Beispiel #1
0
def wider_pre_conv(layer, n_add_filters, weighted=True):
    '''wider previous conv layer.
    '''
    n_dim = get_n_dim(layer)
    if not weighted:
        return get_conv_class(n_dim)(
            layer.input_channel,
            layer.filters + n_add_filters,
            kernel_size=layer.kernel_size,
        )

    n_pre_filters = layer.filters
    rand = np.random.randint(n_pre_filters, size=n_add_filters)
    teacher_w, teacher_b = layer.get_weights()

    student_w = teacher_w.copy()
    student_b = teacher_b.copy()
    # target layer update (i)
    for i, _ in enumerate(rand):
        teacher_index = rand[i]
        new_weight = teacher_w[teacher_index, ...]
        new_weight = new_weight[np.newaxis, ...]
        student_w = np.concatenate((student_w, new_weight), axis=0)
        student_b = np.append(student_b, teacher_b[teacher_index])
    new_pre_layer = get_conv_class(n_dim)(layer.input_channel,
                                          n_pre_filters + n_add_filters,
                                          layer.kernel_size)
    new_pre_layer.set_weights(
        (add_noise(student_w, teacher_w), add_noise(student_b, teacher_b)))
    return new_pre_layer
Beispiel #2
0
def wider_bn(layer, start_dim, total_dim, n_add, weighted=True):
    '''wider batch norm layer.
    '''
    n_dim = get_n_dim(layer)
    if not weighted:
        return get_batch_norm_class(n_dim)(layer.num_features + n_add)

    weights = layer.get_weights()

    new_weights = [
        add_noise(np.ones(n_add, dtype=np.float32), np.array([0, 1])),
        add_noise(np.zeros(n_add, dtype=np.float32), np.array([0, 1])),
        add_noise(np.zeros(n_add, dtype=np.float32), np.array([0, 1])),
        add_noise(np.ones(n_add, dtype=np.float32), np.array([0, 1])),
    ]

    student_w = tuple()
    for weight, new_weight in zip(weights, new_weights):
        temp_w = weight.copy()
        temp_w = np.concatenate(
            (temp_w[:start_dim], new_weight, temp_w[start_dim:total_dim]))
        student_w += (temp_w, )
    new_layer = get_batch_norm_class(n_dim)(layer.num_features + n_add)
    new_layer.set_weights(student_w)
    return new_layer
Beispiel #3
0
def deeper_conv_block(conv_layer, kernel_size, weighted=True):
    '''deeper conv layer.
    '''
    n_dim = get_n_dim(conv_layer)
    filter_shape = (kernel_size, ) * 2
    n_filters = conv_layer.filters
    weight = np.zeros((n_filters, n_filters) + filter_shape)
    center = tuple(map(lambda x: int((x - 1) / 2), filter_shape))
    for i in range(n_filters):
        filter_weight = np.zeros((n_filters, ) + filter_shape)
        index = (i, ) + center
        filter_weight[index] = 1
        weight[i, ...] = filter_weight
    bias = np.zeros(n_filters)
    new_conv_layer = get_conv_class(n_dim)(conv_layer.filters,
                                           n_filters,
                                           kernel_size=kernel_size)
    bn = get_batch_norm_class(n_dim)(n_filters)

    if weighted:
        new_conv_layer.set_weights(
            (add_noise(weight,
                       np.array([0, 1])), add_noise(bias, np.array([0, 1]))))
        new_weights = [
            add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])),
            add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])),
            add_noise(np.zeros(n_filters, dtype=np.float32), np.array([0, 1])),
            add_noise(np.ones(n_filters, dtype=np.float32), np.array([0, 1])),
        ]
        bn.set_weights(new_weights)

    return [StubReLU(), new_conv_layer, bn]
Beispiel #4
0
def wider_next_conv(layer, start_dim, total_dim, n_add, weighted=True):
    '''wider next conv layer.
    '''
    n_dim = get_n_dim(layer)
    if not weighted:
        return get_conv_class(n_dim)(layer.input_channel + n_add,
                                     layer.filters,
                                     kernel_size=layer.kernel_size,
                                     stride=layer.stride)
    n_filters = layer.filters
    teacher_w, teacher_b = layer.get_weights()

    new_weight_shape = list(teacher_w.shape)
    new_weight_shape[1] = n_add
    new_weight = np.zeros(tuple(new_weight_shape))

    student_w = np.concatenate(
        (teacher_w[:, :start_dim, ...].copy(), add_noise(
            new_weight, teacher_w), teacher_w[:, start_dim:total_dim,
                                              ...].copy()),
        axis=1)
    new_layer = get_conv_class(n_dim)(layer.input_channel + n_add,
                                      n_filters,
                                      kernel_size=layer.kernel_size,
                                      stride=layer.stride)
    new_layer.set_weights((student_w, teacher_b))
    return new_layer
Beispiel #5
0
def init_conv_weight(layer):
    '''initilize conv layer weight.
    '''
    n_filters = layer.filters
    filter_shape = (layer.kernel_size, ) * get_n_dim(layer)
    weight = np.zeros((n_filters, n_filters) + filter_shape)

    center = tuple(map(lambda x: int((x - 1) / 2), filter_shape))
    for i in range(n_filters):
        filter_weight = np.zeros((n_filters, ) + filter_shape)
        index = (i, ) + center
        filter_weight[index] = 1
        weight[i, ...] = filter_weight
    bias = np.zeros(n_filters)

    layer.set_weights(
        (add_noise(weight, np.array([0,
                                     1])), add_noise(bias, np.array([0, 1]))))