class ImageClassifier(object):
"""
The class for the training phase of Image classification.
"""
def __init__(self, configer):
self.configer = configer
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = AverageMeter()
self.val_losses = AverageMeter()
self.cls_model_manager = ModelManager(configer)
self.cls_data_loader = DataLoader(configer)
self.cls_running_score = ClsRunningScore(configer)
self.cls_net = None
self.train_loader = None
self.val_loader = None
self.optimizer = None
self.scheduler = None
self.runner_state = dict()
self._init_model()
def _init_model(self):
self.cls_net = self.cls_model_manager.get_cls_model()
self.cls_net = RunnerHelper.load_net(self, self.cls_net)
self.optimizer, self.scheduler = Trainer.init(self._get_parameters(), self.configer.get('solver'))
self.train_loader = self.cls_data_loader.get_trainloader()
self.val_loader = self.cls_data_loader.get_valloader()
self.ce_loss = self.cls_model_manager.get_cls_loss()
def _get_parameters(self):
return self.cls_net.parameters()
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, solver_dict=self.configer.get('solver'))
self.data_time.update(time.time() - start_time)
# Forward pass.
out_dict = self.cls_net(data_dict)
# Compute the loss of the train batch & backward.
loss = self.ce_loss(out_dict, data_dict, gathered=self.configer.get('network', 'gathered'))
self.train_losses.update(loss.item(), len(DCHelper.tolist(data_dict['meta'])))
self.optimizer.zero_grad()
loss.backward()
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()
def val(self):
"""
Validation function during the train phase.
"""
self.cls_net.eval()
start_time = time.time()
with torch.no_grad():
for j, data_dict in enumerate(self.val_loader):
# Forward pass.
out_dict = self.cls_net(data_dict)
# Compute the loss of the val batch.
loss = self.ce_loss(out_dict, data_dict, gathered=self.configer.get('network', 'gathered'))
out_dict = RunnerHelper.gather(self, out_dict)
self.cls_running_score.update(out_dict['out'], DCHelper.tolist(data_dict['labels']))
self.val_losses.update(loss.item(), len(DCHelper.tolist(data_dict['meta'])))
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
RunnerHelper.save_net(self, self.cls_net, performance=self.cls_running_score.get_top1_acc())
self.runner_state['performance'] = self.cls_running_score.get_top1_acc()
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s'.format(batch_time=self.batch_time))
Log.info('TestLoss = {loss.avg:.8f}'.format(loss=self.val_losses))
Log.info('Top1 ACC = {}'.format(self.cls_running_score.get_top1_acc()))
Log.info('Top5 ACC = {}'.format(self.cls_running_score.get_top5_acc()))
self.batch_time.reset()
self.val_losses.reset()
self.cls_running_score.reset()
self.cls_net.train()
class ImageClassifier(object):
"""
The class for the training phase of Image classification.
"""
def __init__(self, configer):
self.configer = configer
self.runner_state = dict()
self.batch_time = AverageMeter()
self.data_time = AverageMeter()
self.train_losses = DictAverageMeter()
self.val_losses = DictAverageMeter()
self.cls_model_manager = ModelManager(configer)
self.cls_data_loader = DataLoader(configer)
self.running_score = ClsRunningScore(configer)
self.cls_net = self.cls_model_manager.get_cls_model()
self.solver_dict = self.configer.get('solver')
self.cls_net = RunnerHelper.load_net(self, self.cls_net)
self.optimizer, self.scheduler = Trainer.init(self._get_parameters(), self.solver_dict)
self.train_loader = self.cls_data_loader.get_trainloader()
self.val_loader = self.cls_data_loader.get_valloader()
self.loss = self.cls_model_manager.get_cls_loss()
def _init_model(self):
self.cls_net = self.cls_model_manager.get_cls_model()
self.cls_net = RunnerHelper.load_net(self, self.cls_net)
self.optimizer, self.scheduler = Trainer.init(self._get_parameters(), self.configer.get('solver'))
self.train_loader = self.cls_data_loader.get_trainloader()
self.val_loader = self.cls_data_loader.get_valloader()
self.ce_loss = self.cls_model_manager.get_cls_loss()
def _get_parameters(self):
if self.solver_dict.get('optim.wdall', default=True):
lr_1 = []
lr_2 = []
params_dict = dict(self.cls_net.named_parameters())
for key, value in params_dict.items():
if value.requires_grad:
if 'backbone' in key:
if self.configer.get('solver.lr.bb_lr_scale') == 0.0:
value.requires_grad = False
else:
lr_1.append(value)
else:
lr_2.append(value)
params = [
{'params': lr_1, 'lr': self.solver_dict['lr']['base_lr'] * self.configer.get('solver.lr.bb_lr_scale')},
{'params': lr_2, 'lr': self.solver_dict['lr']['base_lr']}]
else:
no_decay_list = []
decay_list = []
no_decay_name = []
decay_name = []
for m in self.cls_net.modules():
if (hasattr(m, 'groups') and m.groups > 1) or isinstance(m, torch.nn.BatchNorm2d) \
or m.__class__.__name__ == 'GL':
no_decay_list += m.parameters(recurse=False)
for name, p in m.named_parameters(recurse=False):
no_decay_name.append(m.__class__.__name__ + name)
else:
for name, p in m.named_parameters(recurse=False):
if 'bias' in name:
no_decay_list.append(p)
no_decay_name.append(m.__class__.__name__ + name)
else:
decay_list.append(p)
decay_name.append(m.__class__.__name__ + name)
Log.info('no decay list = {}'.format(no_decay_name))
Log.info('decay list = {}'.format(decay_name))
params = [{'params': no_decay_list, 'weight_decay': 0}, {'params': decay_list}]
return params
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 val(self):
"""
Validation function during the train phase.
"""
self.cls_net.eval()
start_time = time.time()
with torch.no_grad():
for j, data_dict in enumerate(self.val_loader):
# Forward pass.
data_dict = RunnerHelper.to_device(self, data_dict)
out = self.cls_net(data_dict)
loss_dict = self.loss(out)
out_dict, label_dict, _ = RunnerHelper.gather(self, out)
self.running_score.update(out_dict, label_dict)
self.val_losses.update({key: loss.item() for key, loss in loss_dict.items()}, data_dict['img'].size(0))
# Update the vars of the val phase.
self.batch_time.update(time.time() - start_time)
start_time = time.time()
RunnerHelper.save_net(self, self.cls_net)
# Print the log info & reset the states.
Log.info('Test Time {batch_time.sum:.3f}s'.format(batch_time=self.batch_time))
Log.info('TestLoss = {}'.format(self.val_losses.info()))
Log.info('Top1 ACC = {}'.format(RunnerHelper.dist_avg(self, self.running_score.get_top1_acc())))
Log.info('Top3 ACC = {}'.format(RunnerHelper.dist_avg(self, self.running_score.get_top3_acc())))
Log.info('Top5 ACC = {}'.format(RunnerHelper.dist_avg(self, self.running_score.get_top5_acc())))
self.batch_time.reset()
self.batch_time.reset()
self.val_losses.reset()
self.running_score.reset()
self.cls_net.train()