Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1 | # Copyright (c) 2020-2022, ARM Limited. |
| 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 | 473eb38 | 2022-08-02 04:21:30 +0000 | [diff] [blame] | 104 | class Gelu: |
| 105 | def __init__(self, name): |
| 106 | self.result_name = name |
| 107 | |
| 108 | def eval(self, a): |
| 109 | return tf.nn.gelu(a, name=self.result_name) |
| 110 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 111 | class Concat: |
| 112 | def __init__(self, axis, name): |
| 113 | self.axis = axis |
| 114 | self.result_name = name |
| 115 | |
| 116 | def eval(self, a, b): |
| 117 | return tf.concat([a, b], self.axis, name=self.result_name) |
| 118 | |
| 119 | class BitwiseAnd: |
| 120 | def __init__(self, name): |
| 121 | self.result_name = name |
| 122 | |
| 123 | def eval(self, a, b): |
| 124 | return tf.bitwise.bitwise_and(a, b, name=self.result_name) |
| 125 | |
| 126 | class BitwiseOr: |
| 127 | def __init__(self, name): |
| 128 | self.result_name = name |
| 129 | |
| 130 | def eval(self, a, b): |
| 131 | return tf.bitwise.bitwise_or(a, b, name=self.result_name) |
| 132 | |
| 133 | class BitwiseNot: |
| 134 | def __init__(self, name): |
| 135 | self.result_name = name |
| 136 | |
| 137 | def eval(self, a): |
| 138 | return tf.bitwise.invert(a, name=self.result_name) |
| 139 | |
| 140 | class BitwiseXor: |
| 141 | def __init__(self, name): |
| 142 | self.result_name = name |
| 143 | |
| 144 | def eval(self, a, b): |
| 145 | return tf.bitwise.bitwise_xor(a, b, name=self.result_name) |
| 146 | |
| 147 | class LogicalAnd: |
| 148 | def __init__(self, name): |
| 149 | self.result_name = name |
| 150 | |
| 151 | def eval(self, a, b): |
| 152 | return tf.math.logical_and(a, b, name=self.result_name) |
| 153 | |
| 154 | class LogicalOr: |
| 155 | def __init__(self, name): |
| 156 | self.result_name = name |
| 157 | |
| 158 | def eval(self, a, b): |
| 159 | return tf.math.logical_or(a, b, name=self.result_name) |
| 160 | |
| 161 | class LogicalNot: |
| 162 | def __init__(self, name): |
| 163 | self.result_name = name |
| 164 | |
| 165 | def eval(self, a): |
| 166 | return tf.math.logical_not(a, name=self.result_name) |
| 167 | |
| 168 | class ReduceAny: |
| 169 | def __init__(self, axis_list, keepdims, name): |
| 170 | self.axis_list = axis_list |
| 171 | self.keepdims = keepdims |
| 172 | self.result_name = name |
| 173 | |
| 174 | def eval(self, a): |
| 175 | return tf.math.reduce_any( |
| 176 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 177 | ) |
| 178 | |
| 179 | class ReduceAll: |
| 180 | def __init__(self, axis_list, keepdims, name): |
| 181 | self.axis_list = axis_list |
| 182 | self.keepdims = keepdims |
| 183 | self.result_name = name |
| 184 | |
| 185 | def eval(self, a): |
| 186 | return tf.math.reduce_all( |
| 187 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 188 | ) |
| 189 | |
| 190 | class ReduceMin: |
| 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_min( |
| 198 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 199 | ) |
| 200 | |
| 201 | class ReduceMax: |
| 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_max( |
| 209 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 210 | ) |
| 211 | |
| 212 | class ReduceSum: |
| 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_sum( |
| 220 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 221 | ) |
| 222 | |
| 223 | class ReduceMean: |
| 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_mean( |
| 231 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 232 | ) |
| 233 | |
| 234 | class ReduceProduct: |
| 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_prod( |
| 242 | a, self.axis_list, keepdims=self.keepdims, name=self.result_name |
| 243 | ) |
| 244 | |
| 245 | class Min: |
| 246 | def __init__(self, name): |
| 247 | self.result_name = name |
| 248 | |
| 249 | def eval(self, a, b): |
| 250 | return tf.math.minimum(a, b, name=self.result_name) |
| 251 | |
| 252 | class Max: |
| 253 | def __init__(self, name): |
| 254 | self.result_name = name |
| 255 | |
| 256 | def eval(self, a, b): |
| 257 | return tf.math.maximum(a, b, name=self.result_name) |
| 258 | |
| 259 | class Pow: |
| 260 | def __init__(self, name): |
| 261 | self.result_name = name |
| 262 | |
| 263 | def eval(self, a, b): |
| 264 | return tf.math.pow(a, b, name=self.result_name) |
| 265 | |
| 266 | class Abs: |
| 267 | def __init__(self, name): |
| 268 | self.result_name = name |
| 269 | |
| 270 | def eval(self, a): |
| 271 | return tf.math.abs(a, name=self.result_name) |
| 272 | |
| 273 | class Ceil: |
| 274 | def __init__(self, name): |
| 275 | self.result_name = name |
| 276 | |
| 277 | def eval(self, a): |
| 278 | return tf.math.ceil(a, name=self.result_name) |
| 279 | |
| 280 | class Floor: |
| 281 | def __init__(self, name): |
| 282 | self.result_name = name |
| 283 | |
| 284 | def eval(self, a): |
| 285 | return tf.math.floor(a, name=self.result_name) |
| 286 | |
| 287 | class Log: |
| 288 | def __init__(self, name): |
| 289 | self.result_name = name |
| 290 | |
| 291 | def eval(self, a): |
| 292 | return tf.math.log(a, name=self.result_name) |
| 293 | |
| 294 | class Negate: |
| 295 | def __init__(self, name): |
| 296 | self.result_name = name |
| 297 | |
| 298 | def eval(self, a): |
| 299 | return tf.math.negative(a, name=self.result_name) |
| 300 | |
| 301 | class Rsqrt: |
| 302 | def __init__(self, name): |
| 303 | self.result_name = name |
| 304 | |
| 305 | def eval(self, a): |
| 306 | return tf.math.rsqrt(a, name=self.result_name) |
| 307 | |
| 308 | class Sigmoid: |
| 309 | def __init__(self, name): |
| 310 | self.result_name = name |
| 311 | |
| 312 | def eval(self, a): |
| 313 | return tf.math.sigmoid(a, name=self.result_name) |
| 314 | |
| 315 | class Tanh: |
| 316 | def __init__(self, name): |
| 317 | self.result_name = name |
| 318 | |
| 319 | def eval(self, a): |
| 320 | return tf.math.tanh(a, name=self.result_name) |
| 321 | |
| 322 | class Square: |
| 323 | def __init__(self, name): |
| 324 | self.result_name = name |
| 325 | |
| 326 | def eval(self, a): |
| 327 | return tf.math.square(a, name=self.result_name) |
| 328 | |
| 329 | class SquaredDifference: |
| 330 | def __init__(self, name): |
| 331 | self.result_name = name |
| 332 | |
| 333 | def eval(self, a, b): |
| 334 | return tf.math.squared_difference(a, b, name=self.result_name) |
| 335 | |
| 336 | class Equal: |
| 337 | def __init__(self, name): |
| 338 | self.result_name = name |
| 339 | |
| 340 | def eval(self, a, b): |
| 341 | return tf.math.equal(a, b, name=self.result_name) |
| 342 | |
| 343 | class GreaterEqual: |
| 344 | def __init__(self, name): |
| 345 | self.result_name = name |
| 346 | |
| 347 | def eval(self, a, b): |
| 348 | return tf.math.greater_equal(a, b, name=self.result_name) |
| 349 | |
| 350 | class Greater: |
| 351 | def __init__(self, name): |
| 352 | self.result_name = name |
| 353 | |
| 354 | def eval(self, a, b): |
| 355 | return tf.math.greater(a, b, name=self.result_name) |
| 356 | |
| 357 | class Less: |
| 358 | def __init__(self, name): |
| 359 | self.result_name = name |
| 360 | |
| 361 | def eval(self, a, b): |
| 362 | return tf.math.less(a, b, name=self.result_name) |
| 363 | |
| 364 | class LessEqual: |
| 365 | def __init__(self, name): |
| 366 | self.result_name = name |
| 367 | |
| 368 | def eval(self, a, b): |
| 369 | return tf.math.less_equal(a, b, name=self.result_name) |
| 370 | |
| 371 | class Conv2d: |
| 372 | def __init__(self, weight, strides, padding, dilations, name): |
| 373 | self.weight = weight |
| 374 | self.strides = strides |
| 375 | self.padding = padding |
| 376 | self.dilations = dilations |
| 377 | self.result_name = name |
| 378 | |
| 379 | def eval(self, input): |
| 380 | return tf.nn.conv2d( |
| 381 | input, |
| 382 | self.weight, |
| 383 | self.strides, |
| 384 | self.padding, |
| 385 | data_format="NHWC", |
| 386 | dilations=self.dilations, |
| 387 | name=self.result_name, |
| 388 | ) |
| 389 | |
| 390 | class Conv2dRelu: |
| 391 | def __init__(self, weight, name): |
| 392 | self.weight = weight |
| 393 | self.result_name = name |
| 394 | |
| 395 | def eval(self, input): |
| 396 | conv2d = tf.nn.conv2d( |
| 397 | input, |
| 398 | self.weight, |
| 399 | [1, 1, 1, 1], |
| 400 | "SAME", |
| 401 | data_format="NHWC", |
| 402 | dilations=[1, 1, 1, 1], |
| 403 | name="conv2d", |
| 404 | ) |
| 405 | return tf.nn.relu(conv2d, name=self.result_name) |
| 406 | |
| 407 | class Conv2dRelu6: |
| 408 | def __init__(self, weight, name): |
| 409 | self.weight = weight |
| 410 | self.result_name = name |
| 411 | |
| 412 | def eval(self, input): |
| 413 | conv2d = tf.nn.conv2d( |
| 414 | input, |
| 415 | self.weight, |
| 416 | [1, 1, 1, 1], |
| 417 | "SAME", |
| 418 | data_format="NHWC", |
| 419 | dilations=[1, 1, 1, 1], |
| 420 | name="conv2d", |
| 421 | ) |
| 422 | return tf.nn.relu6(conv2d, name=self.result_name) |
| 423 | |
| 424 | class Conv2dReluN1To1: |
| 425 | def __init__(self, weight, name): |
| 426 | self.weight = weight |
| 427 | self.result_name = name |
| 428 | |
| 429 | def eval(self, input): |
| 430 | conv2d = tf.nn.conv2d( |
| 431 | input, |
| 432 | self.weight, |
| 433 | [1, 1, 1, 1], |
| 434 | "SAME", |
| 435 | data_format="NHWC", |
| 436 | dilations=[1, 1, 1, 1], |
| 437 | name="conv2d", |
| 438 | ) |
| 439 | return tf.clip_by_value(conv2d, -1.0, 1.0, name=self.result_name) |
| 440 | |
| 441 | class Conv2dTanh: |
| 442 | def __init__(self, weight, name): |
| 443 | self.weight = weight |
| 444 | self.result_name = name |
| 445 | |
| 446 | def eval(self, input): |
| 447 | conv2d = tf.nn.conv2d( |
| 448 | input, |
| 449 | self.weight, |
| 450 | [1, 1, 1, 1], |
| 451 | "SAME", |
| 452 | data_format="NHWC", |
| 453 | dilations=[1, 1, 1, 1], |
| 454 | name="conv2d", |
| 455 | ) |
| 456 | return tf.math.tanh(conv2d, name=self.result_name) |
| 457 | |
| 458 | class Conv2dWithBias: |
| 459 | def __init__(self, weight, bias, strides, padding, dilations, name): |
| 460 | self.weight = weight |
| 461 | self.bias = bias |
| 462 | self.strides = strides |
| 463 | self.padding = padding |
| 464 | self.dilations = dilations |
| 465 | self.result_name = name |
| 466 | |
| 467 | def eval(self, input): |
| 468 | conv2d_op = tf.nn.conv2d( |
| 469 | input, |
| 470 | self.weight, |
| 471 | self.strides, |
| 472 | self.padding, |
| 473 | data_format="NHWC", |
| 474 | dilations=self.dilations, |
| 475 | name="conv2d", |
| 476 | ) |
| 477 | bias_add_op = tf.nn.bias_add( |
| 478 | conv2d_op, self.bias, data_format="NHWC", name=self.result_name |
| 479 | ) |
| 480 | return bias_add_op |
| 481 | |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 482 | class Conv3d: |
| 483 | def __init__(self, weight, strides, padding, dilations, name): |
| 484 | self.weight = weight |
| 485 | self.strides = strides |
| 486 | self.padding = padding |
| 487 | self.dilations = dilations |
| 488 | self.result_name = name |
| 489 | |
| 490 | def eval(self, input): |
| 491 | return tf.nn.conv3d( |
| 492 | input, |
| 493 | self.weight, |
| 494 | self.strides, |
| 495 | self.padding, |
| 496 | data_format="NDHWC", |
| 497 | dilations=self.dilations, |
| 498 | name=self.result_name, |
| 499 | ) |
| 500 | |
| 501 | class Conv3dWithBias: |
| 502 | def __init__(self, weight, bias, strides, padding, dilations, name): |
| 503 | self.weight = weight |
| 504 | self.bias = bias |
| 505 | self.strides = strides |
| 506 | self.padding = padding |
| 507 | self.dilations = dilations |
| 508 | self.result_name = name |
| 509 | |
| 510 | def eval(self, input): |
| 511 | conv3d_op = tf.nn.conv3d( |
| 512 | input, |
| 513 | self.weight, |
| 514 | self.strides, |
| 515 | self.padding, |
| 516 | data_format="NDHWC", |
| 517 | dilations=self.dilations, |
| 518 | name="conv3d", |
| 519 | ) |
| 520 | bias_add_op = tf.nn.bias_add(conv3d_op, self.bias, name=self.result_name) |
| 521 | return bias_add_op |
| 522 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 523 | class DepthwiseConv2d: |
| 524 | def __init__(self, weight, strides, padding, dilations, name): |
| 525 | self.weight = weight |
| 526 | self.strides = strides |
| 527 | self.padding = padding |
| 528 | self.dilations = dilations |
| 529 | self.result_name = name |
| 530 | |
| 531 | def eval(self, input): |
| 532 | dws_conv2d = tf.nn.depthwise_conv2d( |
| 533 | input, |
| 534 | self.weight, |
| 535 | self.strides, |
| 536 | self.padding, |
| 537 | data_format="NHWC", |
| 538 | dilations=self.dilations, |
| 539 | name="dws_conv2d", |
| 540 | ) |
| 541 | return tf.identity(dws_conv2d, name=self.result_name) |
| 542 | |
| 543 | class DepthwiseConv2dWithBias: |
| 544 | def __init__(self, weight, bias, strides, padding, dilations, name): |
| 545 | self.weight = weight |
| 546 | self.bias = bias |
| 547 | self.strides = strides |
| 548 | self.padding = padding |
| 549 | self.dilations = dilations |
| 550 | self.result_name = name |
| 551 | |
| 552 | def eval(self, input): |
| 553 | dws_conv2d = tf.nn.depthwise_conv2d( |
| 554 | input, |
| 555 | self.weight, |
| 556 | self.strides, |
| 557 | self.padding, |
| 558 | data_format="NHWC", |
| 559 | dilations=self.dilations, |
| 560 | name="dws_conv2d", |
| 561 | ) |
| 562 | bias_add_op = tf.nn.bias_add( |
| 563 | dws_conv2d, self.bias, data_format="NHWC", name=self.result_name |
| 564 | ) |
| 565 | return bias_add_op |
| 566 | |
| 567 | class TransposeConv2d: |
| 568 | def __init__(self, weight, output_shape, strides, padding, name): |
| 569 | self.weight = weight |
| 570 | self.output_shape = output_shape |
| 571 | self.strides = strides |
| 572 | self.padding = padding |
| 573 | self.result_name = name |
| 574 | |
| 575 | def eval(self, input): |
| 576 | return tf.nn.conv2d_transpose( |
| 577 | input, |
| 578 | self.weight, |
| 579 | self.output_shape, |
| 580 | self.strides, |
| 581 | self.padding, |
| 582 | data_format="NHWC", |
| 583 | name=self.result_name, |
| 584 | ) |
| 585 | |
| 586 | class Argmax: |
| 587 | def __init__(self, axis, name): |
| 588 | self.axis = axis |
| 589 | self.result_name = name |
| 590 | |
| 591 | def eval(self, a): |
| 592 | return tf.argmax(a, self.axis, output_type=tf.int32, name=self.result_name) |
| 593 | |
| 594 | class AvgPool2d: |
| 595 | def __init__(self, strides, kernel_size, padding, name): |
| 596 | self.strides = strides |
| 597 | self.kernel_size = kernel_size |
| 598 | self.padding = padding |
| 599 | self.result_name = name |
| 600 | |
| 601 | def eval(self, input): |
| 602 | return tf.nn.avg_pool2d( |
| 603 | input, |
| 604 | strides=self.strides, |
| 605 | ksize=self.kernel_size, |
| 606 | padding=self.padding, |
| 607 | data_format="NHWC", |
| 608 | name=self.result_name, |
| 609 | ) |
| 610 | |
| 611 | class MaxPool2d: |
| 612 | def __init__(self, strides, kernel_size, padding, name): |
| 613 | self.strides = strides |
| 614 | self.kernel_size = kernel_size |
| 615 | self.padding = padding |
| 616 | self.result_name = name |
| 617 | |
| 618 | def eval(self, input): |
| 619 | return tf.nn.max_pool2d( |
| 620 | input, |
| 621 | strides=self.strides, |
| 622 | ksize=self.kernel_size, |
| 623 | padding=self.padding, |
| 624 | data_format="NHWC", |
| 625 | name=self.result_name, |
| 626 | ) |
| 627 | |
| 628 | class Reshape: |
| 629 | def __init__(self, shape, name): |
| 630 | self.shape = shape |
| 631 | self.result_name = name |
| 632 | |
| 633 | def eval(self, a): |
| 634 | reshape_op = tf.reshape(a, self.shape) |
| 635 | return tf.identity(reshape_op, name=self.result_name) |
| 636 | |
| 637 | class Transpose: |
| 638 | def __init__(self, perm, name): |
| 639 | self.perm = perm |
| 640 | self.result_name = name |
| 641 | |
| 642 | def eval(self, a): |
| 643 | return tf.transpose(a, self.perm, name=self.result_name) |
| 644 | |
| 645 | class Slice: |
| 646 | def __init__(self, begin, size, name): |
| 647 | self.begin = begin |
| 648 | self.size = size |
| 649 | self.result_name = name |
| 650 | |
| 651 | def eval(self, a): |
| 652 | return tf.slice(a, begin=self.begin, size=self.size, name=self.result_name) |
| 653 | |
| 654 | class StridedSlice: |
| 655 | def __init__( |
| 656 | self, |
| 657 | begin, |
| 658 | end, |
| 659 | strides, |
| 660 | begin_mask, |
| 661 | end_mask, |
| 662 | ellipsis_mask, |
| 663 | new_axis_mask, |
| 664 | shrink_axis_mask, |
| 665 | name, |
| 666 | ): |
| 667 | self.begin = begin |
| 668 | self.end = end |
| 669 | self.strides = strides |
| 670 | self.begin_mask = begin_mask |
| 671 | self.end_mask = end_mask |
| 672 | self.ellipsis_mask = ellipsis_mask |
| 673 | self.new_axis_mask = new_axis_mask |
| 674 | self.shrink_axis_mask = shrink_axis_mask |
| 675 | self.result_name = name |
| 676 | |
| 677 | def eval(self, a): |
| 678 | return tf.strided_slice( |
| 679 | a, |
| 680 | begin=self.begin, |
| 681 | end=self.end, |
| 682 | strides=self.strides, |
| 683 | begin_mask=self.begin_mask, |
| 684 | end_mask=self.end_mask, |
| 685 | ellipsis_mask=self.ellipsis_mask, |
| 686 | new_axis_mask=self.new_axis_mask, |
| 687 | shrink_axis_mask=self.shrink_axis_mask, |
| 688 | name=self.result_name, |
| 689 | ) |
| 690 | |
| 691 | class Select: |
| 692 | def __init__(self, name): |
| 693 | self.result_name = name |
| 694 | |
| 695 | def eval(self, selector, a, b): |
| 696 | return tf.where(condition=selector, x=a, y=b, name=self.result_name) |
| 697 | |
| 698 | class Addn: |
| 699 | def __init__(self, name): |
| 700 | self.result_name = name |
| 701 | |
| 702 | def eval(self, a, b, c, d): |
| 703 | return tf.add_n([a, b, c, d], name=self.result_name) |
| 704 | |
| 705 | class Concatv2: |
| 706 | def __init__(self, axis, name): |
| 707 | self.axis = axis |
| 708 | self.result_name = name |
| 709 | |
| 710 | def eval(self, a, b, c, d): |
| 711 | return tf.concat([a, b, c, d], axis=self.axis, name=self.result_name) |
| 712 | |
| 713 | class Stack: |
| 714 | def __init__(self, axis, name): |
| 715 | self.axis = axis |
| 716 | self.result_name = name |
| 717 | |
| 718 | def eval(self, a, b, c, d): |
| 719 | return tf.stack([a, b, c, d], axis=self.axis, name=self.result_name) |
| 720 | |
| 721 | class Unstack: |
| 722 | def __init__(self, axis, name): |
| 723 | self.axis = axis |
| 724 | self.result_name = name |
| 725 | |
| 726 | def eval(self, a): |
| 727 | unstack_op = tf.unstack(a, axis=self.axis, name="unstack_op") |
| 728 | result_count = a.shape[self.axis] |
| 729 | |
| 730 | if result_count == 1: |
| 731 | return tf.identity(unstack_op[0], name=self.result_name) |
| 732 | |
| 733 | sums = [] |
| 734 | for i in range(result_count): |
| 735 | sums.append( |
| 736 | tf.math.reduce_sum(unstack_op[i], name="reduce_{}".format(i)) |
| 737 | ) |
| 738 | return tf.stack(sums, 0, name=self.result_name) |
| 739 | |
TatWai Chong | f7008da | 2022-09-09 09:35:40 +0000 | [diff] [blame^] | 740 | class MirrorPad: |
| 741 | def __init__(self, padding, mode, name): |
| 742 | self.padding = padding |
| 743 | self.mode = mode |
| 744 | self.result_name = name |
| 745 | |
| 746 | def eval(self, a): |
| 747 | return tf.pad( |
| 748 | a, |
| 749 | self.padding, |
| 750 | mode=self.mode, |
| 751 | constant_values=0, |
| 752 | name=self.result_name, |
| 753 | ) |
| 754 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 755 | class Pad: |
| 756 | def __init__(self, padding, name): |
| 757 | self.padding = padding |
| 758 | self.result_name = name |
| 759 | |
| 760 | def eval(self, a): |
| 761 | return tf.pad( |
| 762 | a, |
| 763 | self.padding, |
| 764 | mode="CONSTANT", |
| 765 | constant_values=0, |
| 766 | name=self.result_name, |
| 767 | ) |
| 768 | |
| 769 | class ExpandDims: |
| 770 | def __init__(self, axis, name): |
| 771 | self.axis = axis |
| 772 | self.result_name = name |
| 773 | |
| 774 | def eval(self, a): |
| 775 | return tf.expand_dims(a, self.axis, name=self.result_name) |
| 776 | |
| 777 | class Shape: |
| 778 | def __init__(self, name): |
| 779 | self.result_name = name |
| 780 | |
| 781 | def eval(self, a): |
| 782 | return tf.shape(a, name=self.result_name) |
| 783 | |
| 784 | class Rank: |
| 785 | def __init__(self, name): |
| 786 | self.result_name = name |
| 787 | |
| 788 | def eval(self, a): |
| 789 | return tf.rank(a, name=self.result_name) |
| 790 | |
| 791 | class Fill: |
| 792 | def __init__(self, shape, value, name): |
| 793 | self.shape = shape |
| 794 | self.value = value |
| 795 | self.result_name = name |
| 796 | |
| 797 | def eval(self, a): |
| 798 | return tf.fill(self.shape, self.value, name=self.result_name) |
| 799 | |
| 800 | class Elu: |
| 801 | def __init__(self, name): |
| 802 | self.result_name = name |
| 803 | |
| 804 | def eval(self, a): |
| 805 | return tf.nn.elu(a, name=self.result_name) |
| 806 | |
| 807 | class Softmax: |
| 808 | def __init__(self, name): |
| 809 | self.result_name = name |
| 810 | |
| 811 | def eval(self, a): |
| 812 | return tf.nn.softmax(a, name=self.result_name) |
| 813 | |
| 814 | class LogSoftmax: |
| 815 | def __init__(self, name): |
| 816 | self.result_name = name |
| 817 | |
| 818 | def eval(self, a): |
| 819 | return tf.nn.log_softmax(a, name=self.result_name) |
| 820 | |
| 821 | class MatMul: |
| 822 | def __init__(self, name): |
| 823 | self.result_name = name |
| 824 | |
| 825 | def eval(self, a, b): |
| 826 | return tf.linalg.matmul(a, b, name=self.result_name) |
| 827 | |
| 828 | class AddScalar: |
| 829 | def __init__(self, name): |
| 830 | self.result_name = name |
| 831 | |
| 832 | def eval(self, a): |
| 833 | return tf.add(a, 1, name=self.result_name) |
| 834 | |
| 835 | class Add1d: |
| 836 | def __init__(self, name): |
| 837 | self.result_name = name |
| 838 | |
| 839 | def eval(self, a, b): |
| 840 | if len(b.shape) > 1: |
| 841 | b_1d = tf.reduce_sum(b, axis=list(range(0, len(b.shape) - 1, 1))) |
| 842 | else: |
| 843 | b_1d = b |
| 844 | return tf.add(a, b_1d, name=self.result_name) |
| 845 | |
| 846 | class Split: |
| 847 | def __init__(self, num_splits, axis, name): |
| 848 | self.num_splits = num_splits |
| 849 | self.axis = axis |
| 850 | self.result_name = name |
| 851 | |
| 852 | def eval(self, a): |
| 853 | # The split op generates a list of outputs. Since we have difficulty |
| 854 | # serializing a list or array of Numpy arrays, we will reduce each of |
| 855 | # the results |
| 856 | |
| 857 | if not isinstance(self.num_splits, list): |
| 858 | split_op = tf.split( |
| 859 | a, num_or_size_splits=self.num_splits, axis=self.axis, name="split" |
| 860 | ) |
| 861 | result_count = self.num_splits |
| 862 | else: |
| 863 | num_split = np.asarray(self.num_splits, dtype=np.int32) |
| 864 | split_vec_op = tf.compat.v1.constant( |
| 865 | num_split, |
| 866 | shape=num_split.shape, |
| 867 | dtype=tf.int32, |
| 868 | name="const_split_vec", |
| 869 | ) |
| 870 | split_op = tf.split( |
| 871 | a, num_or_size_splits=split_vec_op, axis=self.axis, name="split" |
| 872 | ) |
| 873 | result_count = num_split.shape[0] |
| 874 | |
| 875 | sums = [] |
| 876 | for i in range(result_count): |
| 877 | sums.append(tf.math.reduce_sum(split_op[i], name="reduce_{}".format(i))) |
| 878 | return tf.stack(sums, 0, name=self.result_name) |
| 879 | |
| 880 | class Tile: |
| 881 | def __init__(self, multiples, name): |
| 882 | self.multiples = multiples |
| 883 | self.result_name = name |
| 884 | |
| 885 | def eval(self, a): |
| 886 | t = tf.tile(a, self.multiples, name="tile") |
| 887 | return tf.identity(t, name=self.result_name) |
| 888 | |
| 889 | class Reverse: |
| 890 | def __init__(self, axis, name): |
| 891 | self.axis = axis |
| 892 | self.result_name = name |
| 893 | |
| 894 | def eval(self, a): |
| 895 | return tf.reverse(a, [self.axis], name=self.result_name) |
| 896 | |
| 897 | class Gather: |
| 898 | def __init__(self, indices, batch_dims, axis, name): |
| 899 | self.indices = indices |
| 900 | self.batch_dims = batch_dims |
| 901 | self.axis = axis |
| 902 | self.result_name = name |
| 903 | |
| 904 | def eval(self, a): |
| 905 | return tf.gather( |
| 906 | a, |
| 907 | self.indices, |
| 908 | batch_dims=self.batch_dims, |
| 909 | axis=self.axis, |
| 910 | name=self.result_name, |
| 911 | ) |
| 912 | |
| 913 | class GatherNd: |
| 914 | def __init__(self, indices, name): |
| 915 | self.indices = indices |
| 916 | self.result_name = name |
| 917 | |
| 918 | def eval(self, a): |
| 919 | return tf.gather_nd(a, self.indices, name=self.result_name) |
| 920 | |
| 921 | class ScatterNd: |
| 922 | def __init__(self, shape, indices_shape, N, rng, name): |
| 923 | self.shape = shape |
| 924 | self.indices_shape = indices_shape |
| 925 | self.N = N |
| 926 | self.rng = rng |
| 927 | self.result_name = name |
| 928 | |
| 929 | def eval(self, a): |
| 930 | |
| 931 | # This operator is special. The indices and updates tensors really need |
| 932 | # to be created together, but in the current structure of this tool there |
| 933 | # is no way to do that before now. The number of updates is determined by |
| 934 | # the indices, so we can really only create that after indices; but we |
| 935 | # don't know the type at that time. |
| 936 | # |
| 937 | # Shapes are guaranteed deterministic, but we'll use our rng |
| 938 | # copied from the arggen stage. It's possible that index and |
| 939 | # update *values* will be non-deterministic. |
| 940 | # |
| 941 | # We take the tensor_tensor simply to get the dtype. |
| 942 | |
| 943 | shape_const = tf.constant(self.shape, tf.int32) |
| 944 | |
| 945 | updates_shape = list(self.indices_shape[:-1]) |
| 946 | updates_shape.extend(self.shape[self.indices_shape[-1] :]) |
| 947 | |
| 948 | updates_const = tf.constant(TGen.getRand(updates_shape, a.dtype, self.rng)) |
| 949 | |
| 950 | indices = np.zeros(self.indices_shape, dtype=np.int32) |
| 951 | |
| 952 | # We need to generate the random indices tensor based on the |
| 953 | # limits of 'shape' for each dimension. Surely, there is a faster |
| 954 | # vectorized way to do this, but the tensors are fairly small so we |
| 955 | # will do this one element at a time. Each element needs to be sized based |
| 956 | # on the size of the last dimension. |
| 957 | for idx in np.ndindex(indices.shape): |
| 958 | indices[idx] = self.rng.integers(0, self.shape[idx[-1]], size=1)[0] |
| 959 | # print('{} {}'.format(idx, indices[idx])) |
| 960 | |
| 961 | indices_const = tf.constant(indices, dtype=tf.int32) |
| 962 | |
| 963 | return tf.scatter_nd( |
| 964 | indices=indices_const, |
| 965 | updates=updates_const, |
| 966 | shape=shape_const, |
| 967 | name=self.result_name, |
| 968 | ) |
| 969 | |
| 970 | class SpaceToBatch: |
| 971 | def __init__(self, block_shape, padding, name): |
| 972 | self.block_shape = block_shape |
| 973 | self.padding = padding |
| 974 | self.result_name = name |
| 975 | |
| 976 | def eval(self, a): |
| 977 | return tf.space_to_batch( |
| 978 | a, self.block_shape, self.padding, name=self.result_name |
| 979 | ) |
| 980 | |
| 981 | class BatchToSpace: |
| 982 | def __init__(self, block_shape, cropping, name): |
| 983 | self.block_shape = block_shape |
| 984 | self.cropping = cropping |
| 985 | self.result_name = name |
| 986 | |
| 987 | def eval(self, a): |
| 988 | # transpose to swap depth and batch first. this could avoid adding new shape |
| 989 | block_rank = len(self.block_shape) |
| 990 | perm = [len(a.shape) - 1] |
| 991 | for i in range(block_rank): |
| 992 | perm.append(i + 1) |
| 993 | perm.append(0) |
| 994 | transpose_op = tf.transpose(a, perm) |
| 995 | return tf.batch_to_space( |
| 996 | transpose_op, self.block_shape, self.cropping, name=self.result_name |
| 997 | ) |
| 998 | |
| 999 | class SpaceToDepth: |
| 1000 | def __init__(self, block_shape, name): |
| 1001 | self.block_shape = block_shape |
| 1002 | self.result_name = name |
| 1003 | |
| 1004 | def eval(self, a): |
| 1005 | return tf.nn.space_to_depth(a, self.block_shape, name=self.result_name) |
| 1006 | |
| 1007 | class DepthToSpace: |
| 1008 | def __init__(self, block_shape, name): |
| 1009 | self.block_shape = block_shape |
| 1010 | self.result_name = name |
| 1011 | |
| 1012 | def eval(self, a): |
| 1013 | return tf.nn.depth_to_space(a, self.block_shape, name=self.result_name) |
| 1014 | |
| 1015 | class OneHot: |
| 1016 | def __init__(self, depth, axis, name): |
| 1017 | self.depth = depth |
| 1018 | self.axis = axis |
| 1019 | self.result_name = name |
| 1020 | |
| 1021 | def eval(self, indices, on_value, off_value): |
| 1022 | return tf.one_hot( |
| 1023 | indices, |
| 1024 | self.depth, |
| 1025 | on_value, |
| 1026 | off_value, |
| 1027 | self.axis, |
| 1028 | on_value.dtype, |
| 1029 | self.result_name, |
| 1030 | ) |
| 1031 | |
| 1032 | class Fakequant: |
| 1033 | def __init__(self, num_bits, narrow_range, name): |
| 1034 | self.num_bits = num_bits |
| 1035 | self.narrow_range = narrow_range |
| 1036 | self.result_name = name |
| 1037 | |
| 1038 | def eval(self, a): |
| 1039 | return tf.quantization.fake_quant_with_min_max_args( |
| 1040 | a, |
| 1041 | min=-2.0, |
| 1042 | max=2.0, |
| 1043 | num_bits=self.num_bits, |
| 1044 | narrow_range=self.narrow_range, |
| 1045 | name=self.result_name, |
| 1046 | ) |
| 1047 | |
| 1048 | class ResizeNearest: |
| 1049 | def __init__(self, name): |
| 1050 | self.result_name = name |
| 1051 | |
| 1052 | def eval(self, a): |
| 1053 | out_shape = [] |
| 1054 | out_shape.append(a.shape[1] * 2) |
| 1055 | out_shape.append(a.shape[2] * 2) |
| 1056 | |
| 1057 | # tf.image.resize() will overwrite the node name with result_name + |
| 1058 | # '/BILINEAR' need to add extra identity to force output tensor name to |
| 1059 | # result_name return tf.image.resize(a, out_shape, |
| 1060 | # method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, name=result_name) |
| 1061 | resize = tf.image.resize( |
| 1062 | a, |
| 1063 | out_shape, |
| 1064 | method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, |
| 1065 | name="resize", |
| 1066 | ) |
| 1067 | return tf.identity(resize, name=self.result_name) |
| 1068 | |
| 1069 | class ResizeBilinear: |
| 1070 | def __init__(self, name): |
| 1071 | self.result_name = name |
| 1072 | |
| 1073 | def eval(self, a): |
| 1074 | out_shape = [] |
| 1075 | out_shape.append(a.shape[1] * 2) |
| 1076 | out_shape.append(a.shape[2] * 2) |
| 1077 | |
| 1078 | # tf.image.resize() will overwrite the node name with result_name + |
| 1079 | # '/BILINEAR' need to add extra identity to force output tensor name to |
| 1080 | # result_name return tf.image.resize(a, out_shape, |
| 1081 | # method=tf.image.ResizeMethod.NEAREST_NEIGHBOR, name=result_name) |
| 1082 | resize = tf.image.resize( |
| 1083 | a, out_shape, method=tf.image.ResizeMethod.BILINEAR, name="resize" |
| 1084 | ) |
| 1085 | return tf.identity(resize, name=self.result_name) |
| 1086 | |
TatWai Chong | f732609 | 2022-06-08 12:17:14 -0700 | [diff] [blame] | 1087 | # New tf resize set (align_corners, half_pixel_centers) = (false, true) by default. |
| 1088 | # Test the rest option combinations here. |
| 1089 | # Note that (align_corners, half_pixel_centers) = (true, true) is NOT valid. |
| 1090 | class ResizeBilinearV1AlignCorners: |
| 1091 | def __init__(self, name): |
| 1092 | self.result_name = name |
| 1093 | |
| 1094 | def eval(self, a): |
| 1095 | out_shape = [] |
| 1096 | out_shape.append(a.shape[1] * 2) |
| 1097 | out_shape.append(a.shape[2] * 2) |
| 1098 | |
| 1099 | resize = tf.compat.v1.image.resize_bilinear( |
| 1100 | a, |
| 1101 | out_shape, |
| 1102 | align_corners=True, |
| 1103 | name="resize", |
| 1104 | half_pixel_centers=False, |
| 1105 | ) |
| 1106 | return tf.identity(resize, name=self.result_name) |
| 1107 | |
| 1108 | class ResizeBilinearV1None: |
| 1109 | def __init__(self, name): |
| 1110 | self.result_name = name |
| 1111 | |
| 1112 | def eval(self, a): |
| 1113 | out_shape = [] |
| 1114 | out_shape.append(a.shape[1] * 2) |
| 1115 | out_shape.append(a.shape[2] * 2) |
| 1116 | |
| 1117 | resize = tf.compat.v1.image.resize_bilinear( |
| 1118 | a, |
| 1119 | out_shape, |
| 1120 | align_corners=False, |
| 1121 | name="resize", |
| 1122 | half_pixel_centers=False, |
| 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) |