Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 1 | # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. |
| 2 | # |
| 3 | # SPDX-License-Identifier: Apache-2.0 |
| 4 | # |
| 5 | # Licensed under the Apache License, Version 2.0 (the License); you may |
| 6 | # not use this file except in compliance with the License. |
| 7 | # You may obtain a copy of the License at |
| 8 | # |
| 9 | # www.apache.org/licenses/LICENSE-2.0 |
| 10 | # |
| 11 | # Unless required by applicable law or agreed to in writing, software |
| 12 | # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| 13 | # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | # See the License for the specific language governing permissions and |
| 15 | # limitations under the License. |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 16 | # Description: |
| 17 | # Internal representation of a Neural Network Operation. |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 18 | import enum |
| 19 | |
| 20 | |
| 21 | class NpuBlockType(enum.Enum): |
| 22 | Default = 0 |
| 23 | ConvolutionMxN = 1 |
| 24 | VectorProduct = 2 |
| 25 | Pooling = 3 |
| 26 | ConvolutionDepthWise = 4 |
| 27 | ElementWise = 5 |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 28 | ReduceSum = 6 |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 29 | |
| 30 | |
Michael McGeagh | 8dbf8cf | 2020-09-08 11:09:48 +0100 | [diff] [blame^] | 31 | def create_avgpool_nop(name): |
| 32 | op = Operation("AvgPool", name) |
| 33 | op.attrs["padding"] = b"VALID" |
| 34 | op.attrs["npu_block_type"] = NpuBlockType.Pooling |
| 35 | op.attrs["stride_w"] = 1 |
| 36 | op.attrs["stride_h"] = 1 |
| 37 | op.attrs["filter_width"] = 1 |
| 38 | op.attrs["filter_height"] = 1 |
| 39 | op.attrs["strides"] = [1, 1, 1, 1] |
| 40 | op.attrs["ksize"] = [1, 1, 1, 1] |
| 41 | op.attrs["skirt"] = [0, 0, 0, 0] |
| 42 | op.attrs["explicit_padding"] = [0, 0, 0, 0] |
| 43 | return op |
| 44 | |
| 45 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 46 | class Operation: |
| 47 | """Class representing a Neural Network operation. Has a name, a type, |
| 48 | input and output tensors, as well as an attribute dictionary.""" |
| 49 | |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 50 | __slots__ = ( |
| 51 | "type", |
| 52 | "name", |
| 53 | "op_index", |
| 54 | "attrs", |
| 55 | "inputs", |
| 56 | "outputs", |
| 57 | "flops", |
| 58 | "scheduled_pass", |
| 59 | "run_on_npu", |
| 60 | "activation_lut", |
| 61 | ) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 62 | |
| 63 | def __init__(self, op_type, name): |
| 64 | self.type = op_type |
| 65 | self.name = name |
| 66 | self.attrs = {} |
| 67 | self.inputs = [] |
| 68 | self.outputs = [] |
| 69 | self.flops = 0 |
| 70 | self.run_on_npu = True |
| 71 | self.scheduled_pass = None |
Tim Hall | c8310b1 | 2020-06-17 14:53:11 +0100 | [diff] [blame] | 72 | self.op_index = None # input network operator index |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 73 | self.activation_lut = None |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 74 | |
| 75 | def clone(self, suffix="_clone"): |
| 76 | res = Operation(self.type, self.name + suffix) |
| 77 | |
| 78 | res.attrs = dict(self.attrs) |
| 79 | res.inputs = list(self.inputs) |
| 80 | res.outputs = list(self.outputs) |
| 81 | res.flops = self.flops |
| 82 | res.scheduled_pass = self.scheduled_pass |
Tim Hall | c8310b1 | 2020-06-17 14:53:11 +0100 | [diff] [blame] | 83 | res.op_index = None # not relevant as not part of input network |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 84 | |
| 85 | return res |
| 86 | |
| 87 | def __str__(self): |
| 88 | return "<nng.Operation '%s' type=%s>" % (self.name, self.type) |
| 89 | |
| 90 | __repr__ = __str__ |
| 91 | |
| 92 | def get_ifm_ifm2_weight_bias_ofm_indices(self): |
| 93 | ifm_idx = -1 |
| 94 | ifm2_idx = -1 |
| 95 | weight_idx = -1 |
| 96 | bias_idx = -1 |
| 97 | ofm_idx = -1 |
| 98 | npu_block_type = self.attrs.get("npu_block_type", NpuBlockType.Default) |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 99 | if npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.ConvolutionDepthWise): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 100 | ifm_idx = 0 |
| 101 | weight_idx = 1 |
| 102 | ofm_idx = 0 |
| 103 | |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 104 | if self.type in ("Conv2DBiasAct", "DepthwiseConv2dBiasAct", "TransposeConvAct"): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 105 | if len(self.inputs) >= 3: |
| 106 | bias_idx = 2 |
| 107 | |
Jacob Bohlin | cf7da10 | 2020-05-20 09:03:40 +0200 | [diff] [blame] | 108 | elif self.type == "Conv2DBackpropInputSwitchedBias": |
| 109 | bias_idx = 3 |
| 110 | |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 111 | elif npu_block_type in (NpuBlockType.Pooling, NpuBlockType.ReduceSum): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 112 | ifm_idx = 0 |
| 113 | ofm_idx = 0 |
| 114 | elif npu_block_type == NpuBlockType.VectorProduct: |
| 115 | ifm_idx = 0 |
| 116 | weight_idx = 1 |
| 117 | ofm_idx = 0 |
| 118 | |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 119 | if self.type == "FullyConnectedAct": |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 120 | if len(self.inputs) >= 3: |
| 121 | bias_idx = 2 |
| 122 | |
| 123 | if self.type == "BlockLSTM": |
| 124 | ifm_idx = 3 |
| 125 | weight_idx = 4 |
| 126 | ofm_idx = 6 |
| 127 | |
| 128 | elif npu_block_type == NpuBlockType.ElementWise: |
| 129 | ifm_idx = 0 |
| 130 | ifm2_idx = 1 |
| 131 | ofm_idx = 0 |
| 132 | |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 133 | # LeakyRelu, Abs and CLZ have a single IFM |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 134 | if self.type in ("LeakyRelu", "Abs", "CLZ"): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 135 | ifm2_idx = -1 |
| 136 | |
| 137 | elif self.type == "Conv2DBackpropInput": |
| 138 | ifm_idx = 2 |
| 139 | weight_idx = 1 |
| 140 | ofm_idx = 0 |
| 141 | |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 142 | elif self.type in ("Squeeze", "Reshape", "QuantizedReshape", "ExpandDims"): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 143 | ifm_idx = 0 |
| 144 | ofm_idx = 0 |
| 145 | |
| 146 | elif self.is_split_op(): |
| 147 | ifm_idx = 0 |
| 148 | ofm_idx = 0 |
| 149 | if self.type == "Split": |
| 150 | ifm_idx = 1 |
| 151 | |
| 152 | elif self.is_concat_op(): |
| 153 | ifms, _ = self.get_concat_inputs_axis() |
| 154 | ifm_idx = self.inputs.index(ifms[0]) |
| 155 | if len(ifms) > 1: |
| 156 | ifm2_idx = self.inputs.index(ifms[1]) |
| 157 | ofm_idx = 0 |
| 158 | |
| 159 | return ifm_idx, ifm2_idx, weight_idx, bias_idx, ofm_idx |
| 160 | |
| 161 | def get_ifm_ifm2_weights_ofm(self): |
| 162 | ifm_tensor = None |
| 163 | ifm2_tensor = None |
| 164 | weight_tensor = None |
| 165 | ofm_tensor = None |
| 166 | |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 167 | ifm_idx, ifm2_idx, weight_idx, _, ofm_idx = self.get_ifm_ifm2_weight_bias_ofm_indices() |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 168 | if ifm_idx != -1: |
| 169 | ifm_tensor = self.inputs[ifm_idx] |
| 170 | if ifm2_idx != -1: |
| 171 | ifm2_tensor = self.inputs[ifm2_idx] |
| 172 | if weight_idx != -1: |
| 173 | weight_tensor = self.inputs[weight_idx] |
| 174 | if ofm_idx != -1: |
| 175 | ofm_tensor = self.outputs[ofm_idx] |
| 176 | |
| 177 | return ifm_tensor, ifm2_tensor, weight_tensor, ofm_tensor |
| 178 | |
| 179 | def get_ifm_weights_biases_ofm(self): |
| 180 | ifm_tensor = None |
| 181 | weight_tensor = None |
| 182 | bias_tensor = None |
| 183 | ofm_tensor = None |
| 184 | |
| 185 | ifm_idx, _, weight_idx, bias_idx, ofm_idx = self.get_ifm_ifm2_weight_bias_ofm_indices() |
| 186 | if ifm_idx != -1: |
| 187 | ifm_tensor = self.inputs[ifm_idx] |
| 188 | if weight_idx != -1: |
| 189 | weight_tensor = self.inputs[weight_idx] |
| 190 | if bias_idx != -1: |
| 191 | bias_tensor = self.inputs[bias_idx] |
| 192 | if ofm_idx != -1: |
| 193 | ofm_tensor = self.outputs[ofm_idx] |
| 194 | |
| 195 | return ifm_tensor, weight_tensor, bias_tensor, ofm_tensor |
| 196 | |
Jacob Bohlin | 49d9212 | 2020-08-19 14:36:46 +0200 | [diff] [blame] | 197 | def get_ifm_ifm2_weights_biases_ofm(self): |
| 198 | ifm_tensor = None |
| 199 | ifm2_tensor = None |
| 200 | weight_tensor = None |
| 201 | bias_tensor = None |
| 202 | ofm_tensor = None |
| 203 | |
| 204 | ifm_idx, ifm2_idx, weight_idx, bias_idx, ofm_idx = self.get_ifm_ifm2_weight_bias_ofm_indices() |
| 205 | if ifm_idx != -1: |
| 206 | ifm_tensor = self.inputs[ifm_idx] |
| 207 | if ifm2_idx != -1: |
| 208 | ifm2_tensor = self.inputs[ifm2_idx] |
| 209 | if weight_idx != -1: |
| 210 | weight_tensor = self.inputs[weight_idx] |
| 211 | if bias_idx != -1: |
| 212 | bias_tensor = self.inputs[bias_idx] |
| 213 | if ofm_idx != -1: |
| 214 | ofm_tensor = self.outputs[ofm_idx] |
| 215 | |
| 216 | return ifm_tensor, ifm2_tensor, weight_tensor, bias_tensor, ofm_tensor |
| 217 | |
Louis Verhaard | 98a3499 | 2020-09-01 10:39:04 +0200 | [diff] [blame] | 218 | def get_ofm(self): |
| 219 | _, _, _, ofm = self.get_ifm_ifm2_weights_ofm() |
| 220 | return ofm |
| 221 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 222 | def is_concat_op(self): |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 223 | return self.type in ("Concat", "ConcatV2", "QuantizedConcat", "ConcatTFLite", "PackReshaped") |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 224 | |
| 225 | def get_concat_inputs_axis(self): |
| 226 | assert self.is_concat_op() |
| 227 | |
| 228 | if self.type == "ConcatV2": |
| 229 | axis_tensor = self.inputs[-1] |
| 230 | inputs = self.inputs[:-1] |
| 231 | elif self.type == "Concat": |
| 232 | axis_tensor = self.inputs[0] |
| 233 | inputs = self.inputs[1:] |
| 234 | elif self.type == "QuantizedConcat": |
| 235 | axis_tensor = self.inputs[0] |
| 236 | inputs = self.inputs[1:] |
| 237 | inputs = inputs[: len(inputs) // 3] # Skip min/max |
| 238 | |
| 239 | if self.type == "ConcatTFLite": |
| 240 | inputs = self.inputs |
| 241 | axis = self.attrs["axis"] |
| 242 | elif self.type == "PackReshaped": |
| 243 | # Requires fixup_pack_input to be called before this point |
| 244 | inputs = self.inputs |
| 245 | axis = self.attrs["axis"] |
| 246 | assert len(self.inputs) == self.attrs["values_count"] |
| 247 | else: |
| 248 | assert len(axis_tensor.ops) == 1 and axis_tensor.ops[0].type == "Const" |
| 249 | axis = int(axis_tensor.values) |
| 250 | |
| 251 | return inputs, axis |
| 252 | |
Louis Verhaard | b2fb212 | 2020-06-04 15:51:24 +0200 | [diff] [blame] | 253 | def get_dilation_h_w(self): |
| 254 | _, dilation_h, dilation_w, _ = self.attrs.get("dilation", (1, 1, 1, 1)) |
| 255 | return dilation_h, dilation_w |
| 256 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 257 | def is_split_op(self): |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 258 | return self.type in ("Split", "SplitV", "StridedSlice", "Slice", "UnpackReshaped") |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 259 | |
| 260 | def get_split_inputs_axis(self): |
| 261 | assert self.is_split_op() |
| 262 | |
| 263 | offset_start = None |
| 264 | offset_end = None |
| 265 | axis = None |
| 266 | if self.type == "Split": |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 267 | num_splits = self.attrs.get("num_splits") |
| 268 | axis_tens = self.inputs[0] |
| 269 | assert len(axis_tens.ops) == 1 and axis_tens.ops[0].type == "Const" |
| 270 | axis = int(axis_tens.values) |
| 271 | input_tens = self.inputs[1] |
| 272 | outputs = self.outputs |
| 273 | assert num_splits == len(outputs) |
| 274 | |
Louis Verhaard | 9b8fa12 | 2020-05-15 13:41:13 +0200 | [diff] [blame] | 275 | elif self.type == "SplitV": |
Charles Xu | 53d4752 | 2020-05-04 11:32:05 +0200 | [diff] [blame] | 276 | num_splits = self.attrs.get("num_splits") |
| 277 | input_tens = self.inputs[0] |
| 278 | size_tens = self.inputs[1] |
| 279 | assert len(size_tens.ops) == 1 and size_tens.ops[0].type == "Const" |
| 280 | sizes = size_tens.values |
Patrik Gustavsson | 271ddc3 | 2020-09-01 09:15:27 +0200 | [diff] [blame] | 281 | |
Charles Xu | 53d4752 | 2020-05-04 11:32:05 +0200 | [diff] [blame] | 282 | axis_tens = self.inputs[2] |
| 283 | assert len(axis_tens.ops) == 1 and axis_tens.ops[0].type == "Const" |
| 284 | axis = int(axis_tens.values) |
Patrik Gustavsson | 271ddc3 | 2020-09-01 09:15:27 +0200 | [diff] [blame] | 285 | |
| 286 | for idx, size in enumerate(sizes): |
| 287 | # One but only one size might be set to -1, indicating that size should be inferred |
| 288 | if size == -1: |
| 289 | sizes[idx] = input_tens.shape[axis] - (sum(sizes) + 1) |
| 290 | break |
| 291 | |
Charles Xu | 53d4752 | 2020-05-04 11:32:05 +0200 | [diff] [blame] | 292 | outputs = self.outputs |
| 293 | assert num_splits == len(outputs) |
| 294 | assert sum(sizes) == input_tens.shape[axis] |
| 295 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 296 | elif self.type == "Slice": |
| 297 | input_tens, begin_tens, size_tens = self.inputs |
| 298 | outputs = self.outputs |
| 299 | offset_start = [0] * len(input_tens.shape) |
| 300 | offset_end = [0] * len(input_tens.shape) |
| 301 | |
| 302 | for idx in range(len(begin_tens.values)): |
| 303 | # Check if the op should slice in dimension idx |
| 304 | if size_tens.values[idx] != input_tens.shape[idx]: |
| 305 | offset_start[idx] = begin_tens.values[idx] |
| 306 | offset_end[idx] = size_tens.values[idx] + offset_start[idx] |
| 307 | |
| 308 | elif self.type == "StridedSlice": |
| 309 | input_tens, begin_tens, end_tens, strides_tens = self.inputs |
| 310 | outputs = self.outputs |
| 311 | out_tens = outputs[0] |
| 312 | offset_start = [0] * len(outputs[0].shape) |
| 313 | offset_end = [0] * len(outputs[0].shape) |
| 314 | |
| 315 | # Extract masks |
| 316 | begin_mask = self.attrs["begin_mask"] |
| 317 | ellipsis_mask = self.attrs["ellipsis_mask"] |
| 318 | end_mask = self.attrs["end_mask"] |
| 319 | new_axis_mask = self.attrs["new_axis_mask"] |
| 320 | shrink_axis_mask = self.attrs["shrink_axis_mask"] |
Patrik Gustavsson | cf72890 | 2020-04-30 08:57:23 +0200 | [diff] [blame] | 321 | |
| 322 | # shrink_axis_mask/new_axis_mask/ellipsis_mask is not supported by the Operation class but the operation |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 323 | # may have the attribute modified and handled in the graph optimization phase. |
Patrik Gustavsson | cf72890 | 2020-04-30 08:57:23 +0200 | [diff] [blame] | 324 | assert shrink_axis_mask == new_axis_mask == ellipsis_mask == 0 |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 325 | assert len(input_tens.shape) == len(out_tens.shape) |
| 326 | |
| 327 | for idx in range(len(input_tens.shape)): |
Patrik Gustavsson | 4913433 | 2020-04-29 14:10:32 +0200 | [diff] [blame] | 328 | # Check if slicing is needed in this axis |
| 329 | if end_tens.values[idx] != input_tens.shape[idx] or ( |
| 330 | end_tens.values[idx] == input_tens.shape[idx] and begin_tens.values[idx] != 0 |
| 331 | ): |
| 332 | # If the i:th bit in begin_mask is set then the value on begin[i] should be ignored |
| 333 | if (begin_mask & (1 << idx)) == 0: |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 334 | offset_start[idx] = begin_tens.values[idx] |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 335 | |
Patrik Gustavsson | 4913433 | 2020-04-29 14:10:32 +0200 | [diff] [blame] | 336 | # If the i:th bit in end_mask is set then the value on end[i] should be ignored |
| 337 | if (end_mask & (1 << idx)) == 0: |
| 338 | offset_end[idx] = end_tens.values[idx] |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 339 | |
| 340 | elif self.type == "UnpackReshaped": |
| 341 | # Requires fixup_unpack_output to be called before this point |
| 342 | input_tens = self.inputs[0] |
| 343 | outputs = self.outputs |
| 344 | axis = self.attrs["axis"] |
| 345 | num_splits = self.attrs["num"] |
| 346 | # Number of outputs have to equal the value of the dimension to unpack |
| 347 | assert num_splits == len(outputs) == input_tens.shape[axis] |
| 348 | else: |
| 349 | assert False |
| 350 | |
| 351 | return input_tens, outputs, axis, offset_start, offset_end |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 352 | |
| 353 | def set_activation_lut(self, lut_tensor): |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 354 | self.attrs["fused_activation_function"] = "LUT" |
| 355 | self.activation_lut = lut_tensor |
Michael McGeagh | c5b549b | 2020-08-07 11:54:28 +0100 | [diff] [blame] | 356 | self.add_input_tensor(lut_tensor) |
Michael McGeagh | 5778ffd | 2020-08-06 17:31:02 +0100 | [diff] [blame] | 357 | |
| 358 | def add_input_tensor(self, tens): |
| 359 | self.inputs.append(tens) |
| 360 | if self not in tens.consumer_list: |
| 361 | tens.consumer_list.append(self) |
| 362 | |
Jacob Bohlin | 67e0d8f | 2020-08-20 10:53:02 +0200 | [diff] [blame] | 363 | def set_input_tensor(self, tens, idx): |
| 364 | tens_to_remove = self.inputs[idx] |
| 365 | if tens_to_remove in tens.consumer_list: |
| 366 | tens.consumer_list.remove(tens_to_remove) |
| 367 | |
| 368 | self.inputs[idx] = tens |
| 369 | if self not in tens.consumer_list: |
| 370 | tens.consumer_list.append(self) |
| 371 | |
Michael McGeagh | 5778ffd | 2020-08-06 17:31:02 +0100 | [diff] [blame] | 372 | def set_output_tensor(self, tens): |
| 373 | tens.ops = [self] |
| 374 | self.outputs = [tens] |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 375 | |
| 376 | def needs_bias(self): |
| 377 | return self.type in ( |
| 378 | "Conv2DBiasAct", |
| 379 | "DepthwiseConv2dBiasAct", |
| 380 | "Conv2DBackpropInputSwitchedBias", |
| 381 | "FullyConnectedAct", |
| 382 | ) |
Louis Verhaard | 98a3499 | 2020-09-01 10:39:04 +0200 | [diff] [blame] | 383 | |
| 384 | def get_output_quantization(self): |
| 385 | return self.attrs.get("forced_output_quantization", self.get_ofm().quantization) |