| 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 batch normalisation layer | ||
| 6 | !!!############################################################################# | ||
| 7 | module batchnorm2d_layer | ||
| 8 | use constants, only: real12 | ||
| 9 | use base_layer, only: batch_layer_type, learnable_layer_type | ||
| 10 | use custom_types, only: initialiser_type | ||
| 11 | implicit none | ||
| 12 | |||
| 13 | |||
| 14 | type, extends(batch_layer_type) :: batchnorm2d_layer_type | ||
| 15 | real(real12), allocatable, dimension(:,:,:,:) :: output | ||
| 16 | real(real12), allocatable, dimension(:,:,:,:) :: di ! gradient of input (i.e. delta) | ||
| 17 | contains | ||
| 18 | procedure, pass(this) :: get_output => get_output_batchnorm2d | ||
| 19 | procedure, pass(this) :: init => init_batchnorm2d | ||
| 20 | procedure, pass(this) :: set_batch_size => set_batch_size_batchnorm2d | ||
| 21 | procedure, pass(this) :: print => print_batchnorm2d | ||
| 22 | |||
| 23 | procedure, pass(this) :: forward => forward_rank | ||
| 24 | procedure, pass(this) :: backward => backward_rank | ||
| 25 | procedure, private, pass(this) :: forward_4d | ||
| 26 | procedure, private, pass(this) :: backward_4d | ||
| 27 | |||
| 28 | procedure, pass(this) :: reduce => layer_reduction | ||
| 29 | procedure, pass(this) :: merge => layer_merge | ||
| 30 | procedure :: add_t_t => layer_add !t = type, r = real, i = int | ||
| 31 | generic :: operator(+) => add_t_t !, public | ||
| 32 | end type batchnorm2d_layer_type | ||
| 33 | |||
| 34 | |||
| 35 | interface batchnorm2d_layer_type | ||
| 36 | module function layer_setup( & | ||
| 37 | input_shape, batch_size, & | ||
| 38 | momentum, epsilon, & | ||
| 39 | gamma_init_mean, gamma_init_std, & | ||
| 40 | beta_init_mean, beta_init_std, & | ||
| 41 | kernel_initialiser, bias_initialiser, & | ||
| 42 | moving_mean_initialiser, moving_variance_initialiser & | ||
| 43 | ) result(layer) | ||
| 44 | integer, dimension(:), optional, intent(in) :: input_shape | ||
| 45 | integer, optional, intent(in) :: batch_size | ||
| 46 | real(real12), optional, intent(in) :: momentum, epsilon | ||
| 47 | real(real12), optional, intent(in) :: gamma_init_mean, gamma_init_std | ||
| 48 | real(real12), optional, intent(in) :: beta_init_mean, beta_init_std | ||
| 49 | character(*), optional, intent(in) :: & | ||
| 50 | kernel_initialiser, bias_initialiser, & | ||
| 51 | moving_mean_initialiser, moving_variance_initialiser | ||
| 52 | type(batchnorm2d_layer_type) :: layer | ||
| 53 | end function layer_setup | ||
| 54 | end interface batchnorm2d_layer_type | ||
| 55 | |||
| 56 | |||
| 57 | private | ||
| 58 | public :: batchnorm2d_layer_type | ||
| 59 | public :: read_batchnorm2d_layer | ||
| 60 | |||
| 61 | |||
| 62 | contains | ||
| 63 | |||
| 64 | !!!############################################################################# | ||
| 65 | !!! layer reduction | ||
| 66 | !!!############################################################################# | ||
| 67 | 1 | subroutine layer_reduction(this, rhs) | |
| 68 | implicit none | ||
| 69 | class(batchnorm2d_layer_type), intent(inout) :: this | ||
| 70 | class(learnable_layer_type), intent(in) :: rhs | ||
| 71 | |||
| 72 | select type(rhs) | ||
| 73 | class is(batchnorm2d_layer_type) | ||
| 74 |
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2 | this%dg = this%dg + rhs%dg |
| 75 |
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3 | this%db = this%db + rhs%db |
| 76 | end select | ||
| 77 | |||
| 78 | 1 | end subroutine layer_reduction | |
| 79 | !!!############################################################################# | ||
| 80 | |||
| 81 | |||
| 82 | !!!############################################################################# | ||
| 83 | !!! layer addition | ||
| 84 | !!!############################################################################# | ||
| 85 | 1 | function layer_add(a, b) result(output) | |
| 86 | implicit none | ||
| 87 | class(batchnorm2d_layer_type), intent(in) :: a, b | ||
| 88 | type(batchnorm2d_layer_type) :: output | ||
| 89 | |||
| 90 |
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|
1 | output = a |
| 91 |
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2 | output%dg = output%dg + b%dg |
| 92 |
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2 | output%db = output%db + b%db |
| 93 | |||
| 94 |
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2 | end function layer_add |
| 95 | !!!############################################################################# | ||
| 96 | |||
| 97 | |||
| 98 | !!!############################################################################# | ||
| 99 | !!! layer merge | ||
| 100 | !!!############################################################################# | ||
| 101 | 1 | subroutine layer_merge(this, input) | |
| 102 | implicit none | ||
| 103 | class(batchnorm2d_layer_type), intent(inout) :: this | ||
| 104 | class(learnable_layer_type), intent(in) :: input | ||
| 105 | |||
| 106 | select type(input) | ||
| 107 | class is(batchnorm2d_layer_type) | ||
| 108 |
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2 | this%dg = this%dg + input%dg |
| 109 |
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3 | this%db = this%db + input%db |
| 110 | end select | ||
| 111 | |||
| 112 | 1 | end subroutine layer_merge | |
| 113 | !!!############################################################################# | ||
| 114 | |||
| 115 | |||
| 116 | !!!##########################################################################!!! | ||
| 117 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 118 | !!!##########################################################################!!! | ||
| 119 | |||
| 120 | |||
| 121 | !!!############################################################################# | ||
| 122 | !!! get layer outputs | ||
| 123 | !!!############################################################################# | ||
| 124 | 4 | pure subroutine get_output_batchnorm2d(this, output) | |
| 125 | implicit none | ||
| 126 | class(batchnorm2d_layer_type), intent(in) :: this | ||
| 127 | real(real12), allocatable, dimension(..), intent(out) :: output | ||
| 128 | |||
| 129 | select rank(output) | ||
| 130 | rank(1) | ||
| 131 |
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6 | output = reshape(this%output, [size(this%output)]) |
| 132 | rank(2) | ||
| 133 | output = & | ||
| 134 |
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6 | reshape(this%output, [product(this%output_shape),this%batch_size]) |
| 135 | rank(4) | ||
| 136 |
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|
442 | output = this%output |
| 137 | end select | ||
| 138 | |||
| 139 | 4 | end subroutine get_output_batchnorm2d | |
| 140 | !!!############################################################################# | ||
| 141 | |||
| 142 | |||
| 143 | !!!##########################################################################!!! | ||
| 144 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 145 | !!!##########################################################################!!! | ||
| 146 | |||
| 147 | |||
| 148 | !!!############################################################################# | ||
| 149 | !!! forward propagation assumed rank handler | ||
| 150 | !!!############################################################################# | ||
| 151 | 2 | pure subroutine forward_rank(this, input) | |
| 152 | implicit none | ||
| 153 | class(batchnorm2d_layer_type), intent(inout) :: this | ||
| 154 | real(real12), dimension(..), intent(in) :: input | ||
| 155 | |||
| 156 | select rank(input); rank(4) | ||
| 157 | 2 | call forward_4d(this, input) | |
| 158 | end select | ||
| 159 | 2 | end subroutine forward_rank | |
| 160 | !!!############################################################################# | ||
| 161 | |||
| 162 | |||
| 163 | !!!############################################################################# | ||
| 164 | !!! backward propagation assumed rank handler | ||
| 165 | !!!############################################################################# | ||
| 166 | 1 | pure subroutine backward_rank(this, input, gradient) | |
| 167 | implicit none | ||
| 168 | class(batchnorm2d_layer_type), intent(inout) :: this | ||
| 169 | real(real12), dimension(..), intent(in) :: input | ||
| 170 | real(real12), dimension(..), intent(in) :: gradient | ||
| 171 | |||
| 172 | select rank(input); rank(4) | ||
| 173 | 1 | select rank(gradient); rank(4) | |
| 174 | 1 | call backward_4d(this, input, gradient) | |
| 175 | end select | ||
| 176 | end select | ||
| 177 | 1 | end subroutine backward_rank | |
| 178 | !!!############################################################################# | ||
| 179 | |||
| 180 | |||
| 181 | !!!##########################################################################!!! | ||
| 182 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 183 | !!!##########################################################################!!! | ||
| 184 | |||
| 185 | |||
| 186 | !!!############################################################################# | ||
| 187 | !!! set up layer | ||
| 188 | !!!############################################################################# | ||
| 189 | 4 | module function layer_setup( & | |
| 190 |
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4 | input_shape, batch_size, & |
| 191 | momentum, epsilon, & | ||
| 192 | gamma_init_mean, gamma_init_std, & | ||
| 193 | beta_init_mean, beta_init_std, & | ||
| 194 | kernel_initialiser, bias_initialiser, & | ||
| 195 | moving_mean_initialiser, moving_variance_initialiser & | ||
| 196 | ) result(layer) | ||
| 197 | use initialiser, only: get_default_initialiser | ||
| 198 | implicit none | ||
| 199 | integer, dimension(:), optional, intent(in) :: input_shape | ||
| 200 | integer, optional, intent(in) :: batch_size | ||
| 201 | real(real12), optional, intent(in) :: momentum, epsilon | ||
| 202 | real(real12), optional, intent(in) :: gamma_init_mean, gamma_init_std | ||
| 203 | real(real12), optional, intent(in) :: beta_init_mean, beta_init_std | ||
| 204 | character(*), optional, intent(in) :: & | ||
| 205 | kernel_initialiser, bias_initialiser, & | ||
| 206 | moving_mean_initialiser, moving_variance_initialiser | ||
| 207 | |||
| 208 | type(batchnorm2d_layer_type) :: layer | ||
| 209 | |||
| 210 | |||
| 211 |
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4 | layer%name = "batchnorm2d" |
| 212 | 4 | layer%input_rank = 3 | |
| 213 | !!-------------------------------------------------------------------------- | ||
| 214 | !! initialise batch size | ||
| 215 | !!-------------------------------------------------------------------------- | ||
| 216 |
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4 | if(present(batch_size)) layer%batch_size = batch_size |
| 217 | |||
| 218 | |||
| 219 | !!-------------------------------------------------------------------------- | ||
| 220 | !! set up momentum and epsilon | ||
| 221 | !!-------------------------------------------------------------------------- | ||
| 222 |
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4 | if(present(momentum))then |
| 223 | 1 | layer%momentum = momentum | |
| 224 | else | ||
| 225 | 3 | layer%momentum = 0._real12 | |
| 226 | end if | ||
| 227 |
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4 | if(present(epsilon))then |
| 228 | 1 | layer%epsilon = epsilon | |
| 229 | else | ||
| 230 | 3 | layer%epsilon = 1.E-5_real12 | |
| 231 | end if | ||
| 232 | |||
| 233 | |||
| 234 | !!-------------------------------------------------------------------------- | ||
| 235 | !! set up initialiser mean and standard deviations | ||
| 236 | !!-------------------------------------------------------------------------- | ||
| 237 |
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4 | if(present(gamma_init_mean)) layer%gamma_init_mean = gamma_init_mean |
| 238 |
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4 | if(present(gamma_init_std)) layer%gamma_init_std = gamma_init_std |
| 239 |
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4 | if(present(beta_init_mean)) layer%beta_init_mean = beta_init_mean |
| 240 |
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4 | if(present(beta_init_std)) layer%beta_init_std = beta_init_std |
| 241 | |||
| 242 | |||
| 243 | !!-------------------------------------------------------------------------- | ||
| 244 | !! define gamma and beta initialisers | ||
| 245 | !!-------------------------------------------------------------------------- | ||
| 246 |
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4 | if(present(kernel_initialiser)) layer%kernel_initialiser = kernel_initialiser |
| 247 |
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4 | if(trim(layer%kernel_initialiser).eq.'') & |
| 248 | 3 | layer%kernel_initialiser = 'ones' | |
| 249 | !get_default_initialiser("batch") | ||
| 250 | write(*,'("BATCHNORM2D kernel (gamma) initialiser: ",A)') & | ||
| 251 |
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4 | trim(layer%kernel_initialiser) |
| 252 |
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4 | if(present(bias_initialiser)) layer%bias_initialiser = bias_initialiser |
| 253 |
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4 | if(trim(layer%bias_initialiser).eq.'') & |
| 254 | 3 | layer%bias_initialiser = 'zeros' | |
| 255 | !get_default_initialiser("batch") | ||
| 256 | write(*,'("BATCHNORM2D bias (beta) initialiser: ",A)') & | ||
| 257 |
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4 | trim(layer%bias_initialiser) |
| 258 | |||
| 259 |
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4 | if(present(moving_mean_initialiser)) & |
| 260 |
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1 | layer%moving_mean_initialiser = moving_mean_initialiser |
| 261 |
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4 | if(trim(layer%moving_mean_initialiser).eq.'') & |
| 262 | 3 | layer%moving_mean_initialiser = 'zeros' | |
| 263 | !get_default_initialiser("batch") | ||
| 264 | write(*,'("BATCHNORM2D moving mean initialiser: ",A)') & | ||
| 265 |
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4 | trim(layer%moving_mean_initialiser) |
| 266 |
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4 | if(present(moving_variance_initialiser)) & |
| 267 |
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1 | layer%moving_variance_initialiser = moving_variance_initialiser |
| 268 |
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4 | if(trim(layer%moving_variance_initialiser).eq.'') & |
| 269 | 3 | layer%moving_variance_initialiser = 'ones' | |
| 270 | !get_default_initialiser("batch") | ||
| 271 | write(*,'("BATCHNORM2D moving variance initialiser: ",A)') & | ||
| 272 |
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4 | trim(layer%moving_variance_initialiser) |
| 273 | |||
| 274 | |||
| 275 | !!-------------------------------------------------------------------------- | ||
| 276 | !! initialise layer shape | ||
| 277 | !!-------------------------------------------------------------------------- | ||
| 278 |
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4 | if(present(input_shape)) call layer%init(input_shape=input_shape) |
| 279 | |||
| 280 |
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8 | end function layer_setup |
| 281 | !!!############################################################################# | ||
| 282 | |||
| 283 | |||
| 284 | !!!############################################################################# | ||
| 285 | !!! initialise layer | ||
| 286 | !!!############################################################################# | ||
| 287 |
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3 | subroutine init_batchnorm2d(this, input_shape, batch_size, verbose) |
| 288 | use initialiser, only: initialiser_setup | ||
| 289 | implicit none | ||
| 290 | class(batchnorm2d_layer_type), intent(inout) :: this | ||
| 291 | integer, dimension(:), intent(in) :: input_shape | ||
| 292 | integer, optional, intent(in) :: batch_size | ||
| 293 | integer, optional, intent(in) :: verbose | ||
| 294 | |||
| 295 | integer :: verbose_ = 0 | ||
| 296 | 6 | class(initialiser_type), allocatable :: t_initialiser | |
| 297 | |||
| 298 | |||
| 299 | !!-------------------------------------------------------------------------- | ||
| 300 | !! initialise optional arguments | ||
| 301 | !!-------------------------------------------------------------------------- | ||
| 302 |
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3 | if(present(verbose)) verbose_ = verbose |
| 303 |
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3 | if(present(batch_size)) this%batch_size = batch_size |
| 304 | |||
| 305 | |||
| 306 | !!-------------------------------------------------------------------------- | ||
| 307 | !! initialise input shape | ||
| 308 | !!-------------------------------------------------------------------------- | ||
| 309 |
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3 | if(.not.allocated(this%input_shape)) call this%set_shape(input_shape) |
| 310 | |||
| 311 | |||
| 312 | !!----------------------------------------------------------------------- | ||
| 313 | !! set up number of channels, width, height | ||
| 314 | !!----------------------------------------------------------------------- | ||
| 315 |
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12 | this%output_shape = this%input_shape |
| 316 |
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3 | this%num_channels = this%input_shape(this%input_rank) |
| 317 | |||
| 318 | |||
| 319 | !!----------------------------------------------------------------------- | ||
| 320 | !! allocate mean, variance, gamma, beta, dg, db | ||
| 321 | !!----------------------------------------------------------------------- | ||
| 322 |
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8 | allocate(this%mean(this%num_channels), source=0._real12) |
| 323 |
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8 | allocate(this%variance, source=this%mean) |
| 324 |
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8 | allocate(this%gamma, source=this%mean) |
| 325 |
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8 | allocate(this%beta, source=this%mean) |
| 326 |
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8 | allocate(this%dg, source=this%mean) |
| 327 |
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8 | allocate(this%db, source=this%mean) |
| 328 | |||
| 329 | |||
| 330 | !!----------------------------------------------------------------------- | ||
| 331 | !! initialise gamma | ||
| 332 | !!----------------------------------------------------------------------- | ||
| 333 |
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6 | allocate(t_initialiser, source=initialiser_setup(this%kernel_initialiser)) |
| 334 | 3 | t_initialiser%mean = this%gamma_init_mean | |
| 335 | 3 | t_initialiser%std = this%gamma_init_std | |
| 336 | 12 | call t_initialiser%initialise(this%gamma, & | |
| 337 | fan_in =this%num_channels, & | ||
| 338 |
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3 | fan_out=this%num_channels) |
| 339 |
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3 | deallocate(t_initialiser) |
| 340 | |||
| 341 | !! initialise beta | ||
| 342 | !!----------------------------------------------------------------------- | ||
| 343 |
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6 | allocate(t_initialiser, source=initialiser_setup(this%bias_initialiser)) |
| 344 | 3 | t_initialiser%mean = this%beta_init_mean | |
| 345 | 3 | t_initialiser%std = this%beta_init_std | |
| 346 | 12 | call t_initialiser%initialise(this%beta, & | |
| 347 | fan_in =this%num_channels, & | ||
| 348 |
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3 | fan_out=this%num_channels) |
| 349 |
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3 | deallocate(t_initialiser) |
| 350 | |||
| 351 | |||
| 352 | !!----------------------------------------------------------------------- | ||
| 353 | !! initialise moving mean | ||
| 354 | !!----------------------------------------------------------------------- | ||
| 355 | ✗ | allocate(t_initialiser, & | |
| 356 |
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6 | source=initialiser_setup(this%moving_mean_initialiser)) |
| 357 | 12 | call t_initialiser%initialise(this%mean, & | |
| 358 | fan_in =this%num_channels, & | ||
| 359 |
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3 | fan_out=this%num_channels) |
| 360 |
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3 | deallocate(t_initialiser) |
| 361 | |||
| 362 | !! initialise moving variance | ||
| 363 | !!----------------------------------------------------------------------- | ||
| 364 | ✗ | allocate(t_initialiser, & | |
| 365 |
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6 | source=initialiser_setup(this%moving_variance_initialiser)) |
| 366 | 12 | call t_initialiser%initialise(this%variance, & | |
| 367 | fan_in =this%num_channels, & | ||
| 368 |
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3 | fan_out=this%num_channels) |
| 369 |
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3 | deallocate(t_initialiser) |
| 370 | |||
| 371 | |||
| 372 | !!-------------------------------------------------------------------------- | ||
| 373 | !! initialise batch size-dependent arrays | ||
| 374 | !!-------------------------------------------------------------------------- | ||
| 375 |
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3 | if(this%batch_size.gt.0) call this%set_batch_size(this%batch_size) |
| 376 | |||
| 377 |
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15 | end subroutine init_batchnorm2d |
| 378 | !!!############################################################################# | ||
| 379 | |||
| 380 | |||
| 381 | !!!############################################################################# | ||
| 382 | !!! set batch size | ||
| 383 | !!!############################################################################# | ||
| 384 | 3 | subroutine set_batch_size_batchnorm2d(this, batch_size, verbose) | |
| 385 | implicit none | ||
| 386 | class(batchnorm2d_layer_type), intent(inout) :: this | ||
| 387 | integer, intent(in) :: batch_size | ||
| 388 | integer, optional, intent(in) :: verbose | ||
| 389 | |||
| 390 | integer :: verbose_ = 0 | ||
| 391 | |||
| 392 | |||
| 393 | !!-------------------------------------------------------------------------- | ||
| 394 | !! initialise optional arguments | ||
| 395 | !!-------------------------------------------------------------------------- | ||
| 396 |
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3 | if(present(verbose)) verbose_ = verbose |
| 397 | 3 | this%batch_size = batch_size | |
| 398 | |||
| 399 | |||
| 400 | !!-------------------------------------------------------------------------- | ||
| 401 | !! set norm | ||
| 402 | !!-------------------------------------------------------------------------- | ||
| 403 | this%norm = real( & | ||
| 404 | this%batch_size * & | ||
| 405 |
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9 | product(this%input_shape(1:this%input_rank-1) ),real12) |
| 406 | |||
| 407 | |||
| 408 | !!-------------------------------------------------------------------------- | ||
| 409 | !! allocate arrays | ||
| 410 | !!-------------------------------------------------------------------------- | ||
| 411 |
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3 | if(allocated(this%input_shape))then |
| 412 |
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3 | if(allocated(this%output)) deallocate(this%output) |
| 413 | ✗ | allocate(this%output( & | |
| 414 | 6 | this%output_shape(1), & | |
| 415 | 6 | this%output_shape(2), this%num_channels, & | |
| 416 | this%batch_size), & | ||
| 417 |
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|
239 | source=0._real12) |
| 418 |
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3 | if(allocated(this%di)) deallocate(this%di) |
| 419 |
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|
239 | allocate(this%di, source=this%output) |
| 420 | end if | ||
| 421 | |||
| 422 | 3 | end subroutine set_batch_size_batchnorm2d | |
| 423 | !!!############################################################################# | ||
| 424 | |||
| 425 | |||
| 426 | !!!##########################################################################!!! | ||
| 427 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 428 | !!!##########################################################################!!! | ||
| 429 | |||
| 430 | |||
| 431 | !!!############################################################################# | ||
| 432 | !!! print layer to file | ||
| 433 | !!!############################################################################# | ||
| 434 | ✗ | subroutine print_batchnorm2d(this, file) | |
| 435 | implicit none | ||
| 436 | class(batchnorm2d_layer_type), intent(in) :: this | ||
| 437 | character(*), intent(in) :: file | ||
| 438 | |||
| 439 | integer :: unit | ||
| 440 | integer :: m | ||
| 441 | |||
| 442 | !! open file with new unit | ||
| 443 | !!-------------------------------------------------------------------------- | ||
| 444 | ✗ | open(newunit=unit, file=trim(file), access='append') | |
| 445 | |||
| 446 | !! write convolution initial parameters | ||
| 447 | !!-------------------------------------------------------------------------- | ||
| 448 | ✗ | write(unit,'("BATCHNORM2D")') | |
| 449 | ✗ | write(unit,'(3X,"INPUT_SHAPE = ",3(1X,I0))') this%input_shape | |
| 450 | ✗ | write(unit,'(3X,"MOMENTUM = ",F0.9)') this%momentum | |
| 451 | ✗ | write(unit,'(3X,"EPSILON = ",F0.9)') this%epsilon | |
| 452 | ✗ | write(unit,'(3X,"NUM_CHANNELS = ",I0)') this%num_channels | |
| 453 | ✗ | write(unit,'("GAMMA")') | |
| 454 | ✗ | do m=1,this%num_channels | |
| 455 | ✗ | write(unit,'(5(E16.8E2))') this%gamma(m) | |
| 456 | end do | ||
| 457 | ✗ | write(unit,'("END GAMMA")') | |
| 458 | ✗ | write(unit,'("BETA")') | |
| 459 | ✗ | do m=1,this%num_channels | |
| 460 | ✗ | write(unit,'(5(E16.8E2))') this%beta(m) | |
| 461 | end do | ||
| 462 | ✗ | write(unit,'("END BETA")') | |
| 463 | ✗ | write(unit,'("END BATCHNORM2D")') | |
| 464 | |||
| 465 | !! close unit | ||
| 466 | !!-------------------------------------------------------------------------- | ||
| 467 | ✗ | close(unit) | |
| 468 | |||
| 469 | ✗ | end subroutine print_batchnorm2d | |
| 470 | !!!############################################################################# | ||
| 471 | |||
| 472 | |||
| 473 | !!!############################################################################# | ||
| 474 | !!! read layer from file | ||
| 475 | !!!############################################################################# | ||
| 476 | ✗ | function read_batchnorm2d_layer(unit, verbose) result(layer) | |
| 477 | use infile_tools, only: assign_val, assign_vec | ||
| 478 | use misc, only: to_lower, icount | ||
| 479 | implicit none | ||
| 480 | integer, intent(in) :: unit | ||
| 481 | integer, optional, intent(in) :: verbose | ||
| 482 | |||
| 483 | class(batchnorm2d_layer_type), allocatable :: layer | ||
| 484 | |||
| 485 | integer :: stat, verbose_ = 0 | ||
| 486 | integer :: itmp1, c, i, j, k | ||
| 487 | integer :: num_channels | ||
| 488 | real(real12) :: momentum = 0._real12, epsilon = 1.E-5_real12 | ||
| 489 | logical :: found_gamma=.false., found_beta=.false. | ||
| 490 | character(14) :: kernel_initialiser='', bias_initialiser='' | ||
| 491 | character(256) :: buffer, tag | ||
| 492 | |||
| 493 | integer, dimension(3) :: input_shape | ||
| 494 | ✗ | real(real12), allocatable, dimension(:) :: data_list | |
| 495 | |||
| 496 | |||
| 497 | !!-------------------------------------------------------------------------- | ||
| 498 | !! initialise optional arguments | ||
| 499 | !!-------------------------------------------------------------------------- | ||
| 500 | ✗ | if(present(verbose)) verbose_ = verbose | |
| 501 | |||
| 502 | |||
| 503 | !!-------------------------------------------------------------------------- | ||
| 504 | !! loop over tags in layer card | ||
| 505 | !!-------------------------------------------------------------------------- | ||
| 506 | ✗ | tag_loop: do | |
| 507 | |||
| 508 | !! check for end of file | ||
| 509 | ✗ | read(unit,'(A)',iostat=stat) buffer | |
| 510 | ✗ | if(stat.ne.0)then | |
| 511 | ✗ | write(0,*) "ERROR: file encountered error (EoF?) before END BATCHNORM2D" | |
| 512 | ✗ | stop "Exiting..." | |
| 513 | end if | ||
| 514 | ✗ | if(trim(adjustl(buffer)).eq."") cycle tag_loop | |
| 515 | |||
| 516 | !! check for end of convolution card | ||
| 517 | ✗ | if(trim(adjustl(buffer)).eq."END BATCHNORM2D")then | |
| 518 | ✗ | backspace(unit) | |
| 519 | ✗ | exit tag_loop | |
| 520 | end if | ||
| 521 | |||
| 522 | ✗ | tag=trim(adjustl(buffer)) | |
| 523 | ✗ | if(scan(buffer,"=").ne.0) tag=trim(tag(:scan(tag,"=")-1)) | |
| 524 | |||
| 525 | !! read parameters from save file | ||
| 526 | ✗ | select case(trim(tag)) | |
| 527 | case("INPUT_SHAPE") | ||
| 528 | ✗ | call assign_vec(buffer, input_shape, itmp1) | |
| 529 | case("MOMENTUM") | ||
| 530 | ✗ | call assign_val(buffer, momentum, itmp1) | |
| 531 | case("EPSILON") | ||
| 532 | ✗ | call assign_val(buffer, epsilon, itmp1) | |
| 533 | case("KERNEL_INITIALISER") | ||
| 534 | ✗ | call assign_val(buffer, kernel_initialiser, itmp1) | |
| 535 | case("BIAS_INITIALISER") | ||
| 536 | ✗ | call assign_val(buffer, bias_initialiser, itmp1) | |
| 537 | case("GAMMA") | ||
| 538 | ✗ | found_gamma = .true. | |
| 539 | ✗ | kernel_initialiser = 'zeros' | |
| 540 | ✗ | bias_initialiser = 'zeros' | |
| 541 | ✗ | exit tag_loop | |
| 542 | case("beta") | ||
| 543 | ✗ | found_beta = .true. | |
| 544 | ✗ | kernel_initialiser = 'zeros' | |
| 545 | ✗ | bias_initialiser = 'zeros' | |
| 546 | ✗ | exit tag_loop | |
| 547 | case default | ||
| 548 | !! don't look for "e" due to scientific notation of numbers | ||
| 549 | !! ... i.e. exponent (E+00) | ||
| 550 | ✗ | if(scan(to_lower(trim(adjustl(buffer))),& | |
| 551 | 'abcdfghijklmnopqrstuvwxyz').eq.0)then | ||
| 552 | ✗ | cycle tag_loop | |
| 553 | ✗ | elseif(tag(:3).eq.'END')then | |
| 554 | ✗ | cycle tag_loop | |
| 555 | end if | ||
| 556 | ✗ | stop "Unrecognised line in input file: "//trim(adjustl(buffer)) | |
| 557 | end select | ||
| 558 | end do tag_loop | ||
| 559 | |||
| 560 | |||
| 561 | !!-------------------------------------------------------------------------- | ||
| 562 | !! set transfer activation function | ||
| 563 | !!-------------------------------------------------------------------------- | ||
| 564 | ✗ | num_channels = input_shape(size(input_shape,1)) | |
| 565 | layer = batchnorm2d_layer_type( input_shape=input_shape, & | ||
| 566 | momentum = momentum, epsilon = epsilon & | ||
| 567 | ✗ | ) | |
| 568 | |||
| 569 | |||
| 570 | !!-------------------------------------------------------------------------- | ||
| 571 | !! check if WEIGHTS card was found | ||
| 572 | !!-------------------------------------------------------------------------- | ||
| 573 | ✗ | allocate(data_list(num_channels), source=0._real12) | |
| 574 | ✗ | do i=1,2 | |
| 575 | ✗ | if(found_gamma.or.found_beta)then | |
| 576 | ✗ | c = 1 | |
| 577 | ✗ | k = 1 | |
| 578 | ✗ | data_list = 0._real12 | |
| 579 | ✗ | data_concat_loop: do while(c.le.num_channels) | |
| 580 | ✗ | read(unit,'(A)',iostat=stat) buffer | |
| 581 | ✗ | if(stat.ne.0) exit data_concat_loop | |
| 582 | ✗ | k = icount(buffer) | |
| 583 | ✗ | read(buffer,*,iostat=stat) (data_list(j),j=c,c+k-1) | |
| 584 | ✗ | c = c + k | |
| 585 | end do data_concat_loop | ||
| 586 | ✗ | if(found_gamma)then | |
| 587 | ✗ | layer%gamma = data_list | |
| 588 | ✗ | found_gamma = .false. | |
| 589 | ✗ | elseif(found_beta)then | |
| 590 | ✗ | layer%beta = data_list | |
| 591 | ✗ | found_beta = .false. | |
| 592 | end if | ||
| 593 | ✗ | read(unit,'(A)',iostat=stat) buffer | |
| 594 | ✗ | if(index(trim(adjustl(buffer)),"GAMMA").eq.1) found_gamma = .true. | |
| 595 | ✗ | if(index(trim(adjustl(buffer)),"BETA").eq.1) found_beta = .true. | |
| 596 | end if | ||
| 597 | end do | ||
| 598 | ✗ | deallocate(data_list) | |
| 599 | |||
| 600 | |||
| 601 | !! check for end of layer card | ||
| 602 | !!----------------------------------------------------------------------- | ||
| 603 | ✗ | read(unit,'(A)') buffer | |
| 604 | ✗ | if(trim(adjustl(buffer)).ne."END BATCHNORM2D")then | |
| 605 | ✗ | write(*,*) trim(adjustl(buffer)) | |
| 606 | ✗ | stop "ERROR: END BATCHNORM2D not where expected" | |
| 607 | end if | ||
| 608 | |||
| 609 | ✗ | end function read_batchnorm2d_layer | |
| 610 | !!!############################################################################# | ||
| 611 | |||
| 612 | |||
| 613 | !!!##########################################################################!!! | ||
| 614 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 615 | !!!##########################################################################!!! | ||
| 616 | |||
| 617 | |||
| 618 | !!!############################################################################# | ||
| 619 | !!! forward propagation | ||
| 620 | !!!############################################################################# | ||
| 621 | 10 | pure subroutine forward_4d(this, input) | |
| 622 | implicit none | ||
| 623 | class(batchnorm2d_layer_type), intent(inout) :: this | ||
| 624 |
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2 | real(real12), dimension( & |
| 625 |
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2 | this%input_shape(1), & |
| 626 | this%input_shape(2), & | ||
| 627 | this%num_channels, this%batch_size), & | ||
| 628 | intent(in) :: input | ||
| 629 | |||
| 630 | integer :: m | ||
| 631 | 4 | real(real12), dimension(this%num_channels) :: t_mean, t_variance | |
| 632 | |||
| 633 | |||
| 634 | 2 | select case(this%inference) | |
| 635 | case(.true.) | ||
| 636 | ✗ | do concurrent(m=1:this%num_channels) | |
| 637 | !! normalize each feature | ||
| 638 | ✗ | this%output(:,:,m,:) = this%gamma(m) * (input(:,:,m,:) - this%mean(m)) / & | |
| 639 | sqrt( & | ||
| 640 | ✗ | this%batch_size / (this%batch_size - 1) * this%variance(m) + & | |
| 641 | this%epsilon) + & | ||
| 642 | ✗ | this%beta(m) | |
| 643 | end do | ||
| 644 | case default | ||
| 645 |
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8 | t_mean = 0._real12 |
| 646 |
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8 | t_variance = 0._real12 |
| 647 |
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2 | do concurrent(m=1:this%num_channels) |
| 648 | !! calculate current mean and variance | ||
| 649 |
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|
444 | t_mean(m) = sum(input(:,:,m,:)) / this%norm |
| 650 |
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|
444 | t_variance(m) = sum((input(:,:,m,:) - t_mean(m))**2._real12) / this%norm |
| 651 | |||
| 652 | !! CONVERT TO USING inverse square root of variance (i.e. inverse std) | ||
| 653 | !! would also need to include epsilon in the sqrt denominator | ||
| 654 | |||
| 655 | !! update running averages | ||
| 656 |
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|
6 | if(this%momentum.gt.1.E-8_real12)then |
| 657 | ✗ | this%mean(m) = this%momentum * this%mean(m) + & | |
| 658 | ✗ | (1._real12 - this%momentum) * t_mean(m) | |
| 659 | ✗ | this%variance(m) = this%momentum * this%variance(m) + & | |
| 660 | ✗ | (1._real12 - this%momentum) * t_variance(m) | |
| 661 | else | ||
| 662 |
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6 | this%mean(m) = t_mean(m) |
| 663 |
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6 | this%variance(m) = t_variance(m) |
| 664 | end if | ||
| 665 | |||
| 666 | !! normalize each feature | ||
| 667 |
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6 | this%output(:,:,m,:) = this%gamma(m) * (input(:,:,m,:) - this%mean(m)) / & |
| 668 |
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|
452 | sqrt(this%variance(m) + this%epsilon) + this%beta(m) |
| 669 | |||
| 670 | end do | ||
| 671 | end select | ||
| 672 | |||
| 673 | 2 | end subroutine forward_4d | |
| 674 | !!!############################################################################# | ||
| 675 | |||
| 676 | |||
| 677 | !!!############################################################################# | ||
| 678 | !!! backward propagation | ||
| 679 | !!!############################################################################# | ||
| 680 | 9 | pure subroutine backward_4d(this, input, gradient) | |
| 681 | implicit none | ||
| 682 | class(batchnorm2d_layer_type), intent(inout) :: this | ||
| 683 |
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1 | real(real12), dimension( & |
| 684 |
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1 | this%input_shape(1), & |
| 685 | this%input_shape(2), & | ||
| 686 | this%num_channels,this%batch_size), & | ||
| 687 | intent(in) :: input | ||
| 688 |
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1 | real(real12), dimension( & |
| 689 |
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1 | this%output_shape(1), & |
| 690 | this%output_shape(2), & | ||
| 691 | this%num_channels,this%batch_size), & | ||
| 692 | intent(in) :: gradient | ||
| 693 | |||
| 694 | integer :: m | ||
| 695 | 4 | real(real12), dimension( & | |
| 696 | 4 | this%input_shape(1), & | |
| 697 | this%input_shape(2), & | ||
| 698 |
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|
1 | this%num_channels,this%batch_size) :: x_hat, dx_hat |
| 699 | |||
| 700 | |||
| 701 | 1 | do concurrent(m=1:this%num_channels) | |
| 702 | !! recalculate x_hat (i.e. normalised input) | ||
| 703 |
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6 | x_hat(:,:,m,:) = (input(:,:,m,:) - this%mean(m)) / & |
| 704 |
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|
228 | sqrt(this%variance(m) + this%epsilon) |
| 705 | |||
| 706 | !! calculate gradient of normalised input | ||
| 707 |
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|
222 | dx_hat(:,:,m,:) = gradient(:,:,m,:) * this%gamma(m) |
| 708 | |||
| 709 | !! calculate gradient of inputs | ||
| 710 | 51 | this%di(:,:,m,:) = & | |
| 711 | 6 | 1._real12 / (this%norm * sqrt(this%variance(m) + this%epsilon)) * & | |
| 712 |
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3 | ( this%norm * dx_hat(:,:,m,:) - & |
| 713 |
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6 | sum(dx_hat(:,:,m,:)) - x_hat(:,:,m,:) * & |
| 714 |
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|
669 | sum(dx_hat(:,:,m,:) * x_hat(:,:,m,:))) |
| 715 | |||
| 716 | !! calculate gradient of gamma and beta | ||
| 717 |
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|
222 | this%dg(m) = sum(gradient(:,:,m,:) * x_hat(:,:,m,:)) |
| 718 |
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|
223 | this%db(m) = sum(gradient(:,:,m,:)) |
| 719 | end do | ||
| 720 | |||
| 721 | 1 | end subroutine backward_4d | |
| 722 | !!!############################################################################# | ||
| 723 | |||
| 724 |
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|
32 | end module batchnorm2d_layer |
| 725 | !!!############################################################################# | ||
| 726 |