[dnn/conv] Added bias and forward stride
This commit is contained in:
Philippe Tillet
@@ -1,117 +1,142 @@
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import argparse
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import triton
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'''Train CIFAR10 with PyTorch.'''
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from __future__ import print_function
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torchvision import datasets, transforms
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import torch.nn.functional as F
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import torch.backends.cudnn as cudnn
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torch.manual_seed(0)
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.conv1 = nn.Conv2d(1, 20, (5,5))
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self.conv2 = nn.Conv2d(20, 50, (5,5))
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self.fc1 = nn.Linear(4*4*50, 500)
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self.fc2 = nn.Linear(500, 10)
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import torchvision
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import torchvision.transforms as transforms
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = F.max_pool2d(x, 2, 2)
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x = F.relu(self.conv2(x))
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x = F.max_pool2d(x, 2, 2)
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x = x.view(-1, 4*4*50)
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x = F.relu(self.fc1(x))
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x = self.fc2(x)
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return F.log_softmax(x, dim=1)
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import os
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import argparse
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def train(args, model, device, train_loader, optimizer, epoch):
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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data, target = data.to(device), target.to(device)
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from resnet import *
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from utils import progress_bar
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parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')
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parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
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parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
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args = parser.parse_args()
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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best_acc = 0 # best test accuracy
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start_epoch = 0 # start from epoch 0 or last checkpoint epoch
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# Data
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print('==> Preparing data..')
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transform_train = transforms.Compose([
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transforms.RandomCrop(32, padding=4),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
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])
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transform_test = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
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])
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trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
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trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
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testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
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testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
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classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
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# Model
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print('==> Building model..')
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# net = VGG('VGG19')
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net = ResNet18()
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# net = PreActResNet18()
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# net = GoogLeNet()
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# net = DenseNet121()
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# net = ResNeXt29_2x64d()
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# net = MobileNet()
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# net = MobileNetV2()
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# net = DPN92()
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# net = ShuffleNetG2()
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# net = SENet18()
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#net = ShuffleNetV2(1)
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net = net.to(device)
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if device == 'cuda':
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net = torch.nn.DataParallel(net)
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cudnn.benchmark = True
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if args.resume:
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# Load checkpoint.
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print('==> Resuming from checkpoint..')
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assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
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checkpoint = torch.load('./checkpoint/ckpt.t7')
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net.load_state_dict(checkpoint['net'])
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best_acc = checkpoint['acc']
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start_epoch = checkpoint['epoch']
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
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# Training
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def train(epoch):
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print('\nEpoch: %d' % epoch)
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net.train()
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train_loss = 0
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correct = 0
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total = 0
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for batch_idx, (inputs, targets) in enumerate(trainloader):
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inputs, targets = inputs.to(device), targets.to(device)
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optimizer.zero_grad()
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output = model(data)
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loss = F.nll_loss(output, target)
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outputs = net(inputs)
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loss = criterion(outputs, targets)
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loss.backward()
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optimizer.step()
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if batch_idx % args.log_interval == 0:
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print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
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epoch, batch_idx * len(data), len(train_loader.dataset),
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100. * batch_idx / len(train_loader), loss.item()))
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def test(args, model, device, test_loader):
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model.eval()
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train_loss += loss.item()
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_, predicted = outputs.max(1)
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total += targets.size(0)
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correct += predicted.eq(targets).sum().item()
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progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
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% (train_loss/(batch_idx+1), 100.*correct/total, correct, total))
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def test(epoch):
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global best_acc
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net.eval()
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test_loss = 0
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correct = 0
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total = 0
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with torch.no_grad():
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for data, target in test_loader:
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data, target = data.to(device), target.to(device)
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output = model(data)
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test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
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pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
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correct += pred.eq(target.view_as(pred)).sum().item()
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for batch_idx, (inputs, targets) in enumerate(testloader):
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inputs, targets = inputs.to(device), targets.to(device)
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outputs = net(inputs)
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loss = criterion(outputs, targets)
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test_loss /= len(test_loader.dataset)
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test_loss += loss.item()
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_, predicted = outputs.max(1)
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total += targets.size(0)
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correct += predicted.eq(targets).sum().item()
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print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
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test_loss, correct, len(test_loader.dataset),
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100. * correct / len(test_loader.dataset)))
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progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
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% (test_loss/(batch_idx+1), 100.*correct/total, correct, total))
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def main():
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# Training settings
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parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
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parser.add_argument('--batch-size', type=int, default=64, metavar='N',
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help='input batch size for training (default: 64)')
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parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
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help='input batch size for testing (default: 1000)')
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parser.add_argument('--epochs', type=int, default=10, metavar='N',
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help='number of epochs to train (default: 10)')
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parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
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help='learning rate (default: 0.01)')
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parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
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help='SGD momentum (default: 0.5)')
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parser.add_argument('--no-cuda', action='store_true', default=False,
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help='disables CUDA training')
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parser.add_argument('--seed', type=int, default=1, metavar='S',
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help='random seed (default: 1)')
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parser.add_argument('--log-interval', type=int, default=10, metavar='N',
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help='how many batches to wait before logging training status')
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parser.add_argument('--save-model', action='store_true', default=False,
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help='For Saving the current Model')
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args = parser.parse_args()
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use_cuda = not args.no_cuda and torch.cuda.is_available()
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torch.manual_seed(args.seed)
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device = torch.device("cuda" if use_cuda else "cpu")
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kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
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train_loader = torch.utils.data.DataLoader(
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datasets.MNIST('../data', train=True, download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))
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])),
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batch_size=args.batch_size, shuffle=True, **kwargs)
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test_loader = torch.utils.data.DataLoader(
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datasets.MNIST('../data', train=False, transform=transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.1307,), (0.3081,))
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])),
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batch_size=args.test_batch_size, shuffle=True, **kwargs)
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# Save checkpoint.
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acc = 100.*correct/total
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if acc > best_acc:
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print('Saving..')
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state = {
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'net': net.state_dict(),
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'acc': acc,
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'epoch': epoch,
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}
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if not os.path.isdir('checkpoint'):
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os.mkdir('checkpoint')
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torch.save(state, './checkpoint/ckpt.t7')
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best_acc = acc
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model = Net().to(device)
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optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
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for epoch in range(1, args.epochs + 1):
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train(args, model, device, train_loader, optimizer, epoch)
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test(args, model, device, test_loader)
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if (args.save_model):
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torch.save(model.state_dict(),"mnist_cnn.pt")
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if __name__ == '__main__':
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main()
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for epoch in range(start_epoch, start_epoch+200):
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train(epoch)
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test(epoch)
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