car-detection-bayes/test.py

import argparse

from models import *
from utils.datasets import *
from utils.utils import *

parser = argparse.ArgumentParser(prog='test.py')
parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch')
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file')
parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
parser.add_argument('-weights_path', type=str, default='weights/yolov3.pt', help='path to weights file')
parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label 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=0.3, help='object confidence threshold')
parser.add_argument('-nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
parser.add_argument('-n_cpu', type=int, default=0, help='number of cpu threads to use during batch generation')
parser.add_argument('-img_size', type=int, default=416, help='size of each image dimension')
opt = parser.parse_args()
print(opt, end='\n\n')

cuda = torch.cuda.is_available()
device = torch.device('cuda:0' if cuda else 'cpu')


def main(opt):
    # Configure run
    data_config = parse_data_config(opt.data_config_path)
    nC = int(data_config['classes'])  # number of classes (80 for COCO)
    if platform == 'darwin':  # MacOS (local)
        test_path = data_config['valid']
    else:  # linux (cloud, i.e. gcp)
        test_path = '../coco/5k.part'

    # Initiate model
    model = Darknet(opt.cfg, opt.img_size)

    # Load weights
    if opt.weights_path.endswith('.weights'):  # darknet format
        load_weights(model, opt.weights_path)
    elif opt.weights_path.endswith('.pt'):  # pytorch format
        checkpoint = torch.load(opt.weights_path, map_location='cpu')
        model.load_state_dict(checkpoint['model'])
        del checkpoint

    model.to(device).eval()

    # Get dataloader
    # dataset = load_images_with_labels(test_path)
    # dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
    dataloader = load_images_and_labels(test_path, batch_size=opt.batch_size, img_size=opt.img_size)

    print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
    outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class = [], [], [], [], [], [], [], []
    AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
    for batch_i, (imgs, targets) in enumerate(dataloader):

        with torch.no_grad():
            output = model(imgs.to(device))
            output = non_max_suppression(output, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres)

        # Compute average precision for each sample
        for sample_i, (labels, detections) in enumerate(zip(targets, output)):
            correct = []

            if detections is None:
                # If there are no detections but there are labels mask as zero AP
                if labels.size(0) != 0:
                    mAPs.append(0), mR.append(0), mP.append(0)
                continue

            # Get detections sorted by decreasing confidence scores
            detections = detections.cpu().numpy()
            detections = detections[np.argsort(-detections[:, 4])]

            # If no labels add number of detections as incorrect
            if labels.size(0) == 0:
                # correct.extend([0 for _ in range(len(detections))])
                mAPs.append(0), mR.append(0), mP.append(0)
                continue
            else:
                target_cls = labels[:, 0]

                # Extract target boxes as (x1, y1, x2, y2)
                target_boxes = xywh2xyxy(labels[:, 1:5]) * opt.img_size

                detected = []
                for *pred_bbox, conf, obj_conf, obj_pred in detections:

                    pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1)
                    # Compute iou with target boxes
                    iou = bbox_iou(pred_bbox, target_boxes)
                    # Extract index of largest overlap
                    best_i = np.argmax(iou)
                    # If overlap exceeds threshold and classification is correct mark as correct
                    if iou[best_i] > opt.iou_thres and obj_pred == labels[best_i, 0] and best_i not in detected:
                        correct.append(1)
                        detected.append(best_i)
                    else:
                        correct.append(0)

            # Compute Average Precision (AP) per class
            AP, AP_class, R, P = ap_per_class(tp=correct, conf=detections[:, 4], pred_cls=detections[:, 6],
                                              target_cls=target_cls)

            # Accumulate AP per class
            AP_accum_count += np.bincount(AP_class, minlength=nC)
            AP_accum += np.bincount(AP_class, minlength=nC, weights=AP)

            # Compute mean AP across all classes in this image, and append to image list
            mAPs.append(AP.mean())
            mR.append(R.mean())
            mP.append(P.mean())

            # Means of all images
            mean_mAP = np.mean(mAPs)
            mean_R = np.mean(mR)
            mean_P = np.mean(mP)

            # Print image mAP and running mean mAP
            print(('%11s%11s' + '%11.3g' * 3) % (len(mAPs), dataloader.nF, mean_P, mean_R, mean_mAP))

    # Print mAP per class
    print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP') + '\n\nmAP Per Class:')

    classes = load_classes(opt.class_path)  # Extracts class labels from file
    for i, c in enumerate(classes):
        print('%15s: %-.4f' % (c, AP_accum[i] / AP_accum_count[i]))

    # Return mAP
    return mean_mAP, mean_R, mean_P


if __name__ == '__main__':
    mAP = main(opt)