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test_fd.py
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test_fd.py
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"""MNIST example.
Based on https://github.com/pytorch/examples/blob/master/mnist/main.py
"""
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import tqdm
from tensorboardX import SummaryWriter
from torchvision import datasets, transforms
from pytorch_fd import FDLR
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5, 1)
self.conv2 = nn.Conv2d(20, 50, 5, 1)
self.fc1 = nn.Linear(4*4*50, 500)
self.fc2 = nn.Linear(500, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2, 2)
x = x.view(-1, 4*4*50)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
def train(args, model, device, train_loader, optimizer, epoch, event_writer, scheduler):
model.train()
tqdm_bar = tqdm.tqdm(train_loader)
for batch_idx, (data, target) in enumerate(tqdm_bar):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
scheduler.step()
optimizer.step()
if batch_idx % args.log_interval == 0:
step = (batch_idx+1) * len(data) + (epoch-1) * len(train_loader.dataset)
event_writer.add_scalar('loss', loss, scheduler.last_epoch)
event_writer.add_scalar('lr', optimizer.param_groups[0]['lr'], scheduler.last_epoch)
tqdm_bar.set_description(
f'Train epoch {epoch} Loss: {loss.item():.6f}')
def test(args, model, device, test_loader, event_writer:SummaryWriter, epoch):
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)
test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
acc = correct / len(test_loader.dataset)
event_writer.add_scalar('loss/test_loss', test_loss, epoch - 1)
event_writer.add_scalar('loss/test_acc', acc, epoch - 1)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * acc))
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--scheduler', type=str, default='cosine', choices=['cosine', 'fd', 'step'],
help='which scheduler to use')
parser.add_argument('--optimizer', type=str, default='adam', choices=['adam', 'sgd'],
help='which scheduler to use')
parser.add_argument('--test-batch-size', type=int, default=64, metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=6, metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.0025, metavar='LR',
help='learning rate (default: 0.0025)')
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')
args = parser.parse_args()
use_cuda = torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
writer = SummaryWriter()
model = Net().to(device)
if args.optimizer == 'adam':
optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(.9, .99))
else:
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=.9, weight_decay=0.01)
if args.scheduler == 'cosine':
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, int(1e6))
elif args.scheduler == 'step':
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=200, gamma=0.1)
else:
scheduler = FDLR(optimizer, epsilon=.01, gamma=0.1, writer=writer, use_mom=args.optimizer=='adam')
try:
for epoch in range(1, args.epochs + 1):
train(args, model, device, train_loader, optimizer, epoch, writer, scheduler)
test(args, model, device, test_loader, writer, epoch)
except KeyboardInterrupt:
print('Exit early')
if __name__ == '__main__':
main()