JSmodel.py

import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import coremltools as ct

PATH = './JScifar_net'

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 240)
        self.fc2 = nn.Linear(240, 120)
        self.fc3 = nn.Linear(120, 84)
        self.fc4 = nn.Linear(84, 60)
        self.fc5 = nn.Linear(60, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = F.relu(self.fc4(x))
        x = self.fc5(x)
        return x

# functions to show an image
def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))
    plt.show()

def train():
    trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                            download=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                              shuffle=True, num_workers=2)
    net = Net()
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
    for epoch in range(10):  # loop over the dataset multiple times
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data
            # zero the parameter gradients
            optimizer.zero_grad()
            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            # print statistics
            running_loss += loss.item()
            if i % 2000 == 1999:  # print every 2000 mini-batches
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 2000))
                running_loss = 0.0
    print('Finished Training')
    torch.save(net.state_dict(), PATH + '.pth')

def test():
    testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                           download=True, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                             shuffle=False, num_workers=2)
    net = Net()
    net.load_state_dict(torch.load(PATH + '.pth'))

    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: %d %%' % (
            100 * correct / total))

def toOnnx():
    dummy_input = torch.rand(1, 3, 32, 32)
    input_names = ['image']
    output_names = ['classLabelProbs']
    model = Net()
    model.load_state_dict(torch.load(PATH + '.pth'))
    torch.onnx.export(model,
                      dummy_input,
                      PATH + '.onnx',
                      verbose=True,
                      input_names=input_names,
                      output_names=output_names)

def toMlmodel():
    class_labels = ['air plane', 'automobile', 'bird', 'cat', 'deer', 'dog',
                    'frog', 'horse', 'ship', 'truck']
    model = convert(model=PATH + '.onnx',
                    minimum_ios_deployment_target='13', image_input_names=['image'],
                    mode='classifier',
                    predicted_feature_name='classLabel',
                    class_labels=class_labels)
    model.save(PATH + '.mlmodel')

if __name__ == '__main__':
    train()
    test()
    toOnnx()
    toMlmodel()