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trainer.py
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trainer.py
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import os
import torch
from tqdm import tqdm
from experiment import Experiment
from data.data_loader import DistributedSampler
class Trainer:
def __init__(self, experiment: Experiment):
self.init_device()
self.experiment = experiment
self.structure = experiment.structure
self.logger = experiment.logger
self.model_saver = experiment.train.model_saver
# FIXME: Hack the save model path into logger path
self.model_saver.dir_path = self.logger.save_dir(
self.model_saver.dir_path)
self.current_lr = 0
self.total = 0
def init_device(self):
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
def init_model(self):
model = self.structure.builder.build(
self.device, self.experiment.distributed, self.experiment.local_rank)
return model
def update_learning_rate(self, optimizer, epoch, step):
lr = self.experiment.train.scheduler.learning_rate.get_learning_rate(
epoch, step)
for group in optimizer.param_groups:
group['lr'] = lr
self.current_lr = lr
def train(self):
self.logger.report_time('Start')
self.logger.args(self.experiment)
model = self.init_model()
train_data_loader = self.experiment.train.data_loader
if self.experiment.validation:
validation_loaders = self.experiment.validation.data_loaders
self.steps = 0
if self.experiment.train.checkpoint:
self.experiment.train.checkpoint.restore_model(
model, self.device, self.logger)
epoch, iter_delta = self.experiment.train.checkpoint.restore_counter()
self.steps = epoch * self.total + iter_delta
# Init start epoch and iter
optimizer = self.experiment.train.scheduler.create_optimizer(
model.parameters())
self.logger.report_time('Init')
model.train()
while True:
self.logger.info('Training epoch ' + str(epoch))
self.logger.epoch(epoch)
self.total = len(train_data_loader)
for batch in train_data_loader:
self.update_learning_rate(optimizer, epoch, self.steps)
self.logger.report_time("Data loading")
if self.experiment.validation and\
self.steps % self.experiment.validation.interval == 0 and\
self.steps > self.experiment.validation.exempt:
self.validate(validation_loaders, model, epoch, self.steps)
self.logger.report_time('Validating ')
if self.logger.verbose:
torch.cuda.synchronize()
self.train_step(model, optimizer, batch,
epoch=epoch, step=self.steps)
if self.logger.verbose:
torch.cuda.synchronize()
self.logger.report_time('Forwarding ')
self.model_saver.maybe_save_model(
model, epoch, self.steps, self.logger)
self.steps += 1
self.logger.report_eta(self.steps, self.total, epoch)
epoch += 1
if epoch > self.experiment.train.epochs:
self.model_saver.save_checkpoint(model, 'final')
if self.experiment.validation:
self.validate(validation_loaders, model, epoch, self.steps)
self.logger.info('Training done')
break
iter_delta = 0
def train_step(self, model, optimizer, batch, epoch, step, **kwards):
optimizer.zero_grad()
results = model.forward(batch, training=True)
if len(results) == 2:
l, pred = results
metrics = {}
elif len(results) == 3:
l, pred, metrics = results
if isinstance(l, dict):
line = []
loss = torch.tensor(0.).cuda()
for key, l_val in l.items():
loss += l_val.mean()
line.append('loss_{0}:{1:.4f}'.format(key, l_val.mean()))
else:
loss = l.mean()
loss.backward()
optimizer.step()
if step % self.experiment.logger.log_interval == 0:
if isinstance(l, dict):
line = '\t'.join(line)
log_info = '\t'.join(['step:{:6d}', 'epoch:{:3d}', '{}', 'lr:{:.4f}']).format(step, epoch, line, self.current_lr)
self.logger.info(log_info)
else:
self.logger.info('step: %6d, epoch: %3d, loss: %.6f, lr: %f' % (
step, epoch, loss.item(), self.current_lr))
self.logger.add_scalar('loss', loss, step)
self.logger.add_scalar('learning_rate', self.current_lr, step)
for name, metric in metrics.items():
self.logger.add_scalar(name, metric.mean(), step)
self.logger.info('%s: %6f' % (name, metric.mean()))
self.logger.report_time('Logging')
def validate(self, validation_loaders, model, epoch, step):
all_matircs = {}
model.eval()
for name, loader in validation_loaders.items():
if self.experiment.validation.visualize:
metrics, vis_images = self.validate_step(
loader, model, True)
self.logger.images(
os.path.join('vis', name), vis_images, step)
else:
metrics, vis_images = self.validate_step(loader, model, False)
for _key, metric in metrics.items():
key = name + '/' + _key
if key in all_matircs:
all_matircs[key].update(metric.val, metric.count)
else:
all_matircs[key] = metric
for key, metric in all_matircs.items():
self.logger.info('%s : %f (%d)' % (key, metric.avg, metric.count))
self.logger.metrics(epoch, self.steps, all_matircs)
model.train()
return all_matircs
def validate_step(self, data_loader, model, visualize=False):
raw_metrics = []
vis_images = dict()
for i, batch in tqdm(enumerate(data_loader), total=len(data_loader)):
pred = model.forward(batch, training=False)
output = self.structure.representer.represent(batch, pred)
raw_metric, interested = self.structure.measurer.validate_measure(
batch, output)
raw_metrics.append(raw_metric)
if visualize and self.structure.visualizer:
vis_image = self.structure.visualizer.visualize(
batch, output, interested)
vis_images.update(vis_image)
metrics = self.structure.measurer.gather_measure(
raw_metrics, self.logger)
return metrics, vis_images
def to_np(self, x):
return x.cpu().data.numpy()