Jerry Ge | 9e94af8 | 2022-10-27 09:57:00 -0700 | [diff] [blame] | 1 | # Copyright (c) 2020-2023, ARM Limited. |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 2 | # SPDX-License-Identifier: Apache-2.0 |
| 3 | import numpy as np |
| 4 | import tensorflow as tf |
| 5 | from frameworks.tensor_gen import TGen |
| 6 | |
| 7 | |
| 8 | class TBuilder: |
| 9 | """The member functions build the tensorflow operators into small networks |
| 10 | for our tests""" |
| 11 | |
| 12 | def __init__(self): |
| 13 | pass |
| 14 | |
| 15 | def fake_quant(tensor, tensor_scale, name): |
| 16 | """Helper function for quantizing with a scaling parameters structure.""" |
| 17 | return tf.quantization.fake_quant_with_min_max_args( |
| 18 | tensor, |
| 19 | min=tensor_scale.min, |
| 20 | max=tensor_scale.max, |
| 21 | num_bits=tensor_scale.num_bits, |
| 22 | narrow_range=tensor_scale.narrow_range, |
| 23 | name=name, |
| 24 | ) |
| 25 | |
| 26 | def fake_quant_params(tensor, min, max, scaling, name): |
| 27 | """Helper function for quantizing with individual scaling parameters.""" |
| 28 | return tf.quantization.fake_quant_with_min_max_args( |
| 29 | tensor, |
| 30 | min=min, |
| 31 | max=max, |
| 32 | num_bits=scaling.num_bits, |
| 33 | narrow_range=scaling.narrow_range, |
| 34 | name=name, |
| 35 | ) |
| 36 | |
| 37 | class Add: |
| 38 | def __init__(self, name): |
| 39 | self.result_name = name |
| 40 | |
| 41 | def eval(self, a, b): |
| 42 | return tf.add(a, b, name=self.result_name) |
| 43 | |
| 44 | class Sub: |
| 45 | def __init__(self, name): |
| 46 | self.result_name = name |
| 47 | |
| 48 | def eval(self, a, b): |
| 49 | return tf.subtract(a, b, name=self.result_name) |
| 50 | |
| 51 | class Mul: |
| 52 | def __init__(self, name): |
| 53 | self.result_name = name |
| 54 | |
| 55 | def eval(self, a, b): |
| 56 | return tf.multiply(a, b, name=self.result_name) |
| 57 | |
| 58 | class Exp: |
| 59 | def __init__(self, name): |
| 60 | self.result_name = name |
| 61 | |
| 62 | def eval(self, a): |
| 63 | return tf.exp(a, name=self.result_name) |
| 64 | |
| 65 | class Rcp: |
| 66 | def __init__(self, name): |
| 67 | self.result_name = name |
| 68 | |
| 69 | def eval(self, a): |
| 70 | return tf.math.reciprocal(a, name=self.result_name) |
| 71 | |
| 72 | class Relu: |
| 73 | def __init__(self, name): |
| 74 | self.result_name = name |
| 75 | |
| 76 | def eval(self, a): |
| 77 | return tf.nn.relu(a, name=self.result_name) |
| 78 | |
Jerry Ge | 9391243 | 2022-07-22 10:29:13 -0700 | [diff] [blame] | 79 | class Relu1: |
| 80 | def __init__(self, name): |
| 81 | self.result_name = name |
| 82 | |
| 83 | def eval(self, a): |
| 84 | # TF doesn't have relu_n1_to_1 operator, |
| 85 | # use min and max as a workaround |
| 86 | # alternatively, we can use clip_by_value |
| 87 | return tf.math.minimum(1.0, tf.math.maximum(-1.0, a)) |
| 88 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 89 | class Relu6: |
| 90 | def __init__(self, name): |
| 91 | self.result_name = name |
| 92 | |
| 93 | def eval(self, a): |
| 94 | return tf.nn.relu6(a, name=self.result_name) |
| 95 | |
| 96 | class LeakyRelu: |
| 97 | def __init__(self, alpha, name): |
| 98 | self.alpha = alpha |
| 99 | self.result_name = name |
| 100 | |
| 101 | def eval(self, a): |
| 102 | return tf.nn.leaky_relu(a, alpha=self.alpha, name=self.result_name) |
| 103 | |
TatWai Chong | 41a04fe | 2022-11-03 21:44:32 +0000 | [diff] [blame] | 104 | class Prelu: |
| 105 | def __init__(self, name): |
| 106 | self.result_name = name |
| 107 | self.prelu = tf.keras.layers.PReLU( |
| 108 | alpha_initializer=tf.keras.initializers.RandomNormal( |
| 109 | mean=0.0, stddev=1.0 |
| 110 | ) |
| 111 | ) |
| 112 | |
| 113 | def eval(self, a): |
| 114 | return self.prelu(a) |
| 115 | |
TatWai Chong | 473eb38 | 2022-08-02 04:21:30 +0000 | [diff] [blame] | 116 | class Gelu: |
| 117 | def __init__(self, name): |
| 118 | self.result_name = name |
| 119 | |
| 120 | def eval(self, a): |
| 121 | return tf.nn.gelu(a, name=self.result_name) |
| 122 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 123 | class Concat: |
| 124 | def __init__(self, axis, name): |
| 125 | self.axis = axis |
| 126 | self.result_name = name |
| 127 | |
| 128 | def eval(self, a, b): |
| 129 | return tf.concat([a, b], self.axis, name=self.result_name) |
| 130 | |
| 131 | class BitwiseAnd: |
| 132 | def __init__(self, name): |
| 133 | self.result_name = name |
| 134 | |
| 135 | def eval(self, a, b): |
| 136 | return tf.bitwise.bitwise_and(a, b, name=self.result_name) |
| 137 | |
| 138 | class BitwiseOr: |
| 139 | def __init__(self, name): |
| 140 | self.result_name = name |
| 141 | |
| 142 | def eval(self, a, b): |
| 143 | return tf.bitwise.bitwise_or(a, b, name=self.result_name) |
| 144 | |
| 145 | class BitwiseNot: |
| 146 | def __init__(self, name): |
| 147 | self.result_name = name |
| 148 | |
| 149 | def eval(self, a): |
| 150 | return tf.bitwise.invert(a, name=self.result_name) |
| 151 | |
| 152 | class BitwiseXor: |
| 153 | def __init__(self, name): |
| 154 | self.result_name = name |
| 155 | |
| 156 | def eval(self, a, b): |
| 157 | return tf.bitwise.bitwise_xor(a, b, name=self.result_name) |
| 158 | |
| 159 | class LogicalAnd: |
| 160 | def __init__(self, name): |
| 161 | self.result_name = name |
| 162 | |
| 163 | def eval(self, a, b): |
| 164 | return tf.math.logical_and(a, b, name=self.result_name) |
| 165 | |
| 166 | class LogicalOr: |
| 167 | def __init__(self, name): |
| 168 | self.result_name = name |
| 169 | |
| 170 | def eval(self, a, b): |
| 171 | return tf.math.logical_or(a, b, name=self.result_name) |
| 172 | |
| 173 | class LogicalNot: |
| 174 | def __init__(self, name): |
| 175 | self.result_name = name |
| 176 | |
| 177 | def eval(self, a): |
| 178 | return tf.math.logical_not(a, name=self.result_name) |
| 179 | |
| 180 | class ReduceAny: |
| 181 | def __init__(self, axis_list, keepdims, name): |
| 182 | self.axis_list = axis_list |
| 183 | self.keepdims = keepdims |
| 184 | self.result_name = name |
| 185 | |
| 186 | def eval(self, a): |
| 187 | return tf.math.reduce_any( |
| 188 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 189 | ) |
| 190 | |
| 191 | class ReduceAll: |
| 192 | def __init__(self, axis_list, keepdims, name): |
| 193 | self.axis_list = axis_list |
| 194 | self.keepdims = keepdims |
| 195 | self.result_name = name |
| 196 | |
| 197 | def eval(self, a): |
| 198 | return tf.math.reduce_all( |
| 199 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 200 | ) |
| 201 | |
| 202 | class ReduceMin: |
| 203 | def __init__(self, axis_list, keepdims, name): |
| 204 | self.axis_list = axis_list |
| 205 | self.keepdims = keepdims |
| 206 | self.result_name = name |
| 207 | |
| 208 | def eval(self, a): |
| 209 | return tf.math.reduce_min( |
| 210 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 211 | ) |
| 212 | |
| 213 | class ReduceMax: |
| 214 | def __init__(self, axis_list, keepdims, name): |
| 215 | self.axis_list = axis_list |
| 216 | self.keepdims = keepdims |
| 217 | self.result_name = name |
| 218 | |
| 219 | def eval(self, a): |
| 220 | return tf.math.reduce_max( |
| 221 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 222 | ) |
| 223 | |
| 224 | class ReduceSum: |
| 225 | def __init__(self, axis_list, keepdims, name): |
| 226 | self.axis_list = axis_list |
| 227 | self.keepdims = keepdims |
| 228 | self.result_name = name |
| 229 | |
| 230 | def eval(self, a): |
| 231 | return tf.math.reduce_sum( |
| 232 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 233 | ) |
| 234 | |
| 235 | class ReduceMean: |
| 236 | def __init__(self, axis_list, keepdims, name): |
| 237 | self.axis_list = axis_list |
| 238 | self.keepdims = keepdims |
| 239 | self.result_name = name |
| 240 | |
| 241 | def eval(self, a): |
| 242 | return tf.math.reduce_mean( |
| 243 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 244 | ) |
| 245 | |
| 246 | class ReduceProduct: |
| 247 | def __init__(self, axis_list, keepdims, name): |
| 248 | self.axis_list = axis_list |
| 249 | self.keepdims = keepdims |
| 250 | self.result_name = name |
| 251 | |
| 252 | def eval(self, a): |
| 253 | return tf.math.reduce_prod( |
| 254 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 255 | ) |
| 256 | |
| 257 | class Min: |
| 258 | def __init__(self, name): |
| 259 | self.result_name = name |
| 260 | |
| 261 | def eval(self, a, b): |
| 262 | return tf.math.minimum(a, b, name=self.result_name) |
| 263 | |
| 264 | class Max: |
| 265 | def __init__(self, name): |
| 266 | self.result_name = name |
| 267 | |
| 268 | def eval(self, a, b): |
| 269 | return tf.math.maximum(a, b, name=self.result_name) |
| 270 | |
| 271 | class Pow: |
| 272 | def __init__(self, name): |
| 273 | self.result_name = name |
| 274 | |
| 275 | def eval(self, a, b): |
| 276 | return tf.math.pow(a, b, name=self.result_name) |
| 277 | |
| 278 | class Abs: |
| 279 | def __init__(self, name): |
| 280 | self.result_name = name |
| 281 | |
| 282 | def eval(self, a): |
| 283 | return tf.math.abs(a, name=self.result_name) |
| 284 | |
| 285 | class Ceil: |
| 286 | def __init__(self, name): |
| 287 | self.result_name = name |
| 288 | |
| 289 | def eval(self, a): |
| 290 | return tf.math.ceil(a, name=self.result_name) |
| 291 | |
| 292 | class Floor: |
| 293 | def __init__(self, name): |
| 294 | self.result_name = name |
| 295 | |
| 296 | def eval(self, a): |
| 297 | return tf.math.floor(a, name=self.result_name) |
| 298 | |
| 299 | class Log: |
| 300 | def __init__(self, name): |
| 301 | self.result_name = name |
| 302 | |
| 303 | def eval(self, a): |
| 304 | return tf.math.log(a, name=self.result_name) |
| 305 | |
| 306 | class Negate: |
| 307 | def __init__(self, name): |
| 308 | self.result_name = name |
| 309 | |
| 310 | def eval(self, a): |
| 311 | return tf.math.negative(a, name=self.result_name) |
| 312 | |
| 313 | class Rsqrt: |
| 314 | def __init__(self, name): |
| 315 | self.result_name = name |
| 316 | |
| 317 | def eval(self, a): |
| 318 | return tf.math.rsqrt(a, name=self.result_name) |
| 319 | |
TatWai Chong | d713a4d | 2022-11-10 13:54:28 -0800 | [diff] [blame] | 320 | class Sign: |
| 321 | def __init__(self, name): |
| 322 | self.result_name = name |
| 323 | |
| 324 | def eval(self, a): |
| 325 | return tf.math.sign(a, name=self.result_name) |
| 326 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 327 | class Sigmoid: |
| 328 | def __init__(self, name): |
| 329 | self.result_name = name |
| 330 | |
| 331 | def eval(self, a): |
| 332 | return tf.math.sigmoid(a, name=self.result_name) |
| 333 | |
| 334 | class Tanh: |
| 335 | def __init__(self, name): |
| 336 | self.result_name = name |
| 337 | |
| 338 | def eval(self, a): |
| 339 | return tf.math.tanh(a, name=self.result_name) |
| 340 | |
Luke Hutton | 4160186 | 2022-12-06 17:29:15 +0000 | [diff] [blame] | 341 | class Sin: |
| 342 | def __init__(self, name): |
| 343 | self.result_name = name |
| 344 | |
| 345 | def eval(self, a): |
| 346 | return tf.math.sin(a, name=self.result_name) |
| 347 | |
| 348 | class Cos: |
| 349 | def __init__(self, name): |
| 350 | self.result_name = name |
| 351 | |
| 352 | def eval(self, a): |
| 353 | return tf.math.cos(a, name=self.result_name) |
| 354 | |
Luke Hutton | 2138a19 | 2022-12-15 11:01:39 +0000 | [diff] [blame] | 355 | class Atan2: |
| 356 | def __init__(self, name): |
| 357 | self.result_name = name |
| 358 | |
| 359 | def eval(self, a, b): |
| 360 | return tf.math.atan2(a, b, name=self.result_name) |
| 361 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 362 | class Square: |
| 363 | def __init__(self, name): |
| 364 | self.result_name = name |
| 365 | |
| 366 | def eval(self, a): |
| 367 | return tf.math.square(a, name=self.result_name) |
| 368 | |
| 369 | class SquaredDifference: |
| 370 | def __init__(self, name): |
| 371 | self.result_name = name |
| 372 | |
| 373 | def eval(self, a, b): |
| 374 | return tf.math.squared_difference(a, b, name=self.result_name) |
| 375 | |
| 376 | class Equal: |
| 377 | def __init__(self, name): |
| 378 | self.result_name = name |
| 379 | |
| 380 | def eval(self, a, b): |
| 381 | return tf.math.equal(a, b, name=self.result_name) |
| 382 | |
| 383 | class GreaterEqual: |
| 384 | def __init__(self, name): |
| 385 | self.result_name = name |
| 386 | |
| 387 | def eval(self, a, b): |
| 388 | return tf.math.greater_equal(a, b, name=self.result_name) |
| 389 | |
| 390 | class Greater: |
| 391 | def __init__(self, name): |
| 392 | self.result_name = name |
| 393 | |
| 394 | def eval(self, a, b): |
| 395 | return tf.math.greater(a, b, name=self.result_name) |
| 396 | |
| 397 | class Less: |
| 398 | def __init__(self, name): |
| 399 | self.result_name = name |
| 400 | |
| 401 | def eval(self, a, b): |
| 402 | return tf.math.less(a, b, name=self.result_name) |
| 403 | |
| 404 | class LessEqual: |
| 405 | def __init__(self, name): |
| 406 | self.result_name = name |
| 407 | |
| 408 | def eval(self, a, b): |
| 409 | return tf.math.less_equal(a, b, name=self.result_name) |
| 410 | |
| 411 | class Conv2d: |
| 412 | def __init__(self, weight, strides, padding, dilations, name): |
| 413 | self.weight = weight |
| 414 | self.strides = strides |
| 415 | self.padding = padding |
| 416 | self.dilations = dilations |
| 417 | self.result_name = name |
| 418 | |
| 419 | def eval(self, input): |
| 420 | return tf.nn.conv2d( |
| 421 | input, |
| 422 | self.weight, |
| 423 | self.strides, |
| 424 | self.padding, |
| 425 | data_format="NHWC", |
| 426 | dilations=self.dilations, |
| 427 | name=self.result_name, |
| 428 | ) |
| 429 | |
| 430 | class Conv2dRelu: |
| 431 | def __init__(self, weight, name): |
| 432 | self.weight = weight |
| 433 | self.result_name = name |
| 434 | |
| 435 | def eval(self, input): |
| 436 | conv2d = tf.nn.conv2d( |
| 437 | input, |
| 438 | self.weight, |
| 439 | [1, 1, 1, 1], |
| 440 | "SAME", |
| 441 | data_format="NHWC", |
| 442 | dilations=[1, 1, 1, 1], |
| 443 | name="conv2d", |
| 444 | ) |
| 445 | return tf.nn.relu(conv2d, name=self.result_name) |
| 446 | |
| 447 | class Conv2dRelu6: |
| 448 | def __init__(self, weight, name): |
| 449 | self.weight = weight |
| 450 | self.result_name = name |
| 451 | |
| 452 | def eval(self, input): |
| 453 | conv2d = tf.nn.conv2d( |
| 454 | input, |
| 455 | self.weight, |
| 456 | [1, 1, 1, 1], |
| 457 | "SAME", |
| 458 | data_format="NHWC", |
| 459 | dilations=[1, 1, 1, 1], |
| 460 | name="conv2d", |
| 461 | ) |
| 462 | return tf.nn.relu6(conv2d, name=self.result_name) |
| 463 | |
| 464 | class Conv2dReluN1To1: |
| 465 | def __init__(self, weight, name): |
| 466 | self.weight = weight |
| 467 | self.result_name = name |
| 468 | |
| 469 | def eval(self, input): |
| 470 | conv2d = tf.nn.conv2d( |
| 471 | input, |
| 472 | self.weight, |
| 473 | [1, 1, 1, 1], |
| 474 | "SAME", |
| 475 | data_format="NHWC", |
| 476 | dilations=[1, 1, 1, 1], |
| 477 | name="conv2d", |
| 478 | ) |
| 479 | return tf.clip_by_value(conv2d, -1.0, 1.0, name=self.result_name) |
| 480 | |
| 481 | class Conv2dTanh: |
| 482 | def __init__(self, weight, name): |
| 483 | self.weight = weight |
| 484 | self.result_name = name |
| 485 | |
| 486 | def eval(self, input): |
| 487 | conv2d = tf.nn.conv2d( |
| 488 | input, |
| 489 | self.weight, |
| 490 | [1, 1, 1, 1], |
| 491 | "SAME", |
| 492 | data_format="NHWC", |
| 493 | dilations=[1, 1, 1, 1], |
| 494 | name="conv2d", |
| 495 | ) |
| 496 | return tf.math.tanh(conv2d, name=self.result_name) |
| 497 | |
| 498 | class Conv2dWithBias: |
| 499 | def __init__(self, weight, bias, strides, padding, dilations, name): |
| 500 | self.weight = weight |
| 501 | self.bias = bias |
| 502 | self.strides = strides |
| 503 | self.padding = padding |
| 504 | self.dilations = dilations |
| 505 | self.result_name = name |
| 506 | |
| 507 | def eval(self, input): |
| 508 | conv2d_op = tf.nn.conv2d( |
| 509 | input, |
| 510 | self.weight, |
| 511 | self.strides, |
| 512 | self.padding, |
| 513 | data_format="NHWC", |
| 514 | dilations=self.dilations, |
| 515 | name="conv2d", |
| 516 | ) |
| 517 | bias_add_op = tf.nn.bias_add( |
| 518 | conv2d_op, self.bias, data_format="NHWC", name=self.result_name |
| 519 | ) |
| 520 | return bias_add_op |
| 521 | |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 522 | class Conv3d: |
| 523 | def __init__(self, weight, strides, padding, dilations, name): |
| 524 | self.weight = weight |
| 525 | self.strides = strides |
| 526 | self.padding = padding |
| 527 | self.dilations = dilations |
| 528 | self.result_name = name |
| 529 | |
| 530 | def eval(self, input): |
| 531 | return tf.nn.conv3d( |
| 532 | input, |
| 533 | self.weight, |
| 534 | self.strides, |
| 535 | self.padding, |
| 536 | data_format="NDHWC", |
| 537 | dilations=self.dilations, |
| 538 | name=self.result_name, |
| 539 | ) |
| 540 | |
| 541 | class Conv3dWithBias: |
| 542 | def __init__(self, weight, bias, strides, padding, dilations, name): |
| 543 | self.weight = weight |
| 544 | self.bias = bias |
| 545 | self.strides = strides |
| 546 | self.padding = padding |
| 547 | self.dilations = dilations |
| 548 | self.result_name = name |
| 549 | |
| 550 | def eval(self, input): |
| 551 | conv3d_op = tf.nn.conv3d( |
| 552 | input, |
| 553 | self.weight, |
| 554 | self.strides, |
| 555 | self.padding, |
| 556 | data_format="NDHWC", |
| 557 | dilations=self.dilations, |
| 558 | name="conv3d", |
| 559 | ) |
| 560 | bias_add_op = tf.nn.bias_add(conv3d_op, self.bias, name=self.result_name) |
| 561 | return bias_add_op |
| 562 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 563 | class DepthwiseConv2d: |
| 564 | def __init__(self, weight, strides, padding, dilations, name): |
| 565 | self.weight = weight |
| 566 | self.strides = strides |
| 567 | self.padding = padding |
| 568 | self.dilations = dilations |
| 569 | self.result_name = name |
| 570 | |
| 571 | def eval(self, input): |
| 572 | dws_conv2d = tf.nn.depthwise_conv2d( |
| 573 | input, |
| 574 | self.weight, |
| 575 | self.strides, |
| 576 | self.padding, |
| 577 | data_format="NHWC", |
| 578 | dilations=self.dilations, |
| 579 | name="dws_conv2d", |
| 580 | ) |
| 581 | return tf.identity(dws_conv2d, name=self.result_name) |
| 582 | |
| 583 | class DepthwiseConv2dWithBias: |
| 584 | def __init__(self, weight, bias, strides, padding, dilations, name): |
| 585 | self.weight = weight |
| 586 | self.bias = bias |
| 587 | self.strides = strides |
| 588 | self.padding = padding |
| 589 | self.dilations = dilations |
| 590 | self.result_name = name |
| 591 | |
| 592 | def eval(self, input): |
| 593 | dws_conv2d = tf.nn.depthwise_conv2d( |
| 594 | input, |
| 595 | self.weight, |
| 596 | self.strides, |
| 597 | self.padding, |
| 598 | data_format="NHWC", |
| 599 | dilations=self.dilations, |
| 600 | name="dws_conv2d", |
| 601 | ) |
| 602 | bias_add_op = tf.nn.bias_add( |
| 603 | dws_conv2d, self.bias, data_format="NHWC", name=self.result_name |
| 604 | ) |
| 605 | return bias_add_op |
| 606 | |
| 607 | class TransposeConv2d: |
| 608 | def __init__(self, weight, output_shape, strides, padding, name): |
| 609 | self.weight = weight |
| 610 | self.output_shape = output_shape |
| 611 | self.strides = strides |
| 612 | self.padding = padding |
| 613 | self.result_name = name |
| 614 | |
| 615 | def eval(self, input): |
| 616 | return tf.nn.conv2d_transpose( |
| 617 | input, |
| 618 | self.weight, |
| 619 | self.output_shape, |
| 620 | self.strides, |
| 621 | self.padding, |
| 622 | data_format="NHWC", |
| 623 | name=self.result_name, |
| 624 | ) |
| 625 | |
| 626 | class Argmax: |
| 627 | def __init__(self, axis, name): |
| 628 | self.axis = axis |
| 629 | self.result_name = name |
| 630 | |
| 631 | def eval(self, a): |
| 632 | return tf.argmax(a, self.axis, output_type=tf.int32, name=self.result_name) |
| 633 | |
| 634 | class AvgPool2d: |
| 635 | def __init__(self, strides, kernel_size, padding, name): |
| 636 | self.strides = strides |
| 637 | self.kernel_size = kernel_size |
| 638 | self.padding = padding |
| 639 | self.result_name = name |
| 640 | |
| 641 | def eval(self, input): |
| 642 | return tf.nn.avg_pool2d( |
| 643 | input, |
| 644 | strides=self.strides, |
| 645 | ksize=self.kernel_size, |
| 646 | padding=self.padding, |
| 647 | data_format="NHWC", |
| 648 | name=self.result_name, |
| 649 | ) |
| 650 | |
| 651 | class MaxPool2d: |
| 652 | def __init__(self, strides, kernel_size, padding, name): |
| 653 | self.strides = strides |
| 654 | self.kernel_size = kernel_size |
| 655 | self.padding = padding |
| 656 | self.result_name = name |
| 657 | |
| 658 | def eval(self, input): |
| 659 | return tf.nn.max_pool2d( |
| 660 | input, |
| 661 | strides=self.strides, |
| 662 | ksize=self.kernel_size, |
| 663 | padding=self.padding, |
| 664 | data_format="NHWC", |
| 665 | name=self.result_name, |
| 666 | ) |
| 667 | |
| 668 | class Reshape: |
| 669 | def __init__(self, shape, name): |
| 670 | self.shape = shape |
| 671 | self.result_name = name |
| 672 | |
| 673 | def eval(self, a): |
| 674 | reshape_op = tf.reshape(a, self.shape) |
| 675 | return tf.identity(reshape_op, name=self.result_name) |
| 676 | |
| 677 | class Transpose: |
| 678 | def __init__(self, perm, name): |
| 679 | self.perm = perm |
| 680 | self.result_name = name |
| 681 | |
| 682 | def eval(self, a): |
| 683 | return tf.transpose(a, self.perm, name=self.result_name) |
| 684 | |
| 685 | class Slice: |
| 686 | def __init__(self, begin, size, name): |
| 687 | self.begin = begin |
| 688 | self.size = size |
| 689 | self.result_name = name |
| 690 | |
| 691 | def eval(self, a): |
| 692 | return tf.slice(a, begin=self.begin, size=self.size, name=self.result_name) |
| 693 | |
| 694 | class StridedSlice: |
| 695 | def __init__( |
| 696 | self, |
| 697 | begin, |
| 698 | end, |
| 699 | strides, |
| 700 | begin_mask, |
| 701 | end_mask, |
| 702 | ellipsis_mask, |
| 703 | new_axis_mask, |
| 704 | shrink_axis_mask, |
| 705 | name, |
| 706 | ): |
| 707 | self.begin = begin |
| 708 | self.end = end |
| 709 | self.strides = strides |
| 710 | self.begin_mask = begin_mask |
| 711 | self.end_mask = end_mask |
| 712 | self.ellipsis_mask = ellipsis_mask |
| 713 | self.new_axis_mask = new_axis_mask |
| 714 | self.shrink_axis_mask = shrink_axis_mask |
| 715 | self.result_name = name |
| 716 | |
| 717 | def eval(self, a): |
| 718 | return tf.strided_slice( |
| 719 | a, |
| 720 | begin=self.begin, |
| 721 | end=self.end, |
| 722 | strides=self.strides, |
| 723 | begin_mask=self.begin_mask, |
| 724 | end_mask=self.end_mask, |
| 725 | ellipsis_mask=self.ellipsis_mask, |
| 726 | new_axis_mask=self.new_axis_mask, |
| 727 | shrink_axis_mask=self.shrink_axis_mask, |
| 728 | name=self.result_name, |
| 729 | ) |
| 730 | |
| 731 | class Select: |
| 732 | def __init__(self, name): |
| 733 | self.result_name = name |
| 734 | |
| 735 | def eval(self, selector, a, b): |
| 736 | return tf.where(condition=selector, x=a, y=b, name=self.result_name) |
| 737 | |
| 738 | class Addn: |
| 739 | def __init__(self, name): |
| 740 | self.result_name = name |
| 741 | |
| 742 | def eval(self, a, b, c, d): |
| 743 | return tf.add_n([a, b, c, d], name=self.result_name) |
| 744 | |
| 745 | class Concatv2: |
| 746 | def __init__(self, axis, name): |
| 747 | self.axis = axis |
| 748 | self.result_name = name |
| 749 | |
| 750 | def eval(self, a, b, c, d): |
| 751 | return tf.concat([a, b, c, d], axis=self.axis, name=self.result_name) |
| 752 | |
| 753 | class Stack: |
| 754 | def __init__(self, axis, name): |
| 755 | self.axis = axis |
| 756 | self.result_name = name |
| 757 | |
| 758 | def eval(self, a, b, c, d): |
| 759 | return tf.stack([a, b, c, d], axis=self.axis, name=self.result_name) |
| 760 | |
| 761 | class Unstack: |
| 762 | def __init__(self, axis, name): |
| 763 | self.axis = axis |
| 764 | self.result_name = name |
| 765 | |
| 766 | def eval(self, a): |
| 767 | unstack_op = tf.unstack(a, axis=self.axis, name="unstack_op") |
| 768 | result_count = a.shape[self.axis] |
| 769 | |
| 770 | if result_count == 1: |
| 771 | return tf.identity(unstack_op[0], name=self.result_name) |
| 772 | |
| 773 | sums = [] |
| 774 | for i in range(result_count): |
| 775 | sums.append( |
| 776 | tf.math.reduce_sum(unstack_op[i], name="reduce_{}".format(i)) |
| 777 | ) |
| 778 | return tf.stack(sums, 0, name=self.result_name) |
| 779 | |
TatWai Chong | f7008da | 2022-09-09 09:35:40 +0000 | [diff] [blame] | 780 | class MirrorPad: |
| 781 | def __init__(self, padding, mode, name): |
| 782 | self.padding = padding |
| 783 | self.mode = mode |
| 784 | self.result_name = name |
| 785 | |
| 786 | def eval(self, a): |
| 787 | return tf.pad( |
| 788 | a, |
| 789 | self.padding, |
| 790 | mode=self.mode, |
| 791 | constant_values=0, |
| 792 | name=self.result_name, |
| 793 | ) |
| 794 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 795 | class Pad: |
TatWai Chong | 2226f90 | 2023-02-22 18:38:01 -0800 | [diff] [blame] | 796 | def __init__(self, padding, pad_const, name): |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 797 | self.padding = padding |
TatWai Chong | 2226f90 | 2023-02-22 18:38:01 -0800 | [diff] [blame] | 798 | self.pad_const = pad_const |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 799 | self.result_name = name |
| 800 | |
| 801 | def eval(self, a): |
| 802 | return tf.pad( |
| 803 | a, |
| 804 | self.padding, |
| 805 | mode="CONSTANT", |
TatWai Chong | 2226f90 | 2023-02-22 18:38:01 -0800 | [diff] [blame] | 806 | constant_values=self.pad_const, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 807 | name=self.result_name, |
| 808 | ) |
| 809 | |
| 810 | class ExpandDims: |
| 811 | def __init__(self, axis, name): |
| 812 | self.axis = axis |
| 813 | self.result_name = name |
| 814 | |
| 815 | def eval(self, a): |
| 816 | return tf.expand_dims(a, self.axis, name=self.result_name) |
| 817 | |
| 818 | class Shape: |
| 819 | def __init__(self, name): |
| 820 | self.result_name = name |
| 821 | |
| 822 | def eval(self, a): |
| 823 | return tf.shape(a, name=self.result_name) |
| 824 | |
| 825 | class Rank: |
| 826 | def __init__(self, name): |
| 827 | self.result_name = name |
| 828 | |
| 829 | def eval(self, a): |
| 830 | return tf.rank(a, name=self.result_name) |
| 831 | |
| 832 | class Fill: |
| 833 | def __init__(self, shape, value, name): |
| 834 | self.shape = shape |
| 835 | self.value = value |
| 836 | self.result_name = name |
| 837 | |
| 838 | def eval(self, a): |
| 839 | return tf.fill(self.shape, self.value, name=self.result_name) |
| 840 | |
| 841 | class Elu: |
| 842 | def __init__(self, name): |
| 843 | self.result_name = name |
| 844 | |
| 845 | def eval(self, a): |
| 846 | return tf.nn.elu(a, name=self.result_name) |
| 847 | |
| 848 | class Softmax: |
| 849 | def __init__(self, name): |
| 850 | self.result_name = name |
| 851 | |
| 852 | def eval(self, a): |
| 853 | return tf.nn.softmax(a, name=self.result_name) |
| 854 | |
| 855 | class LogSoftmax: |
| 856 | def __init__(self, name): |
| 857 | self.result_name = name |
| 858 | |
| 859 | def eval(self, a): |
| 860 | return tf.nn.log_softmax(a, name=self.result_name) |
| 861 | |
| 862 | class MatMul: |
| 863 | def __init__(self, name): |
| 864 | self.result_name = name |
| 865 | |
| 866 | def eval(self, a, b): |
| 867 | return tf.linalg.matmul(a, b, name=self.result_name) |
| 868 | |
| 869 | class AddScalar: |
| 870 | def __init__(self, name): |
| 871 | self.result_name = name |
| 872 | |
| 873 | def eval(self, a): |
| 874 | return tf.add(a, 1, name=self.result_name) |
| 875 | |
| 876 | class Add1d: |
| 877 | def __init__(self, name): |
| 878 | self.result_name = name |
| 879 | |
| 880 | def eval(self, a, b): |
| 881 | if len(b.shape) > 1: |
| 882 | b_1d = tf.reduce_sum(b, axis=list(range(0, len(b.shape) - 1, 1))) |
| 883 | else: |
| 884 | b_1d = b |
| 885 | return tf.add(a, b_1d, name=self.result_name) |
| 886 | |
| 887 | class Split: |
| 888 | def __init__(self, num_splits, axis, name): |
| 889 | self.num_splits = num_splits |
| 890 | self.axis = axis |
| 891 | self.result_name = name |
| 892 | |
| 893 | def eval(self, a): |
| 894 | # The split op generates a list of outputs. Since we have difficulty |
| 895 | # serializing a list or array of Numpy arrays, we will reduce each of |
| 896 | # the results |
| 897 | |
| 898 | if not isinstance(self.num_splits, list): |
| 899 | split_op = tf.split( |
| 900 | a, num_or_size_splits=self.num_splits, axis=self.axis, name="split" |
| 901 | ) |
| 902 | result_count = self.num_splits |
| 903 | else: |
| 904 | num_split = np.asarray(self.num_splits, dtype=np.int32) |
| 905 | split_vec_op = tf.compat.v1.constant( |
| 906 | num_split, |
| 907 | shape=num_split.shape, |
| 908 | dtype=tf.int32, |
| 909 | name="const_split_vec", |
| 910 | ) |
| 911 | split_op = tf.split( |
| 912 | a, num_or_size_splits=split_vec_op, axis=self.axis, name="split" |
| 913 | ) |
| 914 | result_count = num_split.shape[0] |
| 915 | |
| 916 | sums = [] |
| 917 | for i in range(result_count): |
| 918 | sums.append(tf.math.reduce_sum(split_op[i], name="reduce_{}".format(i))) |
| 919 | return tf.stack(sums, 0, name=self.result_name) |
| 920 | |
| 921 | class Tile: |
| 922 | def __init__(self, multiples, name): |
| 923 | self.multiples = multiples |
| 924 | self.result_name = name |
| 925 | |
| 926 | def eval(self, a): |
| 927 | t = tf.tile(a, self.multiples, name="tile") |
| 928 | return tf.identity(t, name=self.result_name) |
| 929 | |
| 930 | class Reverse: |
| 931 | def __init__(self, axis, name): |
| 932 | self.axis = axis |
| 933 | self.result_name = name |
| 934 | |
| 935 | def eval(self, a): |
| 936 | return tf.reverse(a, [self.axis], name=self.result_name) |
| 937 | |
| 938 | class Gather: |
| 939 | def __init__(self, indices, batch_dims, axis, name): |
| 940 | self.indices = indices |
| 941 | self.batch_dims = batch_dims |
| 942 | self.axis = axis |
| 943 | self.result_name = name |
| 944 | |
| 945 | def eval(self, a): |
| 946 | return tf.gather( |
| 947 | a, |
| 948 | self.indices, |
| 949 | batch_dims=self.batch_dims, |
| 950 | axis=self.axis, |
| 951 | name=self.result_name, |
| 952 | ) |
| 953 | |
| 954 | class GatherNd: |
| 955 | def __init__(self, indices, name): |
| 956 | self.indices = indices |
| 957 | self.result_name = name |
| 958 | |
| 959 | def eval(self, a): |
| 960 | return tf.gather_nd(a, self.indices, name=self.result_name) |
| 961 | |
| 962 | class ScatterNd: |
| 963 | def __init__(self, shape, indices_shape, N, rng, name): |
| 964 | self.shape = shape |
| 965 | self.indices_shape = indices_shape |
| 966 | self.N = N |
| 967 | self.rng = rng |
| 968 | self.result_name = name |
| 969 | |
| 970 | def eval(self, a): |
| 971 | |
| 972 | # This operator is special. The indices and updates tensors really need |
| 973 | # to be created together, but in the current structure of this tool there |
| 974 | # is no way to do that before now. The number of updates is determined by |
| 975 | # the indices, so we can really only create that after indices; but we |
| 976 | # don't know the type at that time. |
| 977 | # |
| 978 | # Shapes are guaranteed deterministic, but we'll use our rng |
| 979 | # copied from the arggen stage. It's possible that index and |
| 980 | # update *values* will be non-deterministic. |
| 981 | # |
| 982 | # We take the tensor_tensor simply to get the dtype. |
| 983 | |
| 984 | shape_const = tf.constant(self.shape, tf.int32) |
| 985 | |
| 986 | updates_shape = list(self.indices_shape[:-1]) |
| 987 | updates_shape.extend(self.shape[self.indices_shape[-1] :]) |
| 988 | |
| 989 | updates_const = tf.constant(TGen.getRand(updates_shape, a.dtype, self.rng)) |
| 990 | |
| 991 | indices = np.zeros(self.indices_shape, dtype=np.int32) |
| 992 | |
| 993 | # We need to generate the random indices tensor based on the |
| 994 | # limits of 'shape' for each dimension. Surely, there is a faster |
| 995 | # vectorized way to do this, but the tensors are fairly small so we |
| 996 | # will do this one element at a time. Each element needs to be sized based |
| 997 | # on the size of the last dimension. |
| 998 | for idx in np.ndindex(indices.shape): |
| 999 | indices[idx] = self.rng.integers(0, self.shape[idx[-1]], size=1)[0] |
| 1000 | # print('{} {}'.format(idx, indices[idx])) |
| 1001 | |
| 1002 | indices_const = tf.constant(indices, dtype=tf.int32) |
| 1003 | |
| 1004 | return tf.scatter_nd( |
| 1005 | indices=indices_const, |
| 1006 | updates=updates_const, |
| 1007 | shape=shape_const, |
| 1008 | name=self.result_name, |
| 1009 | ) |
| 1010 | |
| 1011 | class SpaceToBatch: |
| 1012 | def __init__(self, block_shape, padding, name): |
| 1013 | self.block_shape = block_shape |
| 1014 | self.padding = padding |
| 1015 | self.result_name = name |
| 1016 | |
| 1017 | def eval(self, a): |
| 1018 | return tf.space_to_batch( |
| 1019 | a, self.block_shape, self.padding, name=self.result_name |
| 1020 | ) |
| 1021 | |
| 1022 | class BatchToSpace: |
| 1023 | def __init__(self, block_shape, cropping, name): |
| 1024 | self.block_shape = block_shape |
| 1025 | self.cropping = cropping |
| 1026 | self.result_name = name |
| 1027 | |
| 1028 | def eval(self, a): |
| 1029 | # transpose to swap depth and batch first. this could avoid adding new shape |
| 1030 | block_rank = len(self.block_shape) |
| 1031 | perm = [len(a.shape) - 1] |
| 1032 | for i in range(block_rank): |
| 1033 | perm.append(i + 1) |
| 1034 | perm.append(0) |
| 1035 | transpose_op = tf.transpose(a, perm) |
| 1036 | return tf.batch_to_space( |
| 1037 | transpose_op, self.block_shape, self.cropping, name=self.result_name |
| 1038 | ) |
| 1039 | |
| 1040 | class SpaceToDepth: |
| 1041 | def __init__(self, block_shape, name): |
| 1042 | self.block_shape = block_shape |
| 1043 | self.result_name = name |
| 1044 | |
| 1045 | def eval(self, a): |
| 1046 | return tf.nn.space_to_depth(a, self.block_shape, name=self.result_name) |
| 1047 | |
| 1048 | class DepthToSpace: |
| 1049 | def __init__(self, block_shape, name): |
| 1050 | self.block_shape = block_shape |
| 1051 | self.result_name = name |
| 1052 | |
| 1053 | def eval(self, a): |
| 1054 | return tf.nn.depth_to_space(a, self.block_shape, name=self.result_name) |
| 1055 | |
| 1056 | class OneHot: |
| 1057 | def __init__(self, depth, axis, name): |
| 1058 | self.depth = depth |
| 1059 | self.axis = axis |
| 1060 | self.result_name = name |
| 1061 | |
| 1062 | def eval(self, indices, on_value, off_value): |
| 1063 | return tf.one_hot( |
| 1064 | indices, |
| 1065 | self.depth, |
| 1066 | on_value, |
| 1067 | off_value, |
| 1068 | self.axis, |
| 1069 | on_value.dtype, |
| 1070 | self.result_name, |
| 1071 | ) |
| 1072 | |
| 1073 | class Fakequant: |
| 1074 | def __init__(self, num_bits, narrow_range, name): |
| 1075 | self.num_bits = num_bits |
| 1076 | self.narrow_range = narrow_range |
| 1077 | self.result_name = name |
| 1078 | |
| 1079 | def eval(self, a): |
| 1080 | return tf.quantization.fake_quant_with_min_max_args( |
| 1081 | a, |
| 1082 | min=-2.0, |
| 1083 | max=2.0, |
| 1084 | num_bits=self.num_bits, |
| 1085 | narrow_range=self.narrow_range, |
| 1086 | name=self.result_name, |
| 1087 | ) |
| 1088 | |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame^] | 1089 | class Resize: |
| 1090 | def __init__(self, mode, align, half, scale, name): |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1091 | self.result_name = name |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame^] | 1092 | self.mode = mode |
| 1093 | self.align = align |
| 1094 | self.half = half |
| 1095 | self.scale = scale |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1096 | |
| 1097 | def eval(self, a): |
| 1098 | out_shape = [] |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame^] | 1099 | out_shape.append(a.shape[1] * self.scale) |
| 1100 | out_shape.append(a.shape[2] * self.scale) |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1101 | |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame^] | 1102 | tf_resize_dict = ( |
| 1103 | {"tf_resize_func": tf.compat.v1.image.resize_nearest_neighbor} |
| 1104 | if (self.mode == "nearest") |
| 1105 | else {"tf_resize_func": tf.compat.v1.image.resize_bilinear} |
| 1106 | ) |
| 1107 | resize = tf_resize_dict["tf_resize_func"]( |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1108 | a, |
| 1109 | out_shape, |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame^] | 1110 | align_corners=self.align, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1111 | name="resize", |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame^] | 1112 | half_pixel_centers=self.half, |
TatWai Chong | f732609 | 2022-06-08 12:17:14 -0700 | [diff] [blame] | 1113 | ) |
| 1114 | return tf.identity(resize, name=self.result_name) |
| 1115 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1116 | class LeftShift: |
| 1117 | def __init__(self, shift, name): |
| 1118 | self.shift = shift |
| 1119 | self.result_name = name |
| 1120 | |
| 1121 | def eval(self, a): |
| 1122 | return tf.bitwise.left_shift(a, self.shift, name=self.result_name) |
| 1123 | |
| 1124 | class RightShift: |
| 1125 | def __init__(self, shift, name): |
| 1126 | self.shift = shift |
| 1127 | self.result_name = name |
| 1128 | |
| 1129 | def eval(self, a): |
| 1130 | return tf.bitwise.right_shift(a, self.shift, name=self.result_name) |
Jerry Ge | 9e94af8 | 2022-10-27 09:57:00 -0700 | [diff] [blame] | 1131 | |
| 1132 | class While: |
| 1133 | def __init__(self, name): |
| 1134 | self.result_name = name |
| 1135 | |
| 1136 | def while_cond(self, x): |
| 1137 | return tf.reduce_sum(x) < self.cap |
| 1138 | |
| 1139 | def while_body(self, x): |
| 1140 | return tf.add(x, tf.math.sigmoid(x)) |
| 1141 | |
| 1142 | def eval(self, a): |
| 1143 | self.cap = tf.cast( |
| 1144 | tf.constant( |
| 1145 | 2.0, |
| 1146 | shape=[ |
| 1147 | 1, |
| 1148 | ], |
| 1149 | ), |
| 1150 | a.dtype, |
| 1151 | ) |
| 1152 | |
| 1153 | result = tf.while_loop( |
| 1154 | self.while_cond, self.while_body, [a], name=self.result_name |
| 1155 | ) |
| 1156 | |
| 1157 | return result[0] |
| 1158 | |
| 1159 | class LSTM: |
| 1160 | def __init__(self, name): |
| 1161 | self.result_name = name |
| 1162 | self.lstm = tf.keras.layers.LSTM( |
| 1163 | 2, |
| 1164 | activation="tanh", |
| 1165 | unroll=False, |
| 1166 | recurrent_activation="sigmoid", |
| 1167 | use_bias=True, |
| 1168 | recurrent_initializer="ones", |
| 1169 | kernel_initializer="ones", |
| 1170 | ) |
| 1171 | |
| 1172 | def eval(self, a): |
| 1173 | return self.lstm(a) |
| 1174 | |
| 1175 | class GRU: |
| 1176 | def __init__(self, name): |
| 1177 | self.result_name = name |
| 1178 | self.lstm = tf.keras.layers.GRU( |
| 1179 | 2, |
| 1180 | recurrent_activation="sigmoid", |
| 1181 | use_bias=True, |
| 1182 | recurrent_initializer="ones", |
| 1183 | kernel_initializer="ones", |
| 1184 | ) |
| 1185 | |
| 1186 | def eval(self, a): |
| 1187 | return self.lstm(a) |
| 1188 | |
| 1189 | class RNN: |
| 1190 | def __init__(self, name): |
| 1191 | self.result_name = name |
| 1192 | basic_cell = tf.keras.layers.SimpleRNNCell( |
| 1193 | units=2, |
| 1194 | activation="sigmoid", |
| 1195 | use_bias=True, |
| 1196 | recurrent_initializer="ones", |
| 1197 | ) |
| 1198 | self.rnn = tf.keras.layers.RNN(basic_cell, unroll=False) |
| 1199 | |
| 1200 | def eval(self, a): |
| 1201 | return self.rnn(a) |
| 1202 | |
| 1203 | class FullyConnected: |
| 1204 | def __init__(self, name): |
| 1205 | self.result_name = name |
| 1206 | self.dense = tf.keras.layers.Dense(2) |
| 1207 | |
| 1208 | def eval(self, a): |
| 1209 | return self.dense(a) |
Luke Hutton | 261b7b6 | 2023-01-10 14:50:31 +0000 | [diff] [blame] | 1210 | |
| 1211 | class RFFT2d: |
| 1212 | def __init__(self, fft_length, name): |
| 1213 | self.fft_length = fft_length |
| 1214 | self.result_name = name |
| 1215 | |
| 1216 | def eval(self, a): |
| 1217 | return tf.signal.rfft2d(a, self.fft_length, name=self.result_name) |