Fourierizer / train.py
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#imports
import os
import csv
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Normalize, RandomCrop, RandomHorizontalFlip, Compose
from fourierizer import Fourierizer
transform = Compose([
RandomCrop(32, padding=4),
RandomHorizontalFlip(),
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
)
# create dataloaders
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
# size checking for loading images
def check_sizes(image_size, patch_size):
sqrt_num_patches, remainder = divmod(image_size, patch_size)
assert remainder == 0, "`image_size` must be divisibe by `patch_size`"
num_patches = sqrt_num_patches ** 2
return num_patches
# create model
# Get cpu or gpu device for training.
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"using {device} device")
# model definition
class FourierizerImageClassification(Fourierizer):
def __init__(
self,
image_size=32,
patch_size=4,
in_channels=3,
num_classes=10,
d_model=256,
d_ffn=512,
num_layers=4,
dropout=0.5
):
num_patches = check_sizes(image_size, patch_size)
super().__init__(d_model, d_ffn,num_layers,dropout)
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) # global average pooling
out = self.classifier(embedding)
return out
model = FourierizerImageClassification().to(device)
print(model)
# Optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(),lr=1e-3)
# Training Loop
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)
#compute prediction error
pred = model(X)
loss = loss_fn(pred,y)
# backpropagation
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(train_accuracy)
return train_loss,train_accuracy
# Test loop
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 Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
test_accuracy = 100*correct
return test_loss, test_accuracy
# apply train and test
logname = "/PATH/Fourierizer/Experiments_cifar10/logs_fourierizer/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!")
# saving trained model
path = "/PATH/Fourierizer/Experiments_cifar10/weights_fourierizer"
model_name = "FourierizerImageClassification_cifar10"
torch.save(model.state_dict(), f"{path}/{model_name}.pth")
print(f"Saved Model State to {path}/{model_name}.pth ")