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train.py
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train.py
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import torch
import time
from utils import get_batch
class Trainer:
def __init__(self, train_loader, test_loader, model, loss, optimizer) -> None:
self.train_loader = train_loader
self.test_loader = test_loader
self.model = model
self.loss = loss
self.optimizer = optimizer
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.interval = 200
def train(self, train_losses, epoch, batch_size, clip) -> list:
# Initialization of RNN hidden, and cell states.
states = self.model.init_hidden(batch_size)
for batch_num, batch in enumerate(self.train_loader): # loop over the data, and jump with step = bptt.
# get the labels
source, target, source_lengths = get_batch(batch)
source = source.to(self.device)
target = target.to(self.device)
x_hat_param, mu, log_var, z, states = self.model(source,source_lengths, states)
# detach hidden states
states = states[0].detach(), states[1].detach()
# compute the loss
mloss, KL_loss, recon_loss = self.loss(mu = mu, log_var = log_var, z = z, x_hat_param = x_hat_param , x = target)
train_losses.append((mloss , KL_loss.item(), recon_loss.item()))
mloss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), clip)
self.optimizer.step()
self.optimizer.zero_grad()
if batch_num % self.interval == 0 and batch_num > 0:
print('| epoch {:3d} | elbo_loss {:5.6f} | kl_loss {:5.6f} | recons_loss {:5.6f} '.format(
epoch, mloss.item(), KL_loss.item(), recon_loss.item()))
return train_losses
def test(self, test_losses, epoch, batch_size) -> list:
with torch.no_grad():
states = self.model.init_hidden(batch_size)
for batch_num, batch in enumerate(self.test_loader): # loop over the data, and jump with step = bptt.
# get the labels
source, target, source_lengths = get_batch(batch)
source = source.to(self.device)
target = target.to(self.device)
x_hat_param, mu, log_var, z, states = self.model(source,source_lengths, states)
# detach hidden states
states = states[0].detach(), states[1].detach()
# compute the loss
mloss, KL_loss, recon_loss = self.loss(mu = mu, log_var = log_var, z = z, x_hat_param = x_hat_param , x = target)
test_losses.append((mloss , KL_loss.item(), recon_loss.item()))
# Statistics.
# if batch_num % 20 ==0:
# print('| epoch {:3d} | elbo_loss {:5.6f} | kl_loss {:5.6f} | recons_loss {:5.6f} '.format(
# epoch, mloss.item(), KL_loss.item(), recon_loss.item()))
return test_losses