!| Main module for FTorch containing types and procedures. ! Generated from `ftorch.fypp` using the [fypp Fortran preprocessor](https://fypp.readthedocs.io/en/stable/index.html). ! ! * License ! FTorch is released under an MIT license. ! See the [LICENSE](https://github.com/Cambridge-ICCS/FTorch/blob/main/LICENSE) ! file for details. module ftorch use, intrinsic :: iso_c_binding, only: c_int, c_int8_t, c_int16_t, c_int32_t, c_int64_t, c_int64_t, & c_float, c_double, c_char, c_ptr, c_null_ptr use, intrinsic :: iso_fortran_env, only: int8, int16, int32, int64, real32, real64 implicit none !> Type for holding a torch neural net (nn.Module). type torch_model type(c_ptr) :: p = c_null_ptr !! pointer to the neural net in memory end type torch_model !> Type for holding a Torch tensor. type torch_tensor type(c_ptr) :: p = c_null_ptr !! pointer to the tensor in memory end type torch_tensor !| Enumerator for Torch data types ! From c_torch.h (torch_data_t) ! Note that 0 `torch_kUInt8` and 5 `torch_kFloat16` are not sypported in Fortran enum, bind(c) enumerator :: torch_kUInt8 = 0 ! not supported in Fortran enumerator :: torch_kInt8 = 1 enumerator :: torch_kInt16 = 2 enumerator :: torch_kInt32 = 3 enumerator :: torch_kInt64 = 4 enumerator :: torch_kFloat16 = 5 ! not supported in Fortran enumerator :: torch_kFloat32 = 6 enumerator :: torch_kFloat64 = 7 end enum !| Enumerator for Torch devices ! From c_torch.h (torch_device_t) enum, bind(c) enumerator :: torch_kCPU = 0 enumerator :: torch_kCUDA = 1 end enum !> Interface for directing `torch_tensor_from_array` to possible input types and ranks interface torch_tensor_from_array module procedure torch_tensor_from_array_int8_1d module procedure torch_tensor_from_array_int8_2d module procedure torch_tensor_from_array_int8_3d module procedure torch_tensor_from_array_int8_4d module procedure torch_tensor_from_array_int16_1d module procedure torch_tensor_from_array_int16_2d module procedure torch_tensor_from_array_int16_3d module procedure torch_tensor_from_array_int16_4d module procedure torch_tensor_from_array_int32_1d module procedure torch_tensor_from_array_int32_2d module procedure torch_tensor_from_array_int32_3d module procedure torch_tensor_from_array_int32_4d module procedure torch_tensor_from_array_int64_1d module procedure torch_tensor_from_array_int64_2d module procedure torch_tensor_from_array_int64_3d module procedure torch_tensor_from_array_int64_4d module procedure torch_tensor_from_array_real32_1d module procedure torch_tensor_from_array_real32_2d module procedure torch_tensor_from_array_real32_3d module procedure torch_tensor_from_array_real32_4d module procedure torch_tensor_from_array_real64_1d module procedure torch_tensor_from_array_real64_2d module procedure torch_tensor_from_array_real64_3d module procedure torch_tensor_from_array_real64_4d end interface interface function torch_from_blob_c(data, ndims, tensor_shape, strides, dtype, & device_type, device_index, & requires_grad) result(tensor_p) & bind(c, name = 'torch_from_blob') use, intrinsic :: iso_c_binding, only : c_bool, c_int, c_int64_t, c_ptr ! Arguments type(c_ptr), value, intent(in) :: data integer(c_int), value, intent(in) :: ndims integer(c_int64_t), intent(in) :: tensor_shape(*) integer(c_int64_t), intent(in) :: strides(*) integer(c_int), value, intent(in) :: dtype integer(c_int), value, intent(in) :: device_type integer(c_int), value, intent(in) :: device_index logical(c_bool), value, intent(in) :: requires_grad type(c_ptr) :: tensor_p end function torch_from_blob_c end interface contains !> Returns a tensor filled with the scalar value 0. subroutine torch_tensor_zeros(tensor, ndims, tensor_shape, dtype, & device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_int, c_int64_t type(torch_tensor), intent(out) :: tensor !! Returned tensor integer(c_int), intent(in) :: ndims !! Number of dimensions of the tensor integer(c_int64_t), intent(in) :: tensor_shape(*) !! Shape of the tensor integer(c_int), intent(in) :: dtype !! Data type of the tensor integer(c_int), intent(in) :: device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical(c_bool), optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor integer(c_int) :: device_index_value !! device index used logical(c_bool) :: requires_grad_value !! Whether gradients need to be computed for the created tensor interface function torch_zeros_c(ndims, tensor_shape, dtype, device_type, device_index, requires_grad) result(tensor) & bind(c, name = 'torch_zeros') use, intrinsic :: iso_c_binding, only : c_bool, c_int, c_int64_t, c_ptr integer(c_int), value, intent(in) :: ndims integer(c_int64_t), intent(in) :: tensor_shape(*) integer(c_int), value, intent(in) :: dtype integer(c_int), value, intent(in) :: device_type integer(c_int), value, intent(in) :: device_index logical(c_bool), value, intent(in) :: requires_grad type(c_ptr) :: tensor end function torch_zeros_c end interface ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = logical(.false., c_bool) else requires_grad_value = requires_grad end if tensor%p = torch_zeros_c(ndims, tensor_shape, dtype, device_type, & device_index_value, requires_grad_value) end subroutine torch_tensor_zeros !> Returns a tensor filled with the scalar value 1. subroutine torch_tensor_ones(tensor, ndims, tensor_shape, dtype, & device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_int, c_int64_t type(torch_tensor), intent(out) :: tensor !! Returned tensor integer(c_int), intent(in) :: ndims !! Number of dimensions of the tensor integer(c_int64_t), intent(in) :: tensor_shape(*) !! Shape of the tensor integer(c_int), intent(in) :: dtype !! Data type of the tensor integer(c_int), intent(in) :: device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical(c_bool), optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor integer(c_int) :: device_index_value !! device index used logical(c_bool) :: requires_grad_value !! Whether gradients need to be computed for the created tensor interface function torch_ones_c(ndims, tensor_shape, dtype, device_type, device_index, requires_grad) result(tensor) & bind(c, name = 'torch_ones') use, intrinsic :: iso_c_binding, only : c_bool, c_int, c_int64_t, c_ptr integer(c_int), value, intent(in) :: ndims integer(c_int64_t), intent(in) :: tensor_shape(*) integer(c_int), value, intent(in) :: dtype integer(c_int), value, intent(in) :: device_type integer(c_int), value, intent(in) :: device_index logical(c_bool), value, intent(in) :: requires_grad type(c_ptr) :: tensor end function torch_ones_c end interface ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = logical(.false., c_bool) else requires_grad_value = requires_grad end if tensor%p = torch_ones_c(ndims, tensor_shape, dtype, device_type, & device_index_value, requires_grad_value) end subroutine torch_tensor_ones ! Torch Tensor API !| Exposes the given data as a tensor without taking ownership of the original data. ! This routine will take an (i, j, k) array and return an (k, j, i) tensor. subroutine torch_tensor_from_blob(tensor, data, ndims, tensor_shape, layout, dtype, & device_type, device_index, & requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_int, c_int64_t, c_ptr type(torch_tensor), intent(out) :: tensor !! Returned tensor type(c_ptr), intent(in) :: data !! Pointer to data integer(c_int), intent(in) :: ndims !! Number of dimensions of the tensor integer(c_int64_t), intent(in) :: tensor_shape(*) !! Shape of the tensor integer(c_int), intent(in) :: layout(*) !! Layout for strides for accessing data integer(c_int), intent(in) :: dtype !! Data type of the tensor integer(c_int), intent(in) :: device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical(c_bool), optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor integer(c_int) :: i !! loop index integer(c_int64_t) :: strides(ndims) !! Strides for accessing data integer(c_int) :: device_index_value !! device index used logical(c_bool) :: requires_grad_value !! Whether gradients need to be computed for the created tensor if (.not. present(requires_grad)) then requires_grad_value = logical(.false., c_bool) else requires_grad_value = requires_grad end if strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * tensor_shape(layout(i - 1)) end do ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif tensor%p = torch_from_blob_c(data, ndims, tensor_shape, strides, dtype, & device_type, device_index_value, & requires_grad_value) end subroutine torch_tensor_from_blob !> Prints the contents of a tensor. subroutine torch_tensor_print(tensor) type(torch_tensor), intent(in) :: tensor !! Input tensor interface subroutine torch_tensor_print_c(tensor) & bind(c, name = 'torch_tensor_print') use, intrinsic :: iso_c_binding, only : c_ptr type(c_ptr), value, intent(in) :: tensor end subroutine torch_tensor_print_c end interface call torch_tensor_print_c(tensor%p) end subroutine torch_tensor_print !> Determines the device index of a tensor. function torch_tensor_get_device_index(tensor) result(device_index) use, intrinsic :: iso_c_binding, only : c_int type(torch_tensor), intent(in) :: tensor !! Input tensor integer(c_int) :: device_index !! Device index of tensor interface function torch_tensor_get_device_index_c(tensor) result(device_index) & bind(c, name = 'torch_tensor_get_device_index') use, intrinsic :: iso_c_binding, only : c_int, c_ptr type(c_ptr), value, intent(in) :: tensor integer(c_int) :: device_index end function torch_tensor_get_device_index_c end interface device_index = torch_tensor_get_device_index_c(tensor%p) end function torch_tensor_get_device_index !> Deallocates a tensor. subroutine torch_tensor_delete(tensor) type(torch_tensor), intent(in) :: tensor !! Input tensor interface subroutine torch_tensor_delete_c(tensor) & bind(c, name = 'torch_tensor_delete') use, intrinsic :: iso_c_binding, only : c_ptr type(c_ptr), value, intent(in) :: tensor end subroutine torch_tensor_delete_c end interface call torch_tensor_delete_c(tensor%p) end subroutine torch_tensor_delete ! Torch Model API !> Loads a TorchScript nn.module (pre-trained PyTorch model saved with TorchScript) subroutine torch_model_load(model, filename, device_type, device_index, requires_grad, is_training) use, intrinsic :: iso_c_binding, only : c_bool, c_int, c_null_char type(torch_model), intent(out) :: model !! Returned deserialized model character(*), intent(in) :: filename !! Filename of saved TorchScript model integer(c_int), optional, intent(in) :: device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor logical, optional, intent(in) :: is_training !! Whether gradients need to be computed for the created tensor integer(c_int) :: device_type_value integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor logical :: is_training_value !! Whether the model is being trained, rather than evaluated interface function torch_jit_load_c(filename, device_type, device_index, requires_grad, is_training) result(model) & bind(c, name = 'torch_jit_load') use, intrinsic :: iso_c_binding, only : c_bool, c_char, c_int, c_ptr character(c_char), intent(in) :: filename(*) integer(c_int), value, intent(in) :: device_type integer(c_int), value, intent(in) :: device_index logical(c_bool), value, intent(in) :: requires_grad logical(c_bool), value, intent(in) :: is_training type(c_ptr) :: model end function torch_jit_load_c end interface ! Process optional arguments if (present(device_type)) then device_type_value = device_type else device_type_value = torch_kCPU endif if (present(device_index)) then device_index_value = device_index else if (device_type_value == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if if (.not. present(is_training)) then is_training_value = .false. else is_training_value = is_training end if ! Need to append c_null_char at end of filename model%p = torch_jit_load_c(trim(adjustl(filename))//c_null_char, & device_type_value, device_index_value, & logical(requires_grad_value, c_bool), & logical(is_training_value, c_bool)) end subroutine torch_model_load !> Performs a forward pass of the model with the input tensors subroutine torch_model_forward(model, input_tensors, output_tensors, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_ptr, c_int, c_loc type(torch_model), intent(in) :: model !! Model type(torch_tensor), intent(in), dimension(:) :: input_tensors !! Array of Input tensors type(torch_tensor), intent(in), dimension(:) :: output_tensors !! Returned output tensors logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor integer :: i integer(c_int) :: n_inputs integer(c_int) :: n_outputs type(c_ptr), dimension(size(input_tensors)), target :: input_ptrs type(c_ptr), dimension(size(output_tensors)), target :: output_ptrs interface subroutine torch_jit_model_forward_c(model, input_tensors, n_inputs, & output_tensors, n_outputs, requires_grad) & bind(c, name = 'torch_jit_module_forward') use, intrinsic :: iso_c_binding, only : c_bool, c_ptr, c_int type(c_ptr), value, intent(in) :: model type(c_ptr), value, intent(in) :: input_tensors integer(c_int), value, intent(in) :: n_inputs type(c_ptr), value, intent(in) :: output_tensors integer(c_int), value, intent(in) :: n_outputs logical(c_bool), value, intent(in) :: requires_grad end subroutine torch_jit_model_forward_c end interface n_inputs = size(input_tensors) n_outputs = size(output_tensors) if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if ! Assign array of pointers to the input tensors do i = 1, n_inputs input_ptrs(i) = input_tensors(i)%p end do ! Assign array of pointers to the output tensors do i = 1, n_outputs output_ptrs(i) = output_tensors(i)%p end do call torch_jit_model_forward_c(model%p, c_loc(input_ptrs), n_inputs, & c_loc(output_ptrs), n_outputs, & logical(requires_grad_value, c_bool)) end subroutine torch_model_forward !> Deallocates a TorchScript model subroutine torch_model_delete(model) type(torch_model), intent(in) :: model !! Torch Model to deallocate interface subroutine torch_jit_model_delete_c(model) & bind(c, name = 'torch_jit_module_delete') use, intrinsic :: iso_c_binding, only : c_ptr type(c_ptr), value, intent(in) :: model end subroutine torch_jit_model_delete_c end interface call torch_jit_model_delete_c(model%p) end subroutine torch_model_delete !> Return a Torch tensor pointing to data_in array of rank 1 containing data of type `int8` subroutine torch_tensor_from_array_int8_1d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int8 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int8), intent(in), target :: data_in(:) !! Input data that tensor will point at integer, intent(in) :: layout(1) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(1) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt8 !! Data type integer(c_int64_t) :: strides(1) !! Strides for accessing data integer(c_int), parameter :: ndims = 1 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int8_1d !> Return a Torch tensor pointing to data_in array of rank 2 containing data of type `int8` subroutine torch_tensor_from_array_int8_2d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int8 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int8), intent(in), target :: data_in(:,:) !! Input data that tensor will point at integer, intent(in) :: layout(2) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(2) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt8 !! Data type integer(c_int64_t) :: strides(2) !! Strides for accessing data integer(c_int), parameter :: ndims = 2 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int8_2d !> Return a Torch tensor pointing to data_in array of rank 3 containing data of type `int8` subroutine torch_tensor_from_array_int8_3d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int8 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int8), intent(in), target :: data_in(:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(3) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(3) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt8 !! Data type integer(c_int64_t) :: strides(3) !! Strides for accessing data integer(c_int), parameter :: ndims = 3 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int8_3d !> Return a Torch tensor pointing to data_in array of rank 4 containing data of type `int8` subroutine torch_tensor_from_array_int8_4d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int8 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int8), intent(in), target :: data_in(:,:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(4) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(4) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt8 !! Data type integer(c_int64_t) :: strides(4) !! Strides for accessing data integer(c_int), parameter :: ndims = 4 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int8_4d !> Return a Torch tensor pointing to data_in array of rank 1 containing data of type `int16` subroutine torch_tensor_from_array_int16_1d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int16 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int16), intent(in), target :: data_in(:) !! Input data that tensor will point at integer, intent(in) :: layout(1) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(1) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt16 !! Data type integer(c_int64_t) :: strides(1) !! Strides for accessing data integer(c_int), parameter :: ndims = 1 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int16_1d !> Return a Torch tensor pointing to data_in array of rank 2 containing data of type `int16` subroutine torch_tensor_from_array_int16_2d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int16 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int16), intent(in), target :: data_in(:,:) !! Input data that tensor will point at integer, intent(in) :: layout(2) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(2) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt16 !! Data type integer(c_int64_t) :: strides(2) !! Strides for accessing data integer(c_int), parameter :: ndims = 2 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int16_2d !> Return a Torch tensor pointing to data_in array of rank 3 containing data of type `int16` subroutine torch_tensor_from_array_int16_3d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int16 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int16), intent(in), target :: data_in(:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(3) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(3) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt16 !! Data type integer(c_int64_t) :: strides(3) !! Strides for accessing data integer(c_int), parameter :: ndims = 3 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int16_3d !> Return a Torch tensor pointing to data_in array of rank 4 containing data of type `int16` subroutine torch_tensor_from_array_int16_4d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int16 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int16), intent(in), target :: data_in(:,:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(4) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(4) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt16 !! Data type integer(c_int64_t) :: strides(4) !! Strides for accessing data integer(c_int), parameter :: ndims = 4 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int16_4d !> Return a Torch tensor pointing to data_in array of rank 1 containing data of type `int32` subroutine torch_tensor_from_array_int32_1d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int32 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int32), intent(in), target :: data_in(:) !! Input data that tensor will point at integer, intent(in) :: layout(1) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(1) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt32 !! Data type integer(c_int64_t) :: strides(1) !! Strides for accessing data integer(c_int), parameter :: ndims = 1 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int32_1d !> Return a Torch tensor pointing to data_in array of rank 2 containing data of type `int32` subroutine torch_tensor_from_array_int32_2d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int32 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int32), intent(in), target :: data_in(:,:) !! Input data that tensor will point at integer, intent(in) :: layout(2) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(2) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt32 !! Data type integer(c_int64_t) :: strides(2) !! Strides for accessing data integer(c_int), parameter :: ndims = 2 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int32_2d !> Return a Torch tensor pointing to data_in array of rank 3 containing data of type `int32` subroutine torch_tensor_from_array_int32_3d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int32 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int32), intent(in), target :: data_in(:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(3) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(3) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt32 !! Data type integer(c_int64_t) :: strides(3) !! Strides for accessing data integer(c_int), parameter :: ndims = 3 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int32_3d !> Return a Torch tensor pointing to data_in array of rank 4 containing data of type `int32` subroutine torch_tensor_from_array_int32_4d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int32 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int32), intent(in), target :: data_in(:,:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(4) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(4) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt32 !! Data type integer(c_int64_t) :: strides(4) !! Strides for accessing data integer(c_int), parameter :: ndims = 4 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int32_4d !> Return a Torch tensor pointing to data_in array of rank 1 containing data of type `int64` subroutine torch_tensor_from_array_int64_1d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int64 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int64), intent(in), target :: data_in(:) !! Input data that tensor will point at integer, intent(in) :: layout(1) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(1) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt64 !! Data type integer(c_int64_t) :: strides(1) !! Strides for accessing data integer(c_int), parameter :: ndims = 1 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int64_1d !> Return a Torch tensor pointing to data_in array of rank 2 containing data of type `int64` subroutine torch_tensor_from_array_int64_2d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int64 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int64), intent(in), target :: data_in(:,:) !! Input data that tensor will point at integer, intent(in) :: layout(2) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(2) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt64 !! Data type integer(c_int64_t) :: strides(2) !! Strides for accessing data integer(c_int), parameter :: ndims = 2 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int64_2d !> Return a Torch tensor pointing to data_in array of rank 3 containing data of type `int64` subroutine torch_tensor_from_array_int64_3d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int64 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int64), intent(in), target :: data_in(:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(3) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(3) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt64 !! Data type integer(c_int64_t) :: strides(3) !! Strides for accessing data integer(c_int), parameter :: ndims = 3 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int64_3d !> Return a Torch tensor pointing to data_in array of rank 4 containing data of type `int64` subroutine torch_tensor_from_array_int64_4d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : int64 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs integer(kind=int64), intent(in), target :: data_in(:,:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(4) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(4) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kInt64 !! Data type integer(c_int64_t) :: strides(4) !! Strides for accessing data integer(c_int), parameter :: ndims = 4 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_int64_4d !> Return a Torch tensor pointing to data_in array of rank 1 containing data of type `real32` subroutine torch_tensor_from_array_real32_1d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : real32 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs real(kind=real32), intent(in), target :: data_in(:) !! Input data that tensor will point at integer, intent(in) :: layout(1) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(1) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kFloat32 !! Data type integer(c_int64_t) :: strides(1) !! Strides for accessing data integer(c_int), parameter :: ndims = 1 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_real32_1d !> Return a Torch tensor pointing to data_in array of rank 2 containing data of type `real32` subroutine torch_tensor_from_array_real32_2d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : real32 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs real(kind=real32), intent(in), target :: data_in(:,:) !! Input data that tensor will point at integer, intent(in) :: layout(2) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(2) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kFloat32 !! Data type integer(c_int64_t) :: strides(2) !! Strides for accessing data integer(c_int), parameter :: ndims = 2 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_real32_2d !> Return a Torch tensor pointing to data_in array of rank 3 containing data of type `real32` subroutine torch_tensor_from_array_real32_3d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : real32 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs real(kind=real32), intent(in), target :: data_in(:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(3) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(3) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kFloat32 !! Data type integer(c_int64_t) :: strides(3) !! Strides for accessing data integer(c_int), parameter :: ndims = 3 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_real32_3d !> Return a Torch tensor pointing to data_in array of rank 4 containing data of type `real32` subroutine torch_tensor_from_array_real32_4d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : real32 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs real(kind=real32), intent(in), target :: data_in(:,:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(4) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(4) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kFloat32 !! Data type integer(c_int64_t) :: strides(4) !! Strides for accessing data integer(c_int), parameter :: ndims = 4 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_real32_4d !> Return a Torch tensor pointing to data_in array of rank 1 containing data of type `real64` subroutine torch_tensor_from_array_real64_1d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : real64 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs real(kind=real64), intent(in), target :: data_in(:) !! Input data that tensor will point at integer, intent(in) :: layout(1) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(1) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kFloat64 !! Data type integer(c_int64_t) :: strides(1) !! Strides for accessing data integer(c_int), parameter :: ndims = 1 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_real64_1d !> Return a Torch tensor pointing to data_in array of rank 2 containing data of type `real64` subroutine torch_tensor_from_array_real64_2d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : real64 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs real(kind=real64), intent(in), target :: data_in(:,:) !! Input data that tensor will point at integer, intent(in) :: layout(2) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(2) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kFloat64 !! Data type integer(c_int64_t) :: strides(2) !! Strides for accessing data integer(c_int), parameter :: ndims = 2 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_real64_2d !> Return a Torch tensor pointing to data_in array of rank 3 containing data of type `real64` subroutine torch_tensor_from_array_real64_3d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : real64 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs real(kind=real64), intent(in), target :: data_in(:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(3) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(3) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kFloat64 !! Data type integer(c_int64_t) :: strides(3) !! Strides for accessing data integer(c_int), parameter :: ndims = 3 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_real64_3d !> Return a Torch tensor pointing to data_in array of rank 4 containing data of type `real64` subroutine torch_tensor_from_array_real64_4d(tensor, data_in, layout, & c_device_type, device_index, requires_grad) use, intrinsic :: iso_c_binding, only : c_bool, c_float, c_int, c_int64_t, c_loc use, intrinsic :: iso_fortran_env, only : real64 ! output tensor type(torch_tensor), intent(out) :: tensor !! Returned tensor ! inputs real(kind=real64), intent(in), target :: data_in(:,:,:,:) !! Input data that tensor will point at integer, intent(in) :: layout(4) !! Control order of indices integer(c_int), intent(in) :: c_device_type !! Device type the tensor will live on (`torch_kCPU` or `torch_kCUDA`) integer(c_int), optional, intent(in) :: device_index !! device index to use for `torch_kCUDA` case logical, optional, intent(in) :: requires_grad !! Whether gradients need to be computed for the created tensor ! local data integer(c_int64_t) :: c_tensor_shape(4) !! Shape of the tensor integer(c_int), parameter :: c_dtype = torch_kFloat64 !! Data type integer(c_int64_t) :: strides(4) !! Strides for accessing data integer(c_int), parameter :: ndims = 4 !! Number of dimension of input data integer :: i integer(c_int) :: device_index_value logical :: requires_grad_value !! Whether gradients need to be computed for the created tensor ! Process optional arguments if (present(device_index)) then device_index_value = device_index else if (c_device_type == torch_kCPU) then device_index_value = -1 else device_index_value = 0 endif if (.not. present(requires_grad)) then requires_grad_value = .false. else requires_grad_value = requires_grad end if c_tensor_shape = shape(data_in) strides(layout(1)) = 1 do i = 2, ndims strides(layout(i)) = strides(layout(i - 1)) * c_tensor_shape(layout(i - 1)) end do tensor%p = torch_from_blob_c(c_loc(data_in), ndims, c_tensor_shape, & strides, c_dtype, c_device_type, & device_index_value, & logical(requires_grad_value, c_bool)) end subroutine torch_tensor_from_array_real64_4d end module ftorch