#!/usr/bin/env python3

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 torch.optim.lr_scheduler import StepLR

TAKE_IMAGE_FROM_TEST_SET_AND_EXIT = False

# Use False to indicate load model from disk
TRAIN_MODEL = True

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(14*14, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.max_pool2d(x, 2)
        x = x.reshape(-1, 1*14*14)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        output = F.softmax(x, dim=1)
        return output

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()))


def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            if TAKE_IMAGE_FROM_TEST_SET_AND_EXIT:
                ############################## Take one image from test set ######################################
                from matplotlib import pyplot as plt
                img_np = data[0].numpy()
                img = img_np.ravel().tolist()

                # Print as C++ array literals:
                print("{" + ",".join(list(map(lambda x: str(x) + "f", img))) + "}")
                plt.imshow(img_np[0], interpolation='nearest')
                plt.show()
                exit(0)
                ############################## Take one image from test set ######################################
            data, target = data.to(device), target.to(device)
            output = model(data)
            # print(output[1])
            # exit(0)
            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=64,
                        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('--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=False,
                        help='For Saving the current Model')
    parser.add_argument('--export-onnx',
                        action='store_true',
                        default=False,
                        help="For exporting models to onnx protobuf.")
    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")

    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
    train_loader = torch.utils.data.DataLoader(datasets.MNIST(
        'data',
        train=True,
        download=True,
        transform=transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.1307, ), (0.3081, ))
        ])),
                                               batch_size=args.batch_size,
                                               shuffle=True,
                                               **kwargs)
    test_loader = torch.utils.data.DataLoader(datasets.MNIST(
        'data',
        train=False,
        transform=transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.1307, ), (0.3081, ))
        ])),
                                              batch_size=args.test_batch_size,
                                              shuffle=True,
                                              **kwargs)

    model = Net().to(device)
    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

    if TRAIN_MODEL:
        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()
    else:
        model.load_state_dict(torch.load("mnist_cnn.pt"))
        test(model, device, test_loader)

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

    if args.export_onnx:
        input_names = ["image"]
        output_names = ["prediction"]
        dummy_input = torch.randn(1, 1, 28, 28)
        torch.onnx.export(model,
                          dummy_input,
                          "mnist.onnx",
                          verbose=True,
                          input_names=input_names,
                          output_names=output_names)


if __name__ == '__main__':
    main()
