microsoft/onnxruntime-extensions
Publicmirrored fromhttps://github.com/microsoft/onnxruntime-extensionsAvailable
onnxruntime_extensions/pnp/_onnx_ops.py
1544lines · modecode
| 1 | # Copyright (c) Microsoft Corporation. All rights reserved. |
| 2 | # Licensed under the MIT License. See License.txt in the project root for |
| 3 | # license information. |
| 4 | ############################################################################### |
| 5 | import warnings |
| 6 | import numpy as np |
| 7 | from onnx import helper, defs as onnx_defs, onnx_pb as onnx_proto |
| 8 | from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE |
| 9 | |
| 10 | |
| 11 | DEFAULT_OPSET_NUMBER = 13 # The maximum opset supported by the converter in the code branch. |
| 12 | # From https://github.com/onnx/onnx/blob/master/docs/Versioning.md |
| 13 | OPSET_TO_IR_VERSION = { |
| 14 | 1: 3, 2: 3, 3: 3, 4: 3, 5: 3, 6: 3, |
| 15 | 7: 3, 8: 3, 9: 4, 10: 5, 11: 6, 12: 7, |
| 16 | 13: 7, 14: 7, 15: 8, 16: 8, 17: 8 |
| 17 | } |
| 18 | if hasattr(helper, 'VERSION_TABLE'): |
| 19 | OPSET_TO_IR_VERSION = {row[2]: row[1] for row in helper.VERSION_TABLE} |
| 20 | |
| 21 | |
| 22 | def _get_main_opset_version(model): |
| 23 | """ |
| 24 | Returns the main opset version. |
| 25 | """ |
| 26 | for op in model.opset_import: |
| 27 | if op.domain == '' or op.domain == 'ai.onnx': |
| 28 | return op.version |
| 29 | return None |
| 30 | |
| 31 | |
| 32 | def onnx_builtin_opset_version(): |
| 33 | return onnx_defs.onnx_opset_version() |
| 34 | |
| 35 | |
| 36 | def get_maximum_opset_supported(): |
| 37 | return min(DEFAULT_OPSET_NUMBER, onnx_builtin_opset_version()) |
| 38 | |
| 39 | |
| 40 | def make_model_ex(graph, imported_opset_pairs, target_default_opset, **kwargs): |
| 41 | onnx_model = helper.make_model(graph, **kwargs) |
| 42 | |
| 43 | # Merge operator sets for the same domain, the largest version number would be kept |
| 44 | purified_operator_set = dict() |
| 45 | for op_domain, op_version in imported_opset_pairs: |
| 46 | if op_domain not in purified_operator_set: |
| 47 | if op_domain == '' or op_domain == 'ai.onnx': |
| 48 | # Initializers are a subset of graph inputs for IR_VERSION <= 3 (target opset < 8). |
| 49 | # Need upgrade opv since initializers are separate for IR_VERSION >= 4 to pass onnx.checker. |
| 50 | if op_version < 8 and target_default_opset is not None and target_default_opset >= 8: |
| 51 | op_version = 8 |
| 52 | purified_operator_set[op_domain] = op_version |
| 53 | else: |
| 54 | purified_operator_set[op_domain] = max(purified_operator_set[op_domain], op_version) |
| 55 | |
| 56 | # Fill operator sets |
| 57 | i = 0 |
| 58 | for op_domain, op_version in purified_operator_set.items(): |
| 59 | if i == 0 and len(onnx_model.opset_import) == 1: |
| 60 | # Overwrite the default operator set created by helper.make_model(...) |
| 61 | op_set = onnx_model.opset_import[0] |
| 62 | else: |
| 63 | # Just create one ONNX element in opset_import |
| 64 | op_set = onnx_model.opset_import.add() |
| 65 | op_set.domain = op_domain |
| 66 | op_set.version = op_version |
| 67 | i += 1 |
| 68 | if op_domain == '' or op_domain == 'ai.onnx': |
| 69 | if target_default_opset < op_version: |
| 70 | raise RuntimeError(('The specified opset %d is too low to convert this model, ' + |
| 71 | 'which requires at least opset %d.') % (target_default_opset, op_version)) |
| 72 | elif target_default_opset > op_version: |
| 73 | warnings.warn('The maximum opset needed by this model is only %d.' % op_version) |
| 74 | else: |
| 75 | pass |
| 76 | |
| 77 | opv = _get_main_opset_version(onnx_model) or target_default_opset |
| 78 | irv = OPSET_TO_IR_VERSION.get(opv, onnx_proto.IR_VERSION) |
| 79 | onnx_model.ir_version = irv |
| 80 | return onnx_model |
| 81 | |
| 82 | |
| 83 | class _ONNXModelOperator: |
| 84 | def __init__(self, name, model, input, output): |
| 85 | self.name = name |
| 86 | self.model = model |
| 87 | self.input = input |
| 88 | self.output = output |
| 89 | |
| 90 | def __repr__(self): |
| 91 | """ |
| 92 | without this method, it's too slow for the debugging. |
| 93 | :return: |
| 94 | """ |
| 95 | return "name: {}, input: {}, output: {}".format(self.name, self.input, self.output) |
| 96 | |
| 97 | @property |
| 98 | def op_type(self): |
| 99 | return 'ModelOp' |
| 100 | |
| 101 | |
| 102 | class ONNXElementContainer: |
| 103 | |
| 104 | opdict_counter = {} |
| 105 | |
| 106 | def __init__(self, target_opset, parent=None): |
| 107 | """ |
| 108 | :param target_opset: number, for example, 7 for ONNX 1.2, and 8 for ONNX 1.3. |
| 109 | """ |
| 110 | self.inputs = [] |
| 111 | self.outputs = [] |
| 112 | self.initializers = [] |
| 113 | self.value_info = [] |
| 114 | self.nodes = [] |
| 115 | self.node_domain_version_pair_sets = set() |
| 116 | self.target_opset = target_opset |
| 117 | self.enable_optimizer = True |
| 118 | self.parent = parent |
| 119 | |
| 120 | # the following property make this container be compatible with onnx.GraphProto |
| 121 | @property |
| 122 | def initializer(self): |
| 123 | return self.initializers |
| 124 | |
| 125 | @property |
| 126 | def input(self): |
| 127 | return self.inputs |
| 128 | |
| 129 | @property |
| 130 | def output(self): |
| 131 | return self.outputs |
| 132 | |
| 133 | @staticmethod |
| 134 | def _make_value_info(variable): |
| 135 | value_info = helper.ValueInfoProto() |
| 136 | value_info.name = variable.full_name |
| 137 | value_info.type.CopyFrom(variable.type.to_onnx_type()) |
| 138 | if variable.type.doc_string: |
| 139 | value_info.doc_string = variable.type.doc_string |
| 140 | return value_info |
| 141 | |
| 142 | def add_input(self, variable): |
| 143 | """ |
| 144 | Add our Variable object defined _parser.py into the the input list of the final ONNX model |
| 145 | |
| 146 | :param variable: The Variable object to be added |
| 147 | """ |
| 148 | self.inputs.append(self._make_value_info(variable)) |
| 149 | |
| 150 | def add_output(self, variable): |
| 151 | """1 |
| 152 | Add our Variable object defined _parser.py into the the output list of the final ONNX model |
| 153 | |
| 154 | :param variable: The Variable object to be added |
| 155 | """ |
| 156 | self.outputs.append(self._make_value_info(variable)) |
| 157 | |
| 158 | def add_initializer(self, name, onnx_type, shape, content): |
| 159 | """ |
| 160 | Add a TensorProto into the initializer list of the final ONNX model |
| 161 | |
| 162 | :param name: Variable name in the produced ONNX model. |
| 163 | :param onnx_type: Element types allowed in ONNX tensor, e.g., TensorProto.FLOAT and TensorProto.STRING. |
| 164 | :param shape: Tensor shape, a list of integers. |
| 165 | :param content: Flattened tensor values (i.e., a float list or a float array). |
| 166 | """ |
| 167 | if any(d is None for d in shape): |
| 168 | raise ValueError('Shape of initializer cannot contain None') |
| 169 | tensor = helper.make_tensor(name, onnx_type, shape, content) |
| 170 | self.initializers.append(tensor) |
| 171 | |
| 172 | def add_value_info(self, variable): |
| 173 | self.value_info.append(self._make_value_info(variable)) |
| 174 | |
| 175 | def add_node(self, op_type, inputs, outputs, op_domain='', op_version=1, **attrs): |
| 176 | """ |
| 177 | Add a NodeProto into the node list of the final ONNX model. If the input operator's domain-version information |
| 178 | cannot be found in our domain-version pool (a Python set), we may add it. |
| 179 | |
| 180 | :param op_type: A string (e.g., Pool and Conv) indicating the type of the NodeProto |
| 181 | :param inputs: A list of strings. They are the input variables' names of the considered NodeProto |
| 182 | :param outputs: A list of strings. They are the output variables' names of the considered NodeProto |
| 183 | :param op_domain: The domain name (e.g., ai.onnx.ml) of the operator we are trying to add. |
| 184 | :param op_version: The version number (e.g., 0 and 1) of the operator we are trying to add. |
| 185 | :param attrs: A Python dictionary. Keys and values are attributes' names and attributes' values, respectively. |
| 186 | """ |
| 187 | |
| 188 | if isinstance(inputs, str): |
| 189 | inputs = [inputs] |
| 190 | if isinstance(outputs, str): |
| 191 | outputs = [outputs] |
| 192 | if not isinstance(inputs, (list, tuple)) or not all(isinstance(s, str) for s in inputs): |
| 193 | type_list = ','.join(list(str(type(s)) for s in inputs)) |
| 194 | raise ValueError('Inputs must be a list of string but get [%s]' % type_list) |
| 195 | if not isinstance(outputs, (list, tuple)) or not all(isinstance(s, str) for s in outputs): |
| 196 | type_list = ','.join(list(str(type(s)) for s in outputs)) |
| 197 | raise ValueError('Outputs must be a list of string but get [%s]' % type_list) |
| 198 | for k, v in attrs.items(): |
| 199 | if v is None: |
| 200 | raise ValueError('Failed to create ONNX node. Undefined attribute pair (%s, %s) found' % (k, v)) |
| 201 | |
| 202 | node = helper.make_node(op_type, inputs, outputs, **attrs) |
| 203 | node.domain = op_domain |
| 204 | |
| 205 | self.node_domain_version_pair_sets.add((op_domain, op_version)) |
| 206 | self.nodes.append(node) |
| 207 | |
| 208 | def add_model_node(self, inputs, outputs, name, model): |
| 209 | self.nodes.append(_ONNXModelOperator(name=name, model=model, input=inputs, output=outputs)) |
| 210 | |
| 211 | @classmethod |
| 212 | def get_unique_operator_name(cls, op_type: str): |
| 213 | name = op_type.lower() |
| 214 | nn = cls.opdict_counter.get(name, 0) |
| 215 | cls.opdict_counter[name] = nn + 1 |
| 216 | return name if nn == 0 else "{}_{}".format(name, nn+1) |
| 217 | |
| 218 | |
| 219 | def _create_name_or_use_existing_one(container, op_type, name): |
| 220 | return name or container.get_unique_operator_name(op_type) |
| 221 | |
| 222 | |
| 223 | class _OpSchema: |
| 224 | _ox = None # will be assigned by ONNXModelBuilder. |
| 225 | |
| 226 | def __init__(self, *args, **kwargs): |
| 227 | # self.op_builder = None |
| 228 | self.apply_fn = args[0] |
| 229 | self.inputs = kwargs['inputs'] if 'inputs' in kwargs else [] |
| 230 | self.outputs = kwargs['outputs'] if 'outputs' in kwargs else [] |
| 231 | |
| 232 | def __call__(self, *args, **kwargs): |
| 233 | assert self._ox is not None, 'no builder instance was created' |
| 234 | return self.apply_fn(self._ox, *args, **kwargs) |
| 235 | |
| 236 | # def __get__(self, instance, owner): |
| 237 | # if owner.__name__ == '_ONNXModelBuilder': |
| 238 | # self.op_builder = instance |
| 239 | # return self |
| 240 | |
| 241 | |
| 242 | def schema(apply_fn=None, *args, **kwargs): |
| 243 | if apply_fn is None: |
| 244 | def wrapper(fn): |
| 245 | return _OpSchema(fn, *args, **kwargs) |
| 246 | return wrapper |
| 247 | else: |
| 248 | # used as a function. |
| 249 | return _OpSchema(apply_fn, *args, **kwargs) |
| 250 | |
| 251 | |
| 252 | class _ONNXOperatorAPI: |
| 253 | _dt = onnx_proto.TensorProto |
| 254 | def get_unique_tensor_name(self, base): pass # implemented by the model builder |
| 255 | |
| 256 | def _apply_unary_operation(self, op_type, input_name, output_name, container, operator_name, **attrs): |
| 257 | name = _create_name_or_use_existing_one(container, op_type, operator_name) |
| 258 | |
| 259 | attrs['name'] = name |
| 260 | if container.target_opset < 6: |
| 261 | attrs['consumed_inputs'] = [0] |
| 262 | op_version = 1 |
| 263 | else: |
| 264 | op_version = 6 |
| 265 | |
| 266 | container.add_node(op_type, input_name, output_name, op_version=op_version, **attrs) |
| 267 | |
| 268 | def _apply_basic_numerical_operation(self, op_type, input_names, output_name, container, operator_name, |
| 269 | axis, broadcast): |
| 270 | name = _create_name_or_use_existing_one(container, op_type, operator_name) |
| 271 | |
| 272 | attrs = {} |
| 273 | if container.target_opset < 7: |
| 274 | # Before ONNX-1.2 (opset 7), broadcasting behavior is Caffe2-like. |
| 275 | if axis is not None: |
| 276 | attrs['axis'] = axis |
| 277 | if broadcast is not None: |
| 278 | attrs['broadcast'] = broadcast |
| 279 | |
| 280 | if container.target_opset < 6: |
| 281 | attrs['consumed_inputs'] = [0, 0] |
| 282 | op_version = 1 |
| 283 | else: |
| 284 | op_version = 6 |
| 285 | else: |
| 286 | # Since ONNX-1.2 (opset 7), broadcasting behavior is Numpy-like, so we don't need to specify any attributes |
| 287 | op_version = 7 |
| 288 | |
| 289 | container.add_node(op_type, input_names, output_name, op_version=op_version, name=name, **attrs) |
| 290 | |
| 291 | def _apply_pointwise_operation(self, op_type, input_names, output_name, container, operator_name): |
| 292 | name = _create_name_or_use_existing_one(container, op_type, operator_name) |
| 293 | attrs = {} |
| 294 | |
| 295 | if container.target_opset < 6: |
| 296 | attrs['consumed_inputs'] = [0] * len(input_names) |
| 297 | op_version = 1 |
| 298 | elif container.target_opset < 8: |
| 299 | op_version = 6 |
| 300 | else: |
| 301 | if container.target_opset < 12 or op_type == 'Mean': |
| 302 | op_version = 8 |
| 303 | else: |
| 304 | op_version = 12 |
| 305 | |
| 306 | container.add_node(op_type, input_names, output_name, op_version=op_version, name=name, **attrs) |
| 307 | |
| 308 | def abs(self, input_name, output_name, container, operator_name=None): |
| 309 | self._apply_unary_operation('Abs', input_name, output_name, container, operator_name=operator_name) |
| 310 | return output_name |
| 311 | |
| 312 | def add(self, input_names, output_name, container, operator_name=None, axis=None, broadcast=None): |
| 313 | self._apply_basic_numerical_operation('Add', input_names, output_name, container, operator_name=operator_name, |
| 314 | axis=axis, broadcast=broadcast) |
| 315 | return output_name |
| 316 | |
| 317 | def argmax(self, input_name, output_name, container, operator_name=None, axis=0, keepdims=1, |
| 318 | select_last_index=0): |
| 319 | name = _create_name_or_use_existing_one(container, 'ArgMax', operator_name) |
| 320 | attrs = {'axis': axis, 'keepdims': keepdims} |
| 321 | if container.target_opset < 11: |
| 322 | op_version = 1 |
| 323 | elif container.target_opset < 12: |
| 324 | op_version = 11 |
| 325 | else: |
| 326 | op_version = 12 |
| 327 | attrs['select_last_index'] = select_last_index |
| 328 | container.add_node('ArgMax', input_name, output_name, op_version=op_version, name=name, **attrs) |
| 329 | return output_name |
| 330 | |
| 331 | def argmin(self, input_name, output_name, container, operator_name=None, axis=0, keepdims=1, |
| 332 | select_last_index=0): |
| 333 | name = _create_name_or_use_existing_one(container, 'ArgMin', operator_name) |
| 334 | attrs = {'axis': axis, 'keepdims': keepdims} |
| 335 | if container.target_opset < 11: |
| 336 | op_version = 1 |
| 337 | elif container.target_opset < 12: |
| 338 | op_version = 11 |
| 339 | else: |
| 340 | op_version = 12 |
| 341 | attrs['select_last_index'] = select_last_index |
| 342 | container.add_node('ArgMin', input_name, output_name, op_version=op_version, name=name, **attrs) |
| 343 | return output_name |
| 344 | |
| 345 | def affine(self, input_name, output_name, container, operator_name=None, alpha=1., beta=0.): |
| 346 | if container.target_opset < 9: |
| 347 | op_type = 'Affine' |
| 348 | name = _create_name_or_use_existing_one(container, 'Affine', operator_name) |
| 349 | attrs = {'name': name, 'alpha': alpha, 'beta': beta} |
| 350 | container.add_node(op_type, input_name, output_name, **attrs) |
| 351 | else: |
| 352 | name = _create_name_or_use_existing_one(container, 'Affine', operator_name) |
| 353 | # Define a and b. |
| 354 | aName = self.get_unique_tensor_name(name + '_alpha') |
| 355 | container.add_initializer(aName, onnx_proto.TensorProto.FLOAT, [1], [alpha]) |
| 356 | bName = self.get_unique_tensor_name(name + '_beta') |
| 357 | container.add_initializer(bName, onnx_proto.TensorProto.FLOAT, [1], [beta]) |
| 358 | |
| 359 | # Compute Z = a * X, where X is the original input. |
| 360 | zName = self.get_unique_tensor_name(name + '_scaled') |
| 361 | self.mul([aName, input_name], zName, container) |
| 362 | |
| 363 | # Compute Y = Z + b, where Y is the final output. |
| 364 | self.add(self, [zName, bName], output_name, container) |
| 365 | return output_name |
| 366 | |
| 367 | def batch_norm(self, input_names, output_names, container, operator_name=None, |
| 368 | epsilon=None, is_test=None, momentum=None, spatial=None): |
| 369 | name = _create_name_or_use_existing_one(container, 'BatchNormalization', operator_name) |
| 370 | attrs = {'name': name, 'epsilon': epsilon, 'momentum': momentum} |
| 371 | |
| 372 | if container.target_opset < 9: |
| 373 | attrs['spatial'] = spatial |
| 374 | if container.target_opset < 7: |
| 375 | attrs['is_test'] = is_test |
| 376 | |
| 377 | if container.target_opset < 6: |
| 378 | attrs['consumed_inputs'] = [0] * len(input_names) |
| 379 | if len(input_names) > 3: |
| 380 | attrs['consumed_inputs'][3] = 1 |
| 381 | if len(input_names) > 4: |
| 382 | attrs['consumed_inputs'][4] = 2 |
| 383 | op_version = 1 |
| 384 | elif container.target_opset < 7: |
| 385 | op_version = 6 |
| 386 | elif container.target_opset < 9: |
| 387 | op_version = 7 |
| 388 | else: |
| 389 | op_version = 9 |
| 390 | |
| 391 | container.add_node('BatchNormalization', input_names, output_names, op_version=op_version, **attrs) |
| 392 | return output_names |
| 393 | |
| 394 | def cast(self, input_name, output_name, container, operator_name=None, to=None): |
| 395 | """ |
| 396 | :param to: enum defined in ONNX TensorProto.DataType, for example, TensorProto.FLOAT and TensorProto.INT64. |
| 397 | """ |
| 398 | name = _create_name_or_use_existing_one(container, 'Cast', operator_name) |
| 399 | attrs = {'name': name} |
| 400 | |
| 401 | d = onnx_proto.TensorProto.DataType.DESCRIPTOR |
| 402 | allowed_type_name_and_type_enum_pairs = {v.number: k for k, v in d.values_by_name.items()} |
| 403 | if to not in allowed_type_name_and_type_enum_pairs: |
| 404 | raise ValueError('Attribute "to" must be one of %s' % allowed_type_name_and_type_enum_pairs.keys()) |
| 405 | |
| 406 | if container.target_opset < 9: |
| 407 | if to in [onnx_proto.TensorProto.STRING, onnx_proto.TensorProto.COMPLEX64, onnx_proto.TensorProto.COMPLEX128]: |
| 408 | raise ValueError('Attribute "to" cannot correspond to a String or Complex TensorProto type.') |
| 409 | |
| 410 | if container.target_opset < 6: |
| 411 | # Convert enum to string, for example, TensorProto.INT64 to 'INT64' |
| 412 | attrs['to'] = allowed_type_name_and_type_enum_pairs[to] |
| 413 | op_version = 1 |
| 414 | else: |
| 415 | # Enum, for example, TensorProto.INT64 |
| 416 | attrs['to'] = to |
| 417 | op_version = 6 |
| 418 | else: |
| 419 | # Enum value, for example, TensorProto.INT64 |
| 420 | # String casting is supported in opset 9 |
| 421 | if to in [onnx_proto.TensorProto.COMPLEX64, onnx_proto.TensorProto.COMPLEX128]: |
| 422 | raise ValueError('Attribute "to" cannot correspond to a Complex TensorProto type.') |
| 423 | attrs['to'] = to |
| 424 | op_version = 9 |
| 425 | |
| 426 | container.add_node('Cast', input_name, output_name, op_version=op_version, **attrs) |
| 427 | return output_name |
| 428 | |
| 429 | def clip(self, input_name, output_name, container, operator_name=None, max=None, min=None): |
| 430 | name = _create_name_or_use_existing_one(container, 'Clip', operator_name) |
| 431 | attrs = {'name': name} |
| 432 | |
| 433 | if container.target_opset < 11: |
| 434 | if max is not None: |
| 435 | attrs['max'] = float(max) |
| 436 | if min is not None: |
| 437 | attrs['min'] = float(min) |
| 438 | |
| 439 | if container.target_opset < 6: |
| 440 | attrs['consumed_inputs'] = [0] |
| 441 | op_version = 1 |
| 442 | else: |
| 443 | op_version = 6 |
| 444 | |
| 445 | container.add_node('Clip', input_name, output_name, op_version=op_version, **attrs) |
| 446 | else: |
| 447 | if container.target_opset < 12: |
| 448 | op_version = 11 |
| 449 | else: |
| 450 | op_version = 12 |
| 451 | if min is None and max is not None: |
| 452 | raise RuntimeError("Operator 'Clip': min must be specified if max is.") |
| 453 | inputs = [input_name] |
| 454 | |
| 455 | if min is not None: |
| 456 | if isinstance(min, (np.ndarray, float, int)): |
| 457 | # add initializer |
| 458 | if isinstance(min, np.ndarray): |
| 459 | if len(min.shape) == 0: |
| 460 | min = [min] |
| 461 | elif min.shape == (1,): |
| 462 | min = list(min[0]) if hasattr(min[0], '__iter__') else list(min) |
| 463 | else: |
| 464 | raise RuntimeError("min must be an array of one element.") |
| 465 | else: |
| 466 | min = [min] |
| 467 | |
| 468 | # container in sklearn-onnx stores the computation type in |
| 469 | # container.dtype. |
| 470 | min_name = self.get_unique_tensor_name('clip_min') |
| 471 | if op_version < 12: |
| 472 | min = np.array(min, dtype=getattr(container, 'dtype', np.float32)) |
| 473 | container.add_initializer(min_name, getattr(container, 'proto_dtype', |
| 474 | onnx_proto.TensorProto.FLOAT), [], [min[0]]) |
| 475 | else: |
| 476 | min = np.array(min) |
| 477 | container.add_initializer(min_name, NP_TYPE_TO_TENSOR_TYPE[min.dtype], [], [min[0]]) |
| 478 | min = min_name |
| 479 | if isinstance(min, str): |
| 480 | inputs.append(min) |
| 481 | else: |
| 482 | raise RuntimeError("Parameter 'min' must be a string or a float.") |
| 483 | |
| 484 | if max is not None: |
| 485 | if min is None: |
| 486 | raise RuntimeError("Parameter 'min' must be specified if 'max' is.") |
| 487 | if isinstance(max, (np.ndarray, float, int)): |
| 488 | # add initializer |
| 489 | if isinstance(max, np.ndarray): |
| 490 | if len(max.shape) == 0: |
| 491 | max = [max] |
| 492 | elif max.shape == (1,): |
| 493 | max = list(max[0]) if hasattr(max[0], '__iter__') else list(max) |
| 494 | else: |
| 495 | raise RuntimeError("max must be an array of one element.") |
| 496 | else: |
| 497 | max = [max] |
| 498 | |
| 499 | max_name = self.get_unique_tensor_name('clip_max') |
| 500 | if op_version < 12: |
| 501 | max = np.array(max, dtype=getattr(container, 'dtype', np.float32)) |
| 502 | container.add_initializer(max_name, getattr(container, 'proto_dtype', |
| 503 | onnx_proto.TensorProto.FLOAT), [], [max[0]]) |
| 504 | else: |
| 505 | max = np.array(max) |
| 506 | container.add_initializer(max_name, NP_TYPE_TO_TENSOR_TYPE[max.dtype], [], [max[0]]) |
| 507 | max = max_name |
| 508 | if isinstance(max, str): |
| 509 | inputs.append(max) |
| 510 | else: |
| 511 | raise RuntimeError("Parameter 'max' must be a string or a float.") |
| 512 | |
| 513 | container.add_node('Clip', inputs, output_name, op_version=op_version, |
| 514 | **attrs) |
| 515 | return output_name |
| 516 | |
| 517 | def concat(self, input_names, output_name, container, operator_name=None, axis=0): |
| 518 | name = _create_name_or_use_existing_one(container, 'Concat', operator_name) |
| 519 | |
| 520 | if container.target_opset < 4: |
| 521 | op_version = 1 |
| 522 | elif container.target_opset < 11: |
| 523 | op_version = 4 |
| 524 | else: |
| 525 | op_version = 11 |
| 526 | |
| 527 | container.add_node('Concat', input_names, output_name, op_version=op_version, name=name, axis=axis) |
| 528 | return output_name |
| 529 | |
| 530 | def concat_from_sequence(self, input_names, output_name, container, operator_name=None, axis=0, new_axis=None): |
| 531 | name = _create_name_or_use_existing_one(container, 'Concat', operator_name) |
| 532 | attrs = {'axis': axis} |
| 533 | if new_axis is not None: |
| 534 | attrs['new_axis'] = new_axis |
| 535 | container.add_node('ConcatFromSequence', input_names, output_name, op_version=11, name=name, **attrs) |
| 536 | return output_name |
| 537 | |
| 538 | def constant(self, input_names, output_name, container, operator_name=None, value=None): |
| 539 | assert len(input_names) == 0 # only a placeholder to standardize the argument list. |
| 540 | name = _create_name_or_use_existing_one(container, 'Constant', operator_name) |
| 541 | |
| 542 | if value is None: |
| 543 | raise ValueError('Attribute "value" is a required argument.') |
| 544 | |
| 545 | if container.target_opset < 9: |
| 546 | op_version = 1 |
| 547 | elif container.target_opset < 11: |
| 548 | op_version = 9 |
| 549 | elif container.target_opset < 12: |
| 550 | op_version = 11 |
| 551 | else: |
| 552 | op_version = 12 |
| 553 | |
| 554 | if op_version < 12: |
| 555 | attrs = {'name': name, 'value': value} |
| 556 | else: |
| 557 | if isinstance(value, float): |
| 558 | attrs = {'name': name, 'value_float': value} |
| 559 | elif isinstance(value, int): |
| 560 | attrs = {'name': name, 'value_int': value} |
| 561 | elif isinstance(value, str): |
| 562 | attrs = {'name': name, 'value_string': value} |
| 563 | else: |
| 564 | attrs = {'name': name, 'value': value} |
| 565 | |
| 566 | container.add_node('Constant', [], output_name, op_version=op_version, **attrs) |
| 567 | return output_name |
| 568 | |
| 569 | def constant_of_shape(self, input_names, output_name, container, operator_name=None, value=None): |
| 570 | attrs = {} |
| 571 | if value is not None: |
| 572 | attrs['value'] = value |
| 573 | name = _create_name_or_use_existing_one(container, 'ConstantOfShape', operator_name) |
| 574 | container.add_node('ConstantOfShape', input_names, output_name, name=name, op_version=9, **attrs) |
| 575 | return output_name |
| 576 | |
| 577 | def conv(self, input_names, output_name, container, operator_name=None, **attrs): |
| 578 | name = _create_name_or_use_existing_one(container, 'Conv', operator_name) |
| 579 | |
| 580 | if container.target_opset < 11: |
| 581 | op_version = 1 |
| 582 | else: |
| 583 | op_version = 11 |
| 584 | |
| 585 | container.add_node('Conv', input_names, output_name, name=name, op_version=op_version, **attrs) |
| 586 | return output_name |
| 587 | |
| 588 | def crop_height_width(self, input_name, output_name, container, operator_name=None, |
| 589 | top_border=0, bottom_border=0, left_border=0, right_border=0): |
| 590 | name = container.get_unique_operator_name('CropHeightWidth') |
| 591 | if container.target_opset < 9: |
| 592 | # If operator set < 9, we can use the experimental Crop in ONNX. |
| 593 | attrs = {'name': name, 'border': [left_border, top_border, right_border, bottom_border]} |
| 594 | container.add_node('Crop', input_name, output_name, **attrs) |
| 595 | else: |
| 596 | # The experimental Crop in ONNX is removed after operator set 9, so we |
| 597 | # switch to ONNX DynamicSlice operator. |
| 598 | |
| 599 | # CoreML only crops H- and W-axes. |
| 600 | axes = [2, 3] |
| 601 | axes_name = self.get_unique_tensor_name(name + '_axes') |
| 602 | container.add_initializer(axes_name, onnx_proto.TensorProto.INT64, |
| 603 | [len(axes)], axes) |
| 604 | |
| 605 | # Number of cropped pixels is the starting index of the remained region. |
| 606 | starts = [top_border, left_border] |
| 607 | starts_name = self.get_unique_tensor_name(name + '_starts') |
| 608 | container.add_initializer(starts_name, onnx_proto.TensorProto.INT64, |
| 609 | [len(starts)], starts) |
| 610 | |
| 611 | # First we assume no cropping is needed at the end of those axes. |
| 612 | # We will change this right below depending on Crop's configuration. |
| 613 | ends = [np.iinfo(np.int64).max] * 2 |
| 614 | |
| 615 | # Crop n pixel means the end index (exclusive) is -n. Note that indexing |
| 616 | # system is zero-based. |
| 617 | if bottom_border > 0: |
| 618 | ends[0] = -bottom_border |
| 619 | if right_border > 0: |
| 620 | ends[1] = -right_border |
| 621 | |
| 622 | # Add the adjusted ends. |
| 623 | ends_name = self.get_unique_tensor_name(name + '_ends') |
| 624 | container.add_initializer(ends_name, onnx_proto.TensorProto.INT64, |
| 625 | [len(ends)], ends) |
| 626 | |
| 627 | # Collect all input names as a list because DynamicSlice has multiple inputs. |
| 628 | input_list = [input_name, starts_name, ends_name, axes_name] |
| 629 | container.add_node('DynamicSlice', input_list, output_name, op_version=9) |
| 630 | return output_name |
| 631 | |
| 632 | def cumsum(self, input_names, output_names, container, operator_name=None, axis=None): |
| 633 | name = _create_name_or_use_existing_one(container, 'cumsum', operator_name) |
| 634 | assert axis is not None, "Axis in Op CumSum must be provided." |
| 635 | axis_name = self.get_unique_tensor_name(name+'_dim') |
| 636 | container.add_initializer(axis_name, |
| 637 | onnx_proto.TensorProto.INT64, |
| 638 | [1], [axis]) |
| 639 | container.add_node('CumSum', input_names + [axis_name], output_names, op_version=11, name=name) |
| 640 | return output_names |
| 641 | |
| 642 | def div(self, input_names, output_name, container, operator_name=None, axis=None, broadcast=None): |
| 643 | self._apply_basic_numerical_operation('Div', input_names, output_name, |
| 644 | container, operator_name, |
| 645 | axis, broadcast) |
| 646 | return output_name |
| 647 | |
| 648 | def elu(self, input_name, output_name, container, operator_name=None, alpha=1.0): |
| 649 | self._apply_unary_operation('Elu', input_name, output_name, container, operator_name, alpha=alpha) |
| 650 | return output_name |
| 651 | |
| 652 | def equal(self, input_names, output_name, container, operator_name=None): |
| 653 | name = _create_name_or_use_existing_one(container, 'equal', operator_name) |
| 654 | if container.target_opset < 7: |
| 655 | op_version = 1 |
| 656 | elif container.target_opset < 9: |
| 657 | op_version = 7 |
| 658 | else: |
| 659 | op_version = 9 |
| 660 | container.add_node('Equal', input_names, output_name, name=name, op_version=op_version) |
| 661 | return output_name |
| 662 | |
| 663 | def exp(self, input_name, output_name, container, operator_name=None): |
| 664 | self._apply_unary_operation('Exp', input_name, output_name, container, operator_name=operator_name) |
| 665 | return output_name |
| 666 | |
| 667 | def floor(self, input_name, output_name, container, operator_name=None): |
| 668 | self._apply_unary_operation('Floor', input_name, output_name, container, operator_name=operator_name) |
| 669 | return output_name |
| 670 | |
| 671 | def flatten(self, input_name, output_name, container, operator_name=None, axis=1): |
| 672 | name = _create_name_or_use_existing_one(container, 'Flatten', operator_name) |
| 673 | if container.target_opset < 9: |
| 674 | op_version = 1 |
| 675 | elif container.target_opset < 11: |
| 676 | op_version = 9 |
| 677 | else: |
| 678 | op_version = 11 |
| 679 | container.add_node('Flatten', input_name, output_name, name=name, op_version=op_version, axis=axis) |
| 680 | return output_name |
| 681 | |
| 682 | def gather(self, input_names, output_name, container, operator_name=None, axis=0): |
| 683 | name = _create_name_or_use_existing_one(container, 'Gather', operator_name) |
| 684 | if container.target_opset < 11: |
| 685 | op_version = 1 |
| 686 | else: |
| 687 | op_version = 11 |
| 688 | |
| 689 | container.add_node('Gather', input_names, output_name, name=name, op_version=op_version, axis=axis) |
| 690 | return output_name |
| 691 | |
| 692 | def gemm(self, input_name, output_name, container, operator_name=None, alpha=1.0, beta=1.0, |
| 693 | transA=0, transB=0): |
| 694 | """ |
| 695 | Applies operator `gemm <https://github.com/onnx/onnx/blob/master/docs/Operators.md#gemm>`. |
| 696 | """ |
| 697 | name = _create_name_or_use_existing_one(container, 'Gemm', operator_name) |
| 698 | attrs = {'alpha': alpha, 'beta': beta, 'transA': transA, 'transB': transB} |
| 699 | if container.target_opset < 5: |
| 700 | attrs['op_version'] = 1 |
| 701 | attrs['broadcast'] = 1 |
| 702 | elif container.target_opset < 7: |
| 703 | attrs['op_version'] = 6 |
| 704 | attrs['broadcast'] = 1 |
| 705 | elif container.target_opset < 11: |
| 706 | attrs['op_version'] = 7 |
| 707 | else: |
| 708 | attrs['op_version'] = 11 |
| 709 | |
| 710 | container.add_node('Gemm', input_name, output_name, name=name, **attrs) |
| 711 | return output_name |
| 712 | |
| 713 | @schema(outputs=((_dt.BOOL, []), ),) |
| 714 | def greater(self, input_names, output_name, container, operator_name=None): |
| 715 | name = _create_name_or_use_existing_one(container, 'Greater', operator_name) |
| 716 | if container.target_opset < 7: |
| 717 | op_version = 1 |
| 718 | elif container.target_opset < 9: |
| 719 | op_version = 7 |
| 720 | else: |
| 721 | op_version = 9 |
| 722 | |
| 723 | container.add_node('Greater', input_names, output_name, name=name, op_version=op_version) |
| 724 | return output_name |
| 725 | |
| 726 | def _apply_convert_compare_equal(self, input_names, output_name, container, operator_name, |
| 727 | tf_op_string, onnx_op_string_rev, onnx_op_string): |
| 728 | if container.target_opset < 7: |
| 729 | raise ValueError(tf_op_string + " op is not supported for opset < 7") |
| 730 | elif container.target_opset < 9: |
| 731 | op_version = 7 |
| 732 | elif container.target_opset < 12: |
| 733 | op_version = 9 |
| 734 | else: |
| 735 | op_version = 12 |
| 736 | name = _create_name_or_use_existing_one(container, tf_op_string, operator_name) |
| 737 | if op_version < 9: |
| 738 | compare_input_0 = self.get_unique_tensor_name(name + '_input_0_cast') |
| 739 | container.add_node('Cast', [input_names[0]], compare_input_0, name=name + '_input_0_cast', to=1) |
| 740 | compare_input_1 = self.get_unique_tensor_name(name + '_input_1_cast') |
| 741 | container.add_node('Cast', [input_names[1]], compare_input_1, name=name + '_input_1_cast', to=1) |
| 742 | less_out = self.get_unique_tensor_name(name + '_less_out') |
| 743 | container.add_node(onnx_op_string_rev, [compare_input_0, compare_input_1], less_out, |
| 744 | name=name + '_' + onnx_op_string_rev.lower(), |
| 745 | op_version=op_version) |
| 746 | container.add_node('Not', less_out, output_name, name=name + '_not') |
| 747 | elif op_version < 12: |
| 748 | compare_node = self.get_unique_tensor_name(name + '_compare_node') |
| 749 | container.add_node(onnx_op_string_rev, input_names, compare_node, |
| 750 | name=name + '_' + onnx_op_string_rev.lower(), |
| 751 | op_version=op_version) |
| 752 | container.add_node('Not', [compare_node], output_name, name=name) |
| 753 | else: |
| 754 | container.add_node(onnx_op_string, input_names, output_name, |
| 755 | name=name + '_' + onnx_op_string_rev.lower(), op_version=op_version) |
| 756 | |
| 757 | def greater_or_equal(self, input_names, output_name, container, operator_name=None): |
| 758 | self._apply_convert_compare_equal(input_names, output_name, container, operator_name, |
| 759 | 'GreaterEqual', 'Less', 'GreaterOrEqual') |
| 760 | return output_name |
| 761 | |
| 762 | def less_or_equal(self, input_names, output_name, container, operator_name=None): |
| 763 | self._apply_convert_compare_equal(input_names, output_name, container, |
| 764 | operator_name, 'LessEqual', 'Greater', 'LessOrEqual') |
| 765 | return output_name |
| 766 | |
| 767 | def gru(self, input_names, output_names, container, operator_name=None, output_seq=0, reset_after=0, **attrs): |
| 768 | name = _create_name_or_use_existing_one(container, 'GRU', operator_name) |
| 769 | if container.target_opset < 3: |
| 770 | op_version = 1 |
| 771 | attrs['output_sequence'] = 1 if output_seq else 0 |
| 772 | else: |
| 773 | attrs['linear_before_reset'] = 1 if reset_after else 0 |
| 774 | if container.target_opset <= 5: |
| 775 | attrs['output_sequence'] = 1 if output_seq else 0 |
| 776 | op_version = 3 |
| 777 | else: |
| 778 | op_version = 7 |
| 779 | |
| 780 | container.add_node('GRU', input_names, output_names, name=name, op_version=op_version, **attrs) |
| 781 | return output_names |
| 782 | |
| 783 | def hard_sigmoid(self, input_name, output_name, container, operator_name=None, alpha=None, beta=None): |
| 784 | self._apply_unary_operation('HardSigmoid', input_name, output_name, container, operator_name, |
| 785 | alpha=alpha, beta=beta) |
| 786 | return output_name |
| 787 | |
| 788 | def identity(self, input_name, output_name, container, operator_name=None): |
| 789 | name = _create_name_or_use_existing_one(container, 'Identity', operator_name) |
| 790 | container.add_node('Identity', input_name, output_name, name=name) |
| 791 | return output_name |
| 792 | |
| 793 | def instance_norm(self, input_names, output_name, container, operator_name=None, epsilon=1e-5): |
| 794 | name = _create_name_or_use_existing_one(container, 'InstanceNormalization', operator_name) |
| 795 | attrs = {'name': name, 'epsilon': epsilon} |
| 796 | |
| 797 | if container.target_opset < 2: |
| 798 | attrs['consumed_inputs'] = [0] * len(input_names) |
| 799 | op_version = 1 |
| 800 | else: |
| 801 | op_version = 6 |
| 802 | |
| 803 | container.add_node('InstanceNormalization', input_names, output_name, op_version=op_version, **attrs) |
| 804 | return output_name |
| 805 | |
| 806 | def leaky_relu(self, input_name, output_name, container, operator_name=None, alpha=0.01): |
| 807 | self._apply_unary_operation('LeakyRelu', input_name, output_name, container, operator_name, alpha=alpha) |
| 808 | return output_name |
| 809 | |
| 810 | def less(self, input_names, output_name, container, operator_name=None): |
| 811 | name = _create_name_or_use_existing_one(container, 'Less', operator_name) |
| 812 | if container.target_opset < 7: |
| 813 | op_version = 1 |
| 814 | elif container.target_opset < 9: |
| 815 | op_version = 7 |
| 816 | else: |
| 817 | op_version = 9 |
| 818 | |
| 819 | container.add_node('Less', input_names, output_name, name=name, op_version=op_version) |
| 820 | return output_name |
| 821 | |
| 822 | def log(self, input_name, output_name, container, operator_name=None): |
| 823 | self._apply_unary_operation('Log', input_name, output_name, container, operator_name=operator_name) |
| 824 | return output_name |
| 825 | |
| 826 | def lstm(self, input_names, output_names, container, operator_name=None, output_seq=0, **attrs): |
| 827 | name = _create_name_or_use_existing_one(container, 'LSTM', operator_name) |
| 828 | if container.target_opset <= 6: |
| 829 | attrs['output_sequence'] = 1 if output_seq else 0 |
| 830 | op_version = 1 |
| 831 | else: |
| 832 | op_version = 7 |
| 833 | container.add_node('LSTM', input_names, output_names, name=name, op_version=op_version, **attrs) |
| 834 | return output_names |
| 835 | |
| 836 | def matmul(self, input_names, output_name, container, operator_name=None): |
| 837 | op_type = 'MatMul' |
| 838 | name = _create_name_or_use_existing_one(container, op_type, operator_name) |
| 839 | if container.target_opset <= 9: |
| 840 | op_version = 1 |
| 841 | else: |
| 842 | op_version = 9 |
| 843 | container.add_node(op_type, input_names, output_name, op_version=op_version, name=name) |
| 844 | return output_name |
| 845 | |
| 846 | def max(self, input_names, output_name, container, operator_name=None): |
| 847 | self._apply_pointwise_operation('Max', input_names, output_name, container, operator_name) |
| 848 | return output_name |
| 849 | |
| 850 | def mean(self, input_names, output_name, container, operator_name=None): |
| 851 | self._apply_pointwise_operation('Mean', input_names, output_name, container, operator_name) |
| 852 | return output_name |
| 853 | |
| 854 | def min(self, input_names, output_name, container, operator_name=None): |
| 855 | self._apply_pointwise_operation('Min', input_names, output_name, container, operator_name) |
| 856 | return output_name |
| 857 | |
| 858 | def mul(self, input_names, output_name, container, operator_name=None, axis=None, broadcast=None): |
| 859 | self._apply_basic_numerical_operation('Mul', input_names, output_name, |
| 860 | container, operator_name=operator_name, |
| 861 | axis=axis, broadcast=broadcast) |
| 862 | return output_name |
| 863 | |
| 864 | def neg(self, input_name, output_name, container, operator_name=None): |
| 865 | self._apply_unary_operation('Neg', input_name, output_name, container, operator_name) |
| 866 | return output_name |
| 867 | |
| 868 | def lpnormalization(self, input_name, output_name, container, operator_name=None, axis=1, p=2): |
| 869 | name = _create_name_or_use_existing_one(container, 'LpNormalization', operator_name) |
| 870 | container.add_node('LpNormalization', input_name, output_name, name=name, p=p, axis=axis) |
| 871 | return output_name |
| 872 | |
| 873 | def not_op(self, input_name, output_name, container, operator_name=None): |
| 874 | self._apply_unary_operation('Not', input_name, output_name, container, operator_name) |
| 875 | return output_name |
| 876 | |
| 877 | def or_op(self, input_names, output_names, container, operator_name=None): |
| 878 | name = _create_name_or_use_existing_one(container, 'or', operator_name) |
| 879 | container.add_node('Or', input_names, output_names, op_version=7, name=name) |
| 880 | return output_names |
| 881 | |
| 882 | def pad(self, input_name, output_name, container, operator_name=None, mode=None, pads=None, value=None, |
| 883 | onnx_type=onnx_proto.TensorProto.FLOAT): |
| 884 | name = _create_name_or_use_existing_one(container, 'Pad', operator_name) |
| 885 | attrs = {'name': name} |
| 886 | inputs = input_name if isinstance(input_name, list) else [input_name] |
| 887 | |
| 888 | if mode is not None: |
| 889 | attrs['mode'] = mode |
| 890 | |
| 891 | if container.target_opset < 11: |
| 892 | if isinstance(pads, str): |
| 893 | raise ValueError("Dynamic pad is not supported for opset < 11.") |
| 894 | if value is not None: |
| 895 | attrs['value'] = value |
| 896 | if container.target_opset < 2: |
| 897 | attrs['paddings'] = pads |
| 898 | op_version = 1 |
| 899 | else: |
| 900 | attrs['pads'] = pads |
| 901 | op_version = 2 |
| 902 | else: |
| 903 | op_version = 11 |
| 904 | if isinstance(pads, str): |
| 905 | inputs.append(pads) |
| 906 | else: |
| 907 | pads_name = self.get_unique_tensor_name(name + '_pads') |
| 908 | container.add_initializer(pads_name, onnx_proto.TensorProto.INT64, [len(pads)], pads) |
| 909 | inputs.append(pads_name) |
| 910 | if value is not None: |
| 911 | value_name = self.get_unique_tensor_name(name + '_value') |
| 912 | container.add_initializer(value_name, onnx_type, [], [value]) |
| 913 | inputs.append(value_name) |
| 914 | |
| 915 | container.add_node('Pad', inputs, output_name, op_version=op_version, **attrs) |
| 916 | return output_name |
| 917 | |
| 918 | def parametric_softplus(self, input_name, output_name, container, operator_name=None, alpha=None, beta=None): |
| 919 | if alpha is None: |
| 920 | alpha = [1.0] |
| 921 | if beta is None: |
| 922 | beta = [0.] |
| 923 | |
| 924 | name = _create_name_or_use_existing_one(container, 'ParametricSoftplus', operator_name) |
| 925 | if container.target_opset < 9: |
| 926 | if len(alpha) != 1 or len(beta) != 1: |
| 927 | raise ValueError('alpha and beta must be 1-element lists') |
| 928 | op_type = 'ParametricSoftplus' |
| 929 | attrs = {'name': name, 'alpha': alpha[0], 'beta': beta[0]} |
| 930 | container.add_node(op_type, input_name, output_name, **attrs) |
| 931 | else: |
| 932 | # Define three scalars: a, b, 1. |
| 933 | aName = self.get_unique_tensor_name(name + '_alpha') |
| 934 | aShape = [len(alpha)] if len(alpha) == 1 else [len(alpha), 1, 1] |
| 935 | container.add_initializer(aName, onnx_proto.TensorProto.FLOAT, aShape, alpha) |
| 936 | bShape = [len(beta)] if len(beta) == 1 else [len(beta), 1, 1] |
| 937 | bName = self.get_unique_tensor_name(name + '_beta') |
| 938 | container.add_initializer(bName, onnx_proto.TensorProto.FLOAT, bShape, beta) |
| 939 | oneName = self.get_unique_tensor_name(name + '_one') |
| 940 | container.add_initializer(oneName, onnx_proto.TensorProto.FLOAT, [1], [1.]) |
| 941 | |
| 942 | # c = b * x |
| 943 | cName = self.get_unique_tensor_name(name + '_c') |
| 944 | self.mul([input_name, bName], cName, container) |
| 945 | |
| 946 | # d = exp(c) |
| 947 | dName = self.get_unique_tensor_name(name + '_d') |
| 948 | self.exp(cName, dName, container) |
| 949 | |
| 950 | # e = 1 + d |
| 951 | eName = self.get_unique_tensor_name(name + '_e') |
| 952 | self.add([dName, oneName], eName, container) |
| 953 | |
| 954 | # f = log(e) |
| 955 | fName = self.get_unique_tensor_name(name + '_f') |
| 956 | self.log(eName, fName, container) |
| 957 | |
| 958 | # g = a * f |
| 959 | self.mul([fName, aName], output_name, container) |
| 960 | return output_name |
| 961 | |
| 962 | def pow(self, input_names, output_name, container, operator_name=None, axis=None, broadcast=None): |
| 963 | name = _create_name_or_use_existing_one(container, 'Pow', operator_name) |
| 964 | |
| 965 | attrs = {'name': name} |
| 966 | if container.target_opset < 7: |
| 967 | # Before ONNX-1.2, broadcasting behavior is Caffe2-like. |
| 968 | if axis is not None: |
| 969 | attrs['axis'] = axis |
| 970 | if broadcast is not None: |
| 971 | attrs['broadcast'] = broadcast |
| 972 | op_version = 1 |
| 973 | elif container.target_opset < 12: |
| 974 | # Since ONNX-1.2, broadcasting behavior is Numpy-like, so we don't need to specify any attributes |
| 975 | op_version = 7 |
| 976 | else: |
| 977 | op_version = 12 |
| 978 | |
| 979 | container.add_node('Pow', input_names, output_name, op_version=op_version, **attrs) |
| 980 | return output_name |
| 981 | |
| 982 | def prelu(self, input_name, output_name, container, operator_name=None, slp_rate=None): |
| 983 | name = _create_name_or_use_existing_one(container, 'PRelu', operator_name) |
| 984 | slp_rate_tensor_name = self.get_unique_tensor_name('slp_rate') |
| 985 | s_shape = slp_rate.shape |
| 986 | if container.target_opset < 7: |
| 987 | s_shape = [len(slp_rate.flatten())] |
| 988 | container.add_initializer(slp_rate_tensor_name, onnx_proto.TensorProto.FLOAT, s_shape, slp_rate.flatten()) |
| 989 | |
| 990 | if container.target_opset < 6: |
| 991 | container.add_node('PRelu', [input_name, slp_rate_tensor_name], output_name, op_version=1, name=name, |
| 992 | consumed_inputs=[0, 0]) |
| 993 | else: |
| 994 | if container.target_opset < 7: |
| 995 | op_version = 6 |
| 996 | elif container.target_opset < 9: |
| 997 | op_version = 7 |
| 998 | else: |
| 999 | # opset 9 supports unidirectional broadcasting |
| 1000 | op_version = 9 |
| 1001 | |
| 1002 | container.add_node('PRelu', [input_name, slp_rate_tensor_name], output_name, op_version=op_version, name=name) |
| 1003 | return output_name |
| 1004 | |
| 1005 | def range(self, input_name, output_name, container, operator_name=None): |
| 1006 | name = _create_name_or_use_existing_one(container, 'Range', operator_name) |
| 1007 | container.add_node('Range', input_name, output_name, op_version=11, name=name) |
| 1008 | return output_name |
| 1009 | |
| 1010 | def reciprocal(self, input_name, output_name, container, operator_name=None): |
| 1011 | self._apply_unary_operation('Reciprocal', input_name, output_name, container, operator_name=operator_name) |
| 1012 | return output_name |
| 1013 | |
| 1014 | # Some old ORT supports axis < 0 case, so put rank=0 as default. |
| 1015 | def reducesum(self, input_name, output_name, container, operator_name=None, axes=None, keepdims=1, rank=0): |
| 1016 | name = _create_name_or_use_existing_one(container, 'ReduceSum', operator_name) |
| 1017 | if axes is None: |
| 1018 | axes = [] |
| 1019 | if container.target_opset < 13: |
| 1020 | if container.target_opset < 11: |
| 1021 | op_version = 1 |
| 1022 | axes = [axis if axis >= 0 else axis + rank for axis in axes] |
| 1023 | else: |
| 1024 | op_version = 11 |
| 1025 | container.add_node('ReduceSum', input_name, output_name, name=name, |
| 1026 | op_version=op_version, axes=axes, keepdims=keepdims) |
| 1027 | else: |
| 1028 | if not isinstance(input_name, list): |
| 1029 | input_name = [input_name] |
| 1030 | op_version = 13 |
| 1031 | if isinstance(axes, str): |
| 1032 | container.add_node('ReduceSum', input_name + [axes], output_name, |
| 1033 | op_version=op_version, name=name, keepdims=keepdims) |
| 1034 | elif axes is None or len(axes) == 0: |
| 1035 | container.add_node('ReduceSum', input_name, output_name, |
| 1036 | op_version=op_version, name=name, keepdims=keepdims) |
| 1037 | else: |
| 1038 | axes_name = self.get_unique_tensor_name(name + '_reducesum') |
| 1039 | container.add_initializer(axes_name, onnx_proto.TensorProto.INT64, [len(axes)], axes) |
| 1040 | container.add_node('ReduceSum', input_name + [axes_name], output_name, |
| 1041 | op_version=op_version, name=name, keepdims=keepdims) |
| 1042 | return output_name |
| 1043 | |
| 1044 | def reducemin(self, input_name, output_name, container, operator_name=None, axes=None, keepdims=1, rank=0): |
| 1045 | name = _create_name_or_use_existing_one(container, 'ReduceMin', operator_name) |
| 1046 | if axes is None: |
| 1047 | axes = [] |
| 1048 | if container.target_opset < 13: |
| 1049 | if container.target_opset < 11: |
| 1050 | op_version = 1 |
| 1051 | axes = [axis if axis >= 0 else axis + rank for axis in axes] |
| 1052 | else: |
| 1053 | op_version = 11 |
| 1054 | container.add_node('ReduceMin', input_name, output_name, name=name, |
| 1055 | op_version=op_version, axes=axes, keepdims=keepdims) |
| 1056 | else: |
| 1057 | if not isinstance(input_name, list): |
| 1058 | input_name = [input_name] |
| 1059 | op_version = 13 |
| 1060 | if isinstance(axes, str): |
| 1061 | container.add_node('ReduceMin', input_name + [axes], output_name, |
| 1062 | op_version=op_version, name=name, keepdims=keepdims) |
| 1063 | elif axes is None or len(axes) == 0: |
| 1064 | container.add_node('ReduceMin', input_name, output_name, |
| 1065 | op_version=op_version, name=name, keepdims=keepdims) |
| 1066 | else: |
| 1067 | axes_name = self.get_unique_tensor_name(name + '_reducemin') |
| 1068 | container.add_initializer(axes_name, onnx_proto.TensorProto.INT64, [len(axes)], axes) |
| 1069 | container.add_node('ReduceMin', input_name + [axes_name], output_name, |
| 1070 | op_version=op_version, name=name, keepdims=keepdims) |
| 1071 | return output_name |
| 1072 | |
| 1073 | def relu(self, input_name, output_name, container, operator_name=None): |
| 1074 | self._apply_unary_operation('Relu', input_name, output_name, container, operator_name) |
| 1075 | return output_name |
| 1076 | |
| 1077 | def relu_6(self, input_name, output_name, container, operator_name=None, zero_value=0.0): |
| 1078 | name_relu = _create_name_or_use_existing_one(container, 'relu', operator_name) |
| 1079 | name_relu_op = _create_name_or_use_existing_one(container, 'relu6', operator_name) |
| 1080 | self.relu(input_name, name_relu, container, name_relu_op+'_relu') |
| 1081 | self.clip(name_relu, output_name, container, name_relu_op + '_clip', zero_value+6, zero_value) |
| 1082 | |
| 1083 | def reshape(self, input_name, output_name, container, operator_name=None, desired_shape=None): |
| 1084 | if not isinstance(desired_shape, str) and len(list(i for i in desired_shape if i is not None and i < 0)) > 1: |
| 1085 | raise ValueError('There can only be one -1 in the targeted shape of a Reshape but got %s' % desired_shape) |
| 1086 | |
| 1087 | name = _create_name_or_use_existing_one(container, 'Reshape', operator_name) |
| 1088 | |
| 1089 | if container.target_opset < 5: |
| 1090 | container.add_node('Reshape', input_name, output_name, op_version=1, name=name, shape=desired_shape, |
| 1091 | consumed_inputs=[0]) |
| 1092 | else: |
| 1093 | if isinstance(desired_shape, str): |
| 1094 | desired_shape_name = desired_shape |
| 1095 | else: |
| 1096 | desired_shape_name = self.get_unique_tensor_name('shape_tensor') |
| 1097 | container.add_initializer(desired_shape_name, onnx_proto.TensorProto.INT64, [len(desired_shape)], |
| 1098 | desired_shape) |
| 1099 | |
| 1100 | # Create ONNX Reshape operator |
| 1101 | if isinstance(input_name, list): |
| 1102 | input_name.append(desired_shape_name) |
| 1103 | else: |
| 1104 | input_name = [input_name, desired_shape_name] |
| 1105 | container.add_node('Reshape', input_name, output_name, op_version=5, name=name) |
| 1106 | return output_name |
| 1107 | |
| 1108 | def resize(self, input_name, output_name, container, operator_name=None, mode='nearest', |
| 1109 | coordinate_transformation_mode='asymmetric', scales=None): |
| 1110 | """ |
| 1111 | :param mode: "nearest" or "linear" |
| 1112 | :param scales: a float tensor for scaling (upsampling or downsampling) all input dimensions |
| 1113 | """ |
| 1114 | name = _create_name_or_use_existing_one(container, 'Resize', operator_name) |
| 1115 | attrs = {'name': name} |
| 1116 | attrs['mode'] = mode.lower() |
| 1117 | |
| 1118 | inputs = [input_name] |
| 1119 | |
| 1120 | if container.target_opset < 11: |
| 1121 | op_version = 10 |
| 1122 | else: |
| 1123 | op_version = 11 |
| 1124 | roi_tensor_name = self.get_unique_tensor_name(name + '_roi') |
| 1125 | roi = [0.0] * len(scales) + [1.0] * len(scales) |
| 1126 | container.add_initializer(roi_tensor_name, onnx_proto.TensorProto.FLOAT, [2 * len(scales)], roi) |
| 1127 | inputs.append(roi_tensor_name) |
| 1128 | attrs['coordinate_transformation_mode'] = coordinate_transformation_mode |
| 1129 | if attrs['mode'] == 'nearest': |
| 1130 | attrs['nearest_mode'] = 'floor' |
| 1131 | |
| 1132 | scales_tensor_name = self.get_unique_tensor_name(name + '_scales') |
| 1133 | container.add_initializer(scales_tensor_name, onnx_proto.TensorProto.FLOAT, [len(scales)], scales) |
| 1134 | inputs.append(scales_tensor_name) |
| 1135 | container.add_node('Resize', inputs, output_name, op_version=op_version, **attrs) |
| 1136 | return output_name |
| 1137 | |
| 1138 | def rnn(self, input_names, output_names, container, operator_name=None, output_seq=0, **attrs): |
| 1139 | name = _create_name_or_use_existing_one(container, 'RNN', operator_name) |
| 1140 | if container.target_opset <= 6: |
| 1141 | attrs['output_sequence'] = 1 if output_seq else 0 |
| 1142 | op_version = 1 |
| 1143 | else: |
| 1144 | op_version = 7 |
| 1145 | container.add_node('RNN', input_names, output_names, name=name, op_version=op_version, **attrs) |
| 1146 | return output_names |
| 1147 | |
| 1148 | def shape(self, input_name, output_name, container, operator_name=None): |
| 1149 | name = _create_name_or_use_existing_one(container, 'Shape', operator_name) |
| 1150 | container.add_node('Shape', input_name, output_name, name=name, op_version=1) |
| 1151 | return output_name |
| 1152 | |
| 1153 | def sigmoid(self, input_name, output_name, container, operator_name=None): |
| 1154 | self._apply_unary_operation('Sigmoid', input_name, output_name, container, operator_name) |
| 1155 | return output_name |
| 1156 | |
| 1157 | def softsign(self, input_name, output_name, container, operator_name=None): |
| 1158 | name = _create_name_or_use_existing_one(container, 'Softsign', operator_name) |
| 1159 | container.add_node('Softsign', input_name, output_name, name=name, op_version=1) |
| 1160 | return output_name |
| 1161 | |
| 1162 | # See alpha and gamma at https://github.com/keras-team/keras/blob/master/keras/activations.py#L80-L81 |
| 1163 | def selu(self, input_name, output_name, container, operator_name=None, alpha=1.673263, gamma=1.050701): |
| 1164 | self._apply_unary_operation('Selu', input_name, output_name, container, operator_name, alpha=alpha, gamma=gamma) |
| 1165 | return output_name |
| 1166 | |
| 1167 | def softmax(self, input_name, output_name, container, operator_name=None, axis=None): |
| 1168 | name = _create_name_or_use_existing_one(container, 'Softmax', operator_name) |
| 1169 | if axis is None: |
| 1170 | axis = 1 if container.target_opset < 13 else -1 |
| 1171 | container.add_node('Softmax', input_name, output_name, name=name, axis=axis) |
| 1172 | return output_name |
| 1173 | |
| 1174 | def scaled_tanh(self, input_name, output_name, container, operator_name=None, alpha=None, beta=None): |
| 1175 | if alpha is None: |
| 1176 | alpha = [1.0] |
| 1177 | if beta is None: |
| 1178 | beta = [1.0] |
| 1179 | if len(alpha) != 1 or len(beta) != 1: |
| 1180 | raise ValueError('alpha and beta must be 1-element lists') |
| 1181 | |
| 1182 | name = _create_name_or_use_existing_one(container, 'ScaledTanh', operator_name) |
| 1183 | if container.target_opset < 9: |
| 1184 | attrs = {'name': name, 'alpha': alpha[0], 'beta': beta[0]} |
| 1185 | container.add_node('ScaledTanh', input_name, output_name, **attrs) |
| 1186 | else: |
| 1187 | # Define scalar a, initialize with parameter alpha. |
| 1188 | aName = self.get_unique_tensor_name(name + '_alpha') |
| 1189 | aShape = [len(alpha)] if len(alpha) == 1 else [len(alpha), 1, 1] |
| 1190 | container.add_initializer(aName, onnx_proto.TensorProto.FLOAT, aShape, alpha) |
| 1191 | |
| 1192 | # Define scalar b, initialize with parameter beta. |
| 1193 | bShape = [len(beta)] if len(beta) == 1 else [len(beta), 1, 1] |
| 1194 | bName = self.get_unique_tensor_name(name + '_beta') |
| 1195 | container.add_initializer(bName, onnx_proto.TensorProto.FLOAT, bShape, beta) |
| 1196 | |
| 1197 | # c = b * x |
| 1198 | cName = self.get_unique_tensor_name(name + '_c') |
| 1199 | self.mul([input_name, bName], cName, container) |
| 1200 | |
| 1201 | # d = tanh(c) |
| 1202 | dName = self.get_unique_tensor_name(name + '_d') |
| 1203 | self.tanh(cName, dName, container) |
| 1204 | |
| 1205 | # output = a * d |
| 1206 | self.mul([aName, dName], output_name, container) |
| 1207 | return output_name |
| 1208 | |
| 1209 | def slice(self, input_name, output_name, container, |
| 1210 | operator_name=None, starts=None, ends=None, axes=None, steps=None): |
| 1211 | assert starts is not None, 'the starts in slice op cannot be None' |
| 1212 | assert ends is not None, 'the ends in slice op cannot be None' |
| 1213 | name = _create_name_or_use_existing_one(container, 'Slice', operator_name) |
| 1214 | |
| 1215 | if container.target_opset < 10: |
| 1216 | if axes is None: |
| 1217 | container.add_node('Slice', input_name, output_name, name=name, |
| 1218 | starts=starts, ends=ends, op_version=1) |
| 1219 | else: |
| 1220 | container.add_node('Slice', input_name, output_name, name=name, |
| 1221 | starts=starts, ends=ends, axes=axes, op_version=1) |
| 1222 | else: |
| 1223 | if container.target_opset == 10: |
| 1224 | op_version = 10 |
| 1225 | else: |
| 1226 | op_version = 11 |
| 1227 | inputs = input_name if isinstance(input_name, list) else [input_name] |
| 1228 | if isinstance(starts, str): |
| 1229 | starts_name = starts |
| 1230 | else: |
| 1231 | starts_name = self.get_unique_tensor_name('starts') |
| 1232 | container.add_initializer(starts_name, onnx_proto.TensorProto.INT64, |
| 1233 | [len(starts)], starts) |
| 1234 | |
| 1235 | if isinstance(ends, str): |
| 1236 | ends_name = ends |
| 1237 | else: |
| 1238 | ends_name = self.get_unique_tensor_name('ends') |
| 1239 | container.add_initializer(ends_name, onnx_proto.TensorProto.INT64, |
| 1240 | [len(ends)], ends) |
| 1241 | |
| 1242 | inputs.append(starts_name) |
| 1243 | inputs.append(ends_name) |
| 1244 | if axes: |
| 1245 | if isinstance(axes, str): |
| 1246 | axes_name = axes |
| 1247 | else: |
| 1248 | axes_name = self.get_unique_tensor_name('axes') |
| 1249 | container.add_initializer(axes_name, onnx_proto.TensorProto.INT64, |
| 1250 | [len(axes)], axes) |
| 1251 | inputs.append(axes_name) |
| 1252 | if steps: |
| 1253 | if not axes: |
| 1254 | inputs.append('') |
| 1255 | if isinstance(steps, str): |
| 1256 | steps_name = steps |
| 1257 | else: |
| 1258 | steps_name = self.get_unique_tensor_name('steps') |
| 1259 | container.add_initializer(steps_name, onnx_proto.TensorProto.INT64, |
| 1260 | [len(steps)], steps) |
| 1261 | inputs.append(steps_name) |
| 1262 | container.add_node('Slice', inputs, output_name, name=name, |
| 1263 | op_version=op_version) |
| 1264 | return output_name |
| 1265 | |
| 1266 | def split(self, input_name, output_names, container, operator_name=None, split=None, axis=0): |
| 1267 | name = _create_name_or_use_existing_one(container, 'Split', operator_name) |
| 1268 | if container.target_opset <= 1: |
| 1269 | op_version = 1 |
| 1270 | elif container.target_opset < 11: |
| 1271 | op_version = 2 |
| 1272 | elif container.target_opset < 13: |
| 1273 | op_version = 11 |
| 1274 | else: |
| 1275 | op_version = 13 |
| 1276 | |
| 1277 | attrs = {'name': name} |
| 1278 | if split is not None: |
| 1279 | if container.target_opset < 13: |
| 1280 | attrs['split'] = split |
| 1281 | else: |
| 1282 | if not isinstance(input_name, list): |
| 1283 | input_name = [input_name] |
| 1284 | if isinstance(split, str): |
| 1285 | split_name = split |
| 1286 | else: |
| 1287 | split_name = self.get_unique_tensor_name(name + '_split') |
| 1288 | container.add_initializer(split_name, onnx_proto.TensorProto.INT64, [len(split)], split) |
| 1289 | input_name = input_name + [split_name] |
| 1290 | |
| 1291 | if axis is not None: |
| 1292 | attrs['axis'] = axis |
| 1293 | |
| 1294 | container.add_node('Split', input_name, output_names, op_version=op_version, **attrs) |
| 1295 | return output_names |
| 1296 | |
| 1297 | def sqrt(self, input_name, output_name, container, operator_name=None): |
| 1298 | self._apply_unary_operation('Sqrt', input_name, output_name, container, operator_name=operator_name) |
| 1299 | return output_name |
| 1300 | |
| 1301 | def _apply_squeeze_unsqueeze(self, input_name, output_name, container, squeeze_str, operator_name=None, axes=None, |
| 1302 | rank=0): |
| 1303 | name = _create_name_or_use_existing_one(container, squeeze_str, operator_name) |
| 1304 | if container.target_opset < 13: |
| 1305 | if container.target_opset < 11: |
| 1306 | op_version = 1 |
| 1307 | axes = [axis if axis >= 0 else axis + rank for axis in axes] |
| 1308 | else: |
| 1309 | op_version = 11 |
| 1310 | container.add_node(squeeze_str, input_name, output_name, name=name, op_version=op_version, axes=axes) |
| 1311 | else: |
| 1312 | op_version = 13 |
| 1313 | if not isinstance(input_name, list): |
| 1314 | input_name = [input_name] |
| 1315 | if isinstance(axes, str): |
| 1316 | container.add_node(squeeze_str, input_name + [axes], output_name, op_version=op_version, name=name) |
| 1317 | elif len(axes) == 0: |
| 1318 | container.add_node(squeeze_str, input_name, output_name, op_version=op_version, name=name) |
| 1319 | else: |
| 1320 | axes_name = self.get_unique_tensor_name(name + '_axes') |
| 1321 | container.add_initializer(axes_name, onnx_proto.TensorProto.INT64, [len(axes)], axes) |
| 1322 | container.add_node(squeeze_str, input_name + [axes_name], output_name, op_version=op_version, name=name) |
| 1323 | return output_name |
| 1324 | |
| 1325 | def squeeze(self, input_name, output_name, container, operator_name=None, axes=None, rank=0): |
| 1326 | if axes is None: |
| 1327 | axes = [] |
| 1328 | self._apply_squeeze_unsqueeze(input_name, output_name, container, 'Squeeze', operator_name, axes, rank) |
| 1329 | return output_name |
| 1330 | |
| 1331 | def sub(self, input_names, output_name, container, operator_name=None, axis=None, broadcast=0): |
| 1332 | self._apply_basic_numerical_operation('Sub', input_names, output_name, container, operator_name=operator_name, |
| 1333 | axis=axis, broadcast=broadcast) |
| 1334 | return output_name |
| 1335 | |
| 1336 | def sum(self, input_names, output_name, container, operator_name=None): |
| 1337 | name = _create_name_or_use_existing_one(container, 'Sum', operator_name) |
| 1338 | if container.target_opset < 6: |
| 1339 | op_version = 1 |
| 1340 | else: |
| 1341 | op_version = 6 |
| 1342 | container.add_node('Sum', input_names, output_name, op_version=op_version, name=name) |
| 1343 | return output_name |
| 1344 | |
| 1345 | def tanh(self, input_name, output_name, container, operator_name=None): |
| 1346 | self._apply_unary_operation('Tanh', input_name, output_name, container, operator_name) |
| 1347 | return output_name |
| 1348 | |
| 1349 | def thresholded_relu(self, input_name, output_name, container, operator_name=None, alpha=None): |
| 1350 | if alpha is None: |
| 1351 | alpha = [1.0] |
| 1352 | |
| 1353 | name = _create_name_or_use_existing_one(container, 'ThresholdedRelu', operator_name) |
| 1354 | attrs = {'name': name, 'alpha': alpha[0]} |
| 1355 | if container.target_opset < 10: |
| 1356 | # ThresholdedRelu graduated from an experimental op to a full op in opset 10 |
| 1357 | # onnxruntime maintains support in the ONNX domain for ThresholdedRelu as a contrib op |
| 1358 | attrs['op_domain'] = "ai.onnx" |
| 1359 | op_version = 1 |
| 1360 | else: |
| 1361 | op_version = 10 |
| 1362 | container.add_node('ThresholdedRelu', input_name, output_name, op_version=op_version, **attrs) |
| 1363 | return output_name |
| 1364 | |
| 1365 | def tile(self, input_name, output_name, container, operator_name=None, repeats=None): |
| 1366 | name = _create_name_or_use_existing_one(container, 'Tile', operator_name) |
| 1367 | |
| 1368 | if repeats is None or (not isinstance(repeats, str) and all(repeat_count == 1 for repeat_count in repeats)): |
| 1369 | container.add_node('Identity', input_name, output_name, name=name) |
| 1370 | return output_name |
| 1371 | |
| 1372 | if container.target_opset < 6: |
| 1373 | intermediate_input_name = input_name |
| 1374 | intermediate_output_name = None |
| 1375 | if isinstance(repeats, str): |
| 1376 | raise ValueError('repeats cannot be string type before opset 6') |
| 1377 | |
| 1378 | for axis, repeat_count in enumerate(repeats): |
| 1379 | if repeat_count == 1: |
| 1380 | continue |
| 1381 | |
| 1382 | # Create the 2nd input of Tile |
| 1383 | tile_tensor_name = self.get_unique_tensor_name(name + '_tile') |
| 1384 | container.add_initializer(tile_tensor_name, onnx_proto.TensorProto.FLOAT, [1], [float(repeat_count)]) |
| 1385 | |
| 1386 | # Create the 3rd input of Tile |
| 1387 | axis_tensor_name = self.get_unique_tensor_name(name + '_axis') |
| 1388 | container.add_initializer(axis_tensor_name, onnx_proto.TensorProto.FLOAT, [1], [float(axis)]) |
| 1389 | |
| 1390 | # Create tile for duplicating along one axis. After ONNX-1.2, we can duplicate along multiple axes, |
| 1391 | # so we don't have to iterate through all axes. |
| 1392 | intermediate_output_name = self.get_unique_tensor_name(name + '_input') |
| 1393 | container.add_node('Tile', [intermediate_input_name, tile_tensor_name, axis_tensor_name], |
| 1394 | intermediate_output_name, name=name) |
| 1395 | |
| 1396 | # Use the output produced by this round as the input in the next iteration |
| 1397 | intermediate_input_name = intermediate_output_name |
| 1398 | |
| 1399 | # Create a new name for next Tile |
| 1400 | name = container.get_unique_operator_name('Tile') |
| 1401 | |
| 1402 | # Use the last Tile name for the name of an Identity |
| 1403 | container.add_node('Identity', intermediate_output_name, output_name, op_version=1, name=name) |
| 1404 | else: |
| 1405 | # ONNX-1.2 has a new Tile and we use it here |
| 1406 | if isinstance(repeats, str): |
| 1407 | container.add_node('Tile', input_name + [repeats], output_name, op_version=6, name=name) |
| 1408 | else: |
| 1409 | repeat_tensor_name = self.get_unique_tensor_name(name + '_repeats') |
| 1410 | container.add_initializer(repeat_tensor_name, onnx_proto.TensorProto.INT64, [len(repeats)], repeats) |
| 1411 | container.add_node('Tile', [input_name, repeat_tensor_name], output_name, op_version=6, name=name) |
| 1412 | return output_name |
| 1413 | |
| 1414 | def topk(self, input_name, output_names, container, k, operator_name=None): |
| 1415 | name = _create_name_or_use_existing_one(container, 'TopK', operator_name) |
| 1416 | |
| 1417 | if container.target_opset < 10: |
| 1418 | if isinstance(k, str): |
| 1419 | raise ValueError('topk k cannot be string type before opset 10') |
| 1420 | container.add_node('TopK', input_name, output_names, name=name, k=k, op_version=1) |
| 1421 | else: |
| 1422 | if container.target_opset == 10: |
| 1423 | op_version = 10 |
| 1424 | else: |
| 1425 | op_version = 11 |
| 1426 | |
| 1427 | if isinstance(k, str): |
| 1428 | k_value_name = k |
| 1429 | else: |
| 1430 | k_value_name = self.get_unique_tensor_name('k_value') |
| 1431 | container.add_initializer(k_value_name, onnx_proto.TensorProto.INT64, [1], [k]) |
| 1432 | container.add_node('TopK', input_name + [k_value_name], output_names, name=name, op_version=op_version) |
| 1433 | return output_names |
| 1434 | |
| 1435 | def transpose(self, input_name, output_name, container, operator_name=None, perm=None): |
| 1436 | name = _create_name_or_use_existing_one(container, 'Transpose', operator_name) |
| 1437 | container.add_node('Transpose', input_name, output_name, name=name, perm=perm) |
| 1438 | return output_name |
| 1439 | |
| 1440 | def upsample(self, input_name, output_name, container, operator_name=None, mode='nearest', |
| 1441 | coordinate_transformation_mode='asymmetric', scales=None): |
| 1442 | """ |
| 1443 | :param input_name: |
| 1444 | :param output_name: |
| 1445 | :param container: |
| 1446 | :param operator_name: |
| 1447 | :param mode: nearest or linear |
| 1448 | :param coordinate_transformation_mode: |
| 1449 | :param scales: an integer list of scaling-up rate of all input dimensions |
| 1450 | :return: |
| 1451 | """ |
| 1452 | if container.target_opset < 10: |
| 1453 | name = _create_name_or_use_existing_one(container, 'Upsample', operator_name) |
| 1454 | inputs = [input_name] |
| 1455 | attrs = {'name': name} |
| 1456 | if container.target_opset < 7: |
| 1457 | if len(scales) != 4: |
| 1458 | raise ValueError('Need to specify a 4-element list the the scales of N-, C-, H-, and W-axes') |
| 1459 | attrs['height_scale'] = float(scales[2]) |
| 1460 | attrs['width_scale'] = float(scales[3]) |
| 1461 | attrs['mode'] = mode.upper() |
| 1462 | op_version = 1 |
| 1463 | else: |
| 1464 | attrs['mode'] = mode.lower() |
| 1465 | if container.target_opset < 9: |
| 1466 | attrs['scales'] = list(map(float, scales)) |
| 1467 | op_version = 7 |
| 1468 | else: |
| 1469 | # scales moved from attribute to input in opset 9 |
| 1470 | scales_tensor_name = self.get_unique_tensor_name(name + '_scales') |
| 1471 | container.add_initializer(scales_tensor_name, onnx_proto.TensorProto.FLOAT, [len(scales)], scales) |
| 1472 | inputs = [input_name, scales_tensor_name] |
| 1473 | op_version = 9 |
| 1474 | |
| 1475 | container.add_node('Upsample', inputs, output_name, op_version=op_version, **attrs) |
| 1476 | else: |
| 1477 | # Upsample op is deprecated in ONNX opset 10 |
| 1478 | # We implement Upsample through Resize instead |
| 1479 | self.resize(input_name, output_name, container, operator_name, mode, coordinate_transformation_mode, |
| 1480 | scales) |
| 1481 | return output_name |
| 1482 | |
| 1483 | def unsqueeze(self, input_name, output_name, container, operator_name=None, axes=None, rank=0): |
| 1484 | if axes is None: |
| 1485 | axes = [0] |
| 1486 | self._apply_squeeze_unsqueeze(input_name, output_name, container, 'Unsqueeze', operator_name, axes, rank) |
| 1487 | return output_name |
| 1488 | |
| 1489 | def where(self, input_names, output_names, container, operator_name=None): |
| 1490 | name = _create_name_or_use_existing_one(container, 'where', operator_name) |
| 1491 | container.add_node('Where', input_names, output_names, op_version=9, name=name) |
| 1492 | return output_names |
| 1493 | |
| 1494 | def loop(self, input_names, output_names, container, operator_name=None, body=None): |
| 1495 | name = _create_name_or_use_existing_one(container, 'loop', operator_name) |
| 1496 | trip_count, cond, *states = tuple(input_names) |
| 1497 | trip_count = '' if trip_count is None else trip_count |
| 1498 | cond_name = '' if cond is None else cond |
| 1499 | container.add_node( |
| 1500 | 'Loop', [trip_count, cond_name] + states, output_names, op_version=11, name=name, body=body) |
| 1501 | return output_names |
| 1502 | |
| 1503 | def model_call(self, input_name, output_name, container, operator_name=None, oxml=None): |
| 1504 | name = operator_name |
| 1505 | if name is None: |
| 1506 | name = container.get_unique_operator_name('og') |
| 1507 | |
| 1508 | # The tensor name replacement happens on unfolding ONNX model. |
| 1509 | for idx, nm_ in enumerate(input_name): |
| 1510 | nvi = oxml.graph.input[idx] |
| 1511 | self.identity([nm_], ["{}_{}".format(name, nvi.name)], container) |
| 1512 | container.value_info.append(nvi) |
| 1513 | for idx, nm_ in enumerate(output_name): |
| 1514 | self.identity(["{}_{}".format(name, oxml.graph.output[idx].name)], [nm_], container) |
| 1515 | container.value_info.extend(oxml.graph.output) |
| 1516 | container.add_model_node(input_name, output_name, name=name, model=oxml) |
| 1517 | return output_name |
| 1518 | |
| 1519 | |
| 1520 | class _ONNXModelBuilder(_ONNXOperatorAPI): |
| 1521 | def __init__(self): |
| 1522 | _OpSchema._ox = self |
| 1523 | self._id_count = 0 |
| 1524 | self.opdict_counter = {} |
| 1525 | |
| 1526 | def get_unique_tensor_name(self, hint): |
| 1527 | self._id_count += 1 |
| 1528 | return "v{}_{}".format(hint, str(self._id_count)) |
| 1529 | |
| 1530 | def make_tensor(self, dtype, dims, vals): |
| 1531 | return helper.make_tensor(self.get_unique_tensor_name('ts'), dtype, dims, vals) |
| 1532 | |
| 1533 | def get_unique_operator_type_name(self, op_type): |
| 1534 | nn = self.opdict_counter.get(op_type, 0) |
| 1535 | self.opdict_counter[op_type] = nn + 1 |
| 1536 | return "_Op{}".format(op_type) if nn == 0 else "_Op{}_{}".format(op_type, nn+1) |
| 1537 | |
| 1538 | @classmethod |
| 1539 | def is_raw(cls, func): # without any schema decorator |
| 1540 | return not isinstance(func, _OpSchema) |
| 1541 | |
| 1542 | |
| 1543 | # Singleton |
| 1544 | ox = _ONNXModelBuilder() |
| 1545 | |