100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
import torch
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import torchvision
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from torch import nn
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import utils
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class Trainer:
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def __init__(self, model, optimizer, loss_function, device, train_loader, test_loader, epochs = 10, print_frequency = 10):
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self.model = model
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self.optimizer = optimizer
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self.loss_function = loss_function
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self.device = device
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self.train_loader = train_loader
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self.test_loader = test_loader
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self.epochs = epochs
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self.print_frequency = print_frequency
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def _train_epoch(self, epoch):
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# Training
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if epoch > 0: # test untrained net first
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codes = dict.fromkeys(["μ", "logσ2", "y", "loss"])
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self.model.train()
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means, logvars, labels, losses = list(), list(), list(), list()
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train_loss = 0
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for batch_idx, (x, y) in enumerate(self.train_loader):
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size_batch = list(x.shape)[0] if batch_idx==0 else size_batch
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x = x.to(self.device)
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# ===================forward=====================
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x_hat, mu, logvar = self.model(x)
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loss = self.loss_function(x_hat, x, mu, logvar)
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train_loss += loss.item()
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# ===================backward====================
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self.optimizer.zero_grad()
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loss.backward()
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self.optimizer.step()
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# =====================log=======================
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means.append(mu.detach())
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logvars.append(logvar.detach())
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labels.append(y.detach())
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losses.append(train_loss / ((batch_idx + 1) * size_batch))
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if batch_idx % self.print_frequency == 0:
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print(f'- Epoch: {epoch} [{batch_idx}/{len(self.train_loader.dataset)} ({100. * batch_idx / len(self.train_loader.dataset) :.0f}%)] Average loss: {train_loss / ((batch_idx + 1) * size_batch):.4f}', end="\r")
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# ===================log========================
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codes['μ'] = torch.cat(means).cpu()
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codes['logσ2'] = torch.cat(logvars).cpu()
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codes['y'] = torch.cat(labels).cpu()
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codes['loss'] = losses
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print(f'====> Epoch: {epoch} Average loss: {train_loss / len(self.train_loader.dataset):.4f}')
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return codes
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def _test_epoch(self, epoch):
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# Testing
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codes = dict.fromkeys(["μ", "logσ2", "y", "loss"])
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with torch.no_grad():
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self.model.eval()
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means, logvars, labels, losses = list(), list(), list(), list()
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test_loss = 0
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for batch_idx, (x, y) in enumerate(self.test_loader):
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size_batch = list(x.shape)[0] if batch_idx==0 else size_batch
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x = x.to(self.device)
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# ===================forward=====================
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x_hat, mu, logvar = self.model(x)
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test_loss += self.loss_function(x_hat, x, mu, logvar).item()
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# =====================log=======================
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means.append(mu.detach())
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logvars.append(logvar.detach())
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labels.append(y.detach())
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losses.append(test_loss / ((batch_idx + 1) * size_batch))
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# ===================log========================
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codes['μ'] = torch.cat(means).cpu()
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codes['logσ2'] = torch.cat(logvars).cpu()
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codes['y'] = torch.cat(labels).cpu()
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codes['loss'] = losses
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test_loss /= len(self.test_loader.dataset)
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print(f'====> Test set loss: {test_loss:.4f}')
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return codes
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def train(self):
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codes_train = dict(μ=list(), logσ2=list(), y=list(), loss=list())
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codes_test = dict(μ=list(), logσ2=list(), y=list(), loss=list())
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for epoch in range(self.epochs + 1):
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codes = self._train_epoch(epoch) if epoch>0 else dict.fromkeys(["μ", "logσ2", "y", "loss"])
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for key in codes_train:
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codes_train[key].append(codes[key])
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codes = self._test_epoch(epoch)
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for key in codes_test:
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codes_test[key].append(codes[key])
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if epoch != self.epochs:
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print("---")
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for x, y in self.test_loader:
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x = x.to(self.device)
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x_hat, _, _ = self.model(x)
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pass
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utils.display_images(x, x_hat, 1, f'Epoch {epoch}')
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return codes_train, codes_test
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