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cifar_lwr.py
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cifar_lwr.py
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from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from LearningWithRetrospection import LWR
from torch.utils.tensorboard import SummaryWriter
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(12544, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
return x
global_step = 0
def train(
args, model, device, train_loader, optimizer, epoch, lwr, writer, k, snapshot=None,
):
model.train()
for i, (batch_idx, data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
if args.usekl:
output = model(data)
loss = lwr(batch_idx, output, target, eval=False)
else:
output = model(data)
if epoch >= k:
assert snapshot != None
previous_output = snapshot(data)
else:
previous_output = None
loss = lwr(batch_idx, output, target, previous_output, eval=False)
loss.backward()
optimizer.step()
global global_step
writer.add_scalar("train/loss", loss.item(), global_step)
global_step += 1
if i % args.log_interval == 0:
print(
"Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
epoch,
i * len(data),
len(train_loader.dataset),
100.0 * i / len(train_loader),
loss.item(),
)
)
if args.dry_run:
break
def test(model, device, test_loader, writer):
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)
# sum up batch loss
test_loss += F.cross_entropy(output, target, reduction="sum").item()
# get the index of the max log-probability
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
global global_step
writer.add_scalar("test/loss", test_loss, global_step)
writer.add_scalar("test/acc", correct / len(test_loader.dataset), global_step)
print(
"\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
test_loss,
correct,
len(test_loader.dataset),
100.0 * correct / len(test_loader.dataset),
)
)
class DatasetWrapper(torch.utils.data.Dataset):
def __init__(self, ds):
self.ds = ds
def __len__(self):
return len(self.ds)
def __getitem__(self, idx):
return idx, self.ds[idx][0], self.ds[idx][1]
def main():
# Training settings
parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
parser.add_argument(
"--batch-size",
type=int,
default=256,
metavar="N",
help="input batch size for training (default: 64)",
)
parser.add_argument(
"--test-batch-size",
type=int,
default=1000,
metavar="N",
help="input batch size for testing (default: 1000)",
)
parser.add_argument(
"--epochs",
type=int,
default=20,
metavar="N",
help="number of epochs to train (default: 14)",
)
parser.add_argument(
"--lr",
type=float,
default=1.0,
metavar="LR",
help="learning rate (default: 1.0)",
)
parser.add_argument(
"--gamma",
type=float,
default=0.7,
metavar="M",
help="Learning rate step gamma (default: 0.7)",
)
parser.add_argument(
"--no-cuda", action="store_true", default=False, help="disables CUDA training"
)
parser.add_argument(
"--dry-run",
action="store_true",
default=False,
help="quickly check a single pass",
)
parser.add_argument(
"--seed", type=int, default=1, metavar="S", help="random seed (default: 1)"
)
parser.add_argument(
"--log-interval",
type=int,
default=10,
metavar="N",
help="how many batches to wait before logging training status",
)
parser.add_argument(
"--save-model",
action="store_true",
default=False,
help="For Saving the current Model",
)
parser.add_argument(
"--usekl",
action="store_true",
default=True,
help="Use KL Divergence loss | Uses L1 loss from https://arxiv.org/abs/2006.13593 if False",
)
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
train_kwargs = {"batch_size": args.batch_size}
test_kwargs = {"batch_size": args.test_batch_size}
if use_cuda:
cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
train_kwargs.update(cuda_kwargs)
test_kwargs.update(cuda_kwargs)
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
)
dataset1 = datasets.CIFAR10("./", train=True, download=False, transform=transform)
dataset1 = DatasetWrapper(dataset1)
dataset2 = datasets.CIFAR10("./", train=False, transform=transform)
dataset2 = DatasetWrapper(dataset2)
lwr = LWR(
k=5,
update_rate=0.9,
num_batches_per_epoch=len(dataset1) // train_kwargs["batch_size"],
dataset_length=len(dataset1),
output_shape=(10,),
tau=5,
max_epochs=20,
softmax_dim=1,
use_kl=args.usekl,
)
writer = SummaryWriter("./logs/lwr/")
train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
model = Net().to(device)
snapshot = None
optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
for epoch in range(1, args.epochs + 1):
if (not args.usekl) and (epoch % k == 0):
snapshot = deepcopy(model)
train(
args,
model,
device,
train_loader,
optimizer,
epoch,
lwr,
writer,
k,
snapshot=snapshot,
)
test(model, device, test_loader, writer)
scheduler.step()
if args.save_model:
torch.save(model.state_dict(), "cifar10_cnn.pt")
if __name__ == "__main__":
main()