| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | !!!############################################################################# | ||
| 2 | !!! Code written by Ned Thaddeus Taylor | ||
| 3 | !!! Code part of the ATHENA library - a feedforward neural network library | ||
| 4 | !!!############################################################################# | ||
| 5 | !!! module contains implementation of a 2D convolutional layer | ||
| 6 | !!!############################################################################# | ||
| 7 | !!! Attribution statement: | ||
| 8 | !!! The following procedures are based on code from the neural-fortran library | ||
| 9 | !!! https://github.com/modern-fortran/neural-fortran/blob/main/src/nf/nf_layer.f90 | ||
| 10 | !!! procedures: | ||
| 11 | !!! - get_params* | ||
| 12 | !!! - set_params* | ||
| 13 | !!! - get_gradients* | ||
| 14 | !!! - set_gradients* | ||
| 15 | !!!############################################################################# | ||
| 16 | module conv2d_layer | ||
| 17 | use constants, only: real12 | ||
| 18 | ✗ | use base_layer, only: learnable_layer_type, conv_layer_type | |
| 19 | use custom_types, only: initialiser_type | ||
| 20 | implicit none | ||
| 21 | |||
| 22 | |||
| 23 | type, extends(conv_layer_type) :: conv2d_layer_type | ||
| 24 | real(real12), allocatable, dimension(:,:,:,:) :: weight | ||
| 25 | real(real12), allocatable, dimension(:,:,:,:,:) :: dw ! weight gradient | ||
| 26 | real(real12), allocatable, dimension(:,:,:,:) :: output, z | ||
| 27 | real(real12), allocatable, dimension(:,:,:,:) :: di ! input gradient | ||
| 28 | contains | ||
| 29 | procedure, pass(this) :: get_params => get_params_conv2d | ||
| 30 | procedure, pass(this) :: set_params => set_params_conv2d | ||
| 31 | procedure, pass(this) :: get_gradients => get_gradients_conv2d | ||
| 32 | procedure, pass(this) :: set_gradients => set_gradients_conv2d | ||
| 33 | procedure, pass(this) :: get_output => get_output_conv2d | ||
| 34 | |||
| 35 | procedure, pass(this) :: init => init_conv2d | ||
| 36 | procedure, pass(this) :: set_batch_size => set_batch_size_conv2d | ||
| 37 | procedure, pass(this) :: print => print_conv2d | ||
| 38 | |||
| 39 | procedure, pass(this) :: forward => forward_rank | ||
| 40 | procedure, pass(this) :: backward => backward_rank | ||
| 41 | procedure, private, pass(this) :: forward_4d | ||
| 42 | procedure, private, pass(this) :: backward_4d | ||
| 43 | |||
| 44 | procedure, pass(this) :: reduce => layer_reduction | ||
| 45 | procedure, pass(this) :: merge => layer_merge | ||
| 46 | procedure :: add_t_t => layer_add !t = type, r = real, i = int | ||
| 47 | generic :: operator(+) => add_t_t !, public | ||
| 48 | end type conv2d_layer_type | ||
| 49 | |||
| 50 | |||
| 51 | !!!----------------------------------------------------------------------------- | ||
| 52 | !!! interface for layer set up | ||
| 53 | !!!----------------------------------------------------------------------------- | ||
| 54 | interface conv2d_layer_type | ||
| 55 | module function layer_setup( & | ||
| 56 | input_shape, batch_size, & | ||
| 57 | num_filters, kernel_size, stride, padding, & | ||
| 58 | activation_function, activation_scale, & | ||
| 59 | kernel_initialiser, bias_initialiser, & | ||
| 60 | calc_input_gradients) result(layer) | ||
| 61 | integer, dimension(:), optional, intent(in) :: input_shape | ||
| 62 | integer, optional, intent(in) :: batch_size | ||
| 63 | integer, optional, intent(in) :: num_filters | ||
| 64 | integer, dimension(..), optional, intent(in) :: kernel_size | ||
| 65 | integer, dimension(..), optional, intent(in) :: stride | ||
| 66 | real(real12), optional, intent(in) :: activation_scale | ||
| 67 | character(*), optional, intent(in) :: activation_function, & | ||
| 68 | kernel_initialiser, bias_initialiser, padding | ||
| 69 | logical, optional, intent(in) :: calc_input_gradients | ||
| 70 | type(conv2d_layer_type) :: layer | ||
| 71 | end function layer_setup | ||
| 72 | end interface conv2d_layer_type | ||
| 73 | |||
| 74 | |||
| 75 | private | ||
| 76 | public :: conv2d_layer_type | ||
| 77 | public :: read_conv2d_layer | ||
| 78 | |||
| 79 | |||
| 80 | contains | ||
| 81 | |||
| 82 | !!!############################################################################# | ||
| 83 | !!! layer reduction | ||
| 84 | !!!############################################################################# | ||
| 85 | 1 | subroutine layer_reduction(this, rhs) | |
| 86 | implicit none | ||
| 87 | class(conv2d_layer_type), intent(inout) :: this | ||
| 88 | class(learnable_layer_type), intent(in) :: rhs | ||
| 89 | |||
| 90 | select type(rhs) | ||
| 91 | class is(conv2d_layer_type) | ||
| 92 |
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|
3 | this%db = this%db + rhs%db |
| 93 |
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|
17 | this%dw = this%dw + rhs%dw |
| 94 | end select | ||
| 95 | |||
| 96 | 1 | end subroutine layer_reduction | |
| 97 | !!!############################################################################# | ||
| 98 | |||
| 99 | |||
| 100 | !!!############################################################################# | ||
| 101 | !!! layer addition | ||
| 102 | !!!############################################################################# | ||
| 103 | 1 | function layer_add(a, b) result(output) | |
| 104 | implicit none | ||
| 105 | class(conv2d_layer_type), intent(in) :: a, b | ||
| 106 | type(conv2d_layer_type) :: output | ||
| 107 | |||
| 108 |
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|
1 | output = a |
| 109 |
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|
16 | output%dw = output%dw + b%dw |
| 110 |
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|
3 | output%db = output%db + b%db |
| 111 | |||
| 112 |
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|
2 | end function layer_add |
| 113 | !!!############################################################################# | ||
| 114 | |||
| 115 | |||
| 116 | !!!############################################################################# | ||
| 117 | !!! layer merge | ||
| 118 | !!!############################################################################# | ||
| 119 | 1 | subroutine layer_merge(this, input) | |
| 120 | implicit none | ||
| 121 | class(conv2d_layer_type), intent(inout) :: this | ||
| 122 | class(learnable_layer_type), intent(in) :: input | ||
| 123 | |||
| 124 | select type(input) | ||
| 125 | class is(conv2d_layer_type) | ||
| 126 |
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|
16 | this%dw = this%dw + input%dw |
| 127 |
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|
4 | this%db = this%db + input%db |
| 128 | end select | ||
| 129 | |||
| 130 | 1 | end subroutine layer_merge | |
| 131 | !!!############################################################################# | ||
| 132 | |||
| 133 | |||
| 134 | !!!##########################################################################!!! | ||
| 135 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 136 | !!!##########################################################################!!! | ||
| 137 | |||
| 138 | |||
| 139 | !!!############################################################################# | ||
| 140 | !!! get learnable parameters | ||
| 141 | !!! procedure modified from neural-fortran library | ||
| 142 | !!!############################################################################# | ||
| 143 | 2 | pure function get_params_conv2d(this) result(params) | |
| 144 | implicit none | ||
| 145 | class(conv2d_layer_type), intent(in) :: this | ||
| 146 | real(real12), allocatable, dimension(:) :: params | ||
| 147 | |||
| 148 | params = [ reshape( & | ||
| 149 | this%weight, & | ||
| 150 | 16 | [ this%num_filters * this%num_channels * product(this%knl) ]), & | |
| 151 |
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48 | this%bias ] |
| 152 | |||
| 153 | 2 | end function get_params_conv2d | |
| 154 | !!!############################################################################# | ||
| 155 | |||
| 156 | |||
| 157 | !!!############################################################################# | ||
| 158 | !!! set learnable parameters | ||
| 159 | !!! procedure modified from neural-fortran library | ||
| 160 | !!!############################################################################# | ||
| 161 |
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1 | subroutine set_params_conv2d(this, params) |
| 162 | implicit none | ||
| 163 | class(conv2d_layer_type), intent(inout) :: this | ||
| 164 | real(real12), dimension(:), intent(in) :: params | ||
| 165 | |||
| 166 | this%weight = reshape( & | ||
| 167 | ✗ | params(1:this%num_filters * this%num_channels * product(this%knl)), & | |
| 168 |
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35 | shape(this%weight)) |
| 169 | ✗ | this%bias = params(& | |
| 170 |
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5 | this%num_filters * this%num_channels * product(this%knl) + 1 : ) |
| 171 | |||
| 172 | 1 | end subroutine set_params_conv2d | |
| 173 | !!!############################################################################# | ||
| 174 | |||
| 175 | |||
| 176 | !!!############################################################################# | ||
| 177 | !!! get sample-average gradients | ||
| 178 | !!! sum over batch dimension and divide by batch size | ||
| 179 | !!! procedure modified from neural-fortran library | ||
| 180 | !!!############################################################################# | ||
| 181 | 4 | pure function get_gradients_conv2d(this, clip_method) result(gradients) | |
| 182 | use clipper, only: clip_type | ||
| 183 | implicit none | ||
| 184 | class(conv2d_layer_type), intent(in) :: this | ||
| 185 | type(clip_type), optional, intent(in) :: clip_method | ||
| 186 | real(real12), allocatable, dimension(:) :: gradients | ||
| 187 | |||
| 188 | gradients = [ reshape( & | ||
| 189 | 80 | sum(this%dw,dim=5)/this%batch_size, & | |
| 190 | 16 | [ this%num_filters * this%num_channels * product(this%knl) ]), & | |
| 191 |
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|
1460 | sum(this%db,dim=2)/this%batch_size ] |
| 192 | |||
| 193 |
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4 | if(present(clip_method)) call clip_method%apply(size(gradients),gradients) |
| 194 | |||
| 195 | 4 | end function get_gradients_conv2d | |
| 196 | !!!############################################################################# | ||
| 197 | |||
| 198 | |||
| 199 | !!!############################################################################# | ||
| 200 | !!! set gradients | ||
| 201 | !!! procedure modified from neural-fortran library | ||
| 202 | !!!############################################################################# | ||
| 203 | 2 | subroutine set_gradients_conv2d(this, gradients) | |
| 204 | implicit none | ||
| 205 | class(conv2d_layer_type), intent(inout) :: this | ||
| 206 | real(real12), dimension(..), intent(in) :: gradients | ||
| 207 | |||
| 208 | integer :: s | ||
| 209 | |||
| 210 | select rank(gradients) | ||
| 211 | rank(0) | ||
| 212 |
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16 | this%dw = gradients |
| 213 |
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4 | this%db = gradients |
| 214 | rank(1) | ||
| 215 |
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2 | do s=1,this%batch_size |
| 216 | ✗ | this%dw(:,:,:,:,s) = reshape(gradients(:& | |
| 217 | 4 | this%num_filters * this%num_channels * product(this%knl)), & | |
| 218 |
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|
21 | shape(this%dw(:,:,:,:,s))) |
| 219 | ✗ | this%db(:,s) = gradients(& | |
| 220 |
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|
5 | this%num_filters * this%num_channels * product(this%knl)+1:) |
| 221 | end do | ||
| 222 | end select | ||
| 223 | |||
| 224 | 2 | end subroutine set_gradients_conv2d | |
| 225 | !!!############################################################################# | ||
| 226 | |||
| 227 | |||
| 228 | !!!############################################################################# | ||
| 229 | !!! get layer outputs | ||
| 230 | !!!############################################################################# | ||
| 231 | 5 | pure subroutine get_output_conv2d(this, output) | |
| 232 | implicit none | ||
| 233 | class(conv2d_layer_type), intent(in) :: this | ||
| 234 | real(real12), allocatable, dimension(..), intent(out) :: output | ||
| 235 | |||
| 236 | select rank(output) | ||
| 237 | rank(1) | ||
| 238 |
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6 | output = reshape(this%output, [size(this%output)]) |
| 239 | rank(2) | ||
| 240 | output = & | ||
| 241 |
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12 | reshape(this%output, [product(this%output_shape),this%batch_size]) |
| 242 | rank(4) | ||
| 243 |
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|
111 | output = this%output |
| 244 | end select | ||
| 245 | |||
| 246 | 5 | end subroutine get_output_conv2d | |
| 247 | !!!############################################################################# | ||
| 248 | |||
| 249 | |||
| 250 | !!!##########################################################################!!! | ||
| 251 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 252 | !!!##########################################################################!!! | ||
| 253 | |||
| 254 | |||
| 255 | !!!############################################################################# | ||
| 256 | !!! forward propagation assumed rank handler | ||
| 257 | !!!############################################################################# | ||
| 258 | 14 | pure subroutine forward_rank(this, input) | |
| 259 | implicit none | ||
| 260 | class(conv2d_layer_type), intent(inout) :: this | ||
| 261 | real(real12), dimension(..), intent(in) :: input | ||
| 262 | |||
| 263 | select rank(input); rank(4) | ||
| 264 | 14 | call forward_4d(this, input) | |
| 265 | end select | ||
| 266 | 14 | end subroutine forward_rank | |
| 267 | !!!############################################################################# | ||
| 268 | |||
| 269 | |||
| 270 | !!!############################################################################# | ||
| 271 | !!! backward propagation assumed rank handler | ||
| 272 | !!!############################################################################# | ||
| 273 | 2 | pure subroutine backward_rank(this, input, gradient) | |
| 274 | implicit none | ||
| 275 | class(conv2d_layer_type), intent(inout) :: this | ||
| 276 | real(real12), dimension(..), intent(in) :: input | ||
| 277 | real(real12), dimension(..), intent(in) :: gradient | ||
| 278 | |||
| 279 | select rank(input); rank(4) | ||
| 280 | 2 | select rank(gradient) | |
| 281 | rank(1) | ||
| 282 | ✗ | call backward_4d(this, input, gradient) | |
| 283 | rank(2) | ||
| 284 | 1 | call backward_4d(this, input, gradient) | |
| 285 | rank(4) | ||
| 286 | 1 | call backward_4d(this, input, gradient) | |
| 287 | end select | ||
| 288 | end select | ||
| 289 | 2 | end subroutine backward_rank | |
| 290 | !!!############################################################################# | ||
| 291 | |||
| 292 | |||
| 293 | !!!##########################################################################!!! | ||
| 294 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 295 | !!!##########################################################################!!! | ||
| 296 | |||
| 297 | |||
| 298 | !!!############################################################################# | ||
| 299 | !!! set up layer | ||
| 300 | !!!############################################################################# | ||
| 301 | 30 | module function layer_setup( & | |
| 302 |
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30 | input_shape, batch_size, & |
| 303 | num_filters, kernel_size, stride, padding, & | ||
| 304 | activation_function, activation_scale, & | ||
| 305 | kernel_initialiser, bias_initialiser, & | ||
| 306 | calc_input_gradients) result(layer) | ||
| 307 | !! add in dilation | ||
| 308 | use activation, only: activation_setup | ||
| 309 | use initialiser, only: get_default_initialiser | ||
| 310 | use misc_ml, only: set_padding | ||
| 311 | implicit none | ||
| 312 | integer, dimension(:), optional, intent(in) :: input_shape | ||
| 313 | integer, optional, intent(in) :: batch_size | ||
| 314 | integer, optional, intent(in) :: num_filters | ||
| 315 | integer, dimension(..), optional, intent(in) :: kernel_size | ||
| 316 | integer, dimension(..), optional, intent(in) :: stride | ||
| 317 | real(real12), optional, intent(in) :: activation_scale | ||
| 318 | character(*), optional, intent(in) :: activation_function, & | ||
| 319 | kernel_initialiser, bias_initialiser, padding | ||
| 320 | logical, optional, intent(in) :: calc_input_gradients | ||
| 321 | |||
| 322 | type(conv2d_layer_type) :: layer | ||
| 323 | |||
| 324 | integer :: i | ||
| 325 | real(real12) :: scale | ||
| 326 | character(len=10) :: activation_function_ | ||
| 327 | character(len=20) :: padding_ | ||
| 328 | |||
| 329 | |||
| 330 |
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30 | layer%name = "conv2d" |
| 331 | 30 | layer%input_rank = 3 | |
| 332 | allocate( & | ||
| 333 | ✗ | layer%knl(layer%input_rank-1), & | |
| 334 | ✗ | layer%stp(layer%input_rank-1), & | |
| 335 | ✗ | layer%hlf(layer%input_rank-1), & | |
| 336 | ✗ | layer%pad(layer%input_rank-1), & | |
| 337 |
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|
30 | layer%cen(layer%input_rank-1) ) |
| 338 | !!-------------------------------------------------------------------------- | ||
| 339 | !! initialise batch size | ||
| 340 | !!-------------------------------------------------------------------------- | ||
| 341 |
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30 | if(present(batch_size)) layer%batch_size = batch_size |
| 342 | |||
| 343 | |||
| 344 | !!-------------------------------------------------------------------------- | ||
| 345 | !! determine whether to calculate input gradients | ||
| 346 | !!-------------------------------------------------------------------------- | ||
| 347 |
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30 | if(present(calc_input_gradients))then |
| 348 | ✗ | layer%calc_input_gradients = calc_input_gradients | |
| 349 | ✗ | write(*,*) "CONV2D input gradients turned off" | |
| 350 | else | ||
| 351 | 30 | layer%calc_input_gradients = .true. | |
| 352 | end if | ||
| 353 | |||
| 354 | |||
| 355 | !!-------------------------------------------------------------------------- | ||
| 356 | !! set up number of filters | ||
| 357 | !!-------------------------------------------------------------------------- | ||
| 358 |
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30 | if(present(num_filters))then |
| 359 | 15 | layer%num_filters = num_filters | |
| 360 | else | ||
| 361 | 15 | layer%num_filters = 32 | |
| 362 | end if | ||
| 363 | |||
| 364 | |||
| 365 | !!-------------------------------------------------------------------------- | ||
| 366 | !! set up kernel size | ||
| 367 | !!-------------------------------------------------------------------------- | ||
| 368 |
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30 | if(present(kernel_size))then |
| 369 | 17 | select rank(kernel_size) | |
| 370 | rank(0) | ||
| 371 |
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45 | layer%knl = kernel_size |
| 372 | rank(1) | ||
| 373 |
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2 | layer%knl(1) = kernel_size(1) |
| 374 |
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4 | if(size(kernel_size,dim=1).eq.1)then |
| 375 |
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1 | layer%knl(2) = kernel_size(1) |
| 376 |
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1 | elseif(size(kernel_size,dim=1).eq.2)then |
| 377 |
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1 | layer%knl(2) = kernel_size(2) |
| 378 | end if | ||
| 379 | end select | ||
| 380 | else | ||
| 381 |
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39 | layer%knl = 3 |
| 382 | end if | ||
| 383 | !! odd or even kernel/filter size | ||
| 384 | !!-------------------------------------------------------------------------- | ||
| 385 |
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|
90 | layer%cen = 2 - mod(layer%knl, 2) |
| 386 |
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90 | layer%hlf = (layer%knl-1)/2 |
| 387 | |||
| 388 |
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30 | if(present(padding))then |
| 389 |
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9 | padding_ = padding |
| 390 | else | ||
| 391 | 21 | padding_ = "valid" | |
| 392 | end if | ||
| 393 |
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90 | do i=1,2 |
| 394 |
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90 | call set_padding(layer%pad(i), layer%knl(i), padding_) |
| 395 | end do | ||
| 396 | |||
| 397 | |||
| 398 | !!-------------------------------------------------------------------------- | ||
| 399 | !! set up stride | ||
| 400 | !!-------------------------------------------------------------------------- | ||
| 401 |
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30 | if(present(stride))then |
| 402 | 11 | select rank(stride) | |
| 403 | rank(0) | ||
| 404 |
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27 | layer%stp = stride |
| 405 | rank(1) | ||
| 406 |
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2 | layer%stp(1) = stride(1) |
| 407 |
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4 | if(size(stride,dim=1).eq.1)then |
| 408 |
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1 | layer%stp(2) = stride(1) |
| 409 |
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1 | elseif(size(stride,dim=1).eq.2)then |
| 410 |
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1 | layer%stp(2) = stride(2) |
| 411 | end if | ||
| 412 | end select | ||
| 413 | else | ||
| 414 |
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57 | layer%stp = 1 |
| 415 | end if | ||
| 416 | |||
| 417 | |||
| 418 | !!-------------------------------------------------------------------------- | ||
| 419 | !! set activation and derivative functions based on input name | ||
| 420 | !!-------------------------------------------------------------------------- | ||
| 421 |
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30 | if(present(activation_function))then |
| 422 |
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14 | activation_function_ = activation_function |
| 423 | else | ||
| 424 | 16 | activation_function_ = "none" | |
| 425 | end if | ||
| 426 |
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30 | if(present(activation_scale))then |
| 427 | ✗ | scale = activation_scale | |
| 428 | else | ||
| 429 | 30 | scale = 1._real12 | |
| 430 | end if | ||
| 431 |
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30 | write(*,'("CONV2D activation function: ",A)') trim(activation_function_) |
| 432 | ✗ | allocate(layer%transfer, & | |
| 433 |
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60 | source=activation_setup(activation_function_, scale)) |
| 434 | |||
| 435 | |||
| 436 | !!-------------------------------------------------------------------------- | ||
| 437 | !! define weights (kernels) and biases initialisers | ||
| 438 | !!-------------------------------------------------------------------------- | ||
| 439 |
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30 | if(present(kernel_initialiser)) layer%kernel_initialiser =kernel_initialiser |
| 440 |
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30 | if(trim(layer%kernel_initialiser).eq.'') & |
| 441 |
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8 | layer%kernel_initialiser=get_default_initialiser(activation_function_) |
| 442 |
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30 | write(*,'("CONV2D kernel initialiser: ",A)') trim(layer%kernel_initialiser) |
| 443 |
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30 | if(present(bias_initialiser)) layer%bias_initialiser = bias_initialiser |
| 444 |
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30 | if(trim(layer%bias_initialiser).eq.'') & |
| 445 | layer%bias_initialiser = get_default_initialiser(& | ||
| 446 |
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21 | activation_function_, is_bias=.true.) |
| 447 |
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30 | write(*,'("CONV2D bias initialiser: ",A)') trim(layer%bias_initialiser) |
| 448 | |||
| 449 | |||
| 450 | !!-------------------------------------------------------------------------- | ||
| 451 | !! initialise layer shape | ||
| 452 | !!-------------------------------------------------------------------------- | ||
| 453 |
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30 | if(present(input_shape)) call layer%init(input_shape=input_shape) |
| 454 | |||
| 455 |
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90 | end function layer_setup |
| 456 | !!!############################################################################# | ||
| 457 | |||
| 458 | |||
| 459 | !!!############################################################################# | ||
| 460 | !!! initialise layer | ||
| 461 | !!!############################################################################# | ||
| 462 |
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27 | subroutine init_conv2d(this, input_shape, batch_size, verbose) |
| 463 | use initialiser, only: initialiser_setup | ||
| 464 | implicit none | ||
| 465 | class(conv2d_layer_type), intent(inout) :: this | ||
| 466 | integer, dimension(:), intent(in) :: input_shape | ||
| 467 | integer, optional, intent(in) :: batch_size | ||
| 468 | integer, optional, intent(in) :: verbose | ||
| 469 | |||
| 470 | integer :: verbose_ = 0 | ||
| 471 | integer, dimension(2) :: end_idx | ||
| 472 | 54 | class(initialiser_type), allocatable :: initialiser_ | |
| 473 | |||
| 474 | |||
| 475 | !!-------------------------------------------------------------------------- | ||
| 476 | !! initialise optional arguments | ||
| 477 | !!-------------------------------------------------------------------------- | ||
| 478 |
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27 | if(present(verbose)) verbose_ = verbose |
| 479 |
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27 | if(present(batch_size)) this%batch_size = batch_size |
| 480 | |||
| 481 | |||
| 482 | !!------------------------------------------------------------------------- | ||
| 483 | !! initialise input shape | ||
| 484 | !!-------------------------------------------------------------------------- | ||
| 485 |
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27 | if(.not.allocated(this%input_shape)) call this%set_shape(input_shape) |
| 486 | |||
| 487 | |||
| 488 | !!-------------------------------------------------------------------------- | ||
| 489 | !! allocate output, activation, bias, and weight shapes | ||
| 490 | !!-------------------------------------------------------------------------- | ||
| 491 | !! NOTE: INPUT SHAPE DOES NOT INCLUDE PADDING WIDTH | ||
| 492 | !! THIS IS HANDLED AUTOMATICALLY BY THE CODE | ||
| 493 | !! ... provide the initial input data shape and let us deal with the padding | ||
| 494 |
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27 | this%num_channels = this%input_shape(3) |
| 495 |
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27 | allocate(this%output_shape(3)) |
| 496 |
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27 | this%output_shape(3) = this%num_filters |
| 497 | 135 | this%output_shape(:2) = floor(& | |
| 498 |
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|
81 | (this%input_shape(:2) + 2.0 * this%pad - this%knl)/real(this%stp) ) + 1 |
| 499 | |||
| 500 |
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|
467 | allocate(this%bias(this%num_filters), source=0._real12) |
| 501 | |||
| 502 |
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|
81 | end_idx = this%hlf + (this%cen - 1) |
| 503 | ✗ | allocate(this%weight( & | |
| 504 | 54 | -this%hlf(1):end_idx(1), & | |
| 505 | 54 | -this%hlf(2):end_idx(2), & | |
| 506 |
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|
17329 | this%num_channels,this%num_filters), source=0._real12) |
| 507 | |||
| 508 | |||
| 509 | !!-------------------------------------------------------------------------- | ||
| 510 | !! initialise weights (kernels) | ||
| 511 | !!-------------------------------------------------------------------------- | ||
| 512 |
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54 | allocate(initialiser_, source=initialiser_setup(this%kernel_initialiser)) |
| 513 | 432 | call initialiser_%initialise(this%weight, & | |
| 514 |
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|
81 | fan_in=product(this%knl)+1, fan_out=1) |
| 515 |
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27 | deallocate(initialiser_) |
| 516 | |||
| 517 | !! initialise biases | ||
| 518 | !!-------------------------------------------------------------------------- | ||
| 519 |
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54 | allocate(initialiser_, source=initialiser_setup(this%bias_initialiser)) |
| 520 | 108 | call initialiser_%initialise(this%bias, & | |
| 521 |
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81 | fan_in=product(this%knl)+1, fan_out=1) |
| 522 |
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27 | deallocate(initialiser_) |
| 523 | |||
| 524 | |||
| 525 | !!-------------------------------------------------------------------------- | ||
| 526 | !! initialise batch size-dependent arrays | ||
| 527 | !!-------------------------------------------------------------------------- | ||
| 528 |
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27 | if(this%batch_size.gt.0) call this%set_batch_size(this%batch_size) |
| 529 | |||
| 530 |
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81 | end subroutine init_conv2d |
| 531 | !!!############################################################################# | ||
| 532 | |||
| 533 | |||
| 534 | !!!############################################################################# | ||
| 535 | !!! set batch size | ||
| 536 | !!!############################################################################# | ||
| 537 | 27 | subroutine set_batch_size_conv2d(this, batch_size, verbose) | |
| 538 | implicit none | ||
| 539 | class(conv2d_layer_type), intent(inout) :: this | ||
| 540 | integer, intent(in) :: batch_size | ||
| 541 | integer, optional, intent(in) :: verbose | ||
| 542 | |||
| 543 | integer :: verbose_ = 0 | ||
| 544 | |||
| 545 | |||
| 546 | !!-------------------------------------------------------------------------- | ||
| 547 | !! initialise optional arguments | ||
| 548 | !!-------------------------------------------------------------------------- | ||
| 549 |
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27 | if(present(verbose)) verbose_ = verbose |
| 550 | 27 | this%batch_size = batch_size | |
| 551 | |||
| 552 | |||
| 553 | !!-------------------------------------------------------------------------- | ||
| 554 | !! allocate arrays | ||
| 555 | !!-------------------------------------------------------------------------- | ||
| 556 |
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27 | if(allocated(this%input_shape))then |
| 557 |
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27 | if(allocated(this%output)) deallocate(this%output) |
| 558 | ✗ | allocate(this%output( & | |
| 559 | 54 | this%output_shape(1), & | |
| 560 | 54 | this%output_shape(2), & | |
| 561 | this%num_filters, & | ||
| 562 |
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9709 | this%batch_size), source=0._real12) |
| 563 |
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27 | if(allocated(this%z)) deallocate(this%z) |
| 564 |
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9709 | allocate(this%z, source=this%output) |
| 565 |
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27 | if(allocated(this%di)) deallocate(this%di) |
| 566 | ✗ | allocate(this%di( & | |
| 567 | 54 | this%input_shape(1), & | |
| 568 | 54 | this%input_shape(2), & | |
| 569 | 54 | this%input_shape(3), & | |
| 570 |
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2767 | this%batch_size), source=0._real12) |
| 571 |
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27 | if(allocated(this%dw)) deallocate(this%dw) |
| 572 | ✗ | allocate(this%dw( & | |
| 573 | lbound(this%weight,1):ubound(this%weight,1), & | ||
| 574 | lbound(this%weight,2):ubound(this%weight,2), & | ||
| 575 | this%num_channels, this%num_filters, & | ||
| 576 |
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29471 | this%batch_size), source=0._real12) |
| 577 |
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27 | if(allocated(this%db)) deallocate(this%db) |
| 578 |
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|
805 | allocate(this%db(this%num_filters, this%batch_size), source=0._real12) |
| 579 | end if | ||
| 580 | |||
| 581 | 27 | end subroutine set_batch_size_conv2d | |
| 582 | !!!############################################################################# | ||
| 583 | |||
| 584 | |||
| 585 | !!!##########################################################################!!! | ||
| 586 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 587 | !!!##########################################################################!!! | ||
| 588 | |||
| 589 | |||
| 590 | !!!############################################################################# | ||
| 591 | !!! print layer to file | ||
| 592 | !!!############################################################################# | ||
| 593 | ✗ | subroutine print_conv2d(this, file) | |
| 594 | implicit none | ||
| 595 | class(conv2d_layer_type), intent(in) :: this | ||
| 596 | character(*), intent(in) :: file | ||
| 597 | |||
| 598 | integer :: l, i, itmp1, idx | ||
| 599 | integer :: unit | ||
| 600 | ✗ | character(:), allocatable :: padding_type | |
| 601 | |||
| 602 | |||
| 603 | !! handle different width kernels for x, y, z | ||
| 604 | !!-------------------------------------------------------------------------- | ||
| 605 | ✗ | itmp1 = -1 | |
| 606 | ✗ | do i=1,2 | |
| 607 | ✗ | if(this%pad(i).gt.itmp1)then | |
| 608 | ✗ | itmp1 = this%pad(i) | |
| 609 | ✗ | idx = i | |
| 610 | end if | ||
| 611 | end do | ||
| 612 | |||
| 613 | !! determine padding method | ||
| 614 | !!-------------------------------------------------------------------------- | ||
| 615 | ✗ | padding_type = "" | |
| 616 | ✗ | if(this%pad(idx).eq.this%knl(idx)-1)then | |
| 617 | ✗ | padding_type = "full" | |
| 618 | ✗ | elseif(this%pad(idx).eq.0)then | |
| 619 | ✗ | padding_type = "valid" | |
| 620 | else | ||
| 621 | ✗ | padding_type = "same" | |
| 622 | end if | ||
| 623 | |||
| 624 | !! open file with new unit | ||
| 625 | !!-------------------------------------------------------------------------- | ||
| 626 | ✗ | open(newunit=unit, file=trim(file), access='append') | |
| 627 | |||
| 628 | !! write convolution initial parameters | ||
| 629 | !!-------------------------------------------------------------------------- | ||
| 630 | ✗ | write(unit,'("CONV2D")') | |
| 631 | ✗ | write(unit,'(3X,"INPUT_SHAPE = ",3(1X,I0))') this%input_shape | |
| 632 | ✗ | write(unit,'(3X,"NUM_FILTERS = ",I0)') this%num_filters | |
| 633 | ✗ | if(all(this%knl.eq.this%knl(1)))then | |
| 634 | ✗ | write(unit,'(3X,"KERNEL_SIZE =",1X,I0)') this%knl(1) | |
| 635 | else | ||
| 636 | ✗ | write(unit,'(3X,"KERNEL_SIZE =",2(1X,I0))') this%knl | |
| 637 | end if | ||
| 638 | ✗ | if(all(this%stp.eq.this%stp(1)))then | |
| 639 | ✗ | write(unit,'(3X,"STRIDE =",1X,I0)') this%stp(1) | |
| 640 | else | ||
| 641 | ✗ | write(unit,'(3X,"STRIDE =",2(1X,I0))') this%stp | |
| 642 | end if | ||
| 643 | ✗ | write(unit,'(3X,"PADDING = ",A)') padding_type | |
| 644 | |||
| 645 | ✗ | write(unit,'(3X,"ACTIVATION = ",A)') trim(this%transfer%name) | |
| 646 | ✗ | write(unit,'(3X,"ACTIVATION_SCALE = ",F0.9)') this%transfer%scale | |
| 647 | |||
| 648 | !! write convolution weights and biases | ||
| 649 | !!-------------------------------------------------------------------------- | ||
| 650 | ✗ | write(unit,'("WEIGHTS")') | |
| 651 | ✗ | do l=1,this%num_filters | |
| 652 | ✗ | write(unit,'(5(E16.8E2))', advance="no") this%weight(:,:,:,l) | |
| 653 | ✗ | if(mod(size(this%weight(:,:,:,l)),5).eq.0) write(unit,*) | |
| 654 | ✗ | write(unit,'(E16.8E2)') this%bias(l) | |
| 655 | end do | ||
| 656 | ✗ | write(unit,'("END WEIGHTS")') | |
| 657 | ✗ | write(unit,'("END CONV2D")') | |
| 658 | |||
| 659 | !! close unit | ||
| 660 | !!-------------------------------------------------------------------------- | ||
| 661 | ✗ | close(unit) | |
| 662 | |||
| 663 | ✗ | end subroutine print_conv2d | |
| 664 | !!!############################################################################# | ||
| 665 | |||
| 666 | |||
| 667 | !!!############################################################################# | ||
| 668 | !!! read layer from file | ||
| 669 | !!!############################################################################# | ||
| 670 | ✗ | function read_conv2d_layer(unit, verbose) result(layer) | |
| 671 | use infile_tools, only: assign_val, assign_vec | ||
| 672 | use misc, only: to_lower, icount | ||
| 673 | implicit none | ||
| 674 | integer, intent(in) :: unit | ||
| 675 | integer, optional, intent(in) :: verbose | ||
| 676 | |||
| 677 | class(conv2d_layer_type), allocatable :: layer | ||
| 678 | |||
| 679 | integer :: stat, verbose_ = 0 | ||
| 680 | integer :: j, k, l, c, itmp1 | ||
| 681 | integer :: num_filters, num_inputs | ||
| 682 | real(real12) :: activation_scale | ||
| 683 | logical :: found_weights = .false. | ||
| 684 | character(14) :: kernel_initialiser='', bias_initialiser='' | ||
| 685 | character(20) :: padding, activation_function | ||
| 686 | character(256) :: buffer, tag | ||
| 687 | |||
| 688 | integer, dimension(2) :: kernel_size, stride | ||
| 689 | integer, dimension(3) :: input_shape | ||
| 690 | ✗ | real(real12), allocatable, dimension(:) :: data_list | |
| 691 | |||
| 692 | |||
| 693 | !!-------------------------------------------------------------------------- | ||
| 694 | !! initialise optional arguments | ||
| 695 | !!-------------------------------------------------------------------------- | ||
| 696 | ✗ | if(present(verbose)) verbose_ = verbose | |
| 697 | |||
| 698 | |||
| 699 | !!-------------------------------------------------------------------------- | ||
| 700 | !! loop over tags in layer card | ||
| 701 | !!-------------------------------------------------------------------------- | ||
| 702 | ✗ | tag_loop: do | |
| 703 | |||
| 704 | !! check for end of file | ||
| 705 | !!----------------------------------------------------------------------- | ||
| 706 | ✗ | read(unit,'(A)',iostat=stat) buffer | |
| 707 | ✗ | if(stat.ne.0)then | |
| 708 | ✗ | write(0,*) "ERROR: file encountered error (EoF?) before END CONV2D" | |
| 709 | ✗ | stop "Exiting..." | |
| 710 | end if | ||
| 711 | ✗ | if(trim(adjustl(buffer)).eq."") cycle tag_loop | |
| 712 | |||
| 713 | !! check for end of layer card | ||
| 714 | !!----------------------------------------------------------------------- | ||
| 715 | ✗ | if(trim(adjustl(buffer)).eq."END CONV2D")then | |
| 716 | ✗ | backspace(unit) | |
| 717 | ✗ | exit tag_loop | |
| 718 | end if | ||
| 719 | |||
| 720 | ✗ | tag=trim(adjustl(buffer)) | |
| 721 | ✗ | if(scan(buffer,"=").ne.0) tag=trim(tag(:scan(tag,"=")-1)) | |
| 722 | |||
| 723 | !! read parameters from save file | ||
| 724 | !!----------------------------------------------------------------------- | ||
| 725 | ✗ | select case(trim(tag)) | |
| 726 | case("INPUT_SHAPE") | ||
| 727 | ✗ | call assign_vec(buffer, input_shape, itmp1) | |
| 728 | case("NUM_FILTERS") | ||
| 729 | ✗ | call assign_val(buffer, num_filters, itmp1) | |
| 730 | case("KERNEL_SIZE") | ||
| 731 | ✗ | call assign_vec(buffer, kernel_size, itmp1) | |
| 732 | case("STRIDE") | ||
| 733 | ✗ | call assign_vec(buffer, stride, itmp1) | |
| 734 | case("PADDING") | ||
| 735 | ✗ | call assign_val(buffer, padding, itmp1) | |
| 736 | ✗ | padding = to_lower(padding) | |
| 737 | case("ACTIVATION") | ||
| 738 | ✗ | call assign_val(buffer, activation_function, itmp1) | |
| 739 | case("ACTIVATION_SCALE") | ||
| 740 | ✗ | call assign_val(buffer, activation_scale, itmp1) | |
| 741 | case("KERNEL_INITIALISER") | ||
| 742 | ✗ | call assign_val(buffer, kernel_initialiser, itmp1) | |
| 743 | case("BIAS_INITIALISER") | ||
| 744 | ✗ | call assign_val(buffer, bias_initialiser, itmp1) | |
| 745 | case("WEIGHTS") | ||
| 746 | ✗ | found_weights = .true. | |
| 747 | ✗ | kernel_initialiser = 'zeros' | |
| 748 | ✗ | bias_initialiser = 'zeros' | |
| 749 | ✗ | exit tag_loop | |
| 750 | case default | ||
| 751 | !! don't look for "e" due to scientific notation of numbers | ||
| 752 | !! ... i.e. exponent (E+00) | ||
| 753 | ✗ | if(scan(to_lower(trim(adjustl(buffer))),& | |
| 754 | 'abcdfghijklmnopqrstuvwxyz').eq.0)then | ||
| 755 | ✗ | cycle tag_loop | |
| 756 | ✗ | elseif(tag(:3).eq.'END')then | |
| 757 | ✗ | cycle tag_loop | |
| 758 | end if | ||
| 759 | ✗ | stop "Unrecognised line in input file: "//trim(adjustl(buffer)) | |
| 760 | end select | ||
| 761 | end do tag_loop | ||
| 762 | |||
| 763 | |||
| 764 | !!-------------------------------------------------------------------------- | ||
| 765 | !! allocate layer | ||
| 766 | !!-------------------------------------------------------------------------- | ||
| 767 | layer = conv2d_layer_type( & | ||
| 768 | input_shape = input_shape, & | ||
| 769 | num_filters = num_filters, & | ||
| 770 | kernel_size = kernel_size, stride = stride, & | ||
| 771 | padding = padding, & | ||
| 772 | activation_function = activation_function, & | ||
| 773 | activation_scale = activation_scale, & | ||
| 774 | kernel_initialiser = kernel_initialiser, & | ||
| 775 | ✗ | bias_initialiser = bias_initialiser) | |
| 776 | |||
| 777 | |||
| 778 | !!-------------------------------------------------------------------------- | ||
| 779 | !! check if WEIGHTS card was found | ||
| 780 | !!-------------------------------------------------------------------------- | ||
| 781 | ✗ | if(.not.found_weights)then | |
| 782 | ✗ | write(0,*) "WARNING: WEIGHTS card in CONV2D not found" | |
| 783 | else | ||
| 784 | ✗ | do l=1,num_filters | |
| 785 | ✗ | num_inputs = product(layer%knl) + 1 !+1 for bias | |
| 786 | ✗ | allocate(data_list(num_inputs), source=0._real12) | |
| 787 | ✗ | c = 1 | |
| 788 | ✗ | k = 1 | |
| 789 | ✗ | data_concat_loop: do while(c.le.num_inputs) | |
| 790 | ✗ | read(unit,'(A)',iostat=stat) buffer | |
| 791 | ✗ | if(stat.ne.0) exit data_concat_loop | |
| 792 | ✗ | k = icount(buffer) | |
| 793 | ✗ | read(buffer,*,iostat=stat) (data_list(j),j=c,c+k-1) | |
| 794 | ✗ | c = c + k | |
| 795 | end do data_concat_loop | ||
| 796 | ✗ | layer%weight(:,:,:,l) = & | |
| 797 | reshape(& | ||
| 798 | ✗ | data_list(1:num_inputs-1),& | |
| 799 | ✗ | shape(layer%weight(:,:,:,l))) | |
| 800 | ✗ | layer%bias(l) = data_list(num_inputs) | |
| 801 | ✗ | deallocate(data_list) | |
| 802 | end do | ||
| 803 | |||
| 804 | !! check for end of weights card | ||
| 805 | !!----------------------------------------------------------------------- | ||
| 806 | ✗ | read(unit,'(A)') buffer | |
| 807 | ✗ | if(trim(adjustl(buffer)).ne."END WEIGHTS")then | |
| 808 | ✗ | write(*,*) trim(adjustl(buffer)) | |
| 809 | ✗ | stop "ERROR: END WEIGHTS not where expected" | |
| 810 | end if | ||
| 811 | end if | ||
| 812 | |||
| 813 | |||
| 814 | !!-------------------------------------------------------------------------- | ||
| 815 | !! check for end of layer card | ||
| 816 | !!-------------------------------------------------------------------------- | ||
| 817 | ✗ | read(unit,'(A)') buffer | |
| 818 | ✗ | if(trim(adjustl(buffer)).ne."END CONV2D")then | |
| 819 | ✗ | write(*,*) trim(adjustl(buffer)) | |
| 820 | ✗ | stop "ERROR: END CONV2D not where expected" | |
| 821 | end if | ||
| 822 | |||
| 823 | ✗ | end function read_conv2d_layer | |
| 824 | !!!############################################################################# | ||
| 825 | |||
| 826 | |||
| 827 | !!!##########################################################################!!! | ||
| 828 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 829 | !!!##########################################################################!!! | ||
| 830 | |||
| 831 | |||
| 832 | !!!############################################################################# | ||
| 833 | !!! forward propagation | ||
| 834 | !!!############################################################################# | ||
| 835 | 182 | pure subroutine forward_4d(this, input) | |
| 836 | implicit none | ||
| 837 | class(conv2d_layer_type), intent(inout) :: this | ||
| 838 | real(real12), & | ||
| 839 | dimension( & | ||
| 840 |
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14 | -this%pad(1)+1:this%input_shape(1)+this%pad(1), & |
| 841 |
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|
14 | -this%pad(2)+1:this%input_shape(2)+this%pad(2), & |
| 842 | this%num_channels,this%batch_size), & | ||
| 843 | intent(in) :: input | ||
| 844 | |||
| 845 | integer :: i, j, l, s | ||
| 846 | integer, dimension(2) :: stp_idx, start_idx, end_idx | ||
| 847 | |||
| 848 | |||
| 849 | !! perform the convolution operation | ||
| 850 | !!-------------------------------------------------------------------------- | ||
| 851 | do concurrent( & | ||
| 852 | 28 | i=1:this%output_shape(1):1, & | |
| 853 |
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|
14 | j=1:this%output_shape(2):1) |
| 854 | #if defined(GFORTRAN) | ||
| 855 | stp_idx = ([i,j]-1)*this%stp + 1 + (this%hlf - this%pad) | ||
| 856 | #else | ||
| 857 |
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|
78 | stp_idx(1) = (i-1)*this%stp(1) + 1 + (this%hlf(1) - this%pad(1)) |
| 858 |
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78 | stp_idx(2) = (j-1)*this%stp(2) + 1 + (this%hlf(2) - this%pad(2)) |
| 859 | #endif | ||
| 860 |
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|
234 | start_idx = stp_idx - this%hlf |
| 861 |
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|
234 | end_idx = start_idx + this%knl - 1 |
| 862 | |||
| 863 | 78 | do concurrent(s=1:this%batch_size) | |
| 864 |
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|
510 | this%z(i,j,:,s) = this%bias(:) |
| 865 | end do | ||
| 866 | |||
| 867 |
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|
118 | do concurrent(l=1:this%num_filters, s=1:this%batch_size) |
| 868 | 5664 | this%z(i,j,l,s) = this%z(i,j,l,s) + & | |
| 869 | sum( & | ||
| 870 |
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354 | input( & |
| 871 | start_idx(1):end_idx(1),& | ||
| 872 | start_idx(2):end_idx(2),:,s) * & | ||
| 873 | 6018 | this%weight(:,:,:,l) & | |
| 874 |
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|
15838 | ) |
| 875 | end do | ||
| 876 | end do | ||
| 877 | |||
| 878 | |||
| 879 | !! apply activation function to activation values (z) | ||
| 880 | !!-------------------------------------------------------------------------- | ||
| 881 |
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|
514 | this%output = this%transfer%activate(this%z) |
| 882 | |||
| 883 | 14 | end subroutine forward_4d | |
| 884 | !!!############################################################################# | ||
| 885 | |||
| 886 | |||
| 887 | !!!############################################################################# | ||
| 888 | !!! backward propagation | ||
| 889 | !!! method : gradient descent | ||
| 890 | !!!############################################################################# | ||
| 891 | 34 | pure subroutine backward_4d(this, input, gradient) | |
| 892 | implicit none | ||
| 893 | class(conv2d_layer_type), intent(inout) :: this | ||
| 894 | real(real12), & | ||
| 895 | dimension( & | ||
| 896 |
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2 | -this%pad(1)+1:this%input_shape(1)+this%pad(1), & |
| 897 |
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|
2 | -this%pad(2)+1:this%input_shape(2)+this%pad(2), & |
| 898 | this%num_channels,this%batch_size), & | ||
| 899 | intent(in) :: input | ||
| 900 | real(real12), & | ||
| 901 |
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2 | dimension( & |
| 902 |
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2 | this%output_shape(1), & |
| 903 | this%output_shape(2), & | ||
| 904 | this%num_filters,this%batch_size), & | ||
| 905 | intent(in) :: gradient | ||
| 906 | |||
| 907 | integer :: l, m, i, j, x, y, s | ||
| 908 | integer, dimension(2) :: stp_idx, offset, adjust, end_idx, n_stp | ||
| 909 | integer, dimension(2,2) :: lim, lim_w, lim_g | ||
| 910 | real(real12), & | ||
| 911 | 4 | dimension( & | |
| 912 | 4 | this%output_shape(1),& | |
| 913 | this%output_shape(2),this%num_filters, & | ||
| 914 |
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|
2 | this%batch_size) :: grad_dz |
| 915 | |||
| 916 | |||
| 917 | real(real12), dimension(1) :: bias_diff | ||
| 918 |
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4 | bias_diff = this%transfer%differentiate([1._real12]) |
| 919 | |||
| 920 | |||
| 921 | !! get size of the input and output feature maps | ||
| 922 | !!-------------------------------------------------------------------------- | ||
| 923 |
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|
6 | end_idx = this%hlf + (this%cen - 1) |
| 924 |
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|
6 | offset = 1 + this%hlf - this%pad |
| 925 |
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|
6 | adjust = 2 * max(this%pad, this%hlf) |
| 926 | |||
| 927 | |||
| 928 | !! get gradient multiplied by differential of Z | ||
| 929 | !!-------------------------------------------------------------------------- | ||
| 930 |
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|
232 | grad_dz = 0._real12 |
| 931 | ✗ | grad_dz(& | |
| 932 | 4 | 1:this%output_shape(1),& | |
| 933 | ✗ | 1:this%output_shape(2),:,:) = gradient * & | |
| 934 |
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|
232 | this%transfer%differentiate(this%z) |
| 935 | do concurrent( & | ||
| 936 | 4 | l=1:this%num_filters, s=1:this%batch_size) | |
| 937 |
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|
232 | this%db(l,s) = this%db(l,s) + sum(grad_dz(:,:,l,s)) * bias_diff(1) |
| 938 | end do | ||
| 939 | |||
| 940 | !! apply convolution to compute weight gradients | ||
| 941 | !! offset applied as centre of kernel is 0 ... | ||
| 942 | !! ... whilst the starting index for input is 1 | ||
| 943 | !!-------------------------------------------------------------------------- | ||
| 944 | do concurrent( & | ||
| 945 | s=1:this%batch_size, & | ||
| 946 | l=1:this%num_filters, & | ||
| 947 | m=1:this%num_channels, & | ||
| 948 | 4 | y=-this%hlf(2):end_idx(2):1, & | |
| 949 | 4 | x=-this%hlf(1):end_idx(1):1 & | |
| 950 |
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|
2 | ) |
| 951 | 7920 | this%dw(x,y,m,l,s) = this%dw(x,y,m,l,s) + & | |
| 952 |
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|
792 | sum(grad_dz(:,:,l,s) * & |
| 953 |
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|
1188 | input( & |
| 954 | ✗ | x+offset(1):x+offset(1)-1+size(input,1)-adjust(1):this%stp(1), & | |
| 955 |
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|
9161 | y+offset(2):y+offset(2)-1+size(input,1)-adjust(2):this%stp(2),m,s)) |
| 956 | end do | ||
| 957 | |||
| 958 | |||
| 959 | !! apply strided convolution to obtain input gradients | ||
| 960 | !!-------------------------------------------------------------------------- | ||
| 961 |
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2 | if(this%calc_input_gradients)then |
| 962 |
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|
3 | lim(1,:) = this%knl - 1 |
| 963 |
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|
3 | lim(2,:) = (this%output_shape(:2) - 1) * this%stp + 1 + end_idx |
| 964 |
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|
3 | n_stp = this%output_shape(:2) * this%stp |
| 965 |
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|
126 | this%di = 0._real12 |
| 966 | !! all elements of the output are separated by stride_x (stride_y) | ||
| 967 | do concurrent( & | ||
| 968 | s=1:this%batch_size, & | ||
| 969 | l=1:this%num_filters, & | ||
| 970 | m=1:this%num_channels, & | ||
| 971 | i=1:size(this%di,dim=1):1, & | ||
| 972 | j=1:size(this%di,dim=2):1 & | ||
| 973 | 931 | ) | |
| 974 | |||
| 975 | !! set weight bounds | ||
| 976 | #if defined(GFORTRAN) | ||
| 977 | stp_idx = ([i,j]-offset)/this%stp + 1 | ||
| 978 | #else | ||
| 979 |
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800 | stp_idx(1) = ( i - offset(1) )/this%stp(1) + 1 |
| 980 |
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|
800 | stp_idx(2) = ( j - offset(2) )/this%stp(2) + 1 |
| 981 | #endif | ||
| 982 | !! max( ... | ||
| 983 | !! ... 1. offset of 1st o/p idx from centre of knl (lim) | ||
| 984 | !! ... 2. lwst o/p idx overlap with <<- knl idx (rpt. pattern) | ||
| 985 | !! ...) | ||
| 986 | !! min( ... | ||
| 987 | !! ... 1. offset of last o/p idx from centre of knl (lim) | ||
| 988 | !! ... 2. hghst o/p idx overlap with ->> knl idx (rpt. pattern) | ||
| 989 | !! ...) | ||
| 990 | #if defined(GFORTRAN) | ||
| 991 | lim_w(2,:) = max(lim(1,:)-[i,j], -this%hlf + & | ||
| 992 | mod(n_stp+this%knl-[i,j],this%stp)) | ||
| 993 | lim_w(1,:) = min(lim(2,:)-[i,j], end_idx - & | ||
| 994 | mod(n_stp-1+[i,j],this%stp)) | ||
| 995 | #else | ||
| 996 | 1600 | lim_w(2,1) = max(lim(1,1) - i, -this%hlf(1) + & | |
| 997 |
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|
800 | mod(n_stp(1)+this%knl(1)-i,this%stp(1))) |
| 998 | 1600 | lim_w(2,2) = max(lim(1,2) - j, -this%hlf(2) + & | |
| 999 |
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|
800 | mod(n_stp(2)+this%knl(2)-j,this%stp(2))) |
| 1000 | lim_w(1,1) = min(lim(2,1)-i, end_idx(1) - & | ||
| 1001 |
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800 | mod(n_stp(1)-1+i,this%stp(1))) |
| 1002 | lim_w(1,2) = min(lim(2,2)-j, end_idx(2) - & | ||
| 1003 |
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800 | mod(n_stp(2)-1+j,this%stp(2))) |
| 1004 | #endif | ||
| 1005 |
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|
2400 | if(any(lim_w(2,:).gt.lim_w(1,:))) cycle |
| 1006 | |||
| 1007 | !! set gradient bounds | ||
| 1008 | #if defined(GFORTRAN) | ||
| 1009 | lim_g(1,:) = max(1, [i,j] - offset) | ||
| 1010 | lim_g(2,:) = min(this%output_shape(:2), [i,j] - offset + this%knl - 1) | ||
| 1011 | #else | ||
| 1012 | 800 | lim_g(1,1) = max(1, i - offset(1)) | |
| 1013 | 800 | lim_g(1,2) = max(1, j - offset(2)) | |
| 1014 |
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800 | lim_g(2,1) = min(this%output_shape(1), i - offset(1) + this%knl(1) -1) |
| 1015 |
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|
800 | lim_g(2,2) = min(this%output_shape(2), j - offset(2) + this%knl(2) -1) |
| 1016 | #endif | ||
| 1017 | |||
| 1018 | !! apply full convolution to compute input gradients | ||
| 1019 | 6400 | this%di(i,j,m,s) = & | |
| 1020 | 6400 | this%di(i,j,m,s) + & | |
| 1021 | sum( & | ||
| 1022 |
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1600 | grad_dz( & |
| 1023 | lim_g(1,1):lim_g(2,1), & | ||
| 1024 | lim_g(1,2):lim_g(2,2),l,s) * & | ||
| 1025 |
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800 | this%weight(& |
| 1026 | 1600 | lim_w(1,1):lim_w(2,1):-this%stp(1), & | |
| 1027 |
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|
8163 | lim_w(1,2):lim_w(2,2):-this%stp(2),m,l) ) |
| 1028 | |||
| 1029 | end do | ||
| 1030 | end if | ||
| 1031 | |||
| 1032 | 2 | end subroutine backward_4d | |
| 1033 | !!!############################################################################# | ||
| 1034 | |||
| 1035 |
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|
231 | end module conv2d_layer |
| 1036 | !!!############################################################################# | ||
| 1037 |