mnist_a_softmax.py

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 torch.autograd import Variable
from torch.nn import Parameter
from torchvision import datasets, transforms

class AngleLinear(nn.Module):
    def __init__(self, in_features, out_features, m = 4):
        super(AngleLinear, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.Tensor(in_features,out_features))
        self.weight.data.uniform_(-1, 1).renorm_(2,1,1e-5).mul_(1e5)
        self.m = m
        self.mlambda = [
            lambda x: x**0,
            lambda x: x**1,
            lambda x: 2*x**2-1,
            lambda x: 4*x**3-3*x,
            lambda x: 8*x**4-8*x**2+1,
            lambda x: 16*x**5-20*x**3+5*x
        ]

    def forward(self, input):
        x = input   # size=(B,F)    F is feature len
        w = self.weight # size=(F,Classnum) F=in_features Classnum=out_features

        ww = w.renorm(2,1,1e-5).mul(1e5)
        xlen = x.pow(2).sum(1).pow(0.5) # size=B
        wlen = ww.pow(2).sum(0).pow(0.5) # size=Classnum

        cos_theta = x.mm(ww) # size=(B,Classnum)
        cos_theta = cos_theta / xlen.view(-1,1) / wlen.view(1,-1)
        cos_theta = cos_theta.clamp(-1,1)

        cos_m_theta = self.mlambda[self.m](cos_theta)
        theta = Variable(cos_theta.data.acos())
        k = (self.m*theta/3.14159265).floor()
        n_one = k*0.0 - 1
        phi_theta = (n_one**k) * cos_m_theta - 2*k

        cos_theta = cos_theta * xlen.view(-1,1)
        phi_theta = phi_theta * xlen.view(-1,1)
        output = (cos_theta,phi_theta)
        return output # size=(B,Classnum,2)


class AngleLoss(nn.Module):
    def __init__(self, gamma=0, test=False, mode='mean'):
        super(AngleLoss, self).__init__()
        self.gamma   = gamma
        self.it = 0
        self.LambdaMin = 5.0
        self.LambdaMax = 1500.0
        self.lamb = 1500.0
        self.test = test
        self.mode = mode

    def forward(self, input, target):
        self.it += 1
        cos_theta,phi_theta = input
        target = target.view(-1,1) #size=(B,1)

        index = cos_theta.data * 0.0 #size=(B,Classnum)
        index.scatter_(1,target.data.view(-1,1),1)
        index = index.byte()
        index = Variable(index)

        output = cos_theta * 1.0 #size=(B,Classnum)
        if self.test:
            output[index] = phi_theta[index]
        else:
            self.lamb = max(self.LambdaMin,self.LambdaMax/(1+0.1*self.it ))
            output[index] -= cos_theta[index]*(1.0+0)/(1+self.lamb)
            output[index] += phi_theta[index]*(1.0+0)/(1+self.lamb)

        logpt = F.log_softmax(output, dim=-1)
        logpt = logpt.gather(1,target)
        logpt = logpt.view(-1)
        pt = Variable(logpt.data.exp())

        loss = -1 * (1-pt)**self.gamma * logpt
        if self.mode=='mean':
            loss = loss.mean()
        else:
            loss = loss.sum()

        return loss

class Net(nn.Module):
    def __init__(self, feature=False):
        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 = AngleLinear(500, 10)
        self.feature = feature

    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))
        if self.feature:
            return x
        x = self.fc2(x)
        return x

def train(args, model, device, train_loader, optimizer, epoch):
    loss_func = AngleLoss()
    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 = loss_func(output, target)
        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()))

def test(args, model, device, test_loader):
    loss_func = AngleLoss(test=True,mode='sum')
    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 += loss_func(output, target).item() # sum up batch loss
            pred = output[0].argmax(dim=1, keepdim=True) # get the index of the max 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=10, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                        help='learning rate (default: 0.01)')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    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')
    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 = {'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)


    model = Net().to(device)
    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)
        test(args, model, device, test_loader)

    if (args.save_model):
        torch.save(model.state_dict(),"mnist_cnn.pt")

if __name__ == '__main__':
    main()