digit_classification.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 torchvision import datasets, transforms
from glob import glob
import matplotlib.pyplot as plt
import cv2
import cfg


global_model = None  # lazy initialization
global_features = None


def get_model():
    global global_model
    if global_model is None:
        global_model = Net()
        state_dict_path = cfg.data_dir + "mnist_cnn.pt"
        state_dict = torch.load(state_dict_path)
        global_model.load_state_dict(state_dict)
    return global_model


def get_reference_features():
    global global_features
    if global_features is None:
        global_features = []
        for label in range(1, 8):
            file_paths = glob(f'{cfg.reference_digits_dir}/{label}/*.png')
            features_i = []
            for img_path in file_paths:
                img = cv2.imread(img_path)[:, :, 0]
                features_i.append(get_features(img).unsqueeze(0))
            features_i = torch.cat(features_i, dim=0)
            global_features.append(features_i)
        global_features = torch.cat([f.unsqueeze(0) for f in global_features])
    return global_features


def get_batch(img):
    batch = torch.tensor(img, dtype=torch.float32).view(1, 1, 28, 28)
    transform = transforms.Normalize((0.1307,), (0.3081,))
    batch = transform(batch)
    return batch


def classify_img_by_examples(img, show=False):
    features = get_features(img)
    reference_features = get_reference_features()
    min_mses = ((reference_features - features)**2).mean(axis=2).min(axis=1).values
    pred = min_mses.argmin().item() + 1
    show_prediction(get_batch(img), pred, min_mses) if show else None
    return pred


def show_prediction(batch, prediction, mses):
    plt.gray()
    plt.imshow(batch.view(28, 28, 1))
    mses = [f'{mse:.2f}' for mse in mses]
    plt.gcf().suptitle(f'{prediction}                    {mses}')
    plt.show()


def get_features(img):
    model = get_model()
    batch = get_batch(img)
    pred, features = model(batch)
    return features[0]


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout(0.25)
        self.dropout2 = nn.Dropout(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)
        self.bn1 = nn.BatchNorm2d(32)
        self.bn2 = nn.BatchNorm2d(64)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.bn1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = self.bn2(x)
        x = torch.flatten(x, 1)
        features = self.fc1(x)
        x = F.relu(features)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output, features


def train(args, 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.nll_loss(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()))
            if args.dry_run:
                break


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('\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=256, 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=14, metavar='N',
                        help='number of epochs to train (default: 14)')
    parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
                        help='learning rate (default: 1.0)')
    parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
                        help='Learning rate step gamma (default: 0.7)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--dry-run', action='store_true', default=False,
                        help='quickly check a single pass')
    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=True,
                        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")

    train_kwargs = {'batch_size': args.batch_size}
    test_kwargs = {'batch_size': args.test_batch_size}
    if use_cuda:
        cuda_kwargs = {'num_workers': 1,
                       'pin_memory': True,
                       'shuffle': True}
        train_kwargs.update(cuda_kwargs)
        test_kwargs.update(cuda_kwargs)

    transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
        ])
    dataset1 = datasets.MNIST('training_data', train=True, download=True,
                       transform=transform)
    dataset2 = datasets.MNIST('training_data', train=False,
                       transform=transform)
    train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)
    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)

    model = Net().to(device)
    optimizer = optim.Adam(model.parameters(), lr=2e-4, weight_decay=0.01)

    # scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)
        test(model, device, test_loader)
        # scheduler.step()

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


if __name__ == '__main__':
    main()