def train(self):
"""
Train function of every epoch during train phase.
"""
self.cls_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.runner_state['epoch'] += 1
for i, data_dict in enumerate(self.train_loader):
Trainer.update(self, warm_list=(0, 1),
warm_lr_list=(self.solver_dict['lr']['base_lr']*self.configer.get('solver.lr.bb_lr_scale'),
self.solver_dict['lr']['base_lr']),
solver_dict=self.solver_dict)
self.data_time.update(time.time() - start_time)
data_dict = RunnerHelper.to_device(self, data_dict)
# Forward pass.
out = self.cls_net(data_dict)
loss_dict = self.loss(out)
# Compute the loss of the train batch & backward.
loss = loss_dict['loss']
self.train_losses.update({key: loss.item() for key, loss in loss_dict.items()}, data_dict['img'].size(0))
self.optimizer.zero_grad()
loss.backward()
if self.configer.get('network', 'clip_grad', default=False):
RunnerHelper.clip_grad(self.cls_net, 10.)
self.optimizer.step()
# Update the vars of the train phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.runner_state['iters'] += 1
# Print the log info & reset the states.
if self.runner_state['iters'] % self.solver_dict['display_iter'] == 0:
Log.info('Train Epoch: {0}\tTrain Iteration: {1}\t'
'Time {batch_time.sum:.3f}s / {2}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {2}iters, ({data_time.avg:3f})\n'
'Learning rate = {4}\tLoss = {3}\n'.format(
self.runner_state['epoch'], self.runner_state['iters'],
self.solver_dict['display_iter'], self.train_losses.info(),
RunnerHelper.get_lr(self.optimizer), batch_time=self.batch_time,
data_time=self.data_time))
self.batch_time.reset()
self.data_time.reset()
self.train_losses.reset()
if self.solver_dict['lr']['metric'] == 'iters' and self.runner_state['iters'] == self.solver_dict['max_iters']:
break
if self.runner_state['iters'] % self.solver_dict['save_iters'] == 0 and self.configer.get('local_rank') == 0:
RunnerHelper.save_net(self, self.cls_net)
# Check to val the current model.
if self.runner_state['iters'] % self.solver_dict['test_interval'] == 0:
self.val()
def train(self):
"""
Train function of every epoch during train phase.
"""
self.det_net.train()
start_time = time.time()
# Adjust the learning rate after every epoch.
self.runner_state['epoch'] += 1
for i, data_dict in enumerate(self.train_loader):
Trainer.update(self, solver_dict=self.configer.get('solver'))
self.data_time.update(time.time() - start_time)
# Forward pass.
data_dict = RunnerHelper.to_device(self, data_dict)
out = self.det_net(data_dict)
loss_dict = self.det_loss(out)
loss = loss_dict['loss'].mean()
self.train_losses.update(loss.item(),
len(DCHelper.tolist(data_dict['meta'])))
self.optimizer.zero_grad()
loss.backward()
RunnerHelper.clip_grad(self.det_net, 10.)
self.optimizer.step()
# Update the vars of the train phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
self.runner_state['iters'] += 1
# Print the log info & reset the states.
if self.runner_state['iters'] % self.configer.get(
'solver', 'display_iter') == 0:
Log.info(
'Train Epoch: {0}\tTrain Iteration: {1}\t'
'Time {batch_time.sum:.3f}s / {2}iters, ({batch_time.avg:.3f})\t'
'Data load {data_time.sum:.3f}s / {2}iters, ({data_time.avg:3f})\n'
'Learning rate = {3}\tLoss = {loss.val:.8f} (ave = {loss.avg:.8f})\n'
.format(self.runner_state['epoch'],
self.runner_state['iters'],
self.configer.get('solver', 'display_iter'),
RunnerHelper.get_lr(self.optimizer),
batch_time=self.batch_time,
data_time=self.data_time,
loss=self.train_losses))
self.batch_time.reset()
self.data_time.reset()
self.train_losses.reset()
if self.configer.get('solver', 'lr')['metric'] == 'iters' \
and self.runner_state['iters'] == self.configer.get('solver', 'max_iters'):
break
# Check to val the current model.
if self.runner_state['iters'] % self.configer.get(
'solver', 'test_interval') == 0:
self.val()
