yolov2_train_class.py 文件源码

def evaluate(model, dataset, crop_margin, test_size, batch_size):
    xp = model.xp
    iterator = chainer.iterators.SerialIterator(dataset, batch_size, repeat=False, shuffle=False)
    acc_sum = 0
    iteration = 0
    for batch in iterator:
        image_batch = []
        label_batch = []
        for image_path, category_id, _ in batch:
            image = load_image(image_path)
            image_width, image_height = image.size
            crop_size = min(image_width, image_height) - crop_margin
            crop_rect = ((image_width - crop_size) // 2, (image_height - crop_size) // 2, crop_size, crop_size)
            input_size = test_size
            image_batch.append(transform_image(image, crop_rect, input_size))
            label_batch.append(category_id)

        x = xp.asarray(image_batch)
        t = xp.asarray(label_batch)

        with chainer.using_config('enable_backprop', False):
            with chainer.using_config('train', False):
                y = model(x)
        acc = F.accuracy(y, t)
        acc_sum += float(acc.data) * batch_size
    return acc_sum / len(dataset)