mirror of
https://github.com/xiph/opus.git
synced 2025-05-16 16:38:30 +00:00
112 lines
No EOL
3.3 KiB
Python
112 lines
No EOL
3.3 KiB
Python
import torch
|
|
from tqdm import tqdm
|
|
import sys
|
|
|
|
def train_one_epoch(model, criterion, optimizer, dataloader, device, scheduler, log_interval=10):
|
|
|
|
model.to(device)
|
|
model.train()
|
|
|
|
running_loss = 0
|
|
previous_running_loss = 0
|
|
|
|
# gru states
|
|
gru_a_state = torch.zeros(1, dataloader.batch_size, model.gru_a_units, device=device).to(device)
|
|
gru_b_state = torch.zeros(1, dataloader.batch_size, model.gru_b_units, device=device).to(device)
|
|
gru_states = [gru_a_state, gru_b_state]
|
|
|
|
with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch:
|
|
|
|
for i, batch in enumerate(tepoch):
|
|
|
|
# set gradients to zero
|
|
optimizer.zero_grad()
|
|
|
|
# zero out initial gru states
|
|
gru_a_state.zero_()
|
|
gru_b_state.zero_()
|
|
|
|
# push batch to device
|
|
for key in batch:
|
|
batch[key] = batch[key].to(device)
|
|
|
|
target = batch['target']
|
|
|
|
# calculate model output
|
|
output = model(batch['features'], batch['periods'], batch['signals'], gru_states)
|
|
|
|
# calculate loss
|
|
loss = criterion(output.permute(0, 2, 1), target)
|
|
|
|
# calculate gradients
|
|
loss.backward()
|
|
|
|
# update weights
|
|
optimizer.step()
|
|
|
|
# update learning rate
|
|
scheduler.step()
|
|
|
|
# call sparsifier
|
|
model.sparsify()
|
|
|
|
# update running loss
|
|
running_loss += float(loss.cpu())
|
|
|
|
# update status bar
|
|
if i % log_interval == 0:
|
|
tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}")
|
|
previous_running_loss = running_loss
|
|
|
|
|
|
running_loss /= len(dataloader)
|
|
|
|
return running_loss
|
|
|
|
def evaluate(model, criterion, dataloader, device, log_interval=10):
|
|
|
|
model.to(device)
|
|
model.eval()
|
|
|
|
running_loss = 0
|
|
previous_running_loss = 0
|
|
|
|
# gru states
|
|
gru_a_state = torch.zeros(1, dataloader.batch_size, model.gru_a_units, device=device).to(device)
|
|
gru_b_state = torch.zeros(1, dataloader.batch_size, model.gru_b_units, device=device).to(device)
|
|
gru_states = [gru_a_state, gru_b_state]
|
|
|
|
with torch.no_grad():
|
|
with tqdm(dataloader, unit='batch', file=sys.stdout) as tepoch:
|
|
|
|
for i, batch in enumerate(tepoch):
|
|
|
|
|
|
# zero out initial gru states
|
|
gru_a_state.zero_()
|
|
gru_b_state.zero_()
|
|
|
|
# push batch to device
|
|
for key in batch:
|
|
batch[key] = batch[key].to(device)
|
|
|
|
target = batch['target']
|
|
|
|
# calculate model output
|
|
output = model(batch['features'], batch['periods'], batch['signals'], gru_states)
|
|
|
|
# calculate loss
|
|
loss = criterion(output.permute(0, 2, 1), target)
|
|
|
|
# update running loss
|
|
running_loss += float(loss.cpu())
|
|
|
|
# update status bar
|
|
if i % log_interval == 0:
|
|
tepoch.set_postfix(running_loss=f"{running_loss/(i + 1):8.7f}", current_loss=f"{(running_loss - previous_running_loss)/log_interval:8.7f}")
|
|
previous_running_loss = running_loss
|
|
|
|
|
|
running_loss /= len(dataloader)
|
|
|
|
return running_loss |