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September 27, 2020 03:31
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benchmark pytorch MNIST mixed precision training by Apex
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""" | |
This script is modified from https://github.com/pytorch/examples.git | |
""" | |
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 apex import amp | |
import time | |
class Net(nn.Module): | |
def __init__(self): | |
super(Net, self).__init__() | |
self.conv1 = nn.Conv2d(1, 32, 3, 1) | |
self.conv2 = nn.Conv2d(32, 64, 3, 1) | |
self.dropout1 = nn.Dropout2d(0.25) | |
self.dropout2 = nn.Dropout2d(0.5) | |
self.fc1 = nn.Linear(9216, 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) | |
output = F.log_softmax(x, dim=1) | |
return output | |
def train(args, model, device, train_loader, optimizer, epoch): | |
start_time = time.time() | |
model.train() | |
for batch_idx, (data, target) in enumerate(train_loader): | |
data, target = data.to(device), target.to(device) | |
optimizer.zero_grad() | |
output = model(data) | |
loss = F.nll_loss(output, target) | |
# loss.backward() | |
with amp.scale_loss(loss, optimizer) as scaled_loss: | |
scaled_loss.backward() | |
optimizer.step() | |
if batch_idx % args.log_interval == 0: | |
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( | |
epoch, batch_idx * len(data), len(train_loader.dataset), | |
100. * batch_idx / len(train_loader), loss.item())) | |
if args.dry_run: | |
break | |
end_time=time.time() | |
print("training used time %.5f sec" %(end_time-start_time)) | |
def test(model, device, test_loader): | |
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) | |
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( | |
test_loss, correct, len(test_loader.dataset), | |
100. * correct / len(test_loader.dataset))) | |
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('--test-batch-size', type=int, default=1000, metavar='N', | |
help='input batch size for testing (default: 1000)') | |
parser.add_argument('--epochs', type=int, default=14, 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('--opt-level', type=str) | |
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") | |
kwargs = {'batch_size': args.batch_size} | |
if use_cuda: | |
kwargs.update({'num_workers': 1, | |
'pin_memory': True, | |
'shuffle': True}, | |
) | |
transform=transforms.Compose([ | |
transforms.ToTensor(), | |
transforms.Normalize((0.1307,), (0.3081,)) | |
]) | |
dataset1 = datasets.MNIST('../data', train=True, download=True, | |
transform=transform) | |
dataset2 = datasets.MNIST('../data', train=False, | |
transform=transform) | |
train_loader = torch.utils.data.DataLoader(dataset1,**kwargs) | |
test_loader = torch.utils.data.DataLoader(dataset2, **kwargs) | |
model = Net().to(device) | |
optimizer = optim.Adadelta(model.parameters(), lr=args.lr) | |
model, optimizer = amp.initialize(model, optimizer, opt_level=args.opt_level) | |
scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) | |
for epoch in range(1, args.epochs + 1): | |
train(args, model, device, train_loader, optimizer, epoch) | |
test(model, device, test_loader) | |
scheduler.step() | |
if args.save_model: | |
torch.save(model.state_dict(), "mnist_cnn.pt") | |
if __name__ == '__main__': | |
main() |
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