tra-analysis/analysis-master/build/lib/analysis/titanlearn.py

# Titan Robotics Team 2022: ML Module
# Written by Arthur Lu & Jacob Levine
# Notes:
#    this should be imported as a python module using 'import titanlearn'
#    this should be included in the local directory or environment variable
#    this module is optimized for multhreaded computing
#    this module learns from its mistakes far faster than 2022's captains
# setup:

__version__ = "2.0.1.001"

#changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog:
    2.0.1.001:
        - removed matplotlib import
        - removed graphloss()
    2.0.1.000:
        - added net, dataset, dataloader, and stdtrain template definitions
        - added graphloss function
    2.0.0.001:
        - added clear functions
    2.0.0.000:
        - complete rewrite planned
        - depreciated 1.0.0.xxx versions
        - added simple training loop
    1.0.0.xxx:
        -added generation of ANNS, basic SGD training
"""

__author__ = (
    "Arthur Lu <arthurlu@ttic.edu>,"
    "Jacob Levine <jlevine@ttic.edu>,"
    )

__all__ = [
    'clear',
    'net',
    'dataset',
    'dataloader',
    'train',
    'stdtrainer',
    ]

import torch
from os import system, name
import numpy as np

def clear(): 
    if name == 'nt': 
        _ = system('cls') 
    else: 
        _ = system('clear') 

class net(torch.nn.Module): #template for standard neural net
    def __init__(self):
        super(Net, self).__init__()
        
    def forward(self, input):
        pass

class dataset(torch.utils.data.Dataset): #template for standard dataset
    
    def __init__(self):
        super(torch.utils.data.Dataset).__init__()
        
    def __getitem__(self, index):
        pass
    
    def __len__(self):
        pass

def dataloader(dataset, batch_size, num_workers, shuffle = True):

    return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)

def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)

    dataset_len = trainloader.dataset.__len__()
    iter_count = 0
    running_loss = 0
    running_loss_list = []

    for epoch in range(epochs):  # loop over the dataset multiple times

        for i, data in enumerate(trainloader, 0):

            inputs = data[0].to(device)
            labels = data[1].to(device)

            optimizer.zero_grad()

            outputs = net(inputs)
            loss = criterion(outputs, labels.to(torch.float))
            
            loss.backward()
            optimizer.step()

            # monitoring steps below

            iter_count += 1
            running_loss += loss.item()
            running_loss_list.append(running_loss)
            clear()

            print("training on: " + device)
            print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
            print("current batch loss: " + str(loss.item))
            print("running loss: " + str(running_loss / iter_count))
        
    return net, running_loss_list
    print("finished training")

def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

    net = net.to(device)
    criterion = criterion.to(device)
    optimizer = optimizer.to(device)
    trainloader = dataloader
    
    return train(device, net, epochs, trainloader, optimizer, criterion)