63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
from torch.utils.data import Dataset, DataLoader
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import numpy as np
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import torchvision.models as models
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from torchvision import datasets, transforms
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import torchvision
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import torch.optim as optim
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import torch.nn.functional as F
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import torch.nn as nn
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import torch
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import os
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import utils as utils
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from tqdm import tqdm
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def train(model, train_loader, optimizer, epoch=0):
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model.train()
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totalsize = train_loader.batch_sampler.sampler.num_samples
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batchsize = int(totalsize / train_loader.batch_size / 5)+1
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pbar = tqdm(totalsize)
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for batch_idx, (data, target) in enumerate(train_loader):
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data = utils.SetDevice(data)
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target = utils.SetDevice(target)
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optimizer.zero_grad()
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output = model(data)
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loss = F.nll_loss(output, target)
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loss.backward()
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optimizer.step()
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pbar.update(train_loader.batch_size)
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if batch_idx % batchsize == 0 and batch_idx > 0:
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pbar.set_description("Loss:"+str(loss.item()))
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pbar.close()
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def test(model, test_loader):
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with torch.no_grad():
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model.eval()
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test_loss = 0
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correct = 0
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for data, target in test_loader:
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data = utils.SetDevice(data)
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target = utils.SetDevice(target)
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output = model(data)
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# sum up batch loss
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test_loss += F.nll_loss(output, target, reduction='sum').item()
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# get the index of the max log-probability
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pred = output.max(1, keepdim=True)[1]
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correct += pred.eq(target.view_as(pred)).sum().item()
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test_loss /= len(test_loader.dataset)
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accu = 100. * correct / len(test_loader.dataset)
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# print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
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# .format(test_loss, correct, len(test_loader.dataset), accu))
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return accu
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def TrainEpochs(model, traindata, optimizer, testdata, epoch=100,testepoch=10, line=None):
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epochbar = tqdm(total=epoch)
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for i in range(epoch):
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train(model, traindata, optimizer, epoch=i)
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if line and i % testepoch == 0 and i > 0:
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line.AppendData(test(model, testdata))
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epochbar.update(1)
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epochbar.close()
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