#! /usr/bin/env python3
#! _*_ coding: utf-8 _*_

from __future__ import print_function
import os
import sys
import torch
import os.path as op
import torch.nn as nn
import torch.nn.functional as F
from tensorboardX import SummaryWriter

# Convolutional neural network
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)
        self.bn = nn.BatchNorm2d(20)

    def forward(self, x):
        x = F.max_pool2d(self.conv1(x), 2)
        x = F.relu(x) + F.relu(-x)
        x = F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)
        x = F.relu(x)
        x = self.bn(x)
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = F.softmax(self.fc2(x), dim=1)
        return x

def main():

    model = ConvNet()

    input = torch.rand(32, 1, 28, 28)
    with SummaryWriter(log_dir=op.join(sys.path[0], 'logs/'), comment='ConvNet') as w:
        w.add_graph(model, (input, ))
    
if __name__ == '__main__':
    main()
