train.py 文件源码

def validate(model):
    dice_coefs = []
    for image_path, label_path in zip(df_val["image"], df_val["label"]):
        image = load_nifti(image_path)
        label = load_nifti(label_path)
        centers = [[], [], []]
        for img_len, len_out, center, n_tile in zip(image.shape, args.output_shape, centers, args.n_tiles):
            assert img_len < len_out * n_tile, "{} must be smaller than {} x {}".format(img_len, len_out, n_tile)
            stride = int((img_len - len_out) / (n_tile - 1))
            center.append(len_out / 2)
            for i in range(n_tile - 2):
                center.append(center[-1] + stride)
            center.append(img_len - len_out / 2)
        output = np.zeros((dataset["n_classes"],) + image.shape[:-1])
        for x, y, z in itertools.product(*centers):
            patch = crop_patch(image, [x, y, z], args.input_shape)
            patch = np.expand_dims(patch, 0)
            patch = xp.asarray(patch)
            slices_out = [slice(center - len_out / 2, center + len_out / 2) for len_out, center in zip(args.output_shape, [x, y, z])]
            slices_in = [slice((len_in - len_out) / 2, len_in - (len_in - len_out) / 2) for len_out, len_in, in zip(args.output_shape, args.input_shape)]
            output[slice(None), slices_out[0], slices_out[1], slices_out[2]] += chainer.cuda.to_cpu(model(patch).data[0, slice(None), slices_in[0], slices_in[1], slices_in[2]])
        y = np.argmax(output, axis=0).astype(np.int32)
        dice_coefs.append(dice_coefficients(y, label, labels=range(dataset["n_classes"])))
    dice_coefs = np.array(dice_coefs)
    return np.mean(dice_coefs, axis=0)