def evaluate_one_epoch(val_loader, model, epoch, configs, logger): batch_time = AverageMeter('Time', ':6.3f') data_time = AverageMeter('Data', ':6.3f') conf_thresh = 0.5 nms_thresh = 0.5 iou_threshold = 0.5 progress = ProgressMeter(len(val_loader), [batch_time, data_time], prefix="Evaluate - Epoch: [{}/{}]".format( epoch, configs.num_epochs)) labels = [] sample_metrics = [] # List of tuples (TP, confs, pred) # switch to evaluate mode model.eval() with torch.no_grad(): start_time = time.time() for batch_idx, batch_data in enumerate(tqdm(val_loader)): data_time.update(time.time() - start_time) _, imgs, targets = batch_data # Extract labels labels += targets[:, 1].tolist() # Rescale target targets[:, 2:] *= configs.img_size imgs = imgs.to(configs.device, non_blocking=True) outputs = model(imgs) outputs = post_processing(outputs, conf_thresh=conf_thresh, nms_thresh=nms_thresh) sample_metrics += get_batch_statistics_rotated_bbox( outputs, targets, iou_threshold=iou_threshold) # measure elapsed time # torch.cuda.synchronize() batch_time.update(time.time() - start_time) # Log message if logger is not None: if ((batch_idx + 1) % configs.print_freq) == 0: logger.info(progress.get_message(batch_idx)) start_time = time.time() # Concatenate sample statistics true_positives, pred_scores, pred_labels = [ np.concatenate(x, 0) for x in list(zip(*sample_metrics)) ] precision, recall, AP, f1, ap_class = ap_per_class( true_positives, pred_scores, pred_labels, labels) return precision, recall, AP, f1, ap_class
def evaluate_mAP(val_loader, model, configs, logger): batch_time = AverageMeter('Time', ':6.3f') data_time = AverageMeter('Data', ':6.3f') progress = ProgressMeter(len(val_loader), [batch_time, data_time], prefix="Evaluation phase...") labels = [] sample_metrics = [] # List of tuples (TP, confs, pred) # switch to evaluate mode model.eval() with torch.no_grad(): start_time = time.time() for batch_idx, batch_data in enumerate(tqdm(val_loader)): metadatas, targets = batch_data batch_size = len(metadatas['img_path']) voxelinput = metadatas['voxels'] coorinput = metadatas['coors'] numinput = metadatas['num_points'] dtype = torch.float32 voxelinputr = torch.tensor(voxelinput, dtype=torch.float32, device=configs.device).to(dtype) coorinputr = torch.tensor(coorinput, dtype=torch.int32, device=configs.device) numinputr = torch.tensor(numinput, dtype=torch.int32, device=configs.device) t1 = time_synchronized() outputs = model(voxelinputr, coorinputr, numinputr) outputs = outputs._asdict() outputs['hm_cen'] = _sigmoid(outputs['hm_cen']) outputs['cen_offset'] = _sigmoid(outputs['cen_offset']) # detections size (batch_size, K, 10) detections = decode(outputs['hm_cen'], outputs['cen_offset'], outputs['direction'], outputs['z_coor'], outputs['dim'], K=configs.K) detections = detections.cpu().numpy().astype(np.float32) detections = post_processingv2(detections, configs.num_classes, configs.down_ratio, configs.peak_thresh) for sample_i in range(len(detections)): # print(output.shape) num = targets['count'][sample_i] # print(targets['batch'][sample_i][:num].shape) target = targets['batch'][sample_i][:num] #print(target[:, 8].tolist()) labels += target[:, 8].tolist() sample_metrics += get_batch_statistics_rotated_bbox( detections, targets, iou_threshold=configs.iou_thresh) t2 = time_synchronized() # measure elapsed time # torch.cuda.synchronize() batch_time.update(time.time() - start_time) # Log message if logger is not None: if ((batch_idx + 1) % configs.print_freq) == 0: logger.info(progress.get_message(batch_idx)) start_time = time.time() # Concatenate sample statistics true_positives, pred_scores, pred_labels = [ np.concatenate(x, 0) for x in list(zip(*sample_metrics)) ] precision, recall, AP, f1, ap_class = ap_per_class( true_positives, pred_scores, pred_labels, labels) return precision, recall, AP, f1, ap_class