witnn/tools/Train.py

from torch.utils.data import Dataset, DataLoader
import numpy as np
import torchvision.models as models
from torchvision import datasets, transforms
import torchvision
import torch.optim as optim
import torch.nn.functional as F
import torch.nn as nn
import torch
import os
import utils as utils
from tqdm import tqdm


def train(model, train_loader, optimizer, epoch=0):
    model.train()
    totalsize = train_loader.batch_sampler.sampler.num_samples
    batchsize = int(totalsize / train_loader.batch_size / 5)+1
    pbar = tqdm(totalsize)
    for batch_idx, (data, target) in enumerate(train_loader):
        data = utils.SetDevice(data)
        target = utils.SetDevice(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()

        pbar.update(train_loader.batch_size)
        if batch_idx % batchsize == 0 and batch_idx > 0:
            pbar.set_description("Loss:"+str(loss.item()))
    pbar.close()

def test(model, test_loader):
    with torch.no_grad():
        model.eval()
        test_loss = 0
        correct = 0
        for data, target in test_loader:
            data = utils.SetDevice(data)
            target = utils.SetDevice(target)
            output = model(data)
            # sum up batch loss
            test_loss += F.nll_loss(output, target, reduction='sum').item()
            # get the index of the max log-probability
            pred = output.max(1, keepdim=True)[1]
            correct += pred.eq(target.view_as(pred)).sum().item()
        test_loss /= len(test_loader.dataset)
        accu = 100. * correct / len(test_loader.dataset)
        # print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
        #       .format(test_loss, correct, len(test_loader.dataset), accu))
        return accu


def TrainEpochs(model, traindata, optimizer, testdata, epoch=100,testepoch=10, line=None):
    epochbar = tqdm(total=epoch)
    for i in range(epoch):
        train(model, traindata, optimizer, epoch=i)
        if line and i % testepoch == 0 and i > 0:
            line.AppendData(test(model, testdata))
        epochbar.update(1)
    epochbar.close()