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import torch as th |
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import torch.nn.functional as F |
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import torch.nn as nn |
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import lightning as ltn |
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import argparse |
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import lightning.pytorch as pl |
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from torch import Tensor |
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from torch import nn |
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from lightning.pytorch.callbacks.early_stopping import EarlyStopping |
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parser = argparse.ArgumentParser() |
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parser.add_argument("-n", "--n_epochs", type=int, default=200, help="number of epochs of training") |
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parser.add_argument("-b", "--batch", type=int, default=256, help="batch size of training") |
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parser.add_argument("-m", "--model", type=str, default='mnist0', help="model to execute") |
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opt = parser.parse_args() |
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if th.cuda.is_available(): |
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accelerator = 'gpu' |
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elif th.backends.mps.is_available(): |
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accelerator = 'cpu' |
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else: |
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accelerator = 'cpu' |
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class OptAEGV1(nn.Module): |
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def __init__(self, points=11): |
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super().__init__() |
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self.points = points |
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self.iscale = nn.Parameter(th.normal(0, 1, (1, 1, 1, 1))) |
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self.oscale = nn.Parameter(th.normal(0, 1, (1, 1, 1, 1))) |
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self.theta = th.linspace(-th.pi, th.pi, points) |
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self.velocity = th.linspace(0, th.e, points) |
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self.weight = nn.Parameter(th.normal(0, 1, (points, points))) |
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@th.compile |
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def integral(self, param, index): |
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return th.sum(param[index].view(-1, 1) * th.softmax(self.weight, dim=1)[index, :], dim=1) |
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@th.compile |
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def interplot(self, param, index): |
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lmt = param.size(0) - 1 |
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p0 = index.floor().long() |
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p1 = p0 + 1 |
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pos = index - p0 |
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p0 = p0.clamp(0, lmt) |
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p1 = p1.clamp(0, lmt) |
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v0 = self.integral(param, p0) |
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v1 = self.integral(param, p1) |
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return (1 - pos) * v0 + pos * v1 |
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@th.compile |
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def forward(self, data: Tensor) -> Tensor: |
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if self.theta.device != data.device: |
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self.theta = self.theta.to(data.device) |
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self.velocity = self.velocity.to(data.device) |
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shape = data.size() |
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data = (data - data.mean()) / data.std() * self.iscale |
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data = data.flatten(0) |
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theta = self.interplot(self.theta, th.sigmoid(data) * (self.points - 1)) |
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ds = self.interplot(self.velocity, th.abs(th.tanh(data) * (self.points - 1))) |
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dx = ds * th.cos(theta) |
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dy = ds * th.sin(theta) |
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data = data * th.exp(dy) + dx |
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data = (data - data.mean()) / data.std() * self.oscale |
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return data.view(*shape) |
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class MNISTModel(ltn.LightningModule): |
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def __init__(self): |
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super().__init__() |
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self.learning_rate = 1e-3 |
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self.counter = 0 |
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self.labeled_loss = 0 |
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self.labeled_correct = 0 |
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def configure_optimizers(self): |
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optimizer = th.optim.Adam(self.parameters(), lr=self.learning_rate) |
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scheduler = th.optim.lr_scheduler.CosineAnnealingLR(optimizer, 37) |
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return [optimizer], [scheduler] |
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def training_step(self, train_batch, batch_idx): |
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x, y = train_batch |
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x = x.view(-1, 1, 28, 28) |
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z = self.forward(x) |
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loss = F.nll_loss(z, y) |
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self.log('train_loss', loss, prog_bar=True) |
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return loss |
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def validation_step(self, val_batch, batch_idx): |
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x, y = val_batch |
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x = x.view(-1, 1, 28, 28) |
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z = self.forward(x) |
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loss = F.nll_loss(z, y) |
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self.log('val_loss', loss, prog_bar=True) |
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pred = z.data.max(1, keepdim=True)[1] |
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correct = pred.eq(y.data.view_as(pred)).sum() / y.size()[0] |
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self.log('correct_rate', correct, prog_bar=True) |
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self.labeled_loss += loss.item() * y.size()[0] |
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self.labeled_correct += correct.item() * y.size()[0] |
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self.counter += y.size()[0] |
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def test_step(self, test_batch, batch_idx): |
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x, y = test_batch |
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x = x.view(-1, 1, 28, 28) |
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z = self(x) |
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pred = z.data.max(1, keepdim=True)[1] |
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correct = pred.eq(y.data.view_as(pred)).sum() / y.size()[0] |
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self.log('correct_rate', correct, prog_bar=True) |
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def on_save_checkpoint(self, checkpoint) -> None: |
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import glob, os |
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correct = self.labeled_correct / self.counter |
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loss = self.labeled_loss / self.counter |
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record = '%2.5f-%03d-%1.5f.ckpt' % (correct, checkpoint['epoch'], loss) |
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fname = 'best-%s' % record |
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with open(fname, 'bw') as f: |
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th.save(checkpoint, f) |
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for ix, ckpt in enumerate(sorted(glob.glob('best-*.ckpt'), reverse=True)): |
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if ix > 5: |
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os.unlink(ckpt) |
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self.counter = 0 |
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self.labeled_loss = 0 |
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self.labeled_correct = 0 |
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print() |
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class MNIST_OptAEGV1(MNISTModel): |
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def __init__(self): |
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super().__init__() |
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self.pool = nn.MaxPool2d(2) |
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self.conv0 = nn.Conv2d(1, 2, kernel_size=7, padding=3, bias=False) |
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self.lnon0 = OptAEGV1() |
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self.conv1 = nn.Conv2d(2, 2, kernel_size=7, padding=3, bias=False) |
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self.lnon1 = OptAEGV1() |
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self.conv2 = nn.Conv2d(2, 2, kernel_size=7, padding=3, bias=False) |
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self.lnon2 = OptAEGV1() |
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self.conv3 = nn.Conv2d(2, 2, kernel_size=7, padding=3, bias=False) |
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self.lnon3 = OptAEGV1() |
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self.fc = nn.Linear(2 * 3 * 3, 10) |
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self.lnon4 = OptAEGV1() |
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def forward(self, x): |
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x = self.conv0(x) |
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x = self.lnon0(x) |
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x = self.pool(x) |
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x = self.conv1(x) |
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x = self.lnon1(x) |
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x = self.pool(x) |
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x = self.conv2(x) |
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x = self.lnon2(x) |
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x = self.pool(x) |
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x = th.flatten(x, 1) |
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x = self.fc(x) |
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x = self.lnon4(x) |
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x = F.log_softmax(x, dim=1) |
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return x |
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def test_best(): |
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import glob |
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fname = sorted(glob.glob('best-*.ckpt'), reverse=True)[0] |
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with open(fname, 'rb') as f: |
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checkpoint = th.load(f) |
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model.load_state_dict(checkpoint['state_dict'], strict=False) |
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model.eval() |
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with th.no_grad(): |
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counter, success = 0, 0 |
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for test_batch in test_loader: |
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x, y = test_batch |
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x = x.view(-1, 1, 28, 28) |
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z = model(x) |
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pred = z.data.max(1, keepdim=True)[1] |
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correct = pred.eq(y.data.view_as(pred)).sum() / y.size()[0] |
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print('.', end='', flush=True) |
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if counter % 100 == 0: |
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print('') |
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success += correct.item() |
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counter += 1 |
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print('') |
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print('Accuracy: %2.5f' % (success / counter)) |
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th.save(model, 'mnist-optaeg-v1.pt') |
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if __name__ == '__main__': |
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print('loading data...') |
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from torch.utils.data import DataLoader |
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from torchvision.datasets import MNIST |
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from torchvision import transforms |
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mnist_train = MNIST('datasets', train=True, download=True, transform=transforms.Compose([ |
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transforms.ToTensor(), |
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])) |
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mnist_test = MNIST('datasets', train=False, download=True, transform=transforms.Compose([ |
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transforms.ToTensor(), |
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])) |
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train_loader = DataLoader(mnist_train, shuffle=True, batch_size=opt.batch, num_workers=8) |
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val_loader = DataLoader(mnist_test, batch_size=opt.batch, num_workers=8) |
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test_loader = DataLoader(mnist_test, batch_size=opt.batch, num_workers=8) |
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print('construct trainer...') |
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trainer = pl.Trainer(accelerator=accelerator, precision=32, max_epochs=opt.n_epochs, |
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callbacks=[EarlyStopping(monitor="correct_rate", mode="max", patience=30)]) |
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print('construct model...') |
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model = MNIST_OptAEGV1() |
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print('training...') |
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trainer.fit(model, train_loader, val_loader) |
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print('testing...') |
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test_best() |
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