""" /* Copyright (c) 2023 Amazon Written by Jan Buethe */ /* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. - Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */ """ 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