mnist1n2g_test.py

import os
import argparse
from random import Random

import torch
import torch.nn as nn
from torch.multiprocessing import Process

import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.utils.data
import torch.utils.data.distributed
import torch.optim as optim
from torchvision import datasets, transforms

# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=1024, 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: 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('--init-method', type=str, default='tcp://127.0.0.1:23456')
parser.add_argument('--rank', type=int, default=0)
parser.add_argument('--world-size', type=int, default=2)

args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}

test_dataset = datasets.MNIST('data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ]))



class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x)


model = Net()
if args.cuda:
    model.cuda()
    model = torch.nn.parallel.DistributedDataParallel(model)
model.load_state_dict(torch.load('./pth/mnist1n1g.pth'))


def test():
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        output = model(data)
        test_loss += F.nll_loss(output, target, size_average=False).item() # sum up batch loss
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    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 run(rank, size):
    print(rank, size)
    test()


def init_processes(rank, size, fn, backend='nccl'):
    """ Initialize the distributed environment. """
    # os.environ['MASTER_ADDR'] = '127.0.0.1'
    # os.environ['MASTER_PORT'] = '29500'
    dist.init_process_group(init_method=args.init_method, backend=backend, rank=rank, world_size=size, group_name="pytorch_test")
    fn(rank, size)


if __name__ == "__main__":

    processes = []
    for rank in range(args.world_size):
        p = Process(target=init_processes, args=(rank, args.world_size, run))
        p.start()
        processes.append(p)

    for p in processes:
        p.join()
    # 分发数据
    test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset)
    test_loader = torch.utils.data.DataLoader(test_dataset,
                                              batch_size=args.test_batch_size, shuffle=True, sampler=test_sampler,
                                              **kwargs)
    test_sampler.set_epoch(1)