module athena__conv3d_layer !! Module containing implementation of a 3D convolutional layer !! !! This module implements 3D convolution for processing volumetric data !! such as video, medical imaging, or 3D point clouds. !! !! Mathematical operation: !! \[ !! y_{i,j,k,\,q} !! = !! \sigma\left( !! \sum_{c=1}^{C_{in}} !! \sum_{a=0}^{K_d-1} !! \sum_{b=0}^{K_h-1} !! \sum_{d=0}^{K_w-1} !! x_{i+a,\,j+b,\,k+d,\,c}\; !! w_{a,\,b,\,d,\,c,\,q} !! + b_q !! \right) !! \] !! !! where: !! - \((i,j,k)\) are spatial coordinates in the output !! - \(q\) is the output channel (filter) index !! - \((a,b,d)\) are kernel offsets: depth, height, width !! - \(c\) is the input channel index !! - \(K_d, K_h, K_w\) are kernel dimensions !! - \(\sigma\) is the activation function !! !! Shape: \((D, H, W, C_{in}) \rightarrow (D', H', W', C_{out})\) use coreutils, only: real32, stop_program use athena__base_layer, only: conv_layer_type, base_layer_type, & learnable_layer_type use athena__pad3d_layer, only: pad3d_layer_type use athena__misc_types, only: base_actv_type, base_init_type, & onnx_node_type, onnx_initialiser_type, onnx_tensor_type use diffstruc, only: array_type use athena__diffstruc_extd, only: conv3d, add_bias implicit none private public :: conv3d_layer_type public :: read_conv3d_layer type, extends(conv_layer_type) :: conv3d_layer_type !! Type for 3D convolutional layer with overloaded procedures contains procedure, pass(this) :: set_hyperparams => set_hyperparams_conv3d !! Set hyperparameters for 3D convolutional layer procedure, pass(this) :: read => read_conv3d !! Read 3D convolutional layer from file #ifdef __INTEL_COMPILER procedure, pass(this) :: reduce => reduce_conv3d !! Merge another 3D convolutional layer into this one procedure :: add_t_t => add_conv3d !! Add two 3D convolutional layers #endif procedure, pass(this) :: forward => forward_conv3d !! Forward propagation derived type handler final :: finalise_conv3d !! Finalise 3D convolutional layer end type conv3d_layer_type interface conv3d_layer_type !! Interface for setting up the 3D convolutional layer module function layer_setup( & input_shape, & num_filters, kernel_size, stride, dilation, padding, & use_bias, & activation, & kernel_initialiser, bias_initialiser, & verbose ) result(layer) !! Set up the 3D convolutional layer integer, dimension(:), optional, intent(in) :: input_shape !! Input shape integer, optional, intent(in) :: num_filters !! Number of filters integer, dimension(..), optional, intent(in) :: kernel_size !! Kernel size integer, dimension(..), optional, intent(in) :: stride !! Stride integer, dimension(..), optional, intent(in) :: dilation !! Dilation logical, optional, intent(in) :: use_bias !! Use bias class(*), optional, intent(in) :: activation, & kernel_initialiser, bias_initialiser !! Activation function, kernel initialiser, bias initialiser character(*), optional, intent(in) :: padding !! Padding method integer, optional, intent(in) :: verbose !! Verbosity level type(conv3d_layer_type) :: layer !! Instance of the 3D convolutional layer end function layer_setup end interface conv3d_layer_type contains !############################################################################### subroutine finalise_conv3d(this) !! Finalise 3D convolutional layer implicit none ! Arguments type(conv3d_layer_type), intent(inout) :: this !! Instance of the 3D convolutional layer if(allocated(this%dil)) deallocate(this%dil) if(allocated(this%knl)) deallocate(this%knl) if(allocated(this%stp)) deallocate(this%stp) if(allocated(this%input_shape)) deallocate(this%input_shape) if(allocated(this%output)) deallocate(this%output) if(allocated(this%pad_layer)) deallocate(this%pad_layer) if(this%z(1)%allocated) call this%z(1)%deallocate() if(this%z(2)%allocated) call this%z(2)%deallocate() end subroutine finalise_conv3d !############################################################################### #ifdef __INTEL_COMPILER !############################################################################### subroutine reduce_conv3d(this, input) !! Merge two 3D convolutional layers via parameter summation implicit none class(conv3d_layer_type), intent(inout) :: this class(learnable_layer_type), intent(in) :: input real(real32), allocatable :: params(:), gradients(:) select type(input) type is (conv3d_layer_type) params = this%get_params() + input%get_params() call this%set_params(params) gradients = this%get_gradients() + input%get_gradients() call this%set_gradients(gradients) class default call stop_program("reduce_conv3d: incompatible layer type") end select end subroutine reduce_conv3d !############################################################################### !############################################################################### function add_conv3d(a, b) result(output) !! Add two 3D convolutional layers without whole-object allocatable copy implicit none class(conv3d_layer_type), intent(in) :: a class(learnable_layer_type), intent(in) :: b class(learnable_layer_type), allocatable :: output type(conv3d_layer_type) :: layer character(len=20) :: padding real(real32), allocatable :: params(:), gradients(:) select type(b) type is (conv3d_layer_type) padding = 'valid' if(allocated(a%pad_layer)) padding = a%pad_layer%method call layer%set_hyperparams( & num_filters = a%num_filters, & kernel_size = a%knl, stride = a%stp, dilation = a%dil, & padding = padding, & use_bias = a%use_bias, & activation = a%activation, & kernel_initialiser = a%kernel_init, & bias_initialiser = a%bias_init & ) if(allocated(a%input_shape)) call layer%init(a%input_shape) params = a%get_params() + b%get_params() call layer%set_params(params) gradients = a%get_gradients() + b%get_gradients() call layer%set_gradients(gradients) layer%id = a%id layer%inference = a%inference layer%use_graph_input = a%use_graph_input layer%use_graph_output = a%use_graph_output layer%subtype = a%subtype allocate(output, source=layer) class default call stop_program("add_conv3d: incompatible layer type") end select end function add_conv3d !############################################################################### #endif !##############################################################################! ! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ! !##############################################################################! !############################################################################### module function layer_setup( & input_shape, & num_filters, kernel_size, stride, dilation, padding, & use_bias, & activation, & kernel_initialiser, bias_initialiser, & verbose ) result(layer) !! Set up the 3D convolutional layer use athena__activation, only: activation_setup use athena__initialiser, only: initialiser_setup implicit none ! Arguments integer, dimension(:), optional, intent(in) :: input_shape !! Input shape integer, optional, intent(in) :: num_filters !! Number of filters integer, dimension(..), optional, intent(in) :: kernel_size !! Kernel size integer, dimension(..), optional, intent(in) :: stride !! Stride integer, dimension(..), optional, intent(in) :: dilation !! Dilation logical, optional, intent(in) :: use_bias !! Use bias class(*), optional, intent(in) :: activation !! Activation function class(*), optional, intent(in) :: kernel_initialiser, bias_initialiser !! Activation function, kernel initialiser, and bias initialiser character(*), optional, intent(in) :: padding !! Padding method integer, optional, intent(in) :: verbose !! Verbosity level type(conv3d_layer_type) :: layer !! Instance of the 3D convolutional layer ! Local variables integer :: verbose_ = 0 !! Verbosity level integer :: num_filters_ !! Number of filters logical :: use_bias_ = .true. !! Use bias character(len=20) :: padding_ !! Padding integer, dimension(3) :: kernel_size_, stride_, dilation_ !! Kernel size and stride class(base_actv_type), allocatable :: activation_ !! Activation function class(base_init_type), allocatable :: kernel_initialiser_, bias_initialiser_ !! Kernel and bias initialisers if(present(verbose)) verbose_ = verbose !--------------------------------------------------------------------------- ! Set use_bias !--------------------------------------------------------------------------- if(present(use_bias)) use_bias_ = use_bias !--------------------------------------------------------------------------- ! Set activation functions based on input name !--------------------------------------------------------------------------- if(present(activation))then activation_ = activation_setup(activation) else activation_ = activation_setup("none") end if !--------------------------------------------------------------------------- ! Define weights (kernels) and biases initialisers !--------------------------------------------------------------------------- if(present(kernel_initialiser))then kernel_initialiser_ = initialiser_setup(kernel_initialiser) end if if(present(bias_initialiser))then bias_initialiser_ = initialiser_setup(bias_initialiser) end if !--------------------------------------------------------------------------- ! Set up number of filters !--------------------------------------------------------------------------- if(present(num_filters))then num_filters_ = num_filters else num_filters_ = 32 end if !--------------------------------------------------------------------------- ! Set up kernel size !--------------------------------------------------------------------------- if(present(kernel_size))then select rank(kernel_size) rank(0) kernel_size_ = kernel_size rank(1) kernel_size_(1) = kernel_size(1) if(size(kernel_size,dim=1).eq.1)then kernel_size_(2:) = kernel_size(1) elseif(size(kernel_size,dim=1).eq.3)then kernel_size_(2:) = kernel_size(2:) end if end select else kernel_size_ = 3 end if !--------------------------------------------------------------------------- ! Set up padding name !--------------------------------------------------------------------------- if(present(padding))then padding_ = padding else padding_ = "valid" end if !--------------------------------------------------------------------------- ! Set up stride !--------------------------------------------------------------------------- if(present(stride))then select rank(stride) rank(0) stride_ = stride rank(1) stride_(1) = stride(1) if(size(stride,dim=1).eq.1)then stride_(2:) = stride(1) elseif(size(stride,dim=1).eq.3)then stride_(2:) = stride(2:) end if end select else stride_ = 1 end if !--------------------------------------------------------------------------- ! Set up dilation !--------------------------------------------------------------------------- if(present(dilation))then select rank(dilation) rank(0) dilation_ = dilation rank(1) dilation_(1) = dilation(1) if(size(dilation,dim=1).eq.1)then dilation_(2:) = dilation(1) elseif(size(dilation,dim=1).eq.3)then dilation_(2:) = dilation(2:) end if end select else dilation_ = 1 end if !--------------------------------------------------------------------------- ! Set hyperparameters !--------------------------------------------------------------------------- call layer%set_hyperparams( & num_filters = num_filters_, & kernel_size = kernel_size_, stride = stride_, dilation = dilation_, & padding = padding_, & use_bias = use_bias_, & activation = activation_, & kernel_initialiser = kernel_initialiser_, & bias_initialiser = bias_initialiser_, & verbose = verbose_ & ) !--------------------------------------------------------------------------- ! Initialise layer shape !--------------------------------------------------------------------------- if(present(input_shape)) call layer%init(input_shape=input_shape) end function layer_setup !############################################################################### !############################################################################### subroutine set_hyperparams_conv3d( & this, & num_filters, & kernel_size, stride, dilation, & padding, & use_bias, & activation, & kernel_initialiser, bias_initialiser, & verbose & ) !! Set hyperparameters for 3D convolutional layer use athena__activation, only: activation_setup use athena__initialiser, only: get_default_initialiser, initialiser_setup use coreutils, only: to_lower implicit none ! Arguments class(conv3d_layer_type), intent(inout) :: this !! Instance of the 3D convolutional layer integer, intent(in) :: num_filters !! Number of filters integer, dimension(3), intent(in) :: kernel_size, stride, dilation !! Kernel size, stride, dilation character(*), intent(in) :: padding !! Padding logical, intent(in) :: use_bias !! Use bias class(base_actv_type), allocatable, intent(in) :: activation !! Activation function class(base_init_type), allocatable, intent(in) :: & kernel_initialiser, bias_initialiser !! Kernel and bias initialisers integer, optional, intent(in) :: verbose !! Verbosity level ! Local variables character(len=20) :: padding_ character(len=256) :: buffer this%name = "conv3d" this%type = "conv" this%input_rank = 4 this%output_rank = 4 this%use_bias = use_bias if(allocated(this%dil)) deallocate(this%dil) if(allocated(this%knl)) deallocate(this%knl) if(allocated(this%stp)) deallocate(this%stp) allocate( & this%dil(this%input_rank-1), & this%knl(this%input_rank-1), & this%stp(this%input_rank-1) & ) this%dil = dilation this%knl = kernel_size this%stp = stride this%num_filters = num_filters padding_ = trim(adjustl(padding)) select case(trim(adjustl(to_lower(padding_)))) case("valid", "none", "") case default this%pad_layer = pad3d_layer_type( & padding = [ (this%knl-1)/2 ], & method = padding_ & ) end select if(allocated(this%activation)) deallocate(this%activation) if(.not.allocated(activation))then this%activation = activation_setup("none") else allocate(this%activation, source=activation) end if if(allocated(this%kernel_init)) deallocate(this%kernel_init) if(.not.allocated(kernel_initialiser))then buffer = get_default_initialiser(this%activation%name) this%kernel_init = initialiser_setup(buffer) else allocate(this%kernel_init, source=kernel_initialiser) end if if(allocated(this%bias_init)) deallocate(this%bias_init) if(.not.allocated(bias_initialiser))then buffer = get_default_initialiser( & this%activation%name, & is_bias=.true. & ) this%bias_init = initialiser_setup(buffer) else allocate(this%bias_init, source=bias_initialiser) end if if(present(verbose))then if(abs(verbose).gt.0)then write(*,'("CONV3D activation function: ",A)') & trim(this%activation%name) write(*,'("CONV3D kernel initialiser: ",A)') & trim(this%kernel_init%name) write(*,'("CONV3D bias initialiser: ",A)') & trim(this%bias_init%name) end if end if end subroutine set_hyperparams_conv3d !############################################################################### !##############################################################################! ! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ! !##############################################################################! !############################################################################### subroutine read_conv3d(this, unit, verbose) !! Read 3D convolutional layer from file use athena__tools_infile, only: assign_val, assign_vec, move use coreutils, only: to_lower, to_upper, icount use athena__activation, only: read_activation use athena__initialiser, only: initialiser_setup implicit none ! Arguments class(conv3d_layer_type), intent(inout) :: this !! Instance of the 3D convolutional layer integer, intent(in) :: unit !! Unit number integer, optional, intent(in) :: verbose !! Verbosity level ! Local variables integer :: stat !! Status of read integer :: verbose_ = 0 !! Verbosity level integer :: j, k, l, c, itmp1, iline, num_params !! Loop variables and temporary integer integer :: num_filters !! Number of filters logical :: use_bias = .true. !! Whether to use bias character(14) :: kernel_initialiser_name='', bias_initialiser_name='' !! Kernel and bias initialisers character(20) :: padding='', activation_name='' !! Padding and activation function class(base_actv_type), allocatable :: activation !! Activation function class(base_init_type), allocatable :: kernel_initialiser, bias_initialiser !! Initialisers character(256) :: buffer, tag, err_msg !! Buffer, tag, and error message integer, dimension(3) :: kernel_size, stride, dilation !! Kernel size and stride integer, dimension(4) :: input_shape !! Input shape real(real32), allocatable, dimension(:) :: data_list !! Data list integer :: param_line, final_line !! Parameter line number ! Initialise optional arguments !--------------------------------------------------------------------------- if(present(verbose)) verbose_ = verbose ! Loop over tags in layer card !--------------------------------------------------------------------------- iline = 0 param_line = 0 final_line = 0 tag_loop: do ! Check for end of file !------------------------------------------------------------------------ read(unit,'(A)',iostat=stat) buffer if(stat.ne.0)then write(err_msg,'("file encountered error (EoF?) before END ",A)') & to_upper(this%name) call stop_program(err_msg) return end if if(trim(adjustl(buffer)).eq."") cycle tag_loop ! Check for end of layer card !------------------------------------------------------------------------ if(trim(adjustl(buffer)).eq."END "//to_upper(trim(this%name)))then final_line = iline backspace(unit) exit tag_loop end if iline = iline + 1 tag=trim(adjustl(buffer)) if(scan(buffer,"=").ne.0) tag=trim(tag(:scan(tag,"=")-1)) ! Read parameters from save file !------------------------------------------------------------------------ select case(trim(tag)) case("INPUT_SHAPE") call assign_vec(buffer, input_shape, itmp1) case("NUM_FILTERS") call assign_val(buffer, num_filters, itmp1) case("KERNEL_SIZE") call assign_vec(buffer, kernel_size, itmp1) case("STRIDE") call assign_vec(buffer, stride, itmp1) case("DILATION") call assign_vec(buffer, dilation, itmp1) case("USE_BIAS") call assign_val(buffer, use_bias, itmp1) case("PADDING") call assign_val(buffer, padding, itmp1) padding = to_lower(padding) case("ACTIVATION") iline = iline - 1 backspace(unit) activation = read_activation(unit, iline) case("KERNEL_INITIALISER", "KERNEL_INIT", "KERNEL_INITIALIZER") call assign_val(buffer, kernel_initialiser_name, itmp1) case("BIAS_INITIALISER", "BIAS_INIT", "BIAS_INITIALIZER") call assign_val(buffer, bias_initialiser_name, itmp1) case("WEIGHTS") kernel_initialiser_name = 'zeros' bias_initialiser_name = 'zeros' param_line = iline case default ! Don't look for "e" due to scientific notation of numbers ! ... i.e. exponent (E+00) if(scan(to_lower(trim(adjustl(buffer))),& 'abcdfghijklmnopqrstuvwxyz').eq.0)then cycle tag_loop elseif(tag(:3).eq.'END')then cycle tag_loop end if write(err_msg,'("Unrecognised line in input file: ",A)') & trim(adjustl(buffer)) call stop_program(err_msg) return end select end do tag_loop kernel_initialiser = initialiser_setup(kernel_initialiser_name) bias_initialiser = initialiser_setup(bias_initialiser_name) ! Set hyperparameters and initialise layer !--------------------------------------------------------------------------- call this%set_hyperparams( & num_filters = num_filters, & kernel_size = kernel_size, stride = stride, dilation = dilation, & padding = padding, & use_bias = use_bias, & activation = activation, & kernel_initialiser = kernel_initialiser, & bias_initialiser = bias_initialiser, & verbose = verbose_ & ) call this%init(input_shape = input_shape) ! Check if WEIGHTS card was found !--------------------------------------------------------------------------- if(param_line.eq.0)then write(0,*) "WARNING: WEIGHTS card in "//to_upper(trim(this%name))//" not found" else call move(unit, param_line - iline, iostat=stat) num_params = product(this%knl) * input_shape(4) * num_filters allocate(data_list(num_params), source=0._real32) c = 1 k = 1 data_concat_loop1: do while(c.le.num_params) read(unit,'(A)',iostat=stat) buffer if(stat.ne.0) exit data_concat_loop1 k = icount(buffer) read(buffer,*,iostat=stat) (data_list(j),j=c,c+k-1) c = c + k end do data_concat_loop1 this%params(1)%val(:,1) = data_list deallocate(data_list) allocate(data_list(num_filters), source=0._real32) c = 1 k = 1 data_concat_loop2: do while(c.le.num_filters) read(unit,'(A)',iostat=stat) buffer if(stat.ne.0) exit data_concat_loop2 k = icount(buffer) read(buffer,*,iostat=stat) (data_list(j),j=c,c+k-1) c = c + k end do data_concat_loop2 this%params(2)%val(:,1) = data_list deallocate(data_list) ! Check for end of weights card !------------------------------------------------------------------------ read(unit,'(A)') buffer if(trim(adjustl(buffer)).ne."END WEIGHTS")then write(0,*) trim(adjustl(buffer)) call stop_program("END WEIGHTS not where expected") return end if end if ! Check for end of layer card !--------------------------------------------------------------------------- call move(unit, final_line - iline, iostat=stat) read(unit,'(A)') buffer if(trim(adjustl(buffer)).ne."END "//to_upper(trim(this%name)))then write(0,*) trim(adjustl(buffer)) write(err_msg,'("END ",A," not where expected")') to_upper(this%name) call stop_program(err_msg) return end if end subroutine read_conv3d !############################################################################### !############################################################################### function read_conv3d_layer(unit, verbose) result(layer) !! Read 3D convolutional layer from file and return layer implicit none ! Arguments integer, intent(in) :: unit !! Unit number integer, optional, intent(in) :: verbose !! Verbosity level class(base_layer_type), allocatable :: layer !! Instance of the base layer integer :: verbose_ = 0 !! Verbosity level if(present(verbose)) verbose_ = verbose allocate(layer, source=conv3d_layer_type()) call layer%read(unit, verbose=verbose_) end function read_conv3d_layer !############################################################################### !##############################################################################! ! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ! !##############################################################################! !############################################################################### subroutine build_from_onnx_conv3d( & this, node, initialisers, value_info, verbose & ) !! Read ONNX attributes for 3D convolutional layer use athena__activation, only: activation_setup use athena__initialiser_data, only: data_init_type implicit none ! Arguments class(conv3d_layer_type), intent(inout) :: this !! Instance of the 3D convolutional layer type(onnx_node_type), intent(in) :: node !! ONNX node information type(onnx_initialiser_type), dimension(:), intent(in) :: initialisers !! ONNX initialiser information type(onnx_tensor_type), dimension(:), intent(in) :: value_info !! ONNX value info information integer, intent(in) :: verbose !! Verbosity level ! Local variables integer :: i, weight_idx, bias_idx !! Loop index and temporary integer integer :: num_filters !! Number of filters logical :: use_bias = .true. !! Whether to use bias integer, dimension(3) :: padding, stride, kernel_size, dilation !! Padding, stride, kernel size, and dilation integer, dimension(:), allocatable :: dims !! Dimensions character(256) :: val !! Attribute value class(base_actv_type), allocatable :: activation !! Activation function class(base_init_type), allocatable :: kernel_initialiser, bias_initialiser ! Set default values padding = 0 stride = 1 kernel_size = 3 dilation = 1 do i = 1, size(node%attributes) val = node%attributes(i)%val select case(trim(adjustl(node%attributes(i)%name))) case("pads") read(val,*) padding case("strides") read(val,*) stride case("kernel_shape") read(val,*) kernel_size case("dilations") read(val,*) dilation case default ! Do nothing write(0,*) "WARNING: Unrecognised attribute in ONNX CONV3D layer: ", & trim(adjustl(node%attributes(i)%name)) end select end do weight_idx = -1 bias_idx = -1 allocate(dims(0)) if(size(initialisers).lt.1)then call stop_program("ONNX CONV3D layer requires at least 1 initialiser") return else ! check which initialiser has weights and which has biases do i = 1, size(initialisers) if(allocated(initialisers(i)%dims))then dims = [ dims, product(initialisers(i)%dims) ] end if end do end if select case(size(dims)) case(1) if(mod(dims(1), product(kernel_size)).eq.0)then weight_idx = 1 else call stop_program("ONNX CONV3D layer initialiser dimensions do not & &match kernel size") return end if use_bias = .false. case(2) ! check which is weight and which is bias if(mod(dims(1), product(kernel_size)).eq.0 .and. & dims(1)/product(kernel_size).eq.dims(2))then weight_idx = 1 bias_idx = 2 elseif(mod(dims(2), product(kernel_size)).eq.0 .and. & dims(2)/product(kernel_size).eq.dims(1))then weight_idx = 2 bias_idx = 1 else call stop_program("ONNX CONV3D layer initialiser dimensions do not & &match kernel size") return end if case default call stop_program("ONNX CONV3D layer number of initialisers not & &supported") return end select num_filters = dims(weight_idx) / product(kernel_size) if(num_filters .ne. value_info(1)%dims(2))then call stop_program("ONNX CONV3D layer number of filters does not match & &value info") return end if kernel_initialiser = data_init_type( data = initialisers(weight_idx)%data ) if(use_bias)then bias_initialiser = data_init_type( data = initialisers(bias_idx)%data ) end if activation = activation_setup("none") call this%set_hyperparams( & num_filters = num_filters, & kernel_size = kernel_size, stride = stride, & dilation = dilation, & padding = "valid", & use_bias = use_bias, & activation = activation, & verbose = verbose, & kernel_initialiser = kernel_initialiser, & bias_initialiser = bias_initialiser & ) end subroutine build_from_onnx_conv3d !############################################################################### !##############################################################################! ! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ! !##############################################################################! !############################################################################### subroutine forward_conv3d(this, input) !! Forward propagation implicit none ! Arguments class(conv3d_layer_type), intent(inout) :: this !! Instance of the 3D convolutional layer class(array_type), dimension(:,:), intent(in) :: input !! Input values ! Local variables type(array_type), pointer :: ptr !! Pointer array ! Generate outputs from weights, biases, and inputs !--------------------------------------------------------------------------- select case(allocated(this%pad_layer)) case(.true.) call this%pad_layer%forward(input) ptr => conv3d(this%pad_layer%output(1,1), this%params(1), & this%stp, this%dil & ) case default ptr => conv3d(input(1,1), this%params(1), this%stp, this%dil) end select call this%z(1)%zero_grad() call this%z(1)%assign_and_deallocate_source(ptr) this%z(1)%is_temporary = .false. ptr => add_bias(this%z(1), this%params(2), dim=4, dim_act_on_shape=.true.) ! Apply activation function to activation !--------------------------------------------------------------------------- call this%output(1,1)%zero_grad() if(trim(this%activation%name) .eq. "none")then call this%output(1,1)%assign_and_deallocate_source(ptr) else call this%z(2)%zero_grad() call this%z(2)%assign_and_deallocate_source(ptr) this%z(2)%is_temporary = .false. ptr => this%activation%apply(this%z(2)) call this%output(1,1)%assign_and_deallocate_source(ptr) end if this%output(1,1)%is_temporary = .false. end subroutine forward_conv3d !############################################################################### end module athena__conv3d_layer