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example.py
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example.py
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# This is a simple example of comparing a standard cross entropy loss
# and using the differentiable argmax with Fenchel Young loss.
# The standard PyTorch tutorial code is used as a baseline, with minor
# changes to show how to use the differentable optimizer.
# Comparison results of a simple network, trained over 50 epochs
# | MODE
# Results (%) | 0 1 2
# ----------------------------------------
# Accuracy of 0 : 77 51 75
# Accuracy of 1 : 81 52 68
# Accuracy of 2 : 45 17 47
# Accuracy of 3 : 45 25 46
# Accuracy of 4 : 58 28 63
# Accuracy of 5 : 51 32 60
# Accuracy of 6 : 65 60 69
# Accuracy of 7 : 72 53 67
# Accuracy of 8 : 75 61 67
# Accuracy of 9 : 67 52 80
#
# Note:
# These results are not meant to be SOTA, but simply to show how once
# can use these perturbed optimizers.
# Mode 0 = Cross entropy loss
# Mode 1 = argmax with MSE Loss
# Mode 2 = argmax with Fenchel Young Loss
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import perturbations
import fenchel_young as fy
BATCH_SIZE = 32
EPOCHS = 50
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Simple model
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def argmax(x, axis=-1):
return F.one_hot(torch.argmax(x, dim=axis), list(x.shape)[axis]).float()
pert_argmax = perturbations.perturbed(argmax, num_samples=100, sigma=0.5, noise='gumbel', batched=True, device=device)
def train_epoch(model, data_loader, criterion, optimizer):
model.train()
running_loss = 0.0
for data in data_loader:
inputs, labels = data
inputs = inputs.to(device)
labels = labels.to(device)
# If using argmax, we need to convert single index into one-hot of length 10
if not isinstance(criterion, nn.CrossEntropyLoss):
labels = F.one_hot(labels, 10).float()
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(inputs)
if isinstance(criterion, nn.MSELoss):
outputs = pert_argmax(outputs)
loss = criterion(outputs, labels).mean()
loss.backward()
optimizer.step()
running_loss += loss.item()
epoch_loss = running_loss / len(data_loader)
return epoch_loss
def test(model, data_loader):
model.eval()
class_correct = list(0. for _ in range(10))
class_total = list(0. for _ in range(10))
with torch.no_grad():
for data in data_loader:
images, labels = data
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
c = (predicted == labels).squeeze()
for i in range(len(c)):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1
for i in range(10):
print('Accuracy of %5s : %2d %%' % (i, 100 * class_correct[i] / class_total[i]))
if __name__ == "__main__":
mode_choices = [0, 1, 2]
parser = argparse.ArgumentParser(description='Perturbations Demo.')
parser.add_argument("--mode", dest='mode', choices=mode_choices, default=0, type=int,
help="0: Cross Entropy Loss, 1: argmax with MSE, 2: argmax with Fenchel Young Loss")
args = parser.parse_args()
assert args.mode in mode_choices
model = Net()
model.to(device)
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# Set loss from mode
if args.mode == 0:
print("Using cross entropy loss")
criterion = nn.CrossEntropyLoss()
elif args.mode == 1:
print("Using argmax with MSE loss")
criterion = nn.MSELoss()
else:
print("Using argmax with Fenchel Young loss")
criterion = fy.FenchelYoungLoss(argmax, num_samples=100, sigma=0.5, noise='gumbel', batched=True, device=device)
# Train
for epoch in range(EPOCHS):
loss = train_epoch(model, trainloader, criterion, optimizer)
print('Epoch {:>3}, Loss: {:.4f}'.format(epoch+1, loss))
# Test
test(model, testloader)