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import csv
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Normalize, Compose
from normalizer import Normalizer
transform = Compose([
ToTensor(),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
training_data = datasets.CIFAR10(
root='data',
train=True,
download=True,
transform=transform
)
test_data = datasets.CIFAR10(
root='data',
train=False,
download=True,
transform=transform
)
batch_size = 128
train_dataloader = DataLoader(training_data, batch_size=batch_size,shuffle=True)
test_dataloader = DataLoader(test_data, batch_size=batch_size)
for X, y in test_dataloader:
print(f"Shape of X [N,C,H,W]:{X.shape}")
print(f"Shape of y:{y.shape}{y.dtype}")
break
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"using {device} device")
class NormalizerImageClassification(Normalizer):
def __init__(
self,
image_size=32,
patch_size=4,
in_channels=3,
num_classes=10,
d_model = 256,
num_tokens = 64,
num_layers=4,
):
super().__init__(d_model, num_tokens, num_layers)
self.patcher = nn.Conv2d(
in_channels, d_model, kernel_size=patch_size, stride=patch_size
)
self.classifier = nn.Linear(d_model, num_classes)
def forward(self, x):
patches = self.patcher(x)
batch_size, num_channels, _, _ = patches.shape
patches = patches.permute(0, 2, 3, 1)
patches = patches.view(batch_size, -1, num_channels)
embedding = self.model(patches)
embedding = embedding.mean(dim=1)
out = self.classifier(embedding)
return out
model = NormalizerImageClassification().to(device)
print(model)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(),lr=1e-3)
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
num_batches = len(dataloader)
model.train()
train_loss = 0
correct = 0
for batch, (X,y) in enumerate(dataloader):
X, y = X.to(device), y.to(device)
pred = model(X)
loss = loss_fn(pred,y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
_, labels = torch.max(pred.data, 1)
correct += labels.eq(y.data).type(torch.float).sum()
if batch % 100 == 0:
loss, current = loss.item(), batch * len(X)
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
train_loss /= num_batches
train_accuracy = 100. * correct.item() / size
print(f"Train Metric: \n Accuracy: {(100*correct):>0.1f}% \n")
return train_loss,train_accuracy
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for X,y in dataloader:
X,y = X.to(device), y.to(device)
pred = model(X)
test_loss += loss_fn(pred, y).item()
correct += (pred.argmax(1) == y).type(torch.float).sum().item()
test_loss /= num_batches
correct /= size
print(f"Test Metrics: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
test_accuracy = 100*correct
return test_loss, test_accuracy
logname = "/PATH/Normalizer/Experiments_cifar10/logs_normalizer/logs_cifar10.csv"
if not os.path.exists(logname):
with open(logname, 'w') as logfile:
logwriter = csv.writer(logfile, delimiter=',')
logwriter.writerow(['epoch', 'train loss', 'train acc',
'test loss', 'test acc'])
epochs = 100
for epoch in range(epochs):
print(f"Epoch {epoch+1}\n-----------------------------------")
train_loss, train_acc = train(train_dataloader, model, loss_fn, optimizer)
test_loss, test_acc = test(test_dataloader, model, loss_fn)
with open(logname, 'a') as logfile:
logwriter = csv.writer(logfile, delimiter=',')
logwriter.writerow([epoch+1, train_loss, train_acc,
test_loss, test_acc])
print("Done!")
path = "/PATH/Normalizer/Experiments_cifar10/weights_normalizer"
model_name = "NormalizerImageClassification_cifar10"
torch.save(model.state_dict(), f"{path}/{model_name}.pth")
print(f"Saved Model State to {path}/{model_name}.pth ")
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