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code.py
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code.py
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import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import math
import tqdm
class Net(nn.Module):
def __init__(self):
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.MaxPool2d(2,2),
nn.Dropout(0.2),
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(True),
nn.MaxPool2d(2,2),
nn.Dropout(0.2),
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(True),
nn.MaxPool2d(2,2),
nn.Dropout(0.2),
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(True),
nn.MaxPool2d(2,2),
nn.Dropout(0.2),
)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Linear(512 * 2 * 2, 4096),
nn.ReLU(True),
nn.Dropout(0.2),
nn.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(0.2),
nn.Linear(4096, 1000),
nn.ReLU(True),
nn.Dropout(0.5),
nn.Linear(1000, 10)
)
def forward(self, x):
x = self.features(x)
x = self.classifier(x)
return x
def get_cifar10():
train_transform = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
)
train_dataset = torchvision.datasets.CIFAR10(
"./data/CIFAR10", train=True, transform=train_transform, download=True
)
test_transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
]
)
test_dataset = torchvision.datasets.CIFAR10(
"./data/CIFAR10", train=False, transform=test_transform, download=True
)
return train_dataset, test_dataset
def train_and_test(net, train_loader, test_loader, optimiser):
net.train()
running_loss = []
for inputs, labels in tqdm.tqdm(train_loader):
inputs = inputs.cuda()
labels = labels.cuda()
optimiser.zero_grad()
outputs = net(inputs)
loss = F.cross_entropy(outputs, labels)
loss.backward()
optimiser.step()
running_loss.append(loss.item())
avg_loss = sum(running_loss) / len(running_loss)
print("Training average loss: {}".format(avg_loss))
net.eval()
running_loss = 0.0
running_correct = 0
for inputs, labels in test_loader:
with torch.no_grad():
inputs = inputs.cuda()
labels = labels.cuda()
outputs = net(inputs)
running_loss += F.cross_entropy(outputs, labels, reduction = "sum")
outputs = outputs.max(1)[1]
running_correct += outputs.eq(labels.view_as(outputs)).sum().item()
avg_loss = running_loss / len(test_loader.dataset)
percent_correct = running_correct * 100.0 / len(test_loader.dataset)
print("Testing loss: {}, Accuracy: {}/10000 ({}%)".format(avg_loss, running_correct, percent_correct))
return percent_correct
train_dataset, test_dataset = get_cifar10()
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=128, shuffle=True
)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=128, shuffle=False
)
net = Net()
net = net.cuda()
optimiser = optim.SGD(net.parameters(), lr = 0.05, momentum = 0.9, weight_decay=5e-4)
scheduler = optim.lr_scheduler.StepLR(optimiser, step_size = 30, gamma = 0.1)
best = 0.0
for epoch in range(200):
print("Epoch: {}, LR: {}".format(epoch, optimiser.param_groups[0]['lr']))
p = train_and_test(net, train_loader, test_loader, optimiser)
if p > best:
torch.save(net.state_dict(), "cifar_net.pth")
best = p
print("(Best: {}%)".format(best))
scheduler.step()
torch.save(net.state_dict(), "cifar_net.pth")