| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | module athena__conv3d_layer | ||
| 2 | !! Module containing implementation of a 3D convolutional layer | ||
| 3 | !! | ||
| 4 | !! This module implements 3D convolution for processing volumetric data | ||
| 5 | !! such as video, medical imaging, or 3D point clouds. | ||
| 6 | !! | ||
| 7 | !! Mathematical operation: | ||
| 8 | !! \[ | ||
| 9 | !! y_{i,j,k,\,q} | ||
| 10 | !! = | ||
| 11 | !! \sigma\left( | ||
| 12 | !! \sum_{c=1}^{C_{in}} | ||
| 13 | !! \sum_{a=0}^{K_d-1} | ||
| 14 | !! \sum_{b=0}^{K_h-1} | ||
| 15 | !! \sum_{d=0}^{K_w-1} | ||
| 16 | !! x_{i+a,\,j+b,\,k+d,\,c}\; | ||
| 17 | !! w_{a,\,b,\,d,\,c,\,q} | ||
| 18 | !! + b_q | ||
| 19 | !! \right) | ||
| 20 | !! \] | ||
| 21 | !! | ||
| 22 | !! where: | ||
| 23 | !! - \((i,j,k)\) are spatial coordinates in the output | ||
| 24 | !! - \(q\) is the output channel (filter) index | ||
| 25 | !! - \((a,b,d)\) are kernel offsets: depth, height, width | ||
| 26 | !! - \(c\) is the input channel index | ||
| 27 | !! - \(K_d, K_h, K_w\) are kernel dimensions | ||
| 28 | !! - \(\sigma\) is the activation function | ||
| 29 | !! | ||
| 30 | !! Shape: \((D, H, W, C_{in}) \rightarrow (D', H', W', C_{out})\) | ||
| 31 | use coreutils, only: real32, stop_program | ||
| 32 | use athena__base_layer, only: conv_layer_type, base_layer_type, & | ||
| 33 | learnable_layer_type | ||
| 34 | use athena__pad3d_layer, only: pad3d_layer_type | ||
| 35 | use athena__misc_types, only: base_actv_type, base_init_type, & | ||
| 36 | onnx_node_type, onnx_initialiser_type, onnx_tensor_type | ||
| 37 | use diffstruc, only: array_type | ||
| 38 | use athena__diffstruc_extd, only: conv3d, add_bias | ||
| 39 | implicit none | ||
| 40 | |||
| 41 | |||
| 42 | private | ||
| 43 | |||
| 44 | public :: conv3d_layer_type | ||
| 45 | public :: read_conv3d_layer | ||
| 46 | |||
| 47 | |||
| 48 | type, extends(conv_layer_type) :: conv3d_layer_type | ||
| 49 | !! Type for 3D convolutional layer with overloaded procedures | ||
| 50 | contains | ||
| 51 | procedure, pass(this) :: set_hyperparams => set_hyperparams_conv3d | ||
| 52 | !! Set hyperparameters for 3D convolutional layer | ||
| 53 | procedure, pass(this) :: read => read_conv3d | ||
| 54 | !! Read 3D convolutional layer from file | ||
| 55 | |||
| 56 | #ifdef __INTEL_COMPILER | ||
| 57 | procedure, pass(this) :: reduce => reduce_conv3d | ||
| 58 | !! Merge another 3D convolutional layer into this one | ||
| 59 | procedure :: add_t_t => add_conv3d | ||
| 60 | !! Add two 3D convolutional layers | ||
| 61 | #endif | ||
| 62 | |||
| 63 | procedure, pass(this) :: forward => forward_conv3d | ||
| 64 | !! Forward propagation derived type handler | ||
| 65 | |||
| 66 | final :: finalise_conv3d | ||
| 67 | !! Finalise 3D convolutional layer | ||
| 68 | end type conv3d_layer_type | ||
| 69 | |||
| 70 | interface conv3d_layer_type | ||
| 71 | !! Interface for setting up the 3D convolutional layer | ||
| 72 | module function layer_setup( & | ||
| 73 | input_shape, & | ||
| 74 | num_filters, kernel_size, stride, dilation, padding, & | ||
| 75 | use_bias, & | ||
| 76 | activation, & | ||
| 77 | kernel_initialiser, bias_initialiser, & | ||
| 78 | verbose ) result(layer) | ||
| 79 | !! Set up the 3D convolutional layer | ||
| 80 | integer, dimension(:), optional, intent(in) :: input_shape | ||
| 81 | !! Input shape | ||
| 82 | integer, optional, intent(in) :: num_filters | ||
| 83 | !! Number of filters | ||
| 84 | integer, dimension(..), optional, intent(in) :: kernel_size | ||
| 85 | !! Kernel size | ||
| 86 | integer, dimension(..), optional, intent(in) :: stride | ||
| 87 | !! Stride | ||
| 88 | integer, dimension(..), optional, intent(in) :: dilation | ||
| 89 | !! Dilation | ||
| 90 | logical, optional, intent(in) :: use_bias | ||
| 91 | !! Use bias | ||
| 92 | class(*), optional, intent(in) :: activation, & | ||
| 93 | kernel_initialiser, bias_initialiser | ||
| 94 | !! Activation function, kernel initialiser, bias initialiser | ||
| 95 | character(*), optional, intent(in) :: padding | ||
| 96 | !! Padding method | ||
| 97 | integer, optional, intent(in) :: verbose | ||
| 98 | !! Verbosity level | ||
| 99 | type(conv3d_layer_type) :: layer | ||
| 100 | !! Instance of the 3D convolutional layer | ||
| 101 | end function layer_setup | ||
| 102 | end interface conv3d_layer_type | ||
| 103 | |||
| 104 | |||
| 105 | |||
| 106 | contains | ||
| 107 | |||
| 108 | !############################################################################### | ||
| 109 | 32 | subroutine finalise_conv3d(this) | |
| 110 | !! Finalise 3D convolutional layer | ||
| 111 | implicit none | ||
| 112 | |||
| 113 | ! Arguments | ||
| 114 | type(conv3d_layer_type), intent(inout) :: this | ||
| 115 | !! Instance of the 3D convolutional layer | ||
| 116 | |||
| 117 |
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32 | if(allocated(this%dil)) deallocate(this%dil) |
| 118 |
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32 | if(allocated(this%knl)) deallocate(this%knl) |
| 119 |
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32 | if(allocated(this%stp)) deallocate(this%stp) |
| 120 | |||
| 121 |
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32 | if(allocated(this%input_shape)) deallocate(this%input_shape) |
| 122 |
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32 | if(allocated(this%output)) deallocate(this%output) |
| 123 |
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32 | if(allocated(this%pad_layer)) deallocate(this%pad_layer) |
| 124 |
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32 | if(this%z(1)%allocated) call this%z(1)%deallocate() |
| 125 |
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32 | if(this%z(2)%allocated) call this%z(2)%deallocate() |
| 126 | |||
| 127 | 32 | end subroutine finalise_conv3d | |
| 128 | !############################################################################### | ||
| 129 | |||
| 130 | #ifdef __INTEL_COMPILER | ||
| 131 | !############################################################################### | ||
| 132 | subroutine reduce_conv3d(this, input) | ||
| 133 | !! Merge two 3D convolutional layers via parameter summation | ||
| 134 | implicit none | ||
| 135 | |||
| 136 | class(conv3d_layer_type), intent(inout) :: this | ||
| 137 | class(learnable_layer_type), intent(in) :: input | ||
| 138 | |||
| 139 | real(real32), allocatable :: params(:), gradients(:) | ||
| 140 | |||
| 141 | select type(input) | ||
| 142 | type is (conv3d_layer_type) | ||
| 143 | params = this%get_params() + input%get_params() | ||
| 144 | call this%set_params(params) | ||
| 145 | |||
| 146 | gradients = this%get_gradients() + input%get_gradients() | ||
| 147 | call this%set_gradients(gradients) | ||
| 148 | class default | ||
| 149 | call stop_program("reduce_conv3d: incompatible layer type") | ||
| 150 | end select | ||
| 151 | |||
| 152 | end subroutine reduce_conv3d | ||
| 153 | !############################################################################### | ||
| 154 | |||
| 155 | |||
| 156 | !############################################################################### | ||
| 157 | function add_conv3d(a, b) result(output) | ||
| 158 | !! Add two 3D convolutional layers without whole-object allocatable copy | ||
| 159 | implicit none | ||
| 160 | |||
| 161 | class(conv3d_layer_type), intent(in) :: a | ||
| 162 | class(learnable_layer_type), intent(in) :: b | ||
| 163 | class(learnable_layer_type), allocatable :: output | ||
| 164 | |||
| 165 | type(conv3d_layer_type) :: layer | ||
| 166 | character(len=20) :: padding | ||
| 167 | real(real32), allocatable :: params(:), gradients(:) | ||
| 168 | |||
| 169 | select type(b) | ||
| 170 | type is (conv3d_layer_type) | ||
| 171 | padding = 'valid' | ||
| 172 | if(allocated(a%pad_layer)) padding = a%pad_layer%method | ||
| 173 | |||
| 174 | call layer%set_hyperparams( & | ||
| 175 | num_filters = a%num_filters, & | ||
| 176 | kernel_size = a%knl, stride = a%stp, dilation = a%dil, & | ||
| 177 | padding = padding, & | ||
| 178 | use_bias = a%use_bias, & | ||
| 179 | activation = a%activation, & | ||
| 180 | kernel_initialiser = a%kernel_init, & | ||
| 181 | bias_initialiser = a%bias_init & | ||
| 182 | ) | ||
| 183 | |||
| 184 | if(allocated(a%input_shape)) call layer%init(a%input_shape) | ||
| 185 | |||
| 186 | params = a%get_params() + b%get_params() | ||
| 187 | call layer%set_params(params) | ||
| 188 | |||
| 189 | gradients = a%get_gradients() + b%get_gradients() | ||
| 190 | call layer%set_gradients(gradients) | ||
| 191 | |||
| 192 | layer%id = a%id | ||
| 193 | layer%inference = a%inference | ||
| 194 | layer%use_graph_input = a%use_graph_input | ||
| 195 | layer%use_graph_output = a%use_graph_output | ||
| 196 | layer%subtype = a%subtype | ||
| 197 | |||
| 198 | allocate(output, source=layer) | ||
| 199 | class default | ||
| 200 | call stop_program("add_conv3d: incompatible layer type") | ||
| 201 | end select | ||
| 202 | |||
| 203 | end function add_conv3d | ||
| 204 | !############################################################################### | ||
| 205 | #endif | ||
| 206 | |||
| 207 | !##############################################################################! | ||
| 208 | ! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ! | ||
| 209 | !##############################################################################! | ||
| 210 | |||
| 211 | |||
| 212 | !############################################################################### | ||
| 213 | 31 | module function layer_setup( & | |
| 214 |
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31 | input_shape, & |
| 215 | num_filters, kernel_size, stride, dilation, padding, & | ||
| 216 | use_bias, & | ||
| 217 | activation, & | ||
| 218 | kernel_initialiser, bias_initialiser, & | ||
| 219 |
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93 | verbose ) result(layer) |
| 220 | !! Set up the 3D convolutional layer | ||
| 221 | use athena__activation, only: activation_setup | ||
| 222 | use athena__initialiser, only: initialiser_setup | ||
| 223 | implicit none | ||
| 224 | |||
| 225 | ! Arguments | ||
| 226 | integer, dimension(:), optional, intent(in) :: input_shape | ||
| 227 | !! Input shape | ||
| 228 | integer, optional, intent(in) :: num_filters | ||
| 229 | !! Number of filters | ||
| 230 | integer, dimension(..), optional, intent(in) :: kernel_size | ||
| 231 | !! Kernel size | ||
| 232 | integer, dimension(..), optional, intent(in) :: stride | ||
| 233 | !! Stride | ||
| 234 | integer, dimension(..), optional, intent(in) :: dilation | ||
| 235 | !! Dilation | ||
| 236 | logical, optional, intent(in) :: use_bias | ||
| 237 | !! Use bias | ||
| 238 | class(*), optional, intent(in) :: activation | ||
| 239 | !! Activation function | ||
| 240 | class(*), optional, intent(in) :: kernel_initialiser, bias_initialiser | ||
| 241 | !! Activation function, kernel initialiser, and bias initialiser | ||
| 242 | character(*), optional, intent(in) :: padding | ||
| 243 | !! Padding method | ||
| 244 | integer, optional, intent(in) :: verbose | ||
| 245 | !! Verbosity level | ||
| 246 | |||
| 247 | type(conv3d_layer_type) :: layer | ||
| 248 | !! Instance of the 3D convolutional layer | ||
| 249 | |||
| 250 | ! Local variables | ||
| 251 | integer :: verbose_ = 0 | ||
| 252 | !! Verbosity level | ||
| 253 | integer :: num_filters_ | ||
| 254 | !! Number of filters | ||
| 255 | logical :: use_bias_ = .true. | ||
| 256 | !! Use bias | ||
| 257 | character(len=20) :: padding_ | ||
| 258 | !! Padding | ||
| 259 | integer, dimension(3) :: kernel_size_, stride_, dilation_ | ||
| 260 | !! Kernel size and stride | ||
| 261 | 93 | class(base_actv_type), allocatable :: activation_ | |
| 262 | !! Activation function | ||
| 263 | 31 | class(base_init_type), allocatable :: kernel_initialiser_, bias_initialiser_ | |
| 264 | !! Kernel and bias initialisers | ||
| 265 | |||
| 266 |
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31 | if(present(verbose)) verbose_ = verbose |
| 267 | |||
| 268 | |||
| 269 | !--------------------------------------------------------------------------- | ||
| 270 | ! Set use_bias | ||
| 271 | !--------------------------------------------------------------------------- | ||
| 272 |
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31 | if(present(use_bias)) use_bias_ = use_bias |
| 273 | |||
| 274 | |||
| 275 | !--------------------------------------------------------------------------- | ||
| 276 | ! Set activation functions based on input name | ||
| 277 | !--------------------------------------------------------------------------- | ||
| 278 |
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31 | if(present(activation))then |
| 279 |
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15 | activation_ = activation_setup(activation) |
| 280 | else | ||
| 281 |
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16 | activation_ = activation_setup("none") |
| 282 | end if | ||
| 283 | |||
| 284 | |||
| 285 | !--------------------------------------------------------------------------- | ||
| 286 | ! Define weights (kernels) and biases initialisers | ||
| 287 | !--------------------------------------------------------------------------- | ||
| 288 |
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31 | if(present(kernel_initialiser))then |
| 289 |
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23 | kernel_initialiser_ = initialiser_setup(kernel_initialiser) |
| 290 | end if | ||
| 291 |
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31 | if(present(bias_initialiser))then |
| 292 |
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10 | bias_initialiser_ = initialiser_setup(bias_initialiser) |
| 293 | end if | ||
| 294 | |||
| 295 | |||
| 296 | !--------------------------------------------------------------------------- | ||
| 297 | ! Set up number of filters | ||
| 298 | !--------------------------------------------------------------------------- | ||
| 299 |
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31 | if(present(num_filters))then |
| 300 | 16 | num_filters_ = num_filters | |
| 301 | else | ||
| 302 | 15 | num_filters_ = 32 | |
| 303 | end if | ||
| 304 | |||
| 305 | |||
| 306 | !--------------------------------------------------------------------------- | ||
| 307 | ! Set up kernel size | ||
| 308 | !--------------------------------------------------------------------------- | ||
| 309 |
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31 | if(present(kernel_size))then |
| 310 | 18 | select rank(kernel_size) | |
| 311 | rank(0) | ||
| 312 |
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64 | kernel_size_ = kernel_size |
| 313 | rank(1) | ||
| 314 |
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2 | kernel_size_(1) = kernel_size(1) |
| 315 |
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4 | if(size(kernel_size,dim=1).eq.1)then |
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3 | kernel_size_(2:) = kernel_size(1) |
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1 | elseif(size(kernel_size,dim=1).eq.3)then |
| 318 |
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3 | kernel_size_(2:) = kernel_size(2:) |
| 319 | end if | ||
| 320 | end select | ||
| 321 | else | ||
| 322 |
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52 | kernel_size_ = 3 |
| 323 | end if | ||
| 324 | |||
| 325 | |||
| 326 | !--------------------------------------------------------------------------- | ||
| 327 | ! Set up padding name | ||
| 328 | !--------------------------------------------------------------------------- | ||
| 329 |
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31 | if(present(padding))then |
| 330 |
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10 | padding_ = padding |
| 331 | else | ||
| 332 | 21 | padding_ = "valid" | |
| 333 | end if | ||
| 334 | |||
| 335 | |||
| 336 | !--------------------------------------------------------------------------- | ||
| 337 | ! Set up stride | ||
| 338 | !--------------------------------------------------------------------------- | ||
| 339 |
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31 | if(present(stride))then |
| 340 | 12 | select rank(stride) | |
| 341 | rank(0) | ||
| 342 |
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40 | stride_ = stride |
| 343 | rank(1) | ||
| 344 |
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2 | stride_(1) = stride(1) |
| 345 |
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4 | if(size(stride,dim=1).eq.1)then |
| 346 |
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3 | stride_(2:) = stride(1) |
| 347 |
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1 | elseif(size(stride,dim=1).eq.3)then |
| 348 |
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3 | stride_(2:) = stride(2:) |
| 349 | end if | ||
| 350 | end select | ||
| 351 | else | ||
| 352 |
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76 | stride_ = 1 |
| 353 | end if | ||
| 354 | |||
| 355 | |||
| 356 | !--------------------------------------------------------------------------- | ||
| 357 | ! Set up dilation | ||
| 358 | !--------------------------------------------------------------------------- | ||
| 359 |
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31 | if(present(dilation))then |
| 360 | ✗ | select rank(dilation) | |
| 361 | rank(0) | ||
| 362 | ✗ | dilation_ = dilation | |
| 363 | rank(1) | ||
| 364 | ✗ | dilation_(1) = dilation(1) | |
| 365 | ✗ | if(size(dilation,dim=1).eq.1)then | |
| 366 | ✗ | dilation_(2:) = dilation(1) | |
| 367 | ✗ | elseif(size(dilation,dim=1).eq.3)then | |
| 368 | ✗ | dilation_(2:) = dilation(2:) | |
| 369 | end if | ||
| 370 | end select | ||
| 371 | else | ||
| 372 |
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124 | dilation_ = 1 |
| 373 | end if | ||
| 374 | |||
| 375 | |||
| 376 | !--------------------------------------------------------------------------- | ||
| 377 | ! Set hyperparameters | ||
| 378 | !--------------------------------------------------------------------------- | ||
| 379 | call layer%set_hyperparams( & | ||
| 380 | num_filters = num_filters_, & | ||
| 381 | kernel_size = kernel_size_, stride = stride_, dilation = dilation_, & | ||
| 382 | padding = padding_, & | ||
| 383 | use_bias = use_bias_, & | ||
| 384 | activation = activation_, & | ||
| 385 | kernel_initialiser = kernel_initialiser_, & | ||
| 386 | bias_initialiser = bias_initialiser_, & | ||
| 387 | verbose = verbose_ & | ||
| 388 | 31 | ) | |
| 389 | |||
| 390 | |||
| 391 | !--------------------------------------------------------------------------- | ||
| 392 | ! Initialise layer shape | ||
| 393 | !--------------------------------------------------------------------------- | ||
| 394 |
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31 | if(present(input_shape)) call layer%init(input_shape=input_shape) |
| 395 | |||
| 396 |
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124 | end function layer_setup |
| 397 | !############################################################################### | ||
| 398 | |||
| 399 | |||
| 400 | !############################################################################### | ||
| 401 | 32 | subroutine set_hyperparams_conv3d( & | |
| 402 | this, & | ||
| 403 | num_filters, & | ||
| 404 | kernel_size, stride, dilation, & | ||
| 405 | padding, & | ||
| 406 | use_bias, & | ||
| 407 | activation, & | ||
| 408 | kernel_initialiser, bias_initialiser, & | ||
| 409 | verbose & | ||
| 410 | ) | ||
| 411 | !! Set hyperparameters for 3D convolutional layer | ||
| 412 | use athena__activation, only: activation_setup | ||
| 413 | use athena__initialiser, only: get_default_initialiser, initialiser_setup | ||
| 414 | use coreutils, only: to_lower | ||
| 415 | implicit none | ||
| 416 | |||
| 417 | ! Arguments | ||
| 418 | class(conv3d_layer_type), intent(inout) :: this | ||
| 419 | !! Instance of the 3D convolutional layer | ||
| 420 | integer, intent(in) :: num_filters | ||
| 421 | !! Number of filters | ||
| 422 | integer, dimension(3), intent(in) :: kernel_size, stride, dilation | ||
| 423 | !! Kernel size, stride, dilation | ||
| 424 | character(*), intent(in) :: padding | ||
| 425 | !! Padding | ||
| 426 | logical, intent(in) :: use_bias | ||
| 427 | !! Use bias | ||
| 428 | class(base_actv_type), allocatable, intent(in) :: activation | ||
| 429 | !! Activation function | ||
| 430 | class(base_init_type), allocatable, intent(in) :: & | ||
| 431 | kernel_initialiser, bias_initialiser | ||
| 432 | !! Kernel and bias initialisers | ||
| 433 | integer, optional, intent(in) :: verbose | ||
| 434 | !! Verbosity level | ||
| 435 | |||
| 436 | ! Local variables | ||
| 437 | character(len=20) :: padding_ | ||
| 438 | character(len=256) :: buffer | ||
| 439 | |||
| 440 |
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32 | this%name = "conv3d" |
| 441 | 32 | this%type = "conv" | |
| 442 | 32 | this%input_rank = 4 | |
| 443 | 32 | this%output_rank = 4 | |
| 444 | 32 | this%use_bias = use_bias | |
| 445 |
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32 | if(allocated(this%dil)) deallocate(this%dil) |
| 446 |
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32 | if(allocated(this%knl)) deallocate(this%knl) |
| 447 |
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32 | if(allocated(this%stp)) deallocate(this%stp) |
| 448 | allocate( & | ||
| 449 | ✗ | this%dil(this%input_rank-1), & | |
| 450 | ✗ | this%knl(this%input_rank-1), & | |
| 451 | ✗ | this%stp(this%input_rank-1) & | |
| 452 |
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32 | ) |
| 453 |
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160 | this%dil = dilation |
| 454 |
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160 | this%knl = kernel_size |
| 455 |
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160 | this%stp = stride |
| 456 | 32 | this%num_filters = num_filters | |
| 457 |
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32 | padding_ = trim(adjustl(padding)) |
| 458 | |||
| 459 | 95 | select case(trim(adjustl(to_lower(padding_)))) | |
| 460 | case("valid", "none", "") | ||
| 461 | case default | ||
| 462 | this%pad_layer = pad3d_layer_type( & | ||
| 463 | ✗ | padding = [ (this%knl-1)/2 ], & | |
| 464 | method = padding_ & | ||
| 465 |
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95 | ) |
| 466 | end select | ||
| 467 |
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32 | if(allocated(this%activation)) deallocate(this%activation) |
| 468 |
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32 | if(.not.allocated(activation))then |
| 469 | ✗ | this%activation = activation_setup("none") | |
| 470 | else | ||
| 471 |
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32 | allocate(this%activation, source=activation) |
| 472 | end if | ||
| 473 |
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32 | if(allocated(this%kernel_init)) deallocate(this%kernel_init) |
| 474 |
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32 | if(.not.allocated(kernel_initialiser))then |
| 475 |
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8 | buffer = get_default_initialiser(this%activation%name) |
| 476 |
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8 | this%kernel_init = initialiser_setup(buffer) |
| 477 | else | ||
| 478 |
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24 | allocate(this%kernel_init, source=kernel_initialiser) |
| 479 | end if | ||
| 480 |
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32 | if(allocated(this%bias_init)) deallocate(this%bias_init) |
| 481 |
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32 | if(.not.allocated(bias_initialiser))then |
| 482 | buffer = get_default_initialiser( & | ||
| 483 | this%activation%name, & | ||
| 484 | is_bias=.true. & | ||
| 485 |
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21 | ) |
| 486 |
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21 | this%bias_init = initialiser_setup(buffer) |
| 487 | else | ||
| 488 |
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11 | allocate(this%bias_init, source=bias_initialiser) |
| 489 | end if | ||
| 490 |
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32 | if(present(verbose))then |
| 491 |
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32 | if(abs(verbose).gt.0)then |
| 492 | write(*,'("CONV3D activation function: ",A)') & | ||
| 493 |
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11 | trim(this%activation%name) |
| 494 | write(*,'("CONV3D kernel initialiser: ",A)') & | ||
| 495 |
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11 | trim(this%kernel_init%name) |
| 496 | write(*,'("CONV3D bias initialiser: ",A)') & | ||
| 497 |
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11 | trim(this%bias_init%name) |
| 498 | end if | ||
| 499 | end if | ||
| 500 | |||
| 501 |
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32 | end subroutine set_hyperparams_conv3d |
| 502 | !############################################################################### | ||
| 503 | |||
| 504 | |||
| 505 | !##############################################################################! | ||
| 506 | ! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ! | ||
| 507 | !##############################################################################! | ||
| 508 | |||
| 509 | |||
| 510 | !############################################################################### | ||
| 511 | 1 | subroutine read_conv3d(this, unit, verbose) | |
| 512 | !! Read 3D convolutional layer from file | ||
| 513 | use athena__tools_infile, only: assign_val, assign_vec, move | ||
| 514 | use coreutils, only: to_lower, to_upper, icount | ||
| 515 | use athena__activation, only: read_activation | ||
| 516 | use athena__initialiser, only: initialiser_setup | ||
| 517 | implicit none | ||
| 518 | |||
| 519 | ! Arguments | ||
| 520 | class(conv3d_layer_type), intent(inout) :: this | ||
| 521 | !! Instance of the 3D convolutional layer | ||
| 522 | integer, intent(in) :: unit | ||
| 523 | !! Unit number | ||
| 524 | integer, optional, intent(in) :: verbose | ||
| 525 | !! Verbosity level | ||
| 526 | |||
| 527 | ! Local variables | ||
| 528 | integer :: stat | ||
| 529 | !! Status of read | ||
| 530 | integer :: verbose_ = 0 | ||
| 531 | !! Verbosity level | ||
| 532 | integer :: j, k, l, c, itmp1, iline, num_params | ||
| 533 | !! Loop variables and temporary integer | ||
| 534 | integer :: num_filters | ||
| 535 | !! Number of filters | ||
| 536 | logical :: use_bias = .true. | ||
| 537 | !! Whether to use bias | ||
| 538 | character(14) :: kernel_initialiser_name='', bias_initialiser_name='' | ||
| 539 | !! Kernel and bias initialisers | ||
| 540 | character(20) :: padding='', activation_name='' | ||
| 541 | !! Padding and activation function | ||
| 542 | 3 | class(base_actv_type), allocatable :: activation | |
| 543 | !! Activation function | ||
| 544 | 5 | class(base_init_type), allocatable :: kernel_initialiser, bias_initialiser | |
| 545 | !! Initialisers | ||
| 546 | character(256) :: buffer, tag, err_msg | ||
| 547 | !! Buffer, tag, and error message | ||
| 548 | integer, dimension(3) :: kernel_size, stride, dilation | ||
| 549 | !! Kernel size and stride | ||
| 550 | integer, dimension(4) :: input_shape | ||
| 551 | !! Input shape | ||
| 552 | 1 | real(real32), allocatable, dimension(:) :: data_list | |
| 553 | !! Data list | ||
| 554 | integer :: param_line, final_line | ||
| 555 | !! Parameter line number | ||
| 556 | |||
| 557 | |||
| 558 | ! Initialise optional arguments | ||
| 559 | !--------------------------------------------------------------------------- | ||
| 560 |
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1 | if(present(verbose)) verbose_ = verbose |
| 561 | |||
| 562 | |||
| 563 | ! Loop over tags in layer card | ||
| 564 | !--------------------------------------------------------------------------- | ||
| 565 | 1 | iline = 0 | |
| 566 | 1 | param_line = 0 | |
| 567 | 1 | final_line = 0 | |
| 568 | 27 | tag_loop: do | |
| 569 | |||
| 570 | ! Check for end of file | ||
| 571 | !------------------------------------------------------------------------ | ||
| 572 | 28 | read(unit,'(A)',iostat=stat) buffer | |
| 573 |
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28 | if(stat.ne.0)then |
| 574 | write(err_msg,'("file encountered error (EoF?) before END ",A)') & | ||
| 575 | ✗ | to_upper(this%name) | |
| 576 | ✗ | call stop_program(err_msg) | |
| 577 | ✗ | return | |
| 578 | end if | ||
| 579 |
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28 | if(trim(adjustl(buffer)).eq."") cycle tag_loop |
| 580 | |||
| 581 | ! Check for end of layer card | ||
| 582 | !------------------------------------------------------------------------ | ||
| 583 |
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56 | if(trim(adjustl(buffer)).eq."END "//to_upper(trim(this%name)))then |
| 584 | 1 | final_line = iline | |
| 585 | 1 | backspace(unit) | |
| 586 | 28 | exit tag_loop | |
| 587 | end if | ||
| 588 | 27 | iline = iline + 1 | |
| 589 | |||
| 590 |
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27 | tag=trim(adjustl(buffer)) |
| 591 |
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27 | if(scan(buffer,"=").ne.0) tag=trim(tag(:scan(tag,"=")-1)) |
| 592 | |||
| 593 | ! Read parameters from save file | ||
| 594 | !------------------------------------------------------------------------ | ||
| 595 | 54 | select case(trim(tag)) | |
| 596 | case("INPUT_SHAPE") | ||
| 597 | 1 | call assign_vec(buffer, input_shape, itmp1) | |
| 598 | case("NUM_FILTERS") | ||
| 599 | 2 | call assign_val(buffer, num_filters, itmp1) | |
| 600 | case("KERNEL_SIZE") | ||
| 601 | 1 | call assign_vec(buffer, kernel_size, itmp1) | |
| 602 | case("STRIDE") | ||
| 603 | 1 | call assign_vec(buffer, stride, itmp1) | |
| 604 | case("DILATION") | ||
| 605 | 1 | call assign_vec(buffer, dilation, itmp1) | |
| 606 | case("USE_BIAS") | ||
| 607 | 1 | call assign_val(buffer, use_bias, itmp1) | |
| 608 | case("PADDING") | ||
| 609 | ✗ | call assign_val(buffer, padding, itmp1) | |
| 610 | ✗ | padding = to_lower(padding) | |
| 611 | case("ACTIVATION") | ||
| 612 | 1 | iline = iline - 1 | |
| 613 | 1 | backspace(unit) | |
| 614 |
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1 | activation = read_activation(unit, iline) |
| 615 | case("KERNEL_INITIALISER", "KERNEL_INIT", "KERNEL_INITIALIZER") | ||
| 616 | ✗ | call assign_val(buffer, kernel_initialiser_name, itmp1) | |
| 617 | case("BIAS_INITIALISER", "BIAS_INIT", "BIAS_INITIALIZER") | ||
| 618 | ✗ | call assign_val(buffer, bias_initialiser_name, itmp1) | |
| 619 | case("WEIGHTS") | ||
| 620 | 1 | kernel_initialiser_name = 'zeros' | |
| 621 | 1 | bias_initialiser_name = 'zeros' | |
| 622 | 1 | param_line = iline | |
| 623 | case default | ||
| 624 | ! Don't look for "e" due to scientific notation of numbers | ||
| 625 | ! ... i.e. exponent (E+00) | ||
| 626 |
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38 | if(scan(to_lower(trim(adjustl(buffer))),& |
| 627 | 'abcdfghijklmnopqrstuvwxyz').eq.0)then | ||
| 628 | 19 | cycle tag_loop | |
| 629 |
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1 | elseif(tag(:3).eq.'END')then |
| 630 | 19 | cycle tag_loop | |
| 631 | end if | ||
| 632 | write(err_msg,'("Unrecognised line in input file: ",A)') & | ||
| 633 | ✗ | trim(adjustl(buffer)) | |
| 634 | ✗ | call stop_program(err_msg) | |
| 635 |
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54 | return |
| 636 | end select | ||
| 637 | end do tag_loop | ||
| 638 |
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1 | kernel_initialiser = initialiser_setup(kernel_initialiser_name) |
| 639 |
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|
1 | bias_initialiser = initialiser_setup(bias_initialiser_name) |
| 640 | |||
| 641 | |||
| 642 | ! Set hyperparameters and initialise layer | ||
| 643 | !--------------------------------------------------------------------------- | ||
| 644 | call this%set_hyperparams( & | ||
| 645 | num_filters = num_filters, & | ||
| 646 | kernel_size = kernel_size, stride = stride, dilation = dilation, & | ||
| 647 | padding = padding, & | ||
| 648 | use_bias = use_bias, & | ||
| 649 | activation = activation, & | ||
| 650 | kernel_initialiser = kernel_initialiser, & | ||
| 651 | bias_initialiser = bias_initialiser, & | ||
| 652 | verbose = verbose_ & | ||
| 653 | 1 | ) | |
| 654 | 1 | call this%init(input_shape = input_shape) | |
| 655 | |||
| 656 | |||
| 657 | ! Check if WEIGHTS card was found | ||
| 658 | !--------------------------------------------------------------------------- | ||
| 659 |
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1 | if(param_line.eq.0)then |
| 660 | ✗ | write(0,*) "WARNING: WEIGHTS card in "//to_upper(trim(this%name))//" not found" | |
| 661 | else | ||
| 662 | 1 | call move(unit, param_line - iline, iostat=stat) | |
| 663 |
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4 | num_params = product(this%knl) * input_shape(4) * num_filters |
| 664 |
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1 | allocate(data_list(num_params), source=0._real32) |
| 665 | 1 | c = 1 | |
| 666 | 1 | k = 1 | |
| 667 |
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18 | data_concat_loop1: do while(c.le.num_params) |
| 668 | 17 | read(unit,'(A)',iostat=stat) buffer | |
| 669 |
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17 | if(stat.ne.0) exit data_concat_loop1 |
| 670 | 17 | k = icount(buffer) | |
| 671 |
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98 | read(buffer,*,iostat=stat) (data_list(j),j=c,c+k-1) |
| 672 | 17 | c = c + k | |
| 673 | end do data_concat_loop1 | ||
| 674 |
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|
82 | this%params(1)%val(:,1) = data_list |
| 675 |
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1 | deallocate(data_list) |
| 676 |
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1 | allocate(data_list(num_filters), source=0._real32) |
| 677 | 1 | c = 1 | |
| 678 | 1 | k = 1 | |
| 679 |
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2 | data_concat_loop2: do while(c.le.num_filters) |
| 680 | 1 | read(unit,'(A)',iostat=stat) buffer | |
| 681 |
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1 | if(stat.ne.0) exit data_concat_loop2 |
| 682 | 1 | k = icount(buffer) | |
| 683 |
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2 | read(buffer,*,iostat=stat) (data_list(j),j=c,c+k-1) |
| 684 | 1 | c = c + k | |
| 685 | end do data_concat_loop2 | ||
| 686 |
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2 | this%params(2)%val(:,1) = data_list |
| 687 |
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1 | deallocate(data_list) |
| 688 | |||
| 689 | ! Check for end of weights card | ||
| 690 | !------------------------------------------------------------------------ | ||
| 691 | 1 | read(unit,'(A)') buffer | |
| 692 |
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1 | if(trim(adjustl(buffer)).ne."END WEIGHTS")then |
| 693 | ✗ | write(0,*) trim(adjustl(buffer)) | |
| 694 | ✗ | call stop_program("END WEIGHTS not where expected") | |
| 695 | ✗ | return | |
| 696 | end if | ||
| 697 | end if | ||
| 698 | |||
| 699 | |||
| 700 | ! Check for end of layer card | ||
| 701 | !--------------------------------------------------------------------------- | ||
| 702 | 1 | call move(unit, final_line - iline, iostat=stat) | |
| 703 | 1 | read(unit,'(A)') buffer | |
| 704 |
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2 | if(trim(adjustl(buffer)).ne."END "//to_upper(trim(this%name)))then |
| 705 | ✗ | write(0,*) trim(adjustl(buffer)) | |
| 706 | ✗ | write(err_msg,'("END ",A," not where expected")') to_upper(this%name) | |
| 707 | ✗ | call stop_program(err_msg) | |
| 708 | 1 | return | |
| 709 | end if | ||
| 710 | |||
| 711 |
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|
1 | end subroutine read_conv3d |
| 712 | !############################################################################### | ||
| 713 | |||
| 714 | |||
| 715 | !############################################################################### | ||
| 716 | 1 | function read_conv3d_layer(unit, verbose) result(layer) | |
| 717 | !! Read 3D convolutional layer from file and return layer | ||
| 718 | implicit none | ||
| 719 | |||
| 720 | ! Arguments | ||
| 721 | integer, intent(in) :: unit | ||
| 722 | !! Unit number | ||
| 723 | integer, optional, intent(in) :: verbose | ||
| 724 | !! Verbosity level | ||
| 725 | class(base_layer_type), allocatable :: layer | ||
| 726 | !! Instance of the base layer | ||
| 727 | |||
| 728 | integer :: verbose_ = 0 | ||
| 729 | !! Verbosity level | ||
| 730 | |||
| 731 |
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1 | if(present(verbose)) verbose_ = verbose |
| 732 |
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|
2 | allocate(layer, source=conv3d_layer_type()) |
| 733 | 1 | call layer%read(unit, verbose=verbose_) | |
| 734 | |||
| 735 | 2 | end function read_conv3d_layer | |
| 736 | !############################################################################### | ||
| 737 | |||
| 738 | |||
| 739 | !##############################################################################! | ||
| 740 | ! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ! | ||
| 741 | !##############################################################################! | ||
| 742 | |||
| 743 | |||
| 744 | !############################################################################### | ||
| 745 | ✗ | subroutine build_from_onnx_conv3d( & | |
| 746 | ✗ | this, node, initialisers, value_info, verbose & | |
| 747 | ) | ||
| 748 | !! Read ONNX attributes for 3D convolutional layer | ||
| 749 | use athena__activation, only: activation_setup | ||
| 750 | use athena__initialiser_data, only: data_init_type | ||
| 751 | implicit none | ||
| 752 | |||
| 753 | ! Arguments | ||
| 754 | class(conv3d_layer_type), intent(inout) :: this | ||
| 755 | !! Instance of the 3D convolutional layer | ||
| 756 | type(onnx_node_type), intent(in) :: node | ||
| 757 | !! ONNX node information | ||
| 758 | type(onnx_initialiser_type), dimension(:), intent(in) :: initialisers | ||
| 759 | !! ONNX initialiser information | ||
| 760 | type(onnx_tensor_type), dimension(:), intent(in) :: value_info | ||
| 761 | !! ONNX value info information | ||
| 762 | integer, intent(in) :: verbose | ||
| 763 | !! Verbosity level | ||
| 764 | |||
| 765 | ! Local variables | ||
| 766 | integer :: i, weight_idx, bias_idx | ||
| 767 | !! Loop index and temporary integer | ||
| 768 | integer :: num_filters | ||
| 769 | !! Number of filters | ||
| 770 | logical :: use_bias = .true. | ||
| 771 | !! Whether to use bias | ||
| 772 | integer, dimension(3) :: padding, stride, kernel_size, dilation | ||
| 773 | !! Padding, stride, kernel size, and dilation | ||
| 774 | ✗ | integer, dimension(:), allocatable :: dims | |
| 775 | !! Dimensions | ||
| 776 | character(256) :: val | ||
| 777 | !! Attribute value | ||
| 778 | ✗ | class(base_actv_type), allocatable :: activation | |
| 779 | !! Activation function | ||
| 780 | ✗ | class(base_init_type), allocatable :: kernel_initialiser, bias_initialiser | |
| 781 | |||
| 782 | ! Set default values | ||
| 783 | ✗ | padding = 0 | |
| 784 | ✗ | stride = 1 | |
| 785 | ✗ | kernel_size = 3 | |
| 786 | ✗ | dilation = 1 | |
| 787 | |||
| 788 | ✗ | do i = 1, size(node%attributes) | |
| 789 | ✗ | val = node%attributes(i)%val | |
| 790 | ✗ | select case(trim(adjustl(node%attributes(i)%name))) | |
| 791 | case("pads") | ||
| 792 | ✗ | read(val,*) padding | |
| 793 | case("strides") | ||
| 794 | ✗ | read(val,*) stride | |
| 795 | case("kernel_shape") | ||
| 796 | ✗ | read(val,*) kernel_size | |
| 797 | case("dilations") | ||
| 798 | ✗ | read(val,*) dilation | |
| 799 | case default | ||
| 800 | ! Do nothing | ||
| 801 | ✗ | write(0,*) "WARNING: Unrecognised attribute in ONNX CONV3D layer: ", & | |
| 802 | ✗ | trim(adjustl(node%attributes(i)%name)) | |
| 803 | end select | ||
| 804 | end do | ||
| 805 | |||
| 806 | ✗ | weight_idx = -1 | |
| 807 | ✗ | bias_idx = -1 | |
| 808 | ✗ | allocate(dims(0)) | |
| 809 | ✗ | if(size(initialisers).lt.1)then | |
| 810 | ✗ | call stop_program("ONNX CONV3D layer requires at least 1 initialiser") | |
| 811 | ✗ | return | |
| 812 | else | ||
| 813 | ! check which initialiser has weights and which has biases | ||
| 814 | ✗ | do i = 1, size(initialisers) | |
| 815 | ✗ | if(allocated(initialisers(i)%dims))then | |
| 816 | ✗ | dims = [ dims, product(initialisers(i)%dims) ] | |
| 817 | end if | ||
| 818 | end do | ||
| 819 | end if | ||
| 820 | |||
| 821 | ✗ | select case(size(dims)) | |
| 822 | case(1) | ||
| 823 | ✗ | if(mod(dims(1), product(kernel_size)).eq.0)then | |
| 824 | ✗ | weight_idx = 1 | |
| 825 | else | ||
| 826 | call stop_program("ONNX CONV3D layer initialiser dimensions do not & | ||
| 827 | ✗ | &match kernel size") | |
| 828 | ✗ | return | |
| 829 | end if | ||
| 830 | ✗ | use_bias = .false. | |
| 831 | case(2) | ||
| 832 | ! check which is weight and which is bias | ||
| 833 | ✗ | if(mod(dims(1), product(kernel_size)).eq.0 .and. & | |
| 834 | ✗ | dims(1)/product(kernel_size).eq.dims(2))then | |
| 835 | ✗ | weight_idx = 1 | |
| 836 | ✗ | bias_idx = 2 | |
| 837 | ✗ | elseif(mod(dims(2), product(kernel_size)).eq.0 .and. & | |
| 838 | ✗ | dims(2)/product(kernel_size).eq.dims(1))then | |
| 839 | ✗ | weight_idx = 2 | |
| 840 | ✗ | bias_idx = 1 | |
| 841 | else | ||
| 842 | call stop_program("ONNX CONV3D layer initialiser dimensions do not & | ||
| 843 | ✗ | &match kernel size") | |
| 844 | ✗ | return | |
| 845 | end if | ||
| 846 | case default | ||
| 847 | call stop_program("ONNX CONV3D layer number of initialisers not & | ||
| 848 | ✗ | &supported") | |
| 849 | ✗ | return | |
| 850 | end select | ||
| 851 | |||
| 852 | ✗ | num_filters = dims(weight_idx) / product(kernel_size) | |
| 853 | ✗ | if(num_filters .ne. value_info(1)%dims(2))then | |
| 854 | call stop_program("ONNX CONV3D layer number of filters does not match & | ||
| 855 | ✗ | &value info") | |
| 856 | ✗ | return | |
| 857 | end if | ||
| 858 | |||
| 859 | ✗ | kernel_initialiser = data_init_type( data = initialisers(weight_idx)%data ) | |
| 860 | ✗ | if(use_bias)then | |
| 861 | ✗ | bias_initialiser = data_init_type( data = initialisers(bias_idx)%data ) | |
| 862 | end if | ||
| 863 | |||
| 864 | ✗ | activation = activation_setup("none") | |
| 865 | call this%set_hyperparams( & | ||
| 866 | num_filters = num_filters, & | ||
| 867 | kernel_size = kernel_size, stride = stride, & | ||
| 868 | dilation = dilation, & | ||
| 869 | padding = "valid", & | ||
| 870 | use_bias = use_bias, & | ||
| 871 | activation = activation, & | ||
| 872 | verbose = verbose, & | ||
| 873 | kernel_initialiser = kernel_initialiser, & | ||
| 874 | bias_initialiser = bias_initialiser & | ||
| 875 | ✗ | ) | |
| 876 | |||
| 877 | ✗ | end subroutine build_from_onnx_conv3d | |
| 878 | !############################################################################### | ||
| 879 | |||
| 880 | |||
| 881 | !##############################################################################! | ||
| 882 | ! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ! | ||
| 883 | !##############################################################################! | ||
| 884 | |||
| 885 | |||
| 886 | !############################################################################### | ||
| 887 |
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15 | subroutine forward_conv3d(this, input) |
| 888 | !! Forward propagation | ||
| 889 | implicit none | ||
| 890 | |||
| 891 | ! Arguments | ||
| 892 | class(conv3d_layer_type), intent(inout) :: this | ||
| 893 | !! Instance of the 3D convolutional layer | ||
| 894 | class(array_type), dimension(:,:), intent(in) :: input | ||
| 895 | !! Input values | ||
| 896 | |||
| 897 | ! Local variables | ||
| 898 | type(array_type), pointer :: ptr | ||
| 899 | !! Pointer array | ||
| 900 | |||
| 901 | |||
| 902 | ! Generate outputs from weights, biases, and inputs | ||
| 903 | !--------------------------------------------------------------------------- | ||
| 904 | 15 | select case(allocated(this%pad_layer)) | |
| 905 | case(.true.) | ||
| 906 | ✗ | call this%pad_layer%forward(input) | |
| 907 | ✗ | ptr => conv3d(this%pad_layer%output(1,1), this%params(1), & | |
| 908 | this%stp, this%dil & | ||
| 909 | ✗ | ) | |
| 910 | case default | ||
| 911 |
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15 | ptr => conv3d(input(1,1), this%params(1), this%stp, this%dil) |
| 912 | end select | ||
| 913 | 15 | call this%z(1)%zero_grad() | |
| 914 | 15 | call this%z(1)%assign_and_deallocate_source(ptr) | |
| 915 | 15 | this%z(1)%is_temporary = .false. | |
| 916 |
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15 | ptr => add_bias(this%z(1), this%params(2), dim=4, dim_act_on_shape=.true.) |
| 917 | |||
| 918 | ! Apply activation function to activation | ||
| 919 | !--------------------------------------------------------------------------- | ||
| 920 |
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15 | call this%output(1,1)%zero_grad() |
| 921 |
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15 | if(trim(this%activation%name) .eq. "none")then |
| 922 |
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1 | call this%output(1,1)%assign_and_deallocate_source(ptr) |
| 923 | else | ||
| 924 | 14 | call this%z(2)%zero_grad() | |
| 925 | 14 | call this%z(2)%assign_and_deallocate_source(ptr) | |
| 926 | 14 | this%z(2)%is_temporary = .false. | |
| 927 | 14 | ptr => this%activation%apply(this%z(2)) | |
| 928 |
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14 | call this%output(1,1)%assign_and_deallocate_source(ptr) |
| 929 | end if | ||
| 930 |
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15 | this%output(1,1)%is_temporary = .false. |
| 931 | |||
| 932 | 15 | end subroutine forward_conv3d | |
| 933 | !############################################################################### | ||
| 934 | |||
| 935 |
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|
644 | end module athena__conv3d_layer |
| 936 |