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122 lines
3.0 KiB
Python
122 lines
3.0 KiB
Python
# Titan Robotics Team 2022: ML Module
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# Written by Arthur Lu & Jacob Levine
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# Notes:
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# this should be imported as a python module using 'import titanlearn'
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# this should be included in the local directory or environment variable
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# this module is optimized for multhreaded computing
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# this module learns from its mistakes far faster than 2022's captains
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# setup:
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__version__ = "2.0.1.001"
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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2.0.1.001:
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- removed matplotlib import
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- removed graphloss()
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2.0.1.000:
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- added net, dataset, dataloader, and stdtrain template definitions
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- added graphloss function
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2.0.0.001:
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- added clear functions
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2.0.0.000:
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- complete rewrite planned
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- depreciated 1.0.0.xxx versions
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- added simple training loop
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1.0.0.xxx:
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-added generation of ANNS, basic SGD training
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"""
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>,"
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"Jacob Levine <jlevine@ttic.edu>,"
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)
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__all__ = [
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'clear',
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'net',
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'dataset',
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'dataloader',
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'train',
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'stdtrainer',
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]
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import torch
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from os import system, name
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import numpy as np
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def clear():
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if name == 'nt':
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_ = system('cls')
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else:
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_ = system('clear')
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class net(torch.nn.Module): #template for standard neural net
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def __init__(self):
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super(Net, self).__init__()
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def forward(self, input):
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pass
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class dataset(torch.utils.data.Dataset): #template for standard dataset
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def __init__(self):
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super(torch.utils.data.Dataset).__init__()
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def __getitem__(self, index):
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pass
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def __len__(self):
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pass
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def dataloader(dataset, batch_size, num_workers, shuffle = True):
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return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
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def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)
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dataset_len = trainloader.dataset.__len__()
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iter_count = 0
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running_loss = 0
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running_loss_list = []
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for epoch in range(epochs): # loop over the dataset multiple times
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for i, data in enumerate(trainloader, 0):
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inputs = data[0].to(device)
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labels = data[1].to(device)
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optimizer.zero_grad()
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outputs = net(inputs)
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loss = criterion(outputs, labels.to(torch.float))
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loss.backward()
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optimizer.step()
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# monitoring steps below
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iter_count += 1
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running_loss += loss.item()
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running_loss_list.append(running_loss)
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clear()
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print("training on: " + device)
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print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
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print("current batch loss: " + str(loss.item))
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print("running loss: " + str(running_loss / iter_count))
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return net, running_loss_list
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print("finished training")
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def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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net = net.to(device)
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criterion = criterion.to(device)
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optimizer = optimizer.to(device)
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trainloader = dataloader
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return train(device, net, epochs, trainloader, optimizer, criterion) |