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__(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()
class FCClassifier(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_loss_manager = LossManager(configer) self.cls_model_manager = ClsModelManager(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.image_classifier() self.cls_net = RunnerHelper.load_net(self, self.cls_net) self.optimizer, self.scheduler = Trainer.init(self, self._get_parameters()) self.train_loader = self.cls_data_loader.get_trainloader() self.val_loader = self.cls_data_loader.get_valloader() self.ce_loss = self.cls_loss_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) inputs = data_dict['img'] labels = data_dict['label'] self.data_time.update(time.time() - start_time) # Change the data type. inputs, labels = RunnerHelper.to_device(self, inputs, labels) # Forward pass. outputs = self.cls_net(inputs) # Compute the loss of the train batch & backward. loss = self.ce_loss(outputs, labels) self.train_losses.update(loss.item(), inputs.size(0)) 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('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): inputs = data_dict['img'] labels = data_dict['label'] # Change the data type. inputs, labels = RunnerHelper.to_device(self, inputs, labels) # Forward pass. outputs = self.cls_net(inputs) outputs = RunnerHelper.gather(self, outputs) # Compute the loss of the val batch. loss = self.ce_loss(outputs, labels) self.cls_running_score.update(outputs, labels) self.val_losses.update(loss.item(), inputs.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, 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()