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 # data_tuple: (inputs, heatmap, maskmap, vecmap) for i, data_dict in enumerate(self.train_loader): Trainer.update(self, warm_list=(0, ), warm_lr_list=(self.configer.get('solver', 'lr')['base_lr'], ), solver_dict=self.configer.get('solver')) self.data_time.update(time.time() - start_time) # Forward pass. out_dict = self.det_net(data_dict) # Compute the loss of the train batch & backward. loss = out_dict['loss'].mean() 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 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 _init_model(self): self.gan_net = self.model_manager.gan_model() self.gan_net = RunnerHelper.load_net(self, self.gan_net) self.optimizer_G, self.scheduler_G = Trainer.init( self._get_parameters()[0], self.configer.get('solver')) self.optimizer_D, self.scheduler_D = Trainer.init( self._get_parameters()[1], self.configer.get('solver')) self.train_loader = self.seg_data_loader.get_trainloader() self.val_loader = self.seg_data_loader.get_valloader()
def _init_model(self): self.det_net = self.det_model_manager.object_detector() self.det_net = RunnerHelper.load_net(self, self.det_net) self.optimizer, self.scheduler = Trainer.init(self._get_parameters(), self.configer.get('solver')) self.train_loader = self.det_data_loader.get_trainloader() self.val_loader = self.det_data_loader.get_valloader()
def _init_model(self): # torch.multiprocessing.set_sharing_strategy('file_system') self.det_net = self.det_model_manager.object_detector() self.det_net = RunnerHelper.load_net(self, self.det_net) self.optimizer, self.scheduler = Trainer.init(self._get_parameters(), self.configer.get('solver')) self.train_loader = self.det_data_loader.get_trainloader() self.val_loader = self.det_data_loader.get_valloader() self.det_loss = self.det_model_manager.get_det_loss()
def _init_model(self): self.pose_net = self.pose_model_manager.get_single_pose_model() self.pose_net = RunnerHelper.load_net(self, self.pose_net) self.optimizer, self.scheduler = Trainer.init( self._get_parameters(), self.configer.get('solver')) self.train_loader = self.pose_data_loader.get_trainloader() self.val_loader = self.pose_data_loader.get_valloader() self.cpm_loss = self.pose_model_manager.get_pose_loss()
def _init_model(self): self.pose_net = self.pose_model_manager.get_multi_pose_model() self.pose_net = RunnerHelper.load_net(self, self.pose_net) self.optimizer, self.scheduler = Trainer.init(self._get_parameters(), self.configer.get('solver')) self.train_loader = self.pose_data_loader.get_trainloader() self.val_loader = self.pose_data_loader.get_valloader() self.weights = self.configer.get('network', 'loss_weights') self.mse_loss = self.pose_model_manager.get_pose_loss()
def _init_model(self): self.seg_net = self.seg_model_manager.get_seg_model() #print('5555') self.seg_net = RunnerHelper.load_net(self, self.seg_net) #print('6666') self.optimizer, self.scheduler = Trainer.init( self._get_parameters(), self.configer.get('solver')) self.train_loader = self.seg_data_loader.get_trainloader() self.val_loader = self.seg_data_loader.get_valloader() self.loss = self.seg_model_manager.get_seg_loss()
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 train(self): """ Train function of every epoch during train phase. """ self.pose_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, backbone_list=(0, ), solver_dict=self.configer.get('solver')) self.data_time.update(time.time() - start_time) # Forward pass. out = self.pose_net(data_dict) # Compute the loss of the train batch & backward. loss_dict = self.pose_loss(out) 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() 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 = {4}\tLoss = {3}\n'.format( self.runner_state['epoch'], self.runner_state['iters'], self.configer.get('solver', '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.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()