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 | |
Jerry Ge | 2eea5bf | 2022-10-11 16:27:05 +0000 | [diff] [blame^] | 89 | class Relu0To1: |
| 90 | def __init__(self, name): |
| 91 | self.result_name = name |
| 92 | |
| 93 | def eval(self, a): |
| 94 | # TF doesn't have relu_0_to_1 operator, |
| 95 | # use min and max as a workaround |
| 96 | # alternatively, we can use clip_by_value |
| 97 | return tf.math.minimum(1.0, tf.math.maximum(0.0, a)) |
| 98 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 99 | class Relu6: |
| 100 | def __init__(self, name): |
| 101 | self.result_name = name |
| 102 | |
| 103 | def eval(self, a): |
| 104 | return tf.nn.relu6(a, name=self.result_name) |
| 105 | |
| 106 | class LeakyRelu: |
| 107 | def __init__(self, alpha, name): |
| 108 | self.alpha = alpha |
| 109 | self.result_name = name |
| 110 | |
| 111 | def eval(self, a): |
| 112 | return tf.nn.leaky_relu(a, alpha=self.alpha, name=self.result_name) |
| 113 | |
TatWai Chong | 41a04fe | 2022-11-03 21:44:32 +0000 | [diff] [blame] | 114 | class Prelu: |
| 115 | def __init__(self, name): |
| 116 | self.result_name = name |
| 117 | self.prelu = tf.keras.layers.PReLU( |
| 118 | alpha_initializer=tf.keras.initializers.RandomNormal( |
| 119 | mean=0.0, stddev=1.0 |
| 120 | ) |
| 121 | ) |
| 122 | |
| 123 | def eval(self, a): |
| 124 | return self.prelu(a) |
| 125 | |
TatWai Chong | 473eb38 | 2022-08-02 04:21:30 +0000 | [diff] [blame] | 126 | class Gelu: |
| 127 | def __init__(self, name): |
| 128 | self.result_name = name |
| 129 | |
| 130 | def eval(self, a): |
| 131 | return tf.nn.gelu(a, name=self.result_name) |
| 132 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 133 | class Concat: |
| 134 | def __init__(self, axis, name): |
| 135 | self.axis = axis |
| 136 | self.result_name = name |
| 137 | |
| 138 | def eval(self, a, b): |
| 139 | return tf.concat([a, b], self.axis, name=self.result_name) |
| 140 | |
| 141 | class BitwiseAnd: |
| 142 | def __init__(self, name): |
| 143 | self.result_name = name |
| 144 | |
| 145 | def eval(self, a, b): |
| 146 | return tf.bitwise.bitwise_and(a, b, name=self.result_name) |
| 147 | |
| 148 | class BitwiseOr: |
| 149 | def __init__(self, name): |
| 150 | self.result_name = name |
| 151 | |
| 152 | def eval(self, a, b): |
| 153 | return tf.bitwise.bitwise_or(a, b, name=self.result_name) |
| 154 | |
| 155 | class BitwiseNot: |
| 156 | def __init__(self, name): |
| 157 | self.result_name = name |
| 158 | |
| 159 | def eval(self, a): |
| 160 | return tf.bitwise.invert(a, name=self.result_name) |
| 161 | |
| 162 | class BitwiseXor: |
| 163 | def __init__(self, name): |
| 164 | self.result_name = name |
| 165 | |
| 166 | def eval(self, a, b): |
| 167 | return tf.bitwise.bitwise_xor(a, b, name=self.result_name) |
| 168 | |
| 169 | class LogicalAnd: |
| 170 | def __init__(self, name): |
| 171 | self.result_name = name |
| 172 | |
| 173 | def eval(self, a, b): |
| 174 | return tf.math.logical_and(a, b, name=self.result_name) |
| 175 | |
| 176 | class LogicalOr: |
| 177 | def __init__(self, name): |
| 178 | self.result_name = name |
| 179 | |
| 180 | def eval(self, a, b): |
| 181 | return tf.math.logical_or(a, b, name=self.result_name) |
| 182 | |
| 183 | class LogicalNot: |
| 184 | def __init__(self, name): |
| 185 | self.result_name = name |
| 186 | |
| 187 | def eval(self, a): |
| 188 | return tf.math.logical_not(a, name=self.result_name) |
| 189 | |
| 190 | class ReduceAny: |
| 191 | def __init__(self, axis_list, keepdims, name): |
| 192 | self.axis_list = axis_list |
| 193 | self.keepdims = keepdims |
| 194 | self.result_name = name |
| 195 | |
| 196 | def eval(self, a): |
| 197 | return tf.math.reduce_any( |
| 198 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 199 | ) |
| 200 | |
| 201 | class ReduceAll: |
| 202 | def __init__(self, axis_list, keepdims, name): |
| 203 | self.axis_list = axis_list |
| 204 | self.keepdims = keepdims |
| 205 | self.result_name = name |
| 206 | |
| 207 | def eval(self, a): |
| 208 | return tf.math.reduce_all( |
| 209 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 210 | ) |
| 211 | |
| 212 | class ReduceMin: |
| 213 | def __init__(self, axis_list, keepdims, name): |
| 214 | self.axis_list = axis_list |
| 215 | self.keepdims = keepdims |
| 216 | self.result_name = name |
| 217 | |
| 218 | def eval(self, a): |
| 219 | return tf.math.reduce_min( |
| 220 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 221 | ) |
| 222 | |
| 223 | class ReduceMax: |
| 224 | def __init__(self, axis_list, keepdims, name): |
| 225 | self.axis_list = axis_list |
| 226 | self.keepdims = keepdims |
| 227 | self.result_name = name |
| 228 | |
| 229 | def eval(self, a): |
| 230 | return tf.math.reduce_max( |
| 231 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 232 | ) |
| 233 | |
| 234 | class ReduceSum: |
| 235 | def __init__(self, axis_list, keepdims, name): |
| 236 | self.axis_list = axis_list |
| 237 | self.keepdims = keepdims |
| 238 | self.result_name = name |
| 239 | |
| 240 | def eval(self, a): |
| 241 | return tf.math.reduce_sum( |
| 242 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 243 | ) |
| 244 | |
| 245 | class ReduceMean: |
| 246 | def __init__(self, axis_list, keepdims, name): |
| 247 | self.axis_list = axis_list |
| 248 | self.keepdims = keepdims |
| 249 | self.result_name = name |
| 250 | |
| 251 | def eval(self, a): |
| 252 | return tf.math.reduce_mean( |
| 253 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 254 | ) |
| 255 | |
| 256 | class ReduceProduct: |
| 257 | def __init__(self, axis_list, keepdims, name): |
| 258 | self.axis_list = axis_list |
| 259 | self.keepdims = keepdims |
| 260 | self.result_name = name |
| 261 | |
| 262 | def eval(self, a): |
| 263 | return tf.math.reduce_prod( |
| 264 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 265 | ) |
| 266 | |
| 267 | class Min: |
| 268 | def __init__(self, name): |
| 269 | self.result_name = name |
| 270 | |
| 271 | def eval(self, a, b): |
| 272 | return tf.math.minimum(a, b, name=self.result_name) |
| 273 | |
| 274 | class Max: |
| 275 | def __init__(self, name): |
| 276 | self.result_name = name |
| 277 | |
| 278 | def eval(self, a, b): |
| 279 | return tf.math.maximum(a, b, name=self.result_name) |
| 280 | |
| 281 | class Pow: |
| 282 | def __init__(self, name): |
| 283 | self.result_name = name |
| 284 | |
| 285 | def eval(self, a, b): |
| 286 | return tf.math.pow(a, b, name=self.result_name) |
| 287 | |
| 288 | class Abs: |
| 289 | def __init__(self, name): |
| 290 | self.result_name = name |
| 291 | |
| 292 | def eval(self, a): |
| 293 | return tf.math.abs(a, name=self.result_name) |
| 294 | |
| 295 | class Ceil: |
| 296 | def __init__(self, name): |
| 297 | self.result_name = name |
| 298 | |
| 299 | def eval(self, a): |
| 300 | return tf.math.ceil(a, name=self.result_name) |
| 301 | |
| 302 | class Floor: |
| 303 | def __init__(self, name): |
| 304 | self.result_name = name |
| 305 | |
| 306 | def eval(self, a): |
| 307 | return tf.math.floor(a, name=self.result_name) |
| 308 | |
| 309 | class Log: |
| 310 | def __init__(self, name): |
| 311 | self.result_name = name |
| 312 | |
| 313 | def eval(self, a): |
| 314 | return tf.math.log(a, name=self.result_name) |
| 315 | |
| 316 | class Negate: |
| 317 | def __init__(self, name): |
| 318 | self.result_name = name |
| 319 | |
| 320 | def eval(self, a): |
| 321 | return tf.math.negative(a, name=self.result_name) |
| 322 | |
| 323 | class Rsqrt: |
| 324 | def __init__(self, name): |
| 325 | self.result_name = name |
| 326 | |
| 327 | def eval(self, a): |
| 328 | return tf.math.rsqrt(a, name=self.result_name) |
| 329 | |
TatWai Chong | d713a4d | 2022-11-10 13:54:28 -0800 | [diff] [blame] | 330 | class Sign: |
| 331 | def __init__(self, name): |
| 332 | self.result_name = name |
| 333 | |
| 334 | def eval(self, a): |
| 335 | return tf.math.sign(a, name=self.result_name) |
| 336 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 337 | class Sigmoid: |
| 338 | def __init__(self, name): |
| 339 | self.result_name = name |
| 340 | |
| 341 | def eval(self, a): |
| 342 | return tf.math.sigmoid(a, name=self.result_name) |
| 343 | |
| 344 | class Tanh: |
| 345 | def __init__(self, name): |
| 346 | self.result_name = name |
| 347 | |
| 348 | def eval(self, a): |
| 349 | return tf.math.tanh(a, name=self.result_name) |
| 350 | |
Luke Hutton | 4160186 | 2022-12-06 17:29:15 +0000 | [diff] [blame] | 351 | class Sin: |
| 352 | def __init__(self, name): |
| 353 | self.result_name = name |
| 354 | |
| 355 | def eval(self, a): |
| 356 | return tf.math.sin(a, name=self.result_name) |
| 357 | |
| 358 | class Cos: |
| 359 | def __init__(self, name): |
| 360 | self.result_name = name |
| 361 | |
| 362 | def eval(self, a): |
| 363 | return tf.math.cos(a, name=self.result_name) |
| 364 | |
Luke Hutton | 2138a19 | 2022-12-15 11:01:39 +0000 | [diff] [blame] | 365 | class Atan2: |
| 366 | def __init__(self, name): |
| 367 | self.result_name = name |
| 368 | |
| 369 | def eval(self, a, b): |
| 370 | return tf.math.atan2(a, b, name=self.result_name) |
| 371 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 372 | class Square: |
| 373 | def __init__(self, name): |
| 374 | self.result_name = name |
| 375 | |
| 376 | def eval(self, a): |
| 377 | return tf.math.square(a, name=self.result_name) |
| 378 | |
| 379 | class SquaredDifference: |
| 380 | def __init__(self, name): |
| 381 | self.result_name = name |
| 382 | |
| 383 | def eval(self, a, b): |
| 384 | return tf.math.squared_difference(a, b, name=self.result_name) |
| 385 | |
| 386 | class Equal: |
| 387 | def __init__(self, name): |
| 388 | self.result_name = name |
| 389 | |
| 390 | def eval(self, a, b): |
| 391 | return tf.math.equal(a, b, name=self.result_name) |
| 392 | |
| 393 | class GreaterEqual: |
| 394 | def __init__(self, name): |
| 395 | self.result_name = name |
| 396 | |
| 397 | def eval(self, a, b): |
| 398 | return tf.math.greater_equal(a, b, name=self.result_name) |
| 399 | |
| 400 | class Greater: |
| 401 | def __init__(self, name): |
| 402 | self.result_name = name |
| 403 | |
| 404 | def eval(self, a, b): |
| 405 | return tf.math.greater(a, b, name=self.result_name) |
| 406 | |
| 407 | class Less: |
| 408 | def __init__(self, name): |
| 409 | self.result_name = name |
| 410 | |
| 411 | def eval(self, a, b): |
| 412 | return tf.math.less(a, b, name=self.result_name) |
| 413 | |
| 414 | class LessEqual: |
| 415 | def __init__(self, name): |
| 416 | self.result_name = name |
| 417 | |
| 418 | def eval(self, a, b): |
| 419 | return tf.math.less_equal(a, b, name=self.result_name) |
| 420 | |
| 421 | class Conv2d: |
| 422 | def __init__(self, weight, strides, padding, dilations, name): |
| 423 | self.weight = weight |
| 424 | self.strides = strides |
| 425 | self.padding = padding |
| 426 | self.dilations = dilations |
| 427 | self.result_name = name |
| 428 | |
| 429 | def eval(self, input): |
| 430 | return tf.nn.conv2d( |
| 431 | input, |
| 432 | self.weight, |
| 433 | self.strides, |
| 434 | self.padding, |
| 435 | data_format="NHWC", |
| 436 | dilations=self.dilations, |
| 437 | name=self.result_name, |
| 438 | ) |
| 439 | |
| 440 | class Conv2dRelu: |
| 441 | def __init__(self, weight, name): |
| 442 | self.weight = weight |
| 443 | self.result_name = name |
| 444 | |
| 445 | def eval(self, input): |
| 446 | conv2d = tf.nn.conv2d( |
| 447 | input, |
| 448 | self.weight, |
| 449 | [1, 1, 1, 1], |
| 450 | "SAME", |
| 451 | data_format="NHWC", |
| 452 | dilations=[1, 1, 1, 1], |
| 453 | name="conv2d", |
| 454 | ) |
| 455 | return tf.nn.relu(conv2d, name=self.result_name) |
| 456 | |
| 457 | class Conv2dRelu6: |
| 458 | def __init__(self, weight, name): |
| 459 | self.weight = weight |
| 460 | self.result_name = name |
| 461 | |
| 462 | def eval(self, input): |
| 463 | conv2d = tf.nn.conv2d( |
| 464 | input, |
| 465 | self.weight, |
| 466 | [1, 1, 1, 1], |
| 467 | "SAME", |
| 468 | data_format="NHWC", |
| 469 | dilations=[1, 1, 1, 1], |
| 470 | name="conv2d", |
| 471 | ) |
| 472 | return tf.nn.relu6(conv2d, name=self.result_name) |
| 473 | |
| 474 | class Conv2dReluN1To1: |
| 475 | def __init__(self, weight, name): |
| 476 | self.weight = weight |
| 477 | self.result_name = name |
| 478 | |
| 479 | def eval(self, input): |
| 480 | conv2d = tf.nn.conv2d( |
| 481 | input, |
| 482 | self.weight, |
| 483 | [1, 1, 1, 1], |
| 484 | "SAME", |
| 485 | data_format="NHWC", |
| 486 | dilations=[1, 1, 1, 1], |
| 487 | name="conv2d", |
| 488 | ) |
| 489 | return tf.clip_by_value(conv2d, -1.0, 1.0, name=self.result_name) |
| 490 | |
| 491 | class Conv2dTanh: |
| 492 | def __init__(self, weight, name): |
| 493 | self.weight = weight |
| 494 | self.result_name = name |
| 495 | |
| 496 | def eval(self, input): |
| 497 | conv2d = tf.nn.conv2d( |
| 498 | input, |
| 499 | self.weight, |
| 500 | [1, 1, 1, 1], |
| 501 | "SAME", |
| 502 | data_format="NHWC", |
| 503 | dilations=[1, 1, 1, 1], |
| 504 | name="conv2d", |
| 505 | ) |
| 506 | return tf.math.tanh(conv2d, name=self.result_name) |
| 507 | |
| 508 | class Conv2dWithBias: |
| 509 | def __init__(self, weight, bias, strides, padding, dilations, name): |
| 510 | self.weight = weight |
| 511 | self.bias = bias |
| 512 | self.strides = strides |
| 513 | self.padding = padding |
| 514 | self.dilations = dilations |
| 515 | self.result_name = name |
| 516 | |
| 517 | def eval(self, input): |
| 518 | conv2d_op = tf.nn.conv2d( |
| 519 | input, |
| 520 | self.weight, |
| 521 | self.strides, |
| 522 | self.padding, |
| 523 | data_format="NHWC", |
| 524 | dilations=self.dilations, |
| 525 | name="conv2d", |
| 526 | ) |
| 527 | bias_add_op = tf.nn.bias_add( |
| 528 | conv2d_op, self.bias, data_format="NHWC", name=self.result_name |
| 529 | ) |
| 530 | return bias_add_op |
| 531 | |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 532 | class Conv3d: |
| 533 | def __init__(self, weight, strides, padding, dilations, name): |
| 534 | self.weight = weight |
| 535 | self.strides = strides |
| 536 | self.padding = padding |
| 537 | self.dilations = dilations |
| 538 | self.result_name = name |
| 539 | |
| 540 | def eval(self, input): |
| 541 | return tf.nn.conv3d( |
| 542 | input, |
| 543 | self.weight, |
| 544 | self.strides, |
| 545 | self.padding, |
| 546 | data_format="NDHWC", |
| 547 | dilations=self.dilations, |
| 548 | name=self.result_name, |
| 549 | ) |
| 550 | |
| 551 | class Conv3dWithBias: |
| 552 | def __init__(self, weight, bias, strides, padding, dilations, name): |
| 553 | self.weight = weight |
| 554 | self.bias = bias |
| 555 | self.strides = strides |
| 556 | self.padding = padding |
| 557 | self.dilations = dilations |
| 558 | self.result_name = name |
| 559 | |
| 560 | def eval(self, input): |
| 561 | conv3d_op = tf.nn.conv3d( |
| 562 | input, |
| 563 | self.weight, |
| 564 | self.strides, |
| 565 | self.padding, |
| 566 | data_format="NDHWC", |
| 567 | dilations=self.dilations, |
| 568 | name="conv3d", |
| 569 | ) |
| 570 | bias_add_op = tf.nn.bias_add(conv3d_op, self.bias, name=self.result_name) |
| 571 | return bias_add_op |
| 572 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 573 | class DepthwiseConv2d: |
| 574 | def __init__(self, weight, strides, padding, dilations, name): |
| 575 | self.weight = weight |
| 576 | self.strides = strides |
| 577 | self.padding = padding |
| 578 | self.dilations = dilations |
| 579 | self.result_name = name |
| 580 | |
| 581 | def eval(self, input): |
| 582 | dws_conv2d = tf.nn.depthwise_conv2d( |
| 583 | input, |
| 584 | self.weight, |
| 585 | self.strides, |
| 586 | self.padding, |
| 587 | data_format="NHWC", |
| 588 | dilations=self.dilations, |
| 589 | name="dws_conv2d", |
| 590 | ) |
| 591 | return tf.identity(dws_conv2d, name=self.result_name) |
| 592 | |
| 593 | class DepthwiseConv2dWithBias: |
| 594 | def __init__(self, weight, bias, strides, padding, dilations, name): |
| 595 | self.weight = weight |
| 596 | self.bias = bias |
| 597 | self.strides = strides |
| 598 | self.padding = padding |
| 599 | self.dilations = dilations |
| 600 | self.result_name = name |
| 601 | |
| 602 | def eval(self, input): |
| 603 | dws_conv2d = tf.nn.depthwise_conv2d( |
| 604 | input, |
| 605 | self.weight, |
| 606 | self.strides, |
| 607 | self.padding, |
| 608 | data_format="NHWC", |
| 609 | dilations=self.dilations, |
| 610 | name="dws_conv2d", |
| 611 | ) |
| 612 | bias_add_op = tf.nn.bias_add( |
| 613 | dws_conv2d, self.bias, data_format="NHWC", name=self.result_name |
| 614 | ) |
| 615 | return bias_add_op |
| 616 | |
| 617 | class TransposeConv2d: |
| 618 | def __init__(self, weight, output_shape, strides, padding, name): |
| 619 | self.weight = weight |
| 620 | self.output_shape = output_shape |
| 621 | self.strides = strides |
| 622 | self.padding = padding |
| 623 | self.result_name = name |
| 624 | |
| 625 | def eval(self, input): |
| 626 | return tf.nn.conv2d_transpose( |
| 627 | input, |
| 628 | self.weight, |
| 629 | self.output_shape, |
| 630 | self.strides, |
| 631 | self.padding, |
| 632 | data_format="NHWC", |
| 633 | name=self.result_name, |
| 634 | ) |
| 635 | |
| 636 | class Argmax: |
| 637 | def __init__(self, axis, name): |
| 638 | self.axis = axis |
| 639 | self.result_name = name |
| 640 | |
| 641 | def eval(self, a): |
| 642 | return tf.argmax(a, self.axis, output_type=tf.int32, name=self.result_name) |
| 643 | |
| 644 | class AvgPool2d: |
| 645 | def __init__(self, strides, kernel_size, padding, name): |
| 646 | self.strides = strides |
| 647 | self.kernel_size = kernel_size |
| 648 | self.padding = padding |
| 649 | self.result_name = name |
| 650 | |
| 651 | def eval(self, input): |
| 652 | return tf.nn.avg_pool2d( |
| 653 | input, |
| 654 | strides=self.strides, |
| 655 | ksize=self.kernel_size, |
| 656 | padding=self.padding, |
| 657 | data_format="NHWC", |
| 658 | name=self.result_name, |
| 659 | ) |
| 660 | |
| 661 | class MaxPool2d: |
| 662 | def __init__(self, strides, kernel_size, padding, name): |
| 663 | self.strides = strides |
| 664 | self.kernel_size = kernel_size |
| 665 | self.padding = padding |
| 666 | self.result_name = name |
| 667 | |
| 668 | def eval(self, input): |
| 669 | return tf.nn.max_pool2d( |
| 670 | input, |
| 671 | strides=self.strides, |
| 672 | ksize=self.kernel_size, |
| 673 | padding=self.padding, |
| 674 | data_format="NHWC", |
| 675 | name=self.result_name, |
| 676 | ) |
| 677 | |
| 678 | class Reshape: |
| 679 | def __init__(self, shape, name): |
| 680 | self.shape = shape |
| 681 | self.result_name = name |
| 682 | |
| 683 | def eval(self, a): |
| 684 | reshape_op = tf.reshape(a, self.shape) |
| 685 | return tf.identity(reshape_op, name=self.result_name) |
| 686 | |
| 687 | class Transpose: |
| 688 | def __init__(self, perm, name): |
| 689 | self.perm = perm |
| 690 | self.result_name = name |
| 691 | |
| 692 | def eval(self, a): |
| 693 | return tf.transpose(a, self.perm, name=self.result_name) |
| 694 | |
| 695 | class Slice: |
| 696 | def __init__(self, begin, size, name): |
| 697 | self.begin = begin |
| 698 | self.size = size |
| 699 | self.result_name = name |
| 700 | |
| 701 | def eval(self, a): |
| 702 | return tf.slice(a, begin=self.begin, size=self.size, name=self.result_name) |
| 703 | |
| 704 | class StridedSlice: |
| 705 | def __init__( |
| 706 | self, |
| 707 | begin, |
| 708 | end, |
| 709 | strides, |
| 710 | begin_mask, |
| 711 | end_mask, |
| 712 | ellipsis_mask, |
| 713 | new_axis_mask, |
| 714 | shrink_axis_mask, |
| 715 | name, |
| 716 | ): |
| 717 | self.begin = begin |
| 718 | self.end = end |
| 719 | self.strides = strides |
| 720 | self.begin_mask = begin_mask |
| 721 | self.end_mask = end_mask |
| 722 | self.ellipsis_mask = ellipsis_mask |
| 723 | self.new_axis_mask = new_axis_mask |
| 724 | self.shrink_axis_mask = shrink_axis_mask |
| 725 | self.result_name = name |
| 726 | |
| 727 | def eval(self, a): |
| 728 | return tf.strided_slice( |
| 729 | a, |
| 730 | begin=self.begin, |
| 731 | end=self.end, |
| 732 | strides=self.strides, |
| 733 | begin_mask=self.begin_mask, |
| 734 | end_mask=self.end_mask, |
| 735 | ellipsis_mask=self.ellipsis_mask, |
| 736 | new_axis_mask=self.new_axis_mask, |
| 737 | shrink_axis_mask=self.shrink_axis_mask, |
| 738 | name=self.result_name, |
| 739 | ) |
| 740 | |
| 741 | class Select: |
| 742 | def __init__(self, name): |
| 743 | self.result_name = name |
| 744 | |
| 745 | def eval(self, selector, a, b): |
| 746 | return tf.where(condition=selector, x=a, y=b, name=self.result_name) |
| 747 | |
| 748 | class Addn: |
| 749 | def __init__(self, name): |
| 750 | self.result_name = name |
| 751 | |
| 752 | def eval(self, a, b, c, d): |
| 753 | return tf.add_n([a, b, c, d], name=self.result_name) |
| 754 | |
| 755 | class Concatv2: |
| 756 | def __init__(self, axis, name): |
| 757 | self.axis = axis |
| 758 | self.result_name = name |
| 759 | |
| 760 | def eval(self, a, b, c, d): |
| 761 | return tf.concat([a, b, c, d], axis=self.axis, name=self.result_name) |
| 762 | |
| 763 | class Stack: |
| 764 | def __init__(self, axis, name): |
| 765 | self.axis = axis |
| 766 | self.result_name = name |
| 767 | |
| 768 | def eval(self, a, b, c, d): |
| 769 | return tf.stack([a, b, c, d], axis=self.axis, name=self.result_name) |
| 770 | |
| 771 | class Unstack: |
| 772 | def __init__(self, axis, name): |
| 773 | self.axis = axis |
| 774 | self.result_name = name |
| 775 | |
| 776 | def eval(self, a): |
| 777 | unstack_op = tf.unstack(a, axis=self.axis, name="unstack_op") |
| 778 | result_count = a.shape[self.axis] |
| 779 | |
| 780 | if result_count == 1: |
| 781 | return tf.identity(unstack_op[0], name=self.result_name) |
| 782 | |
| 783 | sums = [] |
| 784 | for i in range(result_count): |
| 785 | sums.append( |
| 786 | tf.math.reduce_sum(unstack_op[i], name="reduce_{}".format(i)) |
| 787 | ) |
| 788 | return tf.stack(sums, 0, name=self.result_name) |
| 789 | |
TatWai Chong | f7008da | 2022-09-09 09:35:40 +0000 | [diff] [blame] | 790 | class MirrorPad: |
| 791 | def __init__(self, padding, mode, name): |
| 792 | self.padding = padding |
| 793 | self.mode = mode |
| 794 | self.result_name = name |
| 795 | |
| 796 | def eval(self, a): |
| 797 | return tf.pad( |
| 798 | a, |
| 799 | self.padding, |
| 800 | mode=self.mode, |
| 801 | constant_values=0, |
| 802 | name=self.result_name, |
| 803 | ) |
| 804 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 805 | class Pad: |
TatWai Chong | 2226f90 | 2023-02-22 18:38:01 -0800 | [diff] [blame] | 806 | def __init__(self, padding, pad_const, name): |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 807 | self.padding = padding |
TatWai Chong | 2226f90 | 2023-02-22 18:38:01 -0800 | [diff] [blame] | 808 | self.pad_const = pad_const |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 809 | self.result_name = name |
| 810 | |
| 811 | def eval(self, a): |
| 812 | return tf.pad( |
| 813 | a, |
| 814 | self.padding, |
| 815 | mode="CONSTANT", |
TatWai Chong | 2226f90 | 2023-02-22 18:38:01 -0800 | [diff] [blame] | 816 | constant_values=self.pad_const, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 817 | name=self.result_name, |
| 818 | ) |
| 819 | |
| 820 | class ExpandDims: |
| 821 | def __init__(self, axis, name): |
| 822 | self.axis = axis |
| 823 | self.result_name = name |
| 824 | |
| 825 | def eval(self, a): |
| 826 | return tf.expand_dims(a, self.axis, name=self.result_name) |
| 827 | |
| 828 | class Shape: |
| 829 | def __init__(self, name): |
| 830 | self.result_name = name |
| 831 | |
| 832 | def eval(self, a): |
| 833 | return tf.shape(a, name=self.result_name) |
| 834 | |
| 835 | class Rank: |
| 836 | def __init__(self, name): |
| 837 | self.result_name = name |
| 838 | |
| 839 | def eval(self, a): |
| 840 | return tf.rank(a, name=self.result_name) |
| 841 | |
| 842 | class Fill: |
| 843 | def __init__(self, shape, value, name): |
| 844 | self.shape = shape |
| 845 | self.value = value |
| 846 | self.result_name = name |
| 847 | |
| 848 | def eval(self, a): |
| 849 | return tf.fill(self.shape, self.value, name=self.result_name) |
| 850 | |
| 851 | class Elu: |
| 852 | def __init__(self, name): |
| 853 | self.result_name = name |
| 854 | |
| 855 | def eval(self, a): |
| 856 | return tf.nn.elu(a, name=self.result_name) |
| 857 | |
| 858 | class Softmax: |
| 859 | def __init__(self, name): |
| 860 | self.result_name = name |
| 861 | |
| 862 | def eval(self, a): |
| 863 | return tf.nn.softmax(a, name=self.result_name) |
| 864 | |
| 865 | class LogSoftmax: |
| 866 | def __init__(self, name): |
| 867 | self.result_name = name |
| 868 | |
| 869 | def eval(self, a): |
| 870 | return tf.nn.log_softmax(a, name=self.result_name) |
| 871 | |
| 872 | class MatMul: |
| 873 | def __init__(self, name): |
| 874 | self.result_name = name |
| 875 | |
| 876 | def eval(self, a, b): |
| 877 | return tf.linalg.matmul(a, b, name=self.result_name) |
| 878 | |
| 879 | class AddScalar: |
| 880 | def __init__(self, name): |
| 881 | self.result_name = name |
| 882 | |
| 883 | def eval(self, a): |
| 884 | return tf.add(a, 1, name=self.result_name) |
| 885 | |
| 886 | class Add1d: |
| 887 | def __init__(self, name): |
| 888 | self.result_name = name |
| 889 | |
| 890 | def eval(self, a, b): |
| 891 | if len(b.shape) > 1: |
| 892 | b_1d = tf.reduce_sum(b, axis=list(range(0, len(b.shape) - 1, 1))) |
| 893 | else: |
| 894 | b_1d = b |
| 895 | return tf.add(a, b_1d, name=self.result_name) |
| 896 | |
| 897 | class Split: |
| 898 | def __init__(self, num_splits, axis, name): |
| 899 | self.num_splits = num_splits |
| 900 | self.axis = axis |
| 901 | self.result_name = name |
| 902 | |
| 903 | def eval(self, a): |
| 904 | # The split op generates a list of outputs. Since we have difficulty |
| 905 | # serializing a list or array of Numpy arrays, we will reduce each of |
| 906 | # the results |
| 907 | |
| 908 | if not isinstance(self.num_splits, list): |
| 909 | split_op = tf.split( |
| 910 | a, num_or_size_splits=self.num_splits, axis=self.axis, name="split" |
| 911 | ) |
| 912 | result_count = self.num_splits |
| 913 | else: |
| 914 | num_split = np.asarray(self.num_splits, dtype=np.int32) |
| 915 | split_vec_op = tf.compat.v1.constant( |
| 916 | num_split, |
| 917 | shape=num_split.shape, |
| 918 | dtype=tf.int32, |
| 919 | name="const_split_vec", |
| 920 | ) |
| 921 | split_op = tf.split( |
| 922 | a, num_or_size_splits=split_vec_op, axis=self.axis, name="split" |
| 923 | ) |
| 924 | result_count = num_split.shape[0] |
| 925 | |
| 926 | sums = [] |
| 927 | for i in range(result_count): |
| 928 | sums.append(tf.math.reduce_sum(split_op[i], name="reduce_{}".format(i))) |
| 929 | return tf.stack(sums, 0, name=self.result_name) |
| 930 | |
| 931 | class Tile: |
| 932 | def __init__(self, multiples, name): |
| 933 | self.multiples = multiples |
| 934 | self.result_name = name |
| 935 | |
| 936 | def eval(self, a): |
| 937 | t = tf.tile(a, self.multiples, name="tile") |
| 938 | return tf.identity(t, name=self.result_name) |
| 939 | |
| 940 | class Reverse: |
| 941 | def __init__(self, axis, name): |
| 942 | self.axis = axis |
| 943 | self.result_name = name |
| 944 | |
| 945 | def eval(self, a): |
| 946 | return tf.reverse(a, [self.axis], name=self.result_name) |
| 947 | |
| 948 | class Gather: |
| 949 | def __init__(self, indices, batch_dims, axis, name): |
| 950 | self.indices = indices |
| 951 | self.batch_dims = batch_dims |
| 952 | self.axis = axis |
| 953 | self.result_name = name |
| 954 | |
| 955 | def eval(self, a): |
| 956 | return tf.gather( |
| 957 | a, |
| 958 | self.indices, |
| 959 | batch_dims=self.batch_dims, |
| 960 | axis=self.axis, |
| 961 | name=self.result_name, |
| 962 | ) |
| 963 | |
| 964 | class GatherNd: |
| 965 | def __init__(self, indices, name): |
| 966 | self.indices = indices |
| 967 | self.result_name = name |
| 968 | |
| 969 | def eval(self, a): |
| 970 | return tf.gather_nd(a, self.indices, name=self.result_name) |
| 971 | |
| 972 | class ScatterNd: |
| 973 | def __init__(self, shape, indices_shape, N, rng, name): |
| 974 | self.shape = shape |
| 975 | self.indices_shape = indices_shape |
| 976 | self.N = N |
| 977 | self.rng = rng |
| 978 | self.result_name = name |
| 979 | |
| 980 | def eval(self, a): |
| 981 | |
| 982 | # This operator is special. The indices and updates tensors really need |
| 983 | # to be created together, but in the current structure of this tool there |
| 984 | # is no way to do that before now. The number of updates is determined by |
| 985 | # the indices, so we can really only create that after indices; but we |
| 986 | # don't know the type at that time. |
| 987 | # |
| 988 | # Shapes are guaranteed deterministic, but we'll use our rng |
| 989 | # copied from the arggen stage. It's possible that index and |
| 990 | # update *values* will be non-deterministic. |
| 991 | # |
| 992 | # We take the tensor_tensor simply to get the dtype. |
| 993 | |
| 994 | shape_const = tf.constant(self.shape, tf.int32) |
| 995 | |
| 996 | updates_shape = list(self.indices_shape[:-1]) |
| 997 | updates_shape.extend(self.shape[self.indices_shape[-1] :]) |
| 998 | |
| 999 | updates_const = tf.constant(TGen.getRand(updates_shape, a.dtype, self.rng)) |
| 1000 | |
| 1001 | indices = np.zeros(self.indices_shape, dtype=np.int32) |
| 1002 | |
| 1003 | # We need to generate the random indices tensor based on the |
| 1004 | # limits of 'shape' for each dimension. Surely, there is a faster |
| 1005 | # vectorized way to do this, but the tensors are fairly small so we |
| 1006 | # will do this one element at a time. Each element needs to be sized based |
| 1007 | # on the size of the last dimension. |
| 1008 | for idx in np.ndindex(indices.shape): |
| 1009 | indices[idx] = self.rng.integers(0, self.shape[idx[-1]], size=1)[0] |
| 1010 | # print('{} {}'.format(idx, indices[idx])) |
| 1011 | |
| 1012 | indices_const = tf.constant(indices, dtype=tf.int32) |
| 1013 | |
| 1014 | return tf.scatter_nd( |
| 1015 | indices=indices_const, |
| 1016 | updates=updates_const, |
| 1017 | shape=shape_const, |
| 1018 | name=self.result_name, |
| 1019 | ) |
| 1020 | |
| 1021 | class SpaceToBatch: |
| 1022 | def __init__(self, block_shape, padding, name): |
| 1023 | self.block_shape = block_shape |
| 1024 | self.padding = padding |
| 1025 | self.result_name = name |
| 1026 | |
| 1027 | def eval(self, a): |
| 1028 | return tf.space_to_batch( |
| 1029 | a, self.block_shape, self.padding, name=self.result_name |
| 1030 | ) |
| 1031 | |
| 1032 | class BatchToSpace: |
| 1033 | def __init__(self, block_shape, cropping, name): |
| 1034 | self.block_shape = block_shape |
| 1035 | self.cropping = cropping |
| 1036 | self.result_name = name |
| 1037 | |
| 1038 | def eval(self, a): |
| 1039 | # transpose to swap depth and batch first. this could avoid adding new shape |
| 1040 | block_rank = len(self.block_shape) |
| 1041 | perm = [len(a.shape) - 1] |
| 1042 | for i in range(block_rank): |
| 1043 | perm.append(i + 1) |
| 1044 | perm.append(0) |
| 1045 | transpose_op = tf.transpose(a, perm) |
| 1046 | return tf.batch_to_space( |
| 1047 | transpose_op, self.block_shape, self.cropping, name=self.result_name |
| 1048 | ) |
| 1049 | |
| 1050 | class SpaceToDepth: |
| 1051 | def __init__(self, block_shape, name): |
| 1052 | self.block_shape = block_shape |
| 1053 | self.result_name = name |
| 1054 | |
| 1055 | def eval(self, a): |
| 1056 | return tf.nn.space_to_depth(a, self.block_shape, name=self.result_name) |
| 1057 | |
| 1058 | class DepthToSpace: |
| 1059 | def __init__(self, block_shape, name): |
| 1060 | self.block_shape = block_shape |
| 1061 | self.result_name = name |
| 1062 | |
| 1063 | def eval(self, a): |
| 1064 | return tf.nn.depth_to_space(a, self.block_shape, name=self.result_name) |
| 1065 | |
| 1066 | class OneHot: |
| 1067 | def __init__(self, depth, axis, name): |
| 1068 | self.depth = depth |
| 1069 | self.axis = axis |
| 1070 | self.result_name = name |
| 1071 | |
| 1072 | def eval(self, indices, on_value, off_value): |
| 1073 | return tf.one_hot( |
| 1074 | indices, |
| 1075 | self.depth, |
| 1076 | on_value, |
| 1077 | off_value, |
| 1078 | self.axis, |
| 1079 | on_value.dtype, |
| 1080 | self.result_name, |
| 1081 | ) |
| 1082 | |
| 1083 | class Fakequant: |
| 1084 | def __init__(self, num_bits, narrow_range, name): |
| 1085 | self.num_bits = num_bits |
| 1086 | self.narrow_range = narrow_range |
| 1087 | self.result_name = name |
| 1088 | |
| 1089 | def eval(self, a): |
| 1090 | return tf.quantization.fake_quant_with_min_max_args( |
| 1091 | a, |
| 1092 | min=-2.0, |
| 1093 | max=2.0, |
| 1094 | num_bits=self.num_bits, |
| 1095 | narrow_range=self.narrow_range, |
| 1096 | name=self.result_name, |
| 1097 | ) |
| 1098 | |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame] | 1099 | class Resize: |
| 1100 | def __init__(self, mode, align, half, scale, name): |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1101 | self.result_name = name |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame] | 1102 | self.mode = mode |
| 1103 | self.align = align |
| 1104 | self.half = half |
| 1105 | self.scale = scale |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1106 | |
| 1107 | def eval(self, a): |
| 1108 | out_shape = [] |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame] | 1109 | out_shape.append(a.shape[1] * self.scale) |
| 1110 | out_shape.append(a.shape[2] * self.scale) |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1111 | |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame] | 1112 | tf_resize_dict = ( |
| 1113 | {"tf_resize_func": tf.compat.v1.image.resize_nearest_neighbor} |
| 1114 | if (self.mode == "nearest") |
| 1115 | else {"tf_resize_func": tf.compat.v1.image.resize_bilinear} |
| 1116 | ) |
| 1117 | resize = tf_resize_dict["tf_resize_func"]( |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1118 | a, |
| 1119 | out_shape, |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame] | 1120 | align_corners=self.align, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1121 | name="resize", |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame] | 1122 | half_pixel_centers=self.half, |
TatWai Chong | f732609 | 2022-06-08 12:17:14 -0700 | [diff] [blame] | 1123 | ) |
| 1124 | return tf.identity(resize, name=self.result_name) |
| 1125 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1126 | class LeftShift: |
| 1127 | def __init__(self, shift, name): |
| 1128 | self.shift = shift |
| 1129 | self.result_name = name |
| 1130 | |
| 1131 | def eval(self, a): |
| 1132 | return tf.bitwise.left_shift(a, self.shift, name=self.result_name) |
| 1133 | |
| 1134 | class RightShift: |
| 1135 | def __init__(self, shift, name): |
| 1136 | self.shift = shift |
| 1137 | self.result_name = name |
| 1138 | |
| 1139 | def eval(self, a): |
| 1140 | return tf.bitwise.right_shift(a, self.shift, name=self.result_name) |
Jerry Ge | 9e94af8 | 2022-10-27 09:57:00 -0700 | [diff] [blame] | 1141 | |
| 1142 | class While: |
| 1143 | def __init__(self, name): |
| 1144 | self.result_name = name |
| 1145 | |
| 1146 | def while_cond(self, x): |
| 1147 | return tf.reduce_sum(x) < self.cap |
| 1148 | |
| 1149 | def while_body(self, x): |
| 1150 | return tf.add(x, tf.math.sigmoid(x)) |
| 1151 | |
| 1152 | def eval(self, a): |
| 1153 | self.cap = tf.cast( |
| 1154 | tf.constant( |
| 1155 | 2.0, |
| 1156 | shape=[ |
| 1157 | 1, |
| 1158 | ], |
| 1159 | ), |
| 1160 | a.dtype, |
| 1161 | ) |
| 1162 | |
| 1163 | result = tf.while_loop( |
| 1164 | self.while_cond, self.while_body, [a], name=self.result_name |
| 1165 | ) |
| 1166 | |
| 1167 | return result[0] |
| 1168 | |
| 1169 | class LSTM: |
| 1170 | def __init__(self, name): |
| 1171 | self.result_name = name |
| 1172 | self.lstm = tf.keras.layers.LSTM( |
| 1173 | 2, |
| 1174 | activation="tanh", |
| 1175 | unroll=False, |
| 1176 | recurrent_activation="sigmoid", |
| 1177 | use_bias=True, |
| 1178 | recurrent_initializer="ones", |
| 1179 | kernel_initializer="ones", |
| 1180 | ) |
| 1181 | |
| 1182 | def eval(self, a): |
| 1183 | return self.lstm(a) |
| 1184 | |
| 1185 | class GRU: |
| 1186 | def __init__(self, name): |
| 1187 | self.result_name = name |
| 1188 | self.lstm = tf.keras.layers.GRU( |
| 1189 | 2, |
| 1190 | recurrent_activation="sigmoid", |
| 1191 | use_bias=True, |
| 1192 | recurrent_initializer="ones", |
| 1193 | kernel_initializer="ones", |
| 1194 | ) |
| 1195 | |
| 1196 | def eval(self, a): |
| 1197 | return self.lstm(a) |
| 1198 | |
| 1199 | class RNN: |
| 1200 | def __init__(self, name): |
| 1201 | self.result_name = name |
| 1202 | basic_cell = tf.keras.layers.SimpleRNNCell( |
| 1203 | units=2, |
| 1204 | activation="sigmoid", |
| 1205 | use_bias=True, |
| 1206 | recurrent_initializer="ones", |
| 1207 | ) |
| 1208 | self.rnn = tf.keras.layers.RNN(basic_cell, unroll=False) |
| 1209 | |
| 1210 | def eval(self, a): |
| 1211 | return self.rnn(a) |
| 1212 | |
| 1213 | class FullyConnected: |
| 1214 | def __init__(self, name): |
| 1215 | self.result_name = name |
| 1216 | self.dense = tf.keras.layers.Dense(2) |
| 1217 | |
| 1218 | def eval(self, a): |
| 1219 | return self.dense(a) |
Luke Hutton | 261b7b6 | 2023-01-10 14:50:31 +0000 | [diff] [blame] | 1220 | |
| 1221 | class RFFT2d: |
| 1222 | def __init__(self, fft_length, name): |
| 1223 | self.fft_length = fft_length |
| 1224 | self.result_name = name |
| 1225 | |
| 1226 | def eval(self, a): |
| 1227 | return tf.signal.rfft2d(a, self.fft_length, name=self.result_name) |