Building detection model

Hi brothers.
I am a Korean student majoring in civil engineering.
The professor asked me to submit a building detection model using CNN for my graduation thesis and I really want to die.
I don’t know anything about computer science. I have 2 weeks left to submit, but I have too much to prepare for graduation.
Can you help me? I think there are a lot of talented brothers here, and I need you to help me out.
I’ll be direct. Make me a Deep Learning model.
I’ve got all the datasets.
Anaconda, Python 3.6.7, Piotochgpu, etc.
I know my request is reckless and irresponsible. But I have to. I have never learned and I have no time.
What else should I add here?

import random
from os import listdir
from os.path import isfile, join

import click
import torch
import torch.nn as nn
from torch.autograd import Variable

from model.deconvnet import DeconvNet
from util.data_load import BuildingData
from util.data_load import get_data_loader
from torchvision.utils import save_image
from util import utils

@click.option('--data_path', type=str, default='../data/')
@click.option('--trn_path', type=str, default='train/')
@click.option('--test_path', type=str, default='test/')
@click.option('--in_ext', type=str, default='.tiff')
@click.option('--out_ext', type=str, default='.tif')
@click.option('--seed', type=int)
@click.option('--cuda', type=bool, default=True)
@click.option('--epochs', type=int, default=10000)
@click.option('--lr', type=float, default=1e-3)
@click.option('--decay', type=float, default=1e-5)
@click.option('--batch_size', type=int, default=100)
@click.option('--init_weights', type=bool, default=True)
@click.option('--early_stop', type=bool, default=True)
@click.option('--stop_iter', type=int, default=20)
def main(data_path, trn_path, test_path, in_ext, out_ext,
         seed, cuda, epochs, lr, decay, batch_size, init_weights,
         early_stop, stop_iter):
    if seed is None:
        seed = random.randint(1, 1000)
    gpu_usage = torch.cuda.is_available() and cuda

    trn_in_path = data_path + trn_path + 'in/'
    trn_list = [f.split('.')[0] for f in listdir(trn_in_path)
                if isfile(join(trn_in_path, f))]
    trn_out_path = data_path + trn_path + 'out/'
    test_in_path = data_path + test_path + 'in/'
    test_list = [f.split('.')[0] for f in listdir(test_in_path)
                if isfile(join(test_in_path, f))]
    test_out_path = data_path + test_path + 'out/'
    trn_dataset = BuildingData(trn_in_path, trn_out_path,
                               trn_list, in_ext, out_ext)
    test_dataset = BuildingData(test_in_path, test_out_path,
                                test_list, in_ext, out_ext)
    trn_dataloader = get_data_loader(trn_dataset, batch_size, True)
    test_dataloader = get_data_loader(test_dataset, len(test_list), True)

    model = DeconvNet()
    if gpu_usage:
        model = model.cuda()
    criterion = nn.MSELoss()
    optimizer = torch.optim.Adam(model.parameters(),
                                 lr=lr, weight_decay=decay)
    for epoch in range(epochs):
        total_mse = 0.
        for batch_idx, (in_image, out_image) in enumerate(trn_dataloader, 0):
            in_image = Variable(in_image)
            out_image = Variable(out_image)
            if gpu_usage:
                in_image = in_image.cuda()
                out_image = out_image.cuda()
            output = model(in_image)
            loss = criterion(output, out_image)
            total_mse += loss.item()

        num_batchs = batch_idx + 1
        mean_mse = total_mse / num_batchs
        print('epoch [{}/{}], loss:{:.4f}'
              .format(epoch + 1, epochs, mean_mse))
        if epoch % 10 == 0:
            pic = utils.to_img(output.cpu().data)
            save_image(pic, '../data/dc_img/image_{}.png'.format(epoch))

if __name__ == '__main__':

It looks like you have already some code written.
Where are you stuck? Do you get any PyTorch-related errors or is your model just not working?

I don’t think someone will completely implement a solution for you, especially as this seems to be your thesis.

PS: I’ve formatted your code and changed the topic for a clearer message.

My friend wrote the above code. But a friend can no longer help. So I asked for it here.
The problem with the code is that the loss value increases too much each time the epoch is increased.
The above code is a file called in the folder given by a friend in a cmd window. The data load or CNN related code is in another file. If it is okay with you, I would like to send you the whole folder by mail.

My exact subject is the detection of buildings in aerial photos using CNN. Aerial photos and answers are prepared in datasets. I hate it when I can’t do anything because I don’t know anything.