def val(self): """ Validation function during the train phase. """ self.det_net.eval() start_time = time.time() with torch.no_grad(): for j, data_dict in enumerate(self.val_loader): inputs = data_dict['img'] batch_gt_bboxes = data_dict['bboxes'] batch_gt_labels = data_dict['labels'] inputs = RunnerHelper.to_device(self, inputs) input_size = [inputs.size(3), inputs.size(2)] # Forward pass. outputs = self.det_net(inputs) feat_list, loc, cls = RunnerHelper.gather(self, outputs) bboxes, labels = self.ssd_target_generator( feat_list, batch_gt_bboxes, batch_gt_labels, input_size) bboxes, labels = RunnerHelper.to_device(bboxes, labels) # Compute the loss of the val batch. loss = self.det_loss(outputs, bboxes, labels, gathered=self.configer.get( 'network', 'gathered')) self.val_losses.update(loss.item(), inputs.size(0)) batch_detections = SingleShotDetectorTest.decode( loc, cls, self.ssd_priorbox_layer(feat_list, input_size), self.configer, input_size) batch_pred_bboxes = self.__get_object_list(batch_detections) # batch_pred_bboxes = self._get_gt_object_list(batch_gt_bboxes, batch_gt_labels) self.det_running_score.update(batch_pred_bboxes, batch_gt_bboxes, batch_gt_labels) # Update the vars of the val phase. self.batch_time.update(time.time() - start_time) start_time = time.time() RunnerHelper.save_net(self, self.det_net, iters=self.runner_state['iters']) # Print the log info & reset the states. Log.info( 'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t' 'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time, loss=self.val_losses)) Log.info('Val mAP: {}'.format(self.det_running_score.get_mAP())) self.det_running_score.reset() self.batch_time.reset() self.val_losses.reset() self.det_net.train()
def val(self): """ Validation function during the train phase. """ self.det_net.eval() start_time = time.time() with torch.no_grad(): for i, data_dict in enumerate(self.val_loader): inputs = data_dict['img'] batch_gt_bboxes = data_dict['bboxes'] batch_gt_labels = data_dict['labels'] input_size = [inputs.size(3), inputs.size(2)] # Forward pass. inputs = RunnerHelper.to_device(self, inputs) feat_list, predictions, detections = self.det_net(inputs) targets, objmask, noobjmask = self.yolo_target_generator( feat_list, batch_gt_bboxes, batch_gt_labels, input_size) targets, objmask, noobjmask = RunnerHelper.to_device( self, targets, objmask, noobjmask) # Compute the loss of the val batch. loss = self.det_loss(predictions, targets, objmask, noobjmask) self.val_losses.update(loss.item(), inputs.size(0)) batch_detections = YOLOv3Test.decode(detections, self.configer, input_size) batch_pred_bboxes = self.__get_object_list( batch_detections, input_size) self.det_running_score.update(batch_pred_bboxes, batch_gt_bboxes, batch_gt_labels) # Update the vars of the val phase. self.batch_time.update(time.time() - start_time) start_time = time.time() RunnerHelper.save_net(self, self.det_net, iters=self.runner_state['iters']) # Print the log info & reset the states. Log.info( 'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t' 'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time, loss=self.val_losses)) Log.info('Val mAP: {}'.format(self.det_running_score.get_mAP())) self.det_running_score.reset() self.batch_time.reset() self.val_losses.reset() self.det_net.train()
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.pose_net.eval() start_time = time.time() with torch.no_grad(): for j, data_dict in enumerate(self.val_loader): inputs = data_dict['img'] heatmap = data_dict['heatmap'] # Change the data type. inputs, heatmap = RunnerHelper.to_device(self, inputs, heatmap) # Forward pass. outputs = self.pose_net(inputs) # Compute the loss of the val batch. loss = self.mse_loss(outputs[-1], heatmap) 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.pose_net, iters=self.runner_state['iters']) # Print the log info & reset the states. Log.info( 'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t' 'Loss {loss.avg:.8f}\n'.format( batch_time=self.batch_time, loss=self.val_losses)) self.batch_time.reset() self.val_losses.reset() self.pose_net.train()
def val(self): """ Validation function during the train phase. """ self.det_net.eval() start_time = time.time() with torch.no_grad(): for j, data_dict in enumerate(self.val_loader): inputs = data_dict['img'] batch_gt_bboxes = data_dict['bboxes'] batch_gt_labels = data_dict['labels'] metas = data_dict['meta'] data_dict['bboxes'] = DCHelper.todc( batch_gt_bboxes, gpu_list=self.configer.get('gpu'), cpu_only=True) data_dict['labels'] = DCHelper.todc( batch_gt_labels, gpu_list=self.configer.get('gpu'), cpu_only=True) data_dict['meta'] = DCHelper.todc( metas, gpu_list=self.configer.get('gpu'), cpu_only=True) # Forward pass. inputs = RunnerHelper.to_device(self, inputs) loss, test_group = self.det_net(data_dict) # Compute the loss of the train batch & backward. loss = loss.mean() self.val_losses.update(loss.item(), inputs.size(0)) test_indices_and_rois, test_roi_locs, test_roi_scores, test_rois_num = test_group batch_detections = FastRCNNTest.decode(test_roi_locs, test_roi_scores, test_indices_and_rois, test_rois_num, self.configer, metas) batch_pred_bboxes = self.__get_object_list(batch_detections) self.det_running_score.update(batch_pred_bboxes, batch_gt_bboxes, batch_gt_labels) # Update the vars of the val phase. self.batch_time.update(time.time() - start_time) start_time = time.time() RunnerHelper.save_net(self, self.det_net, iters=self.runner_state['iters']) # Print the log info & reset the states. Log.info( 'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t' 'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time, loss=self.val_losses)) Log.info('Val mAP: {}\n'.format(self.det_running_score.get_mAP())) self.det_running_score.reset() self.batch_time.reset() self.val_losses.reset() self.det_net.train()
def val(self, data_loader=None): """ Validation function during the train phase. """ self.seg_net.eval() start_time = time.time() data_loader = self.val_loader if data_loader is None else data_loader for j, data_dict in enumerate(data_loader): inputs = data_dict['img'] targets = data_dict['labelmap'] with torch.no_grad(): # Change the data type. inputs, targets = RunnerHelper.to_device(self, inputs, targets) # Forward pass. outputs = self.seg_net(inputs) # Compute the loss of the val batch. loss = self.pixel_loss(outputs, targets, gathered=self.configer.get( 'network', 'gathered')) outputs = RunnerHelper.gather(self, outputs) self.val_losses.update(loss.item(), inputs.size(0)) self._update_running_score(outputs[-1], data_dict['meta']) # Update the vars of the val phase. self.batch_time.update(time.time() - start_time) start_time = time.time() self.runner_state['performance'] = self.seg_running_score.get_mean_iou( ) self.runner_state['val_loss'] = self.val_losses.avg RunnerHelper.save_net( self, self.seg_net, performance=self.seg_running_score.get_mean_iou(), val_loss=self.val_losses.avg) # Print the log info & reset the states. Log.info('Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t' 'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time, loss=self.val_losses)) Log.info('Mean IOU: {}\n'.format( self.seg_running_score.get_mean_iou())) Log.info('Pixel ACC: {}\n'.format( self.seg_running_score.get_pixel_acc())) self.batch_time.reset() self.val_losses.reset() self.seg_running_score.reset() self.seg_net.train()
def val(self): """ Validation function during the train phase. """ self.pose_net.eval() start_time = time.time() with torch.no_grad(): for i, data_dict in enumerate(self.val_loader): inputs = data_dict['img'] maskmap = data_dict['maskmap'] heatmap = data_dict['heatmap'] vecmap = data_dict['vecmap'] # Change the data type. inputs, heatmap, maskmap, vecmap = RunnerHelper.to_device( self, inputs, heatmap, maskmap, vecmap) # Forward pass. paf_out, heatmap_out = self.pose_net(inputs) # Compute the loss of the val batch. loss_heatmap = self.mse_loss(heatmap_out[-1], heatmap, maskmap) loss_associate = self.mse_loss(paf_out[-1], vecmap, maskmap) loss = 2.0 * loss_heatmap + loss_associate self.val_losses.update(loss.item(), inputs.size(0)) self.val_loss_heatmap.update(loss_heatmap.item(), inputs.size(0)) self.val_loss_associate.update(loss_associate.item(), inputs.size(0)) # Update the vars of the val phase. self.batch_time.update(time.time() - start_time) start_time = time.time() self.runner_state['val_loss'] = self.val_losses.avg RunnerHelper.save_net(self, self.pose_net, val_loss=self.val_losses.avg) Log.info('Loss Heatmap:{}, Loss Asso: {}'.format( self.val_loss_heatmap.avg, self.val_loss_associate.avg)) # Print the log info & reset the states. Log.info( 'Test Time {batch_time.sum:.3f}s, ({batch_time.avg:.3f})\t' 'Loss {loss.avg:.8f}\n'.format(batch_time=self.batch_time, loss=self.val_losses)) self.batch_time.reset() self.val_losses.reset() self.val_loss_heatmap.reset() self.val_loss_associate.reset() self.pose_net.train()
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) # Compute the loss of the val batch. loss = self.ce_loss(outputs, labels, gathered=self.configer.get( 'network', 'gathered')) outputs = RunnerHelper.gather(self, outputs) 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()
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 self.scheduler.step(self.train_schedule_loss.avg, epoch=self.configer.get('epoch')) self.train_schedule_loss.reset() # data_tuple: (inputs, heatmap, maskmap, vecmap) for i, data_dict in enumerate(self.train_loader): inputs = data_dict['img'] maskmap = data_dict['maskmap'] heatmap = data_dict['heatmap'] vecmap = data_dict['vecmap'] self.data_time.update(time.time() - start_time) # Change the data type. inputs, heatmap, maskmap, vecmap = RunnerHelper.to_device( self, inputs, heatmap, maskmap, vecmap) # Forward pass. paf_out, heatmap_out = self.pose_net(inputs) # Compute the loss of the train batch & backward. loss_heatmap = self.mse_loss(heatmap_out, heatmap, mask=maskmap, weights=self.weights) loss_associate = self.mse_loss(paf_out, vecmap, mask=maskmap, weights=self.weights) loss = 2.0 * loss_heatmap + loss_associate self.train_losses.update(loss.item(), inputs.size(0)) self.train_schedule_loss.update(loss.item(), inputs.size(0)) self.train_loss_heatmap.update(loss_heatmap.item(), inputs.size(0)) self.train_loss_associate.update(loss_associate.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('Loss Heatmap:{}, Loss Asso: {}'.format( self.train_loss_heatmap.avg, self.train_loss_associate.avg)) 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() self.train_loss_heatmap.reset() self.train_loss_associate.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 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, backbone_list=(0, )) inputs = data_dict['img'] batch_gt_bboxes = data_dict['bboxes'] batch_gt_labels = data_dict['labels'] input_size = [inputs.size(3), inputs.size(2)] self.data_time.update(time.time() - start_time) # Change the data type. inputs = RunnerHelper.to_device(self, inputs) # Forward pass. feat_list, predictions, _ = self.det_net(inputs) targets, objmask, noobjmask = self.yolo_target_generator( feat_list, batch_gt_bboxes, batch_gt_labels, input_size) targets, objmask, noobjmask = RunnerHelper.to_device( self, targets, objmask, noobjmask) # Compute the loss of the train batch & backward. loss = self.det_loss(predictions, targets, objmask, noobjmask) 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.configer.get('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()