import argparse import json from torch.utils.data import DataLoader from models import * from utils.datasets import * from utils.utils import * def test( cfg, data_cfg, weights=None, batch_size=16, img_size=416, iou_thres=0.5, conf_thres=0.001, nms_thres=0.5, save_json=False, model=None ): if model is None: device = torch_utils.select_device() # Initialize model model = Darknet(cfg, img_size).to(device) # Load weights if weights.endswith('.pt'): # pytorch format model.load_state_dict(torch.load(weights, map_location=device)['model']) else: # darknet format _ = load_darknet_weights(model, weights) if torch.cuda.device_count() > 1: model = nn.DataParallel(model) else: device = next(model.parameters()).device # get model device # Configure run data_cfg = parse_data_cfg(data_cfg) nc = int(data_cfg['classes']) # number of classes test_path = data_cfg['valid'] # path to test images names = load_classes(data_cfg['names']) # class names # Dataloader dataset = LoadImagesAndLabels(test_path, img_size, batch_size) dataloader = DataLoader(dataset, batch_size=batch_size, num_workers=4, pin_memory=True, collate_fn=dataset.collate_fn) seen = 0 model.eval() coco91class = coco80_to_coco91_class() print(('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP', 'F1')) loss, p, r, f1, mp, mr, map, mf1 = 0., 0., 0., 0., 0., 0., 0., 0. jdict, stats, ap, ap_class = [], [], [], [] for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc='Computing mAP')): targets = targets.to(device) imgs = imgs.to(device) _, _, height, width = imgs.shape # batch size, channels, height, width # Plot images with bounding boxes if batch_i == 0 and not os.path.exists('test_batch0.jpg'): plot_images(imgs=imgs, targets=targets, fname='test_batch0.jpg') # Run model inf_out, train_out = model(imgs) # inference and training outputs # Compute loss if hasattr(model, 'hyp'): # if model has loss hyperparameters loss_i, _ = compute_loss(train_out, targets, model) loss += loss_i.item() # Run NMS output = non_max_suppression(inf_out, conf_thres=conf_thres, nms_thres=nms_thres) # Statistics per image for si, pred in enumerate(output): labels = targets[targets[:, 0] == si, 1:] nl = len(labels) tcls = labels[:, 0].tolist() if nl else [] # target class seen += 1 if pred is None: if nl: stats.append(([], torch.Tensor(), torch.Tensor(), tcls)) continue # Append to pycocotools JSON dictionary if save_json: # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ... image_id = int(Path(paths[si]).stem.split('_')[-1]) box = pred[:, :4].clone() # xyxy scale_coords(imgs[si].shape[1:], box, shapes[si]) # to original shape box = xyxy2xywh(box) # xywh box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner for di, d in enumerate(pred): jdict.append({ 'image_id': image_id, 'category_id': coco91class[int(d[6])], 'bbox': [float3(x) for x in box[di]], 'score': float(d[4]) }) # Assign all predictions as incorrect correct = [0] * len(pred) if nl: detected = [] tcls_tensor = labels[:, 0] # target boxes tbox = xywh2xyxy(labels[:, 1:5]) tbox[:, [0, 2]] *= width tbox[:, [1, 3]] *= height # Search for correct predictions for i, (*pbox, pconf, pcls_conf, pcls) in enumerate(pred): # Break if all targets already located in image if len(detected) == nl: break # Continue if predicted class not among image classes if pcls.item() not in tcls: continue # Best iou, index between pred and targets m = (pcls == tcls_tensor).nonzero().view(-1) iou, bi = bbox_iou(pbox, tbox[m]).max(0) # If iou > threshold and class is correct mark as correct if iou > iou_thres and m[bi] not in detected: # and pcls == tcls[bi]: correct[i] = 1 detected.append(m[bi]) # Append statistics (correct, conf, pcls, tcls) stats.append((correct, pred[:, 4].cpu(), pred[:, 6].cpu(), tcls)) # Compute statistics stats = [np.concatenate(x, 0) for x in list(zip(*stats))] # to numpy nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class if len(stats): p, r, ap, f1, ap_class = ap_per_class(*stats) mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.mean() # Print results pf = '%20s' + '%10.3g' * 6 # print format print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1), end='\n\n') # Print results per class if nc > 1 and len(stats): for i, c in enumerate(ap_class): print(pf % (names[c], seen, nt[c], p[i], r[i], ap[i], f1[i])) # Save JSON if save_json and map and len(jdict): imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataset.img_files] with open('results.json', 'w') as file: json.dump(jdict, file) from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb cocoGt = COCO('../coco/annotations/instances_val2014.json') # initialize COCO ground truth api cocoDt = cocoGt.loadRes('results.json') # initialize COCO pred api cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') cocoEval.params.imgIds = imgIds # [:32] # only evaluate these images cocoEval.evaluate() cocoEval.accumulate() cocoEval.summarize() map = cocoEval.stats[1] # update mAP to pycocotools mAP # Return results return mp, mr, map, mf1, loss / len(dataloader) if __name__ == '__main__': parser = argparse.ArgumentParser(prog='test.py') parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch') parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path') parser.add_argument('--data-cfg', type=str, default='data/coco.data', help='coco.data file path') parser.add_argument('--weights', type=str, default='weights/yolov3-spp.weights', help='path to weights file') parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected') parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold') parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression') parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') opt = parser.parse_args() print(opt, end='\n\n') with torch.no_grad(): mAP = test( opt.cfg, opt.data_cfg, opt.weights, opt.batch_size, opt.img_size, opt.iou_thres, opt.conf_thres, opt.nms_thres, opt.save_json )