训练模型

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

# 定义网络结构
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # 输入第二维需要为784
        self.hidden1 = nn.Linear(784, 5120, bias=False)
        self.output = nn.Linear(5120, 10, bias=False)

    def forward(self, x):
        x = x.view(x.size()[0], -1)
        x = F.relu((self.hidden1(x)))
        x = F.dropout(x, 0.2)
        x = self.output(x)
        return F.log_softmax(x)

def train(model, device, train_loader, optimizer, epoch):
    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.cross_entropy(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 10 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]	Loss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                       100. * batch_idx / len(train_loader), loss.item()))

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('
Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)
'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

device = torch.device("cpu")

batch_size=64

kwargs={}

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('.', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor()
                   ])),
    batch_size=batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('.', train=False, transform=transforms.Compose([
        transforms.ToTensor()
    ])),
    batch_size=1000, shuffle=True, **kwargs)

model = Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
optimizer = optim.Adam(model.parameters())

for epoch in range(1, 2 + 1):
    train(model, device, train_loader, optimizer, epoch)
    test(model, device, test_loader)

保存模型

# 必须采用state_dict的保存方式，支持异地部署
torch.save(model.state_dict(), "pytorch_mnist/mnist_mlp.pt")

推理代码

在模型代码推理文件customize_service.py中，需要添加一个子类，该子类继承对应模型类型的父类，各模型类型的父类名称和导入语句如请参考表1。

from PIL import Image
import log
from model_service.pytorch_model_service import PTServingBaseService
import torch.nn.functional as F

import torch.nn as nn
import torch
import json

import numpy as np

logger = log.getLogger(__name__)

import torchvision.transforms as transforms

# 定义模型预处理
infer_transformation = transforms.Compose([
    transforms.Resize((28,28)),
    # 需要处理成pytorch tensor
    transforms.ToTensor()
])


import os


class PTVisionService(PTServingBaseService):

    def __init__(self, model_name, model_path):
        # 调用父类构造方法
        super(PTVisionService, self).__init__(model_name, model_path)
        # 调用自定义函数加载模型
        self.model = Mnist(model_path)
        # 加载标签
        self.label = [0,1,2,3,4,5,6,7,8,9]
        # 亦可通过文件标签文件加载
        # model目录下放置label.json文件，此处读取
        dir_path = os.path.dirname(os.path.realpath(self.model_path))
        with open(os.path.join(dir_path, 'label.json')) as f:
            self.label = json.load(f)


    def _preprocess(self, data):

        preprocessed_data = {}
        for k, v in data.items():
            input_batch = []
            for file_name, file_content in v.items():
                with Image.open(file_content) as image1:
                    # 灰度处理
                    image1 = image1.convert("L")
                    if torch.cuda.is_available():
                        input_batch.append(infer_transformation(image1).cuda())
                    else:
                        input_batch.append(infer_transformation(image1))
            input_batch_var = torch.autograd.Variable(torch.stack(input_batch, dim=0), volatile=True)
            print(input_batch_var.shape)
            preprocessed_data[k] = input_batch_var

        return preprocessed_data

    def _postprocess(self, data):
        results = []
        for k, v in data.items():
            result = torch.argmax(v[0])
            result = {k: self.label[result]}
            results.append(result)
        return results

    def _inference(self, data):

        result = {}
        for k, v in data.items():
            result[k] = self.model(v)

        return result

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.hidden1 = nn.Linear(784, 5120, bias=False)
        self.output = nn.Linear(5120, 10, bias=False)

    def forward(self, x):
        x = x.view(x.size()[0], -1)
        x = F.relu((self.hidden1(x)))
        x = F.dropout(x, 0.2)
        x = self.output(x)
        return F.log_softmax(x)



def Mnist(model_path, **kwargs):
    # 生成网络
    model = Net()
    # 加载模型
    if torch.cuda.is_available():
        device = torch.device('cuda')
        model.load_state_dict(torch.load(model_path, map_location="cuda:0"))
    else:
        device = torch.device('cpu')
        model.load_state_dict(torch.load(model_path, map_location=device))
    # CPU或者GPU映射
    model.to(device)
    # 声明为推理模式
    model.eval()

    return model

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