Пример #1
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def test_imagenet_heatmap():
    print("Testing heatmap mode with imagenet head...")

    model = build_resnet_50(fully_convolutional=True)
    images = load_test_image_paths()
    for image_path in images:
        if "ipod" in image_path:
            dim = int(224 * 1)
            pred = predict(model, image_path, target_size=(dim, dim))

    heatmap = pred[0, :, :, 605]
    mean, std = np.mean(heatmap), np.std(heatmap)
    threshold = mean + 2 * std
    assert (threshold > 0)
    heatmap[heatmap < threshold] = 0
    heatmap[heatmap >= threshold] = 1

    print("Binary ipod heatmap for ipod.jpg, scaled to {}x{}:".format(
        dim, dim))
    print(heatmap)
    print("Compare ipod.jpg to see that it localizes the ipod!")

    true_heatmap = np.array([[0., 0., 0., 0., 0., 0., 0.],
                             [0., 0., 0., 0., 0., 0., 0.],
                             [0., 0., 0., 0., 0., 0., 0.],
                             [0., 0., 0., 0., 1., 1., 0.],
                             [0., 0., 0., 0., 1., 1., 0.],
                             [0., 0., 0., 0., 0., 0., 0.],
                             [0., 0., 0., 0., 0., 0., 0.]])

    assert (np.allclose(true_heatmap, heatmap))
Пример #2
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def test_custom_head_heatmap():
    print("Testing fully convolutial mode with custom head...")

    model = build_resnet_50(fully_convolutional=True, cls_head='custom')
    images = load_test_image_paths()
    for image_path in images:
        if "cat3" in image_path:
            dim = int(224 * 1.3)
            pred = predict(model, image_path, target_size=(dim, dim))

    heatmap = pred[0, :, :, 0]
    mean, std = np.mean(heatmap), np.std(heatmap)
    threshold = mean + 2 * std
    assert (threshold > 0)
    heatmap[heatmap < threshold] = 0
    heatmap[heatmap >= threshold] = 1
    print("Binary cat heatmap for cat3.jpg, scaled to {}x{}:".format(dim, dim))
    print(heatmap)
    print("Compare cat3.jpg to see that it localizes the cat!")

    true_heatmap = np.array([[0., 0., 0., 0., 0., 0., 0., 0., 0.],
                             [0., 0., 0., 0., 0., 0., 0., 0., 0.],
                             [0., 0., 0., 0., 0., 0., 0., 0., 0.],
                             [0., 0., 0., 0., 1., 0., 0., 0., 0.],
                             [0., 0., 0., 1., 1., 1., 0., 0., 0.],
                             [0., 0., 0., 1., 1., 1., 0., 0., 0.],
                             [0., 0., 0., 0., 0., 0., 0., 0., 0.],
                             [0., 0., 0., 0., 0., 0., 0., 0., 0.],
                             [0., 0., 0., 0., 0., 0., 0., 0., 0.]])

    assert (np.allclose(true_heatmap, heatmap))
Пример #3
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def extract_features(extension, img_size):
    extractor = Extractor(extension)
    image_files, features = extractor.load_latest_extractions()
    if image_files is None or features is None:
        name = "{}x{}_{}".format(img_size[0], img_size[1], "feature-maps")
        model = build_resnet_50(cls_head=None)
        image_files, features = extractor.extract(name, model, img_size)
    print("Features extracted. Shape: {}".format(features.shape))
    return image_files, features
Пример #4
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def test_imagenet_classification():
    print("Testing classification mode with imagenet head...")

    model = build_resnet_50()
    images = load_test_image_paths()
    preds = []
    for image_path in images:
        preds.append(predict(model, image_path))
    predictios = np.vstack(preds)

    for i, top5 in enumerate(decode_predictions(predictios)):
        current_image = images[i]
        current_class_label = IMAGE_CLASS_LABELS[current_image]
        class_labels_predictions = [pred[1] for pred in top5]
        print("Image: {} | Actual: {} | Top5 predictions: {}".format(
            current_image, current_class_label, class_labels_predictions))
        assert (current_class_label in class_labels_predictions)
Пример #5
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def test_custom_head_classification():
    print("Testing classfication mode with custom head...")

    model = build_resnet_50(cls_head='custom')
    images = load_test_image_paths()
    preds = []
    for image_path in images:
        preds.append(predict(model, image_path))
    predictios = np.vstack(preds)

    for i, pred in enumerate(predictios):
        current_image = images[i]
        is_cat = "cat" in current_image
        print("Image: {} | Cat score: {:3.5f} | No-cat score {:3.5f}".format(
            current_image, pred[0], pred[1]))
        if is_cat:
            assert (pred[0] > 0.95)
            assert (pred[1] < 0.05)
        else:
            assert (pred[0] < 0.05)
            assert (pred[1] > 0.95)
Пример #6
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    parser.add_argument("-ws",
                        "--width",
                        nargs="?",
                        type=int,
                        default=224,
                        help="Image width")
    args = parser.parse_args()

    return args


def extract(name, model, extension, img_size):
    extractor = Extractor(extension)
    image_files, features = extractor.extract(name, model, img_size)


def filename_base(args):
    return "{}x{}-FCN_{}-{}" \
           .format(args.height, args.width, args.fully_convolutional,
                   args.cls_head)


if __name__ == "__main__":
    args = parse_args()
    img_size = (args.height, args.width)

    model = build_resnet_50(args.fully_convolutional, 'imagenet',
                            args.cls_head)
    name = filename_base(args)
    extract(name, model, args.extension, img_size)