Pytorch模型训练套路总结
模型训练
1.完整的模型训练套路
（1）首先导入数据集
import torchvision.datasets
train_data = torchvision.datasets.CIFAR10("dataset", train=True,
transform=torchvision.transforms.ToTensor(),
download=True)
test_data = torchvision.datasets.CIFAR10("dataset", train=False,
transform=torchvision.transforms.ToTensor(),
download=True)
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练集的长度为：{}".format(train_data_size))
print("测试集的长度为：{}".format(test_data_size))Files already downloaded and verified
Files already downloaded and verified
训练集的长度为：50000
测试集的长度为：10000（2）利用DataLoader加载数据集
from torch.utils.data import DataLoader
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)（3） 搭建神经网络
规范写法中，神经网络的架构都被存放于一个叫做model的Python中，此外，为了验证模型中的参数设置是否正确，使用如下的方法进行验证，看输出参数是否是10个
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
class ZBX(nn.Module):
def __init__(self):
super(ZBX, self).__init__()
self.model = nn.Sequential(
Conv2d(3, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, kernel_size=5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(64*4*4, 64),
Linear(64, 10)
)
def forward(self, x):
x = self.model(x)
return x
if __name__ == '__main__':
zbx = ZBX()
input = torch.ones((64, 3, 32, 32))
output = zbx(input)
print(output.shape)torch.Size([64, 10])将上面的模型放入到model.py文件中以后，便可以在train.py文件中使用如下命令调用此模型
from model import *（4）创建网络模型
在train文件中直接使用下面的命令即可调用model文件中的模型
zbx = ZBX()（5）损失函数
这里使用交叉熵损失函数
loss_fn = nn.CrossEntropyLoss()（6）优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(zbx.parameters(), lr=learning_rate)（7）设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10（8）训练
训练框架（不包括测试）：
for i in range(epoch):
print("-------------第{}轮训练开始-------------".format(i+1))
# 训练步骤开始
for data in train_dataloader:
imgs, targets = data
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_train_step = total_train_step + 1
print("训练次数：{}.Loss：{}".format(total_train_step, loss.item()))（9）对模型进行测试
训练+测试的框架：
for i in range(epoch):
print("-------------第{}轮训练开始-------------".format(i+1))
# 训练步骤开始
for data in train_dataloader:
imgs, targets = data
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
print("训练次数：{}.Loss：{}".format(total_train_step, loss.item()))
total_test_loss = 0.0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
print("整体测试集上的Loss:{}".format(total_test_loss))（10）添加tensorboard以观察训练进度
训练+测试+数据可视化框架：
# 添加tensorboard
writer = SummaryWriter("logs_train")
for i in range(epoch):
print("-------------第{}轮训练开始-------------".format(i+1))
# 训练步骤开始
for data in train_dataloader:
imgs, targets = data
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
print("训练次数：{}.Loss：{}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
total_test_loss = 0.0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
total_test_step = total_test_step + 1
print("整体测试集上的Loss:{}".format(total_test_loss))
writer.add_scalar("test_loss", total_test_loss, total_test_step)（11）保存模型
在每一轮结束后添加下面的语句即可
torch.save(zbx, "zbx_{}.pth".format(i+1))
print("模型已保存")（12）关于正确率（分类问题的衡量指标）
比如，我们有两个输入，2*input,放到模型中可以得到一个输出，比如这是一个二分类问题
第一个输入得到的结果为,第二个输入得到的结果为
中的表示对第一个类别预测的概率，表示对第二个类别预测的概率
这样便可以得出，模型在第一个输出上得到的结果为1，第二个也为1，因为在第一个类别上输出的概率最大（这里以0为起始点）
假设真实输入的target中第一个是0类别，第二个是1类别
如何从，这个形式转化为这个形式呢？
可以使用argmax函数preds=([1],[1]) input_target=([0],[1])
比较preds 和input_target，得到的结果为[false, true]
使用sum函数，即[false, true].sum()得到结果为1
import torch
outputs = torch.tensor([[0.1, 0.2],
[0.3, 0.4]])
print(outputs.argmax(1))tensor([1, 1])preds = outputs.argmax(1)
targets = torch.tensor([0, 1])
print(preds == targets)tensor([False, True])print((preds == targets).sum())tensor(1)根据上面的代码可以在训练过程中添加整体正确率这一参数，来观察模型的预测准确度
# 添加tensorboard
writer = SummaryWriter("logs_train")
for i in range(epoch):
print("-------------第{}轮训练开始-------------".format(i+1))
# 训练步骤开始
for data in train_dataloader:
imgs, targets = data
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
print("训练次数：{}.Loss：{}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
# 测试步骤开始
total_test_loss = 0.0
total_accuracy = 0.0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
total_test_step = total_test_step + 1
print("整体测试集上的Loss:{}".format(total_test_loss))
print("整体测试集上的正确率：{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
torch.save(zbx, "zbx_{}.pth".format(i+1))
print("模型已保存")
writer.close()（13）一些细节
在训练开始之前，有些人会添加一句
zbx.train()表示训练开始
在测试开始之前，也会添加一句
zbx.eval()表示测试状态开始，如果你的模型中含有Dropout层等一些特殊层的话，在测试的时候会关掉
总结
model.py
import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
class ZBX(nn.Module):
def __init__(self):
super(ZBX, self).__init__()
self.model = nn.Sequential(
Conv2d(3, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, kernel_size=5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(64*4*4, 64),
Linear(64, 10)
)
def forward(self, x):
x = self.model(x)
return x
if __name__ == '__main__':
zbx = ZBX()
input = torch.ones((64, 3, 32, 32))
output = zbx(input)
print(output.shape)train.py
import torch.optim
import torchvision.datasets
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from model import *
#准备数据集
train_data = torchvision.datasets.CIFAR10("dataset", train=True,
transform=torchvision.transforms.ToTensor(),
download=True)
test_data = torchvision.datasets.CIFAR10("dataset", train=False,
transform=torchvision.transforms.ToTensor(),
download=True)
# length长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练集的长度为：{}".format(train_data_size))
print("测试集的长度为：{}".format(test_data_size))
# 使用DataLoader加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
# 创建网络模型
zbx = ZBX()
# 损失函数
loss_fn = nn.CrossEntropyLoss()
# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(zbx.parameters(), lr=learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10
# 添加tensorboard
writer = SummaryWriter("logs_train")
for i in range(epoch):
print("-------------第{}轮训练开始-------------".format(i+1))
# 训练步骤开始
for data in train_dataloader:
imgs, targets = data
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
print("训练次数：{}.Loss：{}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
total_test_loss = 0.0
total_accuracy = 0.0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
total_test_step = total_test_step + 1
print("整体测试集上的Loss:{}".format(total_test_loss))
print("整体测试集上的正确率：{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
torch.save(zbx, "zbx_{}.pth".format(i+1))
# torch.save(zbx.state_dict(),"zbx_{}.pth".format(i+1))
print("模型已保存")
writer.close()2.使用GPU进行训练
第一种方式（不常用）
含有CUDA方法的只有以下几个部分有
1.网络模型
2.数据（输入，标注）
3.损失函数
在使用cuda方法是只需要条加上
xxx = xxx.cuda()这条语句即可
使用下面的代码train.py+model.py合并，分别在gpu和cpu上面训练，可以比较训练所花费的时间
import torch.optim
import torchvision.datasets
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
import time
#准备数据集
train_data = torchvision.datasets.CIFAR10("dataset", train=True,
transform=torchvision.transforms.ToTensor(),
download=True)
test_data = torchvision.datasets.CIFAR10("dataset", train=False,
transform=torchvision.transforms.ToTensor(),
download=True)
# length长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练集的长度为：{}".format(train_data_size))
print("测试集的长度为：{}".format(test_data_size))
# 使用DataLoader加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
# 创建网络模型
class ZBX(nn.Module):
def __init__(self):
super(ZBX, self).__init__()
self.model = nn.Sequential(
Conv2d(3, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, kernel_size=5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(64*4*4, 64),
Linear(64, 10)
)
def forward(self, x):
x = self.model(x)
return x
zbx = ZBX()
if torch.cuda.is_available():
zbx = zbx.cuda() #网络模型转移到CUDA上面去
# 损失函数
loss_fn = nn.CrossEntropyLoss()
if torch.cuda.is_available():
loss_fn = loss_fn.cuda()
# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(zbx.parameters(), lr=learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10
# 添加tensorboard
writer = SummaryWriter("logs_train")
start_time = time.time()
for i in range(epoch):
print("-------------第{}轮训练开始-------------".format(i+1))
# 训练步骤开始
for data in train_dataloader:
imgs, targets = data
if torch.cuda.is_available():
imgs = imgs.cuda()
targets = targets.cuda()
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
end_time = time.time()
print(end_time - start_time)
print("训练次数：{}.Loss：{}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
total_test_loss = 0.0
total_accuracy = 0.0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
if torch.cuda.is_available():
imgs = imgs.cuda()
targets = targets.cuda()
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
total_test_step = total_test_step + 1
print("整体测试集上的Loss:{}".format(total_test_loss))
print("整体测试集上的正确率：{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
torch.save(zbx, "zbx_{}.pth".format(i+1))
# torch.save(zbx.state_dict(),"zbx_{}.pth".format(i+1))
print("模型已保存")
writer.close()在cpu上进行训练，每100轮所用时间最快为4秒,谷歌colab的cpu达到了每100轮10秒，转移到谷歌colab实验台的gpu上，每100轮训练大概仅需一秒
第二种方式（较为常用）
import torch.optim
import torchvision.datasets
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
import time
# 定义训练的设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#准备数据集
train_data = torchvision.datasets.CIFAR10("dataset", train=True,
transform=torchvision.transforms.ToTensor(),
download=True)
test_data = torchvision.datasets.CIFAR10("dataset", train=False,
transform=torchvision.transforms.ToTensor(),
download=True)
# length长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练集的长度为：{}".format(train_data_size))
print("测试集的长度为：{}".format(test_data_size))
# 使用DataLoader加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
# 创建网络模型
class ZBX(nn.Module):
def __init__(self):
super(ZBX, self).__init__()
self.model = nn.Sequential(
Conv2d(3, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, kernel_size=5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(64*4*4, 64),
Linear(64, 10)
)
def forward(self, x):
x = self.model(x)
return x
zbx = ZBX()
zbx = zbx.to(device)
# 损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn = loss_fn.to(device)
# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(zbx.parameters(), lr=learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10
# 添加tensorboard
writer = SummaryWriter("logs_train")
start_time = time.time()
for i in range(epoch):
print("-------------第{}轮训练开始-------------".format(i+1))
# 训练步骤开始
for data in train_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
end_time = time.time()
print(end_time - start_time)
print("训练次数：{}.Loss：{}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
total_test_loss = 0.0
total_accuracy = 0.0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
total_test_step = total_test_step + 1
print("整体测试集上的Loss:{}".format(total_test_loss))
print("整体测试集上的正确率：{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
torch.save(zbx, "zbx_{}.pth".format(i+1))
# torch.save(zbx.state_dict(),"zbx_{}.pth".format(i+1))
print("模型已保存")
writer.close()3.完整的模型训练套路
利用已经训练好的模型，然后给它提供输入
首先将图片转化为模型要求的输入格式
import torchvision.transforms
from PIL import Image
image_path = "D:\\Micro_Climate_Summer_Task\\Pytorch_For_Deep_Learning\\imgs\\dog.jpg"
image = Image.open(image_path)
print(image)
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32, 32)),
torchvision.transforms.ToTensor()])
image = transform(image)
print(image.shape)<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=488x331 at 0x21C2D241190>
torch.Size([3, 32, 32])然后加载网络模型
class ZBX(nn.Module):
def __init__(self):
super(ZBX, self).__init__()
self.model = nn.Sequential(
Conv2d(3, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, kernel_size=5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(64*4*4, 64),
Linear(64, 10)
)
def forward(self, x):
x = self.model(x)
return x
model = torch.load("zbx_1.pth")
print(model)ZBX(
(model): Sequential(
(0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Flatten(start_dim=1, end_dim=-1)
(7): Linear(in_features=1024, out_features=64, bias=True)
(8): Linear(in_features=64, out_features=10, bias=True)
)
)最后便能得到每个种类所预测的概率
image = torch.reshape(image,(1,3,32,32))
model.eval()
with torch.no_grad():
output = model(image)
print(output)tensor([[-3.1763, -1.0918, 1.0762, 1.2570, 2.6293, 1.1636, 3.1605, 2.2872,
-4.5042, -1.1769]])print(output.argmax(1))tensor([6])此模型只被训练了一轮，要查看训练30轮的情况，可以在colab上直接复制模型
import torch.optim
import torchvision.datasets
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
import time
# 定义训练的设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#准备数据集
train_data = torchvision.datasets.CIFAR10("dataset", train=True,
transform=torchvision.transforms.ToTensor(),
download=True)
test_data = torchvision.datasets.CIFAR10("dataset", train=False,
transform=torchvision.transforms.ToTensor(),
download=True)
# length长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练集的长度为：{}".format(train_data_size))
print("测试集的长度为：{}".format(test_data_size))
# 使用DataLoader加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)
# 创建网络模型
class ZBX(nn.Module):
def __init__(self):
super(ZBX, self).__init__()
self.model = nn.Sequential(
Conv2d(3, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, kernel_size=5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(64*4*4, 64),
Linear(64, 10)
)
def forward(self, x):
x = self.model(x)
return x
zbx = ZBX()
zbx = zbx.to(device)
# 损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn = loss_fn.to(device)
# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(zbx.parameters(), lr=learning_rate)
# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 30
# 添加tensorboard
writer = SummaryWriter("logs_train")
start_time = time.time()
for i in range(epoch):
print("-------------第{}轮训练开始-------------".format(i+1))
# 训练步骤开始
for data in train_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
# 优化器优化模型
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_train_step = total_train_step + 1
if total_train_step % 100 == 0:
end_time = time.time()
print(end_time - start_time)
print("训练次数：{}.Loss：{}".format(total_train_step, loss.item()))
writer.add_scalar("train_loss", loss.item(), total_train_step)
total_test_loss = 0.0
total_accuracy = 0.0
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
imgs = imgs.to(device)
targets = targets.to(device)
outputs = zbx(imgs)
loss = loss_fn(outputs, targets)
total_test_loss = total_test_loss + loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
total_test_step = total_test_step + 1
print("整体测试集上的Loss:{}".format(total_test_loss))
print("整体测试集上的正确率：{}".format(total_accuracy/test_data_size))
writer.add_scalar("test_loss", total_test_loss, total_test_step)
writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
torch.save(zbx, "zbx_{}.pth".format(i+1))
# torch.save(zbx.state_dict(),"zbx_{}.pth".format(i+1))
print("模型已保存")
writer.close()运行以后再将图片传入30轮训练以后得到的模型中。
test.py
import torch
import torchvision.transforms
from PIL import Image
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
image_path = "D:\\Micro_Climate_Summer_Task\\Pytorch_For_Deep_Learning\\imgs\\plane.png"
image = Image.open(image_path)
print(image)
image = image.convert('RGB')
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32, 32)),
torchvision.transforms.ToTensor()])
image = transform(image)
print(image.shape)
class ZBX(nn.Module):
def __init__(self):
super(ZBX, self).__init__()
self.model = nn.Sequential(
Conv2d(3, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 32, kernel_size=5, padding=2),
MaxPool2d(2),
Conv2d(32, 64, kernel_size=5, padding=2),
MaxPool2d(2),
Flatten(),
Linear(64*4*4, 64),
Linear(64, 10)
)
def forward(self, x):
x = self.model(x)
return x
model = torch.load("zbx_29.pth",map_location=torch.device('cpu'))
print(model)
image = torch.reshape(image, (1, 3, 32, 32))
model.eval()
with torch.no_grad():
output = model(image)
print(output)
print(output.argmax(1))完结撒花！！！
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