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Remove unused and duplicate code #420

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Apr 22, 2024
Merged

Remove unused and duplicate code #420

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75f55c8
Remove unused and duplicate code
Dobiasd Apr 22, 2024
1e032ba
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Dobiasd Apr 22, 2024
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6 changes: 0 additions & 6 deletions include/fdeep/convolution.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -59,12 +59,6 @@ namespace internal {
return { shape, filters.size(), biases, use_bias, filter_mats };
}

inline convolution_filter_matrices generate_im2col_single_filter_matrix(
const filter& filter)
{
return generate_im2col_filter_matrix(filter_vec(1, filter));
}

inline tensor init_conv_output_tensor(
std::size_t out_height,
std::size_t out_width,
Expand Down
98 changes: 28 additions & 70 deletions include/fdeep/import_model.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -130,35 +130,36 @@ namespace internal {
return fplus::just(result);
}

inline tensor_shape_variable create_tensor_shape_variable(const nlohmann::json& data)
inline tensor_shape_variable create_tensor_shape_variable_offset(
const nlohmann::json& data, std::size_t offset)
{
assertion(data.is_array(), "tensor_shape_variable needs to be an array");
assertion(data.size() > 0, "need at least one dimension");
if (data.size() == 1)
if (data.size() == 1 + offset)
return tensor_shape_variable(
create_maybe_size_t(data[0]));
if (data.size() == 2)
create_maybe_size_t(data[0 + offset]));
if (data.size() == 2 + offset)
return tensor_shape_variable(
create_maybe_size_t(data[0]),
create_maybe_size_t(data[1]));
if (data.size() == 3)
create_maybe_size_t(data[0 + offset]),
create_maybe_size_t(data[1 + offset]));
if (data.size() == 3 + offset)
return tensor_shape_variable(
create_maybe_size_t(data[0]),
create_maybe_size_t(data[1]),
create_maybe_size_t(data[2]));
if (data.size() == 4)
create_maybe_size_t(data[0 + offset]),
create_maybe_size_t(data[1 + offset]),
create_maybe_size_t(data[2 + offset]));
if (data.size() == 4 + offset)
return tensor_shape_variable(
create_maybe_size_t(data[0]),
create_maybe_size_t(data[1]),
create_maybe_size_t(data[2]),
create_maybe_size_t(data[3]));
if (data.size() == 5)
create_maybe_size_t(data[0 + offset]),
create_maybe_size_t(data[1 + offset]),
create_maybe_size_t(data[2 + offset]),
create_maybe_size_t(data[3 + offset]));
if (data.size() == 5 + offset)
return tensor_shape_variable(
create_maybe_size_t(data[0]),
create_maybe_size_t(data[1]),
create_maybe_size_t(data[2]),
create_maybe_size_t(data[3]),
create_maybe_size_t(data[4]));
create_maybe_size_t(data[0 + offset]),
create_maybe_size_t(data[1 + offset]),
create_maybe_size_t(data[2 + offset]),
create_maybe_size_t(data[3 + offset]),
create_maybe_size_t(data[4 + offset]));

raise_error("tensor_shape_variable needs 1, 2, 3, 4 or 5 dimensions");
return tensor_shape_variable(
Expand All @@ -169,43 +170,14 @@ namespace internal {
fplus::nothing<std::size_t>()); // Is never called
}

inline tensor_shape_variable create_tensor_shape_variable_leading_null(const nlohmann::json& data)
inline tensor_shape_variable create_tensor_shape_variable(const nlohmann::json& data)
{
assertion(data.is_array(), "tensor_shape_variable needs to be an array");
assertion(data.size() > 0, "need at least one dimension");
if (data.size() == 2)
return tensor_shape_variable(
create_maybe_size_t(data[1]));
if (data.size() == 3)
return tensor_shape_variable(
create_maybe_size_t(data[1]),
create_maybe_size_t(data[2]));
if (data.size() == 4)
return tensor_shape_variable(
create_maybe_size_t(data[1]),
create_maybe_size_t(data[2]),
create_maybe_size_t(data[3]));
if (data.size() == 5)
return tensor_shape_variable(
create_maybe_size_t(data[1]),
create_maybe_size_t(data[2]),
create_maybe_size_t(data[3]),
create_maybe_size_t(data[4]));
if (data.size() == 6)
return tensor_shape_variable(
create_maybe_size_t(data[1]),
create_maybe_size_t(data[2]),
create_maybe_size_t(data[3]),
create_maybe_size_t(data[4]),
create_maybe_size_t(data[5]));
return create_tensor_shape_variable_offset(data, 0);
}

raise_error("tensor_shape_variable needs 1, 2, 3, 4 or 5 dimensions");
return tensor_shape_variable(
fplus::nothing<std::size_t>(),
fplus::nothing<std::size_t>(),
fplus::nothing<std::size_t>(),
fplus::nothing<std::size_t>(),
fplus::nothing<std::size_t>()); // Is never called
inline tensor_shape_variable create_tensor_shape_variable_leading_null(const nlohmann::json& data)
{
return create_tensor_shape_variable_offset(data, 1);
}

inline tensor_shape create_tensor_shape(const nlohmann::json& data)
Expand Down Expand Up @@ -390,11 +362,6 @@ namespace internal {
return std::make_shared<model_layer>(name, layers, inputs, outputs);
}

inline void fill_with_zeros(float_vec& xs)
{
std::fill(std::begin(xs), std::end(xs), static_cast<float_type>(0));
}

inline padding create_padding(const std::string& padding_str)
{
return fplus::throw_on_nothing(error("no padding"),
Expand Down Expand Up @@ -1067,15 +1034,6 @@ namespace internal {
return data;
}

inline std::string json_object_get_activation_with_default(const nlohmann::json& config,
const std::string& default_activation)
{
if (json_obj_has_member(config, "activation")) {
return get_activation_type(config["activation"]);
}
return default_activation;
}

inline activation_layer_ptr create_activation_layer_type_name(
const get_param_f& get_param,
const nlohmann::json& data,
Expand Down
10 changes: 0 additions & 10 deletions include/fdeep/recurrent_ops.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -17,11 +17,6 @@ namespace internal {
template <int Count>
using RowVector = Eigen::Matrix<float_type, 1, Count>;

inline float_type linear_activation(float_type x)
{
return x;
}

inline float_type tanh_activation(float_type x)
{
return std::tanh(x);
Expand Down Expand Up @@ -49,11 +44,6 @@ namespace internal {
return (x / static_cast<float_type>(6)) + static_cast<float_type>(0.5);
}

inline float_type relu_activation(float_type x)
{
return std::max<float_type>(x, 0);
}

inline float_type selu_activation(float_type x)
{
const float_type alpha = static_cast<float_type>(1.6732632423543772848170429916717);
Expand Down
29 changes: 0 additions & 29 deletions include/fdeep/tensor.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -187,16 +187,6 @@ namespace internal {
return t.get(tensor_pos(static_cast<std::size_t>(0)));
}

inline tensor from_singleton_value(float_type value)
{
return tensor(tensor_shape(static_cast<std::size_t>(1)), value);
}

inline tensor tensor_with_changed_rank(const tensor& t, std::size_t rank)
{
return tensor(tensor_shape_with_changed_rank(t.shape(), rank), t.as_vector());
}

template <typename F>
tensor transform_tensor(F f, const tensor& m)
{
Expand Down Expand Up @@ -900,17 +890,6 @@ namespace internal {
return tensor(ts.front().shape(), std::move(result_values));
}

// When using this function, make sure the data pointer is not invalidated
// before the last access to the returned matrix happens.
inline MappedRowMajorMatrixXf eigen_row_major_mat_from_shared_values(std::size_t height,
std::size_t width, float_type* data)
{
return MappedRowMajorMatrixXf(
data,
static_cast<EigenIndex>(height),
static_cast<EigenIndex>(width));
}

inline RowMajorMatrixXf eigen_row_major_mat_from_values(std::size_t height,
std::size_t width, const float_vec& values)
{
Expand All @@ -920,14 +899,6 @@ namespace internal {
return m;
}

inline shared_float_vec eigen_row_major_mat_to_values(const RowMajorMatrixXf& m)
{
shared_float_vec result = fplus::make_shared_ref<float_vec>();
result->resize(static_cast<std::size_t>(m.rows() * m.cols()));
std::memcpy(result->data(), m.data(), result->size() * sizeof(float_type));
return result;
}

inline tensor resize2d_nearest(const tensor& in_vol, const shape2& target_size)
{
tensor out_vol(tensor_shape(target_size.height_, target_size.width_, in_vol.shape().depth_), 0);
Expand Down