相关链接:：
超详细——CNN卷积神经网络教程（零基础到实战）
大白话pytorch基本知识点及语法+项目实战
基于 pytorch 搭建神经网络分类模型识别花的种类，输入一张花的照片，输出显示最有可能的前八种花的名称和该种花的照片。
（在该程序中定义相关函数，以便在其他程序中进行调用）
"""""""""""""""加载测试模型open"""""""""""""""
#加载模型
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)
......
"""""""""""""""加载测试模型end"""""""""""""""
用相似的模型的权重初始化，修改全连接层，然后重新训练
pytorch->transforms->resnet->models
pytorch官方transforms神经网络模型
(由于镜像在国外下载较慢，已经下载好了放在文件夹下)
1、一个文件程序写全部代码有两个问题：
1）功能分工不明确
2）每次都要重新跑训练网络
2、模块化编程： 将功能函数分别放到不同的程序中，程序中相互调，可以分别进行功能测试
3、模块化编程两种方式：
A中：
import B
B.function
A中：
from B import function
function
4、注意import循环重载：
利用pycharm这种IDE进行模块化编程，多个.py文件相互import容易发生循环重载
例：A中importB，当顺序执行A，遇到相关数据需要调用B时，停止执行A，去执行B，B执行完了再执行A，如果A、B相互调用的话会报错
当A中importB，当B又需要调用A时，把需要的A中参数定义、函数定义在B中再写一遍
1、用pycharm或其他IDE需要配置python、pytorch环境
2、迁移学习的模型已经下载在文件中，不用重新下载
3、直接单独运行flower_forecast预测程序，可在设置检测图像数据模块中更换照片检测
4、如果想看神经网络搭建过程，直接运行flower_model网络模型训练程序
1、神经网络是个黑盒子，只要不是深入研究，我们做工程性项目重点是用神经网络而不是从0搭建网络，重点是利用神经网络做工程性项目，没有必要搞懂里面每一步（之前踩的坑，又麻烦又浪费时间）！！！
2、简单学习CNN基本原理后，直接去网上找个注释写的相当详细的开源程序去读
3、用好迁移学习，即调用pyorch官网上已经训练好的相关模型，根据我们自己的项目重新训练
4、先把每段代码都写上功能注释（里面一些具体参数和步骤了解即可，一定要写，有助于理解复杂的神经网络流程），再试着动手调别人的网络模型，最后试着这套模板应用到别的项目（数据集）上
5、推荐B站上唐宇迪的课，讲的通俗易懂很详细
4天教会你深度学习|Opencv+PyTorch+CNN+Python入门到实战课程
大帅锅大漂亮都看到这了，点个赞再走吧！
关注博主，分享学习教程，一起HappyCodeing
import numpy as np
import torch
import matplotlib.pyplot as plt
from PIL import Image
from torchvision import transforms, models
filename='checkpoint.pth'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
"""""""""""""""图像增强（数据集预处理处理）open"""""""""""""""
#图像增强：将数据集中照片进行旋转、翻折、放大...得到更多的数据
#ImageFolder假设所有的文件按文件夹保存好，每个文件夹下面存贮同一类别的图片，文件夹的名字为分类的名字
data_transforms = { #data_transforms中指定了所有图像预处理操作,只需要修改训练集和验证集的名字后复制粘贴
'train':
transforms.Compose([transforms.RandomRotation(45),#随机旋转，-45到45度之间随机选
transforms.CenterCrop(224),#从中心开始裁剪
transforms.RandomHorizontalFlip(p=0.5),#随机水平翻转 选择一个概率概率
transforms.RandomVerticalFlip(p=0.5),#随机垂直翻转
transforms.ColorJitter(brightness=0.2, contrast=0.1, saturation=0.1, hue=0.1),#参数1为亮度，参数2为对比度，参数3为饱和度，参数4为色相
transforms.RandomGrayscale(p=0.025),#概率转换成灰度率，3通道就是R=G=B
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])#均值，标准差
]),
'valid':
transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
"""""""""""""""图像增强（数据集预处理处理）end"""""""""""""""
"""""""""""""""处理照片数据函数open"""""""""""""""
#注意tensor的数据需要转换成numpy的格式，而且还需要还原回标准化的结果
def im_convert(tensor):
image = tensor.to("cpu").clone().detach()
image = image.numpy().squeeze()
# 还原回h，w，c
image = image.transpose(1, 2, 0)
# 被标准化过了，还原非标准化样子
image = image * np.array((0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))
image = image.clip(0, 1)
return image
"""""""""""""""处理照片数据函数end"""""""""""""""
"""""""""""""""检测照片预处理函数open"""""""""""""""
def process_image(image_path):
# 读取测试数据
img = Image.open(image_path)
# Resize,thumbnail方法只能进行缩小，所以进行了判断
if img.size[0] > img.size[1]:
img.thumbnail((10000, 256))
else:
img.thumbnail((256, 10000))
# Crop操作，再裁剪
left_margin = (img.width - 224) / 2
bottom_margin = (img.height - 224) / 2
right_margin = left_margin + 224
top_margin = bottom_margin + 224
img = img.crop((left_margin, bottom_margin, right_margin,
top_margin))
# 相同的预处理方法
img = np.array(img) / 255
mean = np.array([0.485, 0.456, 0.406]) # provided mean
std = np.array([0.229, 0.224, 0.225]) # provided std
img = (img - mean) / std
# 注意颜色通道应该放在第一个位置
img = img.transpose((2, 0, 1))
return img
"""""""""""""""检测照片预处理函数end"""""""""""""""
"""""""""""""""展示一张照片函数open"""""""""""""""
def imshow(image, ax=None, title=None):
"""展示数据"""
if ax is None:
fig, ax = plt.subplots()
# 颜色通道还原
image = np.array(image).transpose((1, 2, 0))
# 预处理还原
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
image = std * image + mean
image = np.clip(image, 0, 1)
ax.imshow(image)
ax.set_title(title)
return ax
"""""""""""""""展示一张照片函数end"""""""""""""""
import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
import torch.optim as optim
import torchvision
#pip install torchvision 需要提前安装好这个模块
from torchvision import transforms, models, datasets
#https://pytorch.org/docs/stable/torchvision/index.html
import imageio
import time
import warnings
import random
import sys
import copy
import json
from PIL import Image
#图像增强（数据集预处理处理）
from flower_function import data_transforms
"""""""""""""""读取训练集、测试集open"""""""""""""""
data_dir = './flower_data/'
train_dir = data_dir + '/train'
valid_dir = data_dir + '/valid'
"""""""""""""""读取训练集、测试集end"""""""""""""""
"""""""""""""""构建神经网络的数据集open"""""""""""""""
"""都存到dataloaders中"""
#batch_size是设置一次训练多少张照片
batch_size = 8 #设置越大需要的显存越大
#(os.path.join(data_dir, x), data_transforms[x])，（两个文件夹传进去，传入数据增强的数据）
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'valid']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True) for x in ['train', 'valid']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'valid']}
#class_name 是训练集
class_names = image_datasets['train'].classes
"""
print(image_datasets)
print(dataloaders)
print(dataset_sizes)
"""
#数据集中类别按照123456...标号，文件是各标号对应的名称
with open('cat_to_name.json', 'r') as f:
cat_to_name = json.load(f)
"""
print(cat_to_name) #打印标号集
"""
"""""""""""""""构建神经网络的数据集end"""""""""""""""
"""""""""""""""打印照片操作open"""""""""""""""
"""
fig=plt.figure(figsize=(20, 12))
columns = 4
rows = 2
dataiter = iter(dataloaders['valid'])
inputs, classes = dataiter.next()
#做图，print出来
for idx in range (columns*rows):
ax = fig.add_subplot(rows, columns, idx+1, xticks=[], yticks=[])
ax.set_title(cat_to_name[str(int(class_names[classes[idx]]))])
plt.imshow(im_convert(inputs[idx]))
plt.show()
"""
"""""""""""""""打印照片操作end"""""""""""""""
import os
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
import torch.optim as optim
import torchvision
#pip install torchvision 需要提前安装好这个模块
from torchvision import transforms, models, datasets
#https://pytorch.org/docs/stable/torchvision/index.html
import imageio
import time
import warnings
import random
import sys
import copy
import json
from PIL import Image
#神经网络数据集
from flower_dataset import dataloaders
filename='checkpoint.pth'
"""""""""""""""冻结神经网络权重函数open"""""""""""""""
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting: #这里为true
for param in model.parameters():
param.requires_grad = False #把除了最后全连接层，前面所有层权重冻结不能修改
"""""""""""""""冻结神经网络权重函数end"""""""""""""""
"""""""""""""""修改全连接层函数（官方）open"""""""""""""""
#（模型名字、得到类别个数、模型权重、
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
# 选择合适的模型，不同模型的初始化方法稍微有点区别
model_ft = None
input_size = 0
if model_name == "resnet":
""" Resnet152
"""
#加载模型（下载）
model_ft = models.resnet152(pretrained=use_pretrained)
#有选择性的选需要冻住哪些层
set_parameter_requires_grad(model_ft, feature_extract)
#取出最后一层
num_ftrs = model_ft.fc.in_features
#重新做全连接层（102这里需要修改，因为本任务分类类别是102）
model_ft.fc = nn.Sequential(nn.Linear(num_ftrs, 102),
nn.LogSoftmax(dim=1))
input_size = 224
elif model_name == "alexnet":
""" Alexnet
"""
model_ft = models.alexnet(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
input_size = 224
elif model_name == "vgg":
""" VGG11_bn
"""
model_ft = models.vgg16(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
input_size = 224
elif model_name == "squeezenet":
""" Squeezenet
"""
model_ft = models.squeezenet1_0(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
model_ft.num_classes = num_classes
input_size = 224
elif model_name == "densenet":
""" Densenet
"""
model_ft = models.densenet121(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "inception":
""" Inception v3
Be careful, expects (299,299) sized images and has auxiliary output
"""
model_ft = models.inception_v3(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
# Handle the auxilary net
num_ftrs = model_ft.AuxLogits.fc.in_features
model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
# Handle the primary net
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs,num_classes)
input_size = 299
else:
print("Invalid model name, exiting...")
exit()
return model_ft, input_size
"""""""""""""""修改全连接层函数（官方）end"""""""""""""""
"""""""""""""""训练模型函数open"""""""""""""""
#得到并保存神经网络模型checkpoint.pth
#（模型，数据，损失函数，优化器
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False, filename=filename):
since = time.time()
#保存最好的准确率
best_acc = 0
"""
checkpoint = torch.load(filename)
best_acc = checkpoint['best_acc']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
model.class_to_idx = checkpoint['mapping']
"""
#指定CPU做训练
model.to(device)
val_acc_history = []
train_acc_history = []
train_losses = []
valid_losses = []
LRs = [optimizer.param_groups[0]['lr']]
#最好的一次存下来
best_model_wts = copy.deepcopy(model.state_dict())
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# 训练和验证
for phase in ['train', 'valid']:
if phase == 'train':
model.train() # 训练
else:
model.eval() # 验证
running_loss = 0.0
running_corrects = 0
# 把数据都取个遍
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# 清零
optimizer.zero_grad()
# 只有训练的时候计算和更新梯度
with torch.set_grad_enabled(phase == 'train'):
#resnet不执行这个
if is_inception and phase == 'train':
outputs, aux_outputs = model(inputs)
loss1 = criterion(outputs, labels)
loss2 = criterion(aux_outputs, labels)
loss = loss1 + 0.4 * loss2
else: # resnet执行的是这里
outputs = model(inputs)
loss = criterion(outputs, labels)
_, preds = torch.max(outputs, 1)
# 训练阶段更新权重
if phase == 'train':
loss.backward()
optimizer.step()
# 计算损失
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
epoch_loss = running_loss / len(dataloaders[phase].dataset)
epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
time_elapsed = time.time() - since
print('Time elapsed {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))
# 得到最好那次的模型
if phase == 'valid' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
state = {
'state_dict': model.state_dict(),
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
}
torch.save(state, filename)
if phase == 'valid':
val_acc_history.append(epoch_acc)
valid_losses.append(epoch_loss)
scheduler.step(epoch_loss)
if phase == 'train':
train_acc_history.append(epoch_acc)
train_losses.append(epoch_loss)
print('Optimizer learning rate : {:.7f}'.format(optimizer.param_groups[0]['lr']))
LRs.append(optimizer.param_groups[0]['lr'])
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# 训练完后用最好的一次当做模型最终的结果
model.load_state_dict(best_model_wts)
return model, val_acc_history, train_acc_history, valid_losses, train_losses, LRs
"""""""""""""""训练模型函数end"""""""""""""""
""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
"""""""""""""""加载并修改models中提供的resnet模型open"""""""""""""""
"""直接用训练的好权重当做初始化参数"""
#可选的比较多 ['resnet', 'alexnet', 'vgg', 'squeezenet', 'densenet', 'inception']
model_name = 'resnet'
#是否用人家训练好的特征来做,true用人家权重
feature_extract = True
# 是否用GPU训练
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#设置哪些层需要训练
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)
#GPU计算
model_ft = model_ft.to(device) #device这里放置的是gpu
#模型保存
filename='checkpoint.pth'
# 是否训练所有层
params_to_update = model_ft.parameters()
print("Params to learn:")
if feature_extract:
params_to_update = []
for name,param in model_ft.named_parameters():
if param.requires_grad == True:
params_to_update.append(param)
print("\t",name)
else:
for name,param in model_ft.named_parameters():
if param.requires_grad == True:
print("\t",name)
#优化器设置
optimizer_ft = optim.Adam(params_to_update, lr=1e-2) #lr学习率
#（传入优化器，迭代了多少后要变换学习率，学习率要*多少）
scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)#学习率每7个epoch衰减成原来的1/10
#最后一层已经LogSoftmax()了，所以不能nn.CrossEntropyLoss()来计算了，nn.CrossEntropyLoss()相当于logSoftmax()和nn.NLLLoss()整合
#定义损失函数
criterion = nn.NLLLoss()
"""""""""""""""加载并修改models中提供的resnet模型end"""""""""""""""
"""""""""""""""开始训练全连接层（0-19）open"""""""""""""""
model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs = train_model(model_ft, dataloaders, criterion, optimizer_ft, num_epochs=20, is_inception=(model_name=="inception"))
"""""""""""""""开始训练全连接层（0-19）end"""""""""""""""
"""""""""""""""再继续训练所有层（0-9）open"""""""""""""""
for param in model_ft.parameters():
param.requires_grad = True #所有层都变成true去训练
# 再继续训练所有的参数，学习率调小一点
optimizer = optim.Adam(params_to_update, lr=1e-4) #lr学习率变大一点
scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
# 损失函数
criterion = nn.NLLLoss()
# 在之前训练好的层上再去做训练
checkpoint = torch.load(filename) #传入路径
best_acc = checkpoint['best_acc'] #当前最好的一次准确率
model_ft.load_state_dict(checkpoint['state_dict']) #模型当前结果读进来
optimizer.load_state_dict(checkpoint['optimizer'])
#model_ft.class_to_idx = checkpoint['mapping']
#调用函数，再训练一遍
model_ft, val_acc_history, train_acc_history, valid_losses, train_losses, LRs = train_model(model_ft, dataloaders, criterion, optimizer, num_epochs=10, is_inception=(model_name=="inception"))
"""""""""""""""再继续训练所有层（0-9）end"""""""""""""""
"""""""""""""""测试网络效果open"""""""""""""""
"""probs, classes = predict ('flower_test.jpg', model_ft) """
"""print(probs) """
"""print(classes) """
"""""""""""""""测试网络效果end"""""""""""""""
import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
import torch.optim as optim
import torchvision
#pip install torchvision 需要提前安装好这个模块
from torchvision import transforms, models, datasets
#https://pytorch.org/docs/stable/torchvision/index.html
import imageio
import time
import warnings
import random
import sys
import copy
import json
from PIL import Image
#数据集，标签
from flower_dataset import dataloaders, cat_to_name
#处理照片数据函数，检测照片预处理函数，展示一张照片函数
from flower_function import im_convert, process_image, imshow
"""""""""""""""flower_model中在本程序需要用到的参数和函数本程序中重新写一遍open"""""""""""""""
"""""""""""""""这样就不用调用flower_model程序，就不用再次训练模型了open"""""""""""""""
"""相关参数open"""
feature_extract = True
model_name = 'resnet'
train_on_gpu = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
"""相关参数open"""
"""""""""""""""冻结神经网络权重函数open"""""""""""""""
def set_parameter_requires_grad(model, feature_extracting):
if feature_extracting: #这里为true
for param in model.parameters():
param.requires_grad = False #把除了最后全连接层，前面所有层权重冻结不能修改
"""""""""""""""冻结神经网络权重函数end"""""""""""""""
"""""""""""""""修改全连接层函数open"""""""""""""""
#（模型名字、得到类别个数、模型权重、
def initialize_model(model_name, num_classes, feature_extract, use_pretrained=True):
# 选择合适的模型，不同模型的初始化方法稍微有点区别
model_ft = None
input_size = 0
if model_name == "resnet":
""" Resnet152
"""
#加载模型（下载）
model_ft = models.resnet152(pretrained=use_pretrained)
#有选择性的选需要冻住哪些层
set_parameter_requires_grad(model_ft, feature_extract)
#取出最后一层
num_ftrs = model_ft.fc.in_features
#重新做全连接层（102这里需要修改，因为本任务分类类别是102）
model_ft.fc = nn.Sequential(nn.Linear(num_ftrs, 102),
nn.LogSoftmax(dim=1))
input_size = 224
elif model_name == "alexnet":
""" Alexnet
"""
model_ft = models.alexnet(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
input_size = 224
elif model_name == "vgg":
""" VGG11_bn
"""
model_ft = models.vgg16(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs,num_classes)
input_size = 224
elif model_name == "squeezenet":
""" Squeezenet
"""
model_ft = models.squeezenet1_0(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
model_ft.num_classes = num_classes
input_size = 224
elif model_name == "densenet":
""" Densenet
"""
model_ft = models.densenet121(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, num_classes)
input_size = 224
elif model_name == "inception":
""" Inception v3
Be careful, expects (299,299) sized images and has auxiliary output
"""
model_ft = models.inception_v3(pretrained=use_pretrained)
set_parameter_requires_grad(model_ft, feature_extract)
# Handle the auxilary net
num_ftrs = model_ft.AuxLogits.fc.in_features
model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)
# Handle the primary net
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs,num_classes)
input_size = 299
else:
print("Invalid model name, exiting...")
exit()
return model_ft, input_size
"""""""""""""""修改全连接层函数end"""""""""""""""
"""""""""""""""flower_model中在本程序需要用到的参数和函数本程序中重新写一遍end"""""""""""""""
"""""""""""""""这样就不用调用flower_model程序，就不用再次训练模型了end"""""""""""""""
"""""""""""""""加载测试模型open"""""""""""""""
#加载模型
model_ft, input_size = initialize_model(model_name, 102, feature_extract, use_pretrained=True)
# GPU模式
model_ft = model_ft.to(device)
#保存文件的名字
filename='checkpoint.pth'
# 加载模型
checkpoint = torch.load(filename)
best_acc = checkpoint['best_acc']
model_ft.load_state_dict(checkpoint['state_dict'])
"""""""""""""""加载测试模型end"""""""""""""""
"""""""""""""""设置检测图像数据open"""""""""""""""
image_path = 'flower_test.jpg'
img1 = process_image(image_path) #预处理一下
imshow(img1) #展示函数
# 得到一个batch的测试数据（一次处理8张照片），在这里用模型进行检测
dataiter = iter(dataloaders['valid'])
images, labels = dataiter.next()
model_ft.eval()
if train_on_gpu:
output = model_ft(images.cuda())
else:
output = model_ft(images)
#得到概率最大的那个
_, preds_tensor = torch.max(output, 1)
preds = np.squeeze(preds_tensor.numpy()) if not train_on_gpu else np.squeeze(preds_tensor.cpu().numpy())
"""""""""""""""设置检测图像数据open"""""""""""""""
"""""""""""""""设置展示界面open"""""""""""""""
#设置展示预测结果，这张照片最像的前八类
fig=plt.figure(figsize=(20, 20))
columns = 4
rows = 2
#2*4展示出来
for idx in range (columns*rows):
ax = fig.add_subplot(rows, columns, idx+1, xticks=[], yticks=[])
plt.imshow(im_convert(images[idx]))
ax.set_title("{} ({})".format(cat_to_name[str(preds[idx])], cat_to_name[str(labels[idx].item())]),color=("green" if cat_to_name[str(preds[idx])] == cat_to_name[str(labels[idx].item())] else "red"))
plt.show()
"""""""""""""""设置展示界面end"""""""""""""""