test.py

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,
         weights=None,
         batch_size=2,
         img_size=416,
         conf_thres=0.001,
         nms_thres=0.5,
         save_json=False,
         model=None,
         dataloader=None,
         testing=False):
    # Initialize/load model and set device
    if model is None:
        device = torch_utils.select_device(opt.device, batch_size=batch_size)
        verbose = True

        # Remove previous
        for f in glob.glob('test_batch*.jpg'):
            os.remove(f)

        # Initialize model
        model = Darknet(cfg, img_size).to(device)

        # Load weights
        attempt_download(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:  # called by train.py
        device = next(model.parameters()).device  # get model device
        verbose = False

    # Configure run
    data = parse_data_cfg(data)
    nc = int(data['classes'])  # number of classes
    test_path = data['valid']  # path to test images
    names = load_classes(data['names'])  # class names
    iou_thres = torch.linspace(0.5, 0.95, 10).to(device)  # for mAP@0.5:0.95
    iou_thres = iou_thres[0].view(1)  # for mAP@0.5
    niou = iou_thres.numel()

    # Dataloader
    if dataloader is None:
        dataset = LoadImagesAndLabels(test_path,
                                      img_size,
                                      batch_size,
                                      rect=True)
        batch_size = min(batch_size, len(dataset))
        dataloader = DataLoader(dataset,
                                batch_size=batch_size,
                                num_workers=min([
                                    os.cpu_count(),
                                    batch_size if batch_size > 1 else 0, 8
                                ]),
                                pin_memory=True,
                                collate_fn=dataset.collate_fn)

    seen = 0
    model.eval()
    coco91class = coco80_to_coco91_class()
    if testing:
        s = ('%20s' + '%10s' * 8) % ('Class', 'Images', 'Targets', 'P', 'R',
                                    'mAP@0.5', 'F1', 'TP', 'FP')
    else:
        s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R',
                                     'mAP@0.5', 'F1')
    p, r, f1, mp, mr, map, mf1 = 0., 0., 0., 0., 0., 0., 0.
    loss = torch.zeros(3)
    jdict, stats, ap, ap_class = [], [], [], []
    for batch_i, (imgs, targets, paths,
                  shapes) in enumerate(tqdm(dataloader, desc=s)):
        imgs = imgs.to(
            device).float() / 255.0  # uint8 to float32, 0 - 255 to 0.0 - 1.0
        targets = targets.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,
                        paths=paths,
                        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 += compute_loss(train_out, targets,
                                 model)[1][:3].cpu()  # GIoU, obj, cls

        # 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)
            # print("nl:", nl, pred)
            tcls = labels[:, 0].tolist() if nl else []  # target class
            seen += 1

            if pred is None:
                if nl:
                    stats.append(
                        (torch.zeros(0,
                                     1), torch.Tensor(), torch.Tensor(), tcls))
                continue

            # Append to text file
            # with open('test.txt', 'a') as file:
            #    [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred]

            # 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][0],
                             shapes[si][1])  # 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': [floatn(x, 3) for x in box[di]],
                        'score': floatn(d[4], 5)
                    })

            # Clip boxes to image bounds
            clip_coords(pred, (height, width))

            # Assign all predictions as incorrect
            correct = torch.zeros(len(pred), niou)

            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, j = bbox_iou(pbox, tbox[m]).max(0)
                    m = m[j]

                    # Per iou_thres 'correct' vector
                    if iou > iou_thres[0] and m not in detected:
                        detected.append(m)
                        correct[i] = iou > iou_thres  # 0.5 -> 0.7

            # 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

    # print(len(stats))

    if len(stats):
        """
        def ap_per_class(tp, conf, pred_cls, target_cls):
            Compute the average precision, given the recall and precision curves.
            Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
            # Arguments
                tp:    True positives (nparray, nx1 or nx10).
                conf:  Objectness value from 0-1 (nparray).
                pred_cls: Predicted object classes (nparray).
                target_cls: True object classes (nparray).
            # Returns
                The average precision as computed in py-faster-rcnn.

            # Sort by objectness
            i = np.argsort(-conf)
            tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
        """


        if testing:
            print(stats, len(stats))
            tp_1, conf_1, pred_cls_1, target_cls_1 = stats
            i_1 = np.argsort(-conf_1)
            # print(tp_1[i_1].cumsum(0))
            tp_2 = tp_1[i_1].cumsum(0)[-1]
            fp_2 = (1 - tp_1[i_1]).cumsum(0)[-1]

        # print(tp_2.shape, fp_2.shape)

        p, r, ap, f1, ap_class = ap_per_class(*stats)
        # if niou > 1:
        #       p, r, ap, f1 = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1)  # average across ious
        mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.mean()
        nt = np.bincount(stats[3].astype(np.int64),
                         minlength=nc)  # number of targets per class
    else:
        nt = torch.zeros(1)

    # Print results
    if testing:
        pf = '%20s' + '%10.4g' * 8  # print format
        print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1, tp_2, fp_2))
    else:
        pf = '%20s' + '%10.4g' * 6  # print format
        print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1))

    # Print results per class
    if verbose and 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 dataloader.dataset.img_files
        ]
        with open('results.json', 'w') as file:
            json.dump(jdict, file)

        try:
            from pycocotools.coco import COCO
            from pycocotools.cocoeval import COCOeval
        except:
            print(
                'WARNING: missing pycocotools package, can not compute official COCO mAP. See requirements.txt.'
            )

        # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
        cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')
                      [0])  # 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()
        mf1, map = cocoEval.stats[:
                                  2]  # update to pycocotools results (mAP@0.5:0.95, mAP@0.5)

    # Return results
    maps = np.zeros(nc) + map
    for i, c in enumerate(ap_class):
        maps[c] = ap[i]
    return (mp, mr, map, mf1, *(loss / len(dataloader)).tolist()), maps


if __name__ == '__main__':
    parser = argparse.ArgumentParser(prog='test.py')
    parser.add_argument('--cfg',
                        type=str,
                        default='cfg/yolov3.cfg',
                        help='*.cfg path')
    parser.add_argument('--data',
                        type=str,
                        default='data/dataset1.data',
                        help='*.data path')
    parser.add_argument(
        '--weights',
        type=str,
        default='weights/best.pt',
        help='path to weights file')
    parser.add_argument('--batch-size',
                        type=int,
                        default=2,
                        help='size of each image batch')
    parser.add_argument('--img-size',
                        type=int,
                        default=416,
                        help='inference size (pixels)')
    parser.add_argument('--conf-thres',
                        type=float,
                        default=0.2,
                        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('--device',
                        default='',
                        help='device id (i.e. 0 or 0,1) or cpu')
    opt = parser.parse_args()
    print(opt)

    with torch.no_grad():
        test(opt.cfg,
             opt.data,
             opt.weights,
             opt.batch_size,
             opt.img_size,
             opt.conf_thres,
             opt.nms_thres,
             opt.save_json or any([
                 x in opt.data
                 for x in ['coco.data', 'coco2014.data', 'coco2017.data']
             ]),
             testing=True)