# Code was taken from https://github.com/wangjuan001/hicplus
import torch.nn as nn
import torch.nn.functional as F

conv2d1_filters_numbers = 8
conv2d1_filters_size = 9
conv2d2_filters_numbers = 8
conv2d2_filters_size = 1
conv2d3_filters_numbers = 1
conv2d3_filters_size = 5

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, conv2d1_filters_numbers, conv2d1_filters_size)
        self.conv2 = nn.Conv2d(conv2d1_filters_numbers, conv2d2_filters_numbers, conv2d2_filters_size)
        self.conv3 = nn.Conv2d(conv2d2_filters_numbers, 1, conv2d3_filters_size)

    def forward(self, x):
        #print("start forwardingf")
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.conv3(x)
        x = F.relu(x)
        return x