# Source code for deeprobust.image.netmodels.CNN

    """
    This is an implementatio of a Convolution Neural Network with 2 Convolutional layer.
    """

    from __future__ import print_function
    import argparse
    import torch
    import torch.nn as nn
    import torch.nn.functional as F #233
    import torch.optim as optim
    from torchvision import datasets, transforms
    import numpy as np
    from PIL import Image

    class Net(nn.Module):
        """Model counterparts.
        """

        def __init__(self, in_channel1 = 1, out_channel1 = 32, out_channel2 = 64, H = 28, W = 28):
            super(Net, self).__init__()
            self.H = H
            self.W = W
            self.out_channel2 = out_channel2

            ## define two convolutional layers
            self.conv1 = nn.Conv2d(in_channels = in_channel1,
                                   out_channels = out_channel1,
                                   kernel_size = 5,
                                   stride= 1,
                                   padding = (2,2))
            self.conv2 = nn.Conv2d(in_channels = out_channel1,
                                   out_channels = out_channel2,
                                   kernel_size = 5,
                                   stride = 1,
                                   padding = (2,2))

            ## define two linear layers
            self.fc1 = nn.Linear(8 * 8 * out_channel2, 1024)
            self.fc2 = nn.Linear(1024, 10)

        def forward(self, x):
            x = F.relu(self.conv1(x))
            x = F.max_pool2d(x, 2, 2)
            x = F.relu(self.conv2(x))
            x = F.max_pool2d(x, 2, 2)
            x = x.view(-1, int(self.H/4) * int(self.W/4) * self.out_channel2)
            x = F.relu(self.fc1(x))
            x = self.fc2(x)
            return x

        def get_logits(self, x):
            x = F.relu(self.conv1(x))
            x = F.max_pool2d(x, 2, 2)
            x = F.relu(self.conv2(x))
            x = F.max_pool2d(x, 2, 2)
            x = x.view(-1, 8 * 8 * self.out_channel2)
            x = F.relu(self.fc1(x))
            x = self.fc2(x)
            return x

    def train(model, device, train_loader, optimizer, epoch):
        """train network.

        Parameters
        ----------
        model :
            model
        device :
            device(option:'cpu','cuda')
        train_loader :
            training data loader
        optimizer :
            optimizer
        epoch :
            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()

            #print every 10
            if batch_idx % 10 == 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):
        """test network.

        Parameters
        ----------
        model :
            model
        device :
            device(option:'cpu', 'cuda')
        test_loader :
            testing data 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.cross_entropy(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)))