mirror of
https://github.com/tencentmusic/cube-studio.git
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187 lines
7.6 KiB
Python
187 lines
7.6 KiB
Python
from __future__ import print_function
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# pip install tensorboardX torch torchvision --index-url https://mirrors.aliyun.com/pypi/simple
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import argparse
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import os
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import datetime, time
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from tensorboardX import SummaryWriter
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from torchvision import datasets, transforms
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import torch
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import torch.distributed as dist
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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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import pysnooper
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WORLD_SIZE = int(os.environ.get('WORLD_SIZE', 1))
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# 可以先下载数据到data目录
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class MyMNIST(datasets.MNIST):
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mirrors = ['https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/']
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# resources = [
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# ("https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/train-images-idx3-ubyte.gz",
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# "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
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# ("https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/train-labels-idx1-ubyte.gz",
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# "d53e105ee54ea40749a09fcbcd1e9432"),
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# ("https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/t10k-images-idx3-ubyte.gz",
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# "9fb629c4189551a2d022fa330f9573f3"),
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# ("https://docker-76009.sz.gfp.tencent-cloud.com/kubeflow/pytorch/example/data/t10k-labels-idx1-ubyte.gz",
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# "ec29112dd5afa0611ce80d1b7f02629c")
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# ]
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# 定义模型框架
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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, 1)
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self.conv2 = nn.Conv2d(20, 50, 5, 1)
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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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# 前向计算
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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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# 训练过程
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def train(args, model, device, train_loader, optimizer, epoch, writer):
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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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optimizer.zero_grad()
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output = model(data)
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loss = F.nll_loss(output, target)
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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={:.4f}'.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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niter = epoch * len(train_loader) + batch_idx
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writer.add_scalar('loss', loss.item(), niter)
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# 测试计算计算损失值
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def test(args, model, device, test_loader, writer, epoch):
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model.eval()
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test_loss = 0
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correct = 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.max(1, keepdim=True)[1] # 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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test_loss /= len(test_loader.dataset)
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print('\naccuracy={:.4f}\n'.format(float(correct) / len(test_loader.dataset)))
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writer.add_scalar('accuracy', float(correct) / len(test_loader.dataset), epoch)
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# 计算是不是分布式
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def should_distribute():
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return dist.is_available() and WORLD_SIZE > 1
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# 计算是不是分布式
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def is_distributed():
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return dist.is_available() and dist.is_initialized()
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@pysnooper.snoop()
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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=1, 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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parser.add_argument('--dir', default='logs', metavar='L',
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help='directory where summary logs are stored')
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if dist.is_available():
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parser.add_argument('--backend', type=str, help='Distributed backend',
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choices=[dist.Backend.GLOO, dist.Backend.NCCL, dist.Backend.MPI],
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default=dist.Backend.NCCL)
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args = parser.parse_args()
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print('reveice args:', args)
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# args.no_cuda = True
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# args.backend = dist.Backend.GLOO
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use_cuda = not args.no_cuda and torch.cuda.is_available()
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if use_cuda:
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print('Using CUDA')
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else:
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args.backend = dist.Backend.GLOO
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writer = SummaryWriter(args.dir)
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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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if should_distribute():
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print('Using distributed PyTorch with {} backend'.format(args.backend))
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dist.init_process_group(backend=args.backend)
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kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
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print('bengin load train data %s' % str(datetime.datetime.now()))
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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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print('bengin load test data %s' % str(datetime.datetime.now()))
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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=False, **kwargs)
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print('bengin make net model %s' % str(datetime.datetime.now()))
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model = Net().to(device)
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if is_distributed():
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Distributor = nn.parallel.DistributedDataParallel if use_cuda \
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else nn.parallel.DistributedDataParallelCPU
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model = Distributor(model)
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optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
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print('bengin train model %s' % str(datetime.datetime.now()))
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for epoch in range(1, args.epochs + 1):
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train(args, model, device, train_loader, optimizer, epoch, writer)
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test(args, model, device, test_loader, writer, epoch)
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print('bengin save model %s' % str(datetime.datetime.now()))
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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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print('begin python shell %s' % str(datetime.datetime.now()))
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main()
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print('end python shell %s' % str(datetime.datetime.now()))
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