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import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from torchvision import transforms
import torchvision
from torch.utils.data import DataLoader
transform = transforms.Compose([
transforms.Resize((512, 512)),
transforms.ToTensor(),
])
batch_size = 4
train_set = torchvision.datasets.ImageFolder(root='data/cat_vs_dog/train', transform=transform)
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True,
num_workers=0) # Batch Size定义：一次训练所选取的样本数。 Batch Size的大小影响模型的优化程度和速度。
test_dataset = torchvision.datasets.ImageFolder(root='data/cat_vs_dog/test', transform=transform)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True,
num_workers=0) # Batch Size定义：一次训练所选取的样本数。 Batch Size的大小影响模型的优化程度和速度。
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
net = torchvision.models.resnet50(weights=True)
num_ftrs = net.fc.in_features
net.fc = nn.Linear(num_ftrs, 2) # 将输出维度修改为2
criterion = nn.CrossEntropyLoss()
net = net.to(device)
optimizer = torch.optim.Adam(lr=0.0001, params=net.parameters())
eposhs = 100
for epoch in range(eposhs):
correct = 0
sum_loss = 0
total = 0
for inputs, labels in tqdm(train_loader):
inputs = inputs.to(device)
labels = labels.to(device)
output = net(inputs)
loss = criterion(output, labels)
total = total + labels.size(0)
optimizer.zero_grad()
_, predicted = torch.max(output.data, 1)
loss.backward()
optimizer.step()
correct = correct + (predicted == labels).sum().item()
acc = correct / total
print('准确率是:', acc)
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