_data in validate_loader:val_images, val_labels = val_dataoutputs = net(val_images.to(device))#print("outputs: \n",outputs,"\n")predict_y = torch.max(outputs, dim=1)[1]#print("predict_y: \n",predict_y,"\n")acc += (predict_y == val_labels.to(device)).sum().item() # 预测和标签一致，累加val_accurate = acc / val_num # 一个批次的准确率if val_accurate > best_acc:best_acc = val_accuratetorch.save(net.state_dict(), save_path) # 更新准确率最高的网络参数print('[epoch %d] train_loss: %.3f  test_accuracy: %.3f' %(epoch + 1, running_loss / step, val_accurate))print('Finished Training')# {'daisy':0, 'dandelion':1, 'roses':2, 'sunflower':3, 'tulips':4}
flower_list = train_dataset.class_to_idx #  {0: 'daisy', 1: 'dandelion', 2: 'roses', 3: 'sunflowers', 4: 'tulips'}
cla_dict = dict((val, key) for key, val in flower_list.items())# 将字典写入 json 文件
json_str = json.dumps(cla_dict, indent=4) # 字典转json
with open('class_indices.json', 'w') as json_file: # 对class_indices.json写入操作json_file.write(json_str) # 写入class_indices.json

5 测试

predict.py

import torch
from model import googlenet
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
import json
import osdata_transform = transforms.Compose([transforms.Resize((224, 224)),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])cwd = os.getcwd() # 获取当前路径
predict = '数据/predict'
predict_path = os.path.join(cwd,predict)try:json_file = open('./class_indices.json', 'r')class_indict = json.load(json_file)
except Exception as e:print(e)exit(-1)num_classes=len(class_indict)# cla 为类
for j,cla in class_indict.items():print(">>测试： ",cla)#print("类别\t","概率") path = os.path.join(predict_path,cla)images = [f1 for f1 in os.listdir(path) if ".gif" not in f1] # 过滤gif动图acc_ =  [0 for x in range(0,num_classes)] # 统计类别数for image in images:# 加载图片img = Image.open(path+'/'+image).convert('RGB')# RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 0# .convert('RGB')#plt.imshow(img)# [N, C, H, W]img = data_transform(img)# expand batch dimensionimg = torch.unsqueeze(img, dim=0)# read class_indicttry:json_file = open('./class_indices.json', 'r')class_indict = json.load(json_file)except Exception as e:print(e)exit(-1)# create modelmodel = googlenet(num_classes)# load model weightsmodel_weight_path = "./googlenet.pth"model.load_state_dict(torch.load(model_weight_path))model.eval()with torch.no_grad():# predict classoutput = torch.squeeze(model(img))predict = torch.softmax(output, dim=0)predict_cla = torch.argmax(predict).numpy()print(class_indict[str(predict_cla)],'\t', predict[predict_cla].item())acc_[predict_cla]+=1#print(acc_)#plt.show()#print("acc_: ",acc_)if len(images) == 0:print("{}文件夹为空".format(cla))else:print("{}总共有{}张图片 \n其中，".format(cla,len(images)))#print(class_indict.values(),'\n',str(acc_))print("{}准确率为：{}%".format(cla,100*acc_[int(j)]/len(images)))print("\n")
print(">>测试完毕！")

Anaconda	3
python	3.6
pytorch	1.3
torchvision	0.4

在GoogLeNet文件夹，右键打开终端：

python get_data.py
python spile_data.py
python train.py
python predict.py

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Pytorch搭建FCN网络前言原理代码实现前言 FCN 全卷积网络,用卷积层替代CNN的全连接层,最后通过转置卷积层得到一个和输入尺寸一致的预测结果: 原理为了得到更好的分割结果,论文中提 ...
使用PyTorch搭建ResNet50网络
ResNet18的搭建请移步:使用PyTorch搭建ResNet18网络并使用CIFAR10数据集训练测试 ResNet34的搭建请移步:使用PyTorch搭建ResNet34网络 ResNet101 ...
pytorch 搭建 VGG 网络
目录 1. VGG 网络介绍 2. 搭建VGG 网络 3. code 1. VGG 网络介绍 VGG16 的网络结构如图: VGG 网络是由卷积层和池化层构成基础的CNN 它的CONV卷积层的参数全部 ...
Pytorch搭建LeNet5网络
本讲目标: 介绍Pytorch搭建LeNet5网络的流程. Pytorch八股法搭建LeNet5网络 1.LeNet5网络介绍 2.Pytorch搭建LeNet5网络 2.1搭建LeNet网络 2 ...
PyTorch搭建神经网络求解二分类问题
PyTorch搭建全连接神经网络求解二分类问题在求解线性回归问题的时候,我们已经学习了如何使用梯度下降算法来不断更新权重矩阵,使误差函数不断减小,这一节我们将使用PyTorch搭建一个简单的神经网络 ...
pytorch搭建孪生网络比较人脸相似性
参考文献: 神经网络学习小记录52--Pytorch搭建孪生神经网络(Siamese network)比较图片相似性_Bubbliiiing的博客-CSDN博客_神经网络图片相似性 Python - ...

Pytorch搭建GoogLeNet网络（奥特曼分类）