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| import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
BATCH_SIZE=32
EPOCHS=20
DEVICE = torch.device("cuda")
transforms = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
train_path = "D:\\ship\\train"
train_dataset = datasets.ImageFolder(train_path, transform=transforms)
test_path = "D:\\ship\\test"
test_dataset = datasets.ImageFolder(test_path, transform=transforms)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=6)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=6)
class AlexNet(nn.Module):
def __init__(self):
super(AlexNet, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(3, 96, 11, 4),
nn.ReLU(),
nn.MaxPool2d(3, 2),
nn.Conv2d(96, 256, 5, 1, 2),
nn.ReLU(),
nn.MaxPool2d(3, 2),
nn.Conv2d(256, 384, 3, 1, 1),
nn.ReLU(),
nn.Conv2d(384, 384, 3, 1, 1),
nn.ReLU(),
nn.Conv2d(384, 256, 3, 1, 1),
nn.ReLU(),
nn.MaxPool2d(3, 2)
)
self.fc = nn.Sequential(
nn.Linear(256*5*5, 4096),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(4096, 4096),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(4096, 2),
)
def forward(self, img):
feature = self.conv(img)
output = self.fc(feature.view(img.shape[0], -1))
return output
model=AlexNet().to(DEVICE)
weights = [1.0,3]
class_weights = torch.FloatTensor(weights).to(DEVICE)
criterion = nn.CrossEntropyLoss(weight=class_weights)
optimizer = torch.optim.Adam(model.parameters(), lr=0.00005)
def train(model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output=model(data)
loss=criterion(output,target)
loss.backward()
optimizer.step()
if (batch_idx + 1) % 30 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test(model,device,test_loader):
model.eval()
test_loss=0
correct=0
with torch.no_grad():
for data,target in test_loader:
data,target=data.to(device),target.to(device)
output=model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
for epoch in range(EPOCHS):
train(model,DEVICE,train_loader,optimizer,epoch)
test(model,DEVICE,test_loader)
MODEL_PATH = "./Model.pth"
torch.save(model,MODEL_PATH)
|
