CNN.py

from torch import nn
from torchsummary import summary

class CNNNetwork(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
        )
        self.conv4 = nn.Sequential(
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=2),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
        )
        self.flatten = nn.Flatten()
        self.linear1 = nn.Linear(in_features=128 * 5 * 4, out_features=128)
        self.linear2 = nn.Linear(in_features=128, out_features=2)

    def forward(self, input_data):
        x = self.conv1(input_data)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.linear2(x)
        output1 = x[:, 0]
        output2 = x[:, 1]
        return output1, output2


model = CNNNetwork().cuda()
summary(model, (1, 64, 44))