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 | |
Louis Verhaard | fa2f92a | 2020-09-21 11:56:18 +0200 | [diff] [blame^] | 46 | def get_slice_offsets(input_shape, offset_tens, offset_mask, is_begin=True): |
| 47 | # For strided slice operator: get start or end offsets |
| 48 | offsets = len(input_shape) * [0] if is_begin else input_shape[:] |
| 49 | for idx in range(len(input_shape)): |
| 50 | # If the i:th bit in the mask is set then the value on offset_tens[i] should be ignored |
| 51 | if (offset_mask & (1 << idx)) == 0: |
| 52 | offsets[idx] = offset_tens.values[idx] |
| 53 | if offsets[idx] < 0: |
| 54 | # Convert offset to positive value |
| 55 | offsets[idx] += input_shape[idx] |
| 56 | return offsets |
| 57 | |
| 58 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 59 | class Operation: |
| 60 | """Class representing a Neural Network operation. Has a name, a type, |
| 61 | input and output tensors, as well as an attribute dictionary.""" |
| 62 | |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 63 | __slots__ = ( |
| 64 | "type", |
| 65 | "name", |
| 66 | "op_index", |
| 67 | "attrs", |
| 68 | "inputs", |
| 69 | "outputs", |
| 70 | "flops", |
| 71 | "scheduled_pass", |
| 72 | "run_on_npu", |
| 73 | "activation_lut", |
| 74 | ) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 75 | |
| 76 | def __init__(self, op_type, name): |
| 77 | self.type = op_type |
| 78 | self.name = name |
| 79 | self.attrs = {} |
| 80 | self.inputs = [] |
| 81 | self.outputs = [] |
| 82 | self.flops = 0 |
| 83 | self.run_on_npu = True |
| 84 | self.scheduled_pass = None |
Tim Hall | c8310b1 | 2020-06-17 14:53:11 +0100 | [diff] [blame] | 85 | self.op_index = None # input network operator index |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 86 | self.activation_lut = None |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 87 | |
| 88 | def clone(self, suffix="_clone"): |
| 89 | res = Operation(self.type, self.name + suffix) |
| 90 | |
| 91 | res.attrs = dict(self.attrs) |
| 92 | res.inputs = list(self.inputs) |
| 93 | res.outputs = list(self.outputs) |
| 94 | res.flops = self.flops |
| 95 | res.scheduled_pass = self.scheduled_pass |
Tim Hall | c8310b1 | 2020-06-17 14:53:11 +0100 | [diff] [blame] | 96 | res.op_index = None # not relevant as not part of input network |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 97 | |
| 98 | return res |
| 99 | |
| 100 | def __str__(self): |
| 101 | return "<nng.Operation '%s' type=%s>" % (self.name, self.type) |
| 102 | |
| 103 | __repr__ = __str__ |
| 104 | |
| 105 | def get_ifm_ifm2_weight_bias_ofm_indices(self): |
| 106 | ifm_idx = -1 |
| 107 | ifm2_idx = -1 |
| 108 | weight_idx = -1 |
| 109 | bias_idx = -1 |
| 110 | ofm_idx = -1 |
| 111 | npu_block_type = self.attrs.get("npu_block_type", NpuBlockType.Default) |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 112 | if npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.ConvolutionDepthWise): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 113 | ifm_idx = 0 |
| 114 | weight_idx = 1 |
| 115 | ofm_idx = 0 |
| 116 | |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 117 | if self.type in ("Conv2DBiasAct", "DepthwiseConv2dBiasAct", "TransposeConvAct"): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 118 | if len(self.inputs) >= 3: |
| 119 | bias_idx = 2 |
| 120 | |
Jacob Bohlin | cf7da10 | 2020-05-20 09:03:40 +0200 | [diff] [blame] | 121 | elif self.type == "Conv2DBackpropInputSwitchedBias": |
| 122 | bias_idx = 3 |
| 123 | |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 124 | elif npu_block_type in (NpuBlockType.Pooling, NpuBlockType.ReduceSum): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 125 | ifm_idx = 0 |
| 126 | ofm_idx = 0 |
| 127 | elif npu_block_type == NpuBlockType.VectorProduct: |
| 128 | ifm_idx = 0 |
| 129 | weight_idx = 1 |
| 130 | ofm_idx = 0 |
| 131 | |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 132 | if self.type == "FullyConnectedAct": |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 133 | if len(self.inputs) >= 3: |
| 134 | bias_idx = 2 |
| 135 | |
| 136 | if self.type == "BlockLSTM": |
| 137 | ifm_idx = 3 |
| 138 | weight_idx = 4 |
| 139 | ofm_idx = 6 |
| 140 | |
| 141 | elif npu_block_type == NpuBlockType.ElementWise: |
| 142 | ifm_idx = 0 |
| 143 | ifm2_idx = 1 |
| 144 | ofm_idx = 0 |
| 145 | |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 146 | # LeakyRelu, Abs and CLZ have a single IFM |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 147 | if self.type in ("LeakyRelu", "Abs", "CLZ"): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 148 | ifm2_idx = -1 |
| 149 | |
| 150 | elif self.type == "Conv2DBackpropInput": |
| 151 | ifm_idx = 2 |
| 152 | weight_idx = 1 |
| 153 | ofm_idx = 0 |
| 154 | |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 155 | elif self.type in ("Squeeze", "Reshape", "QuantizedReshape", "ExpandDims"): |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 156 | ifm_idx = 0 |
| 157 | ofm_idx = 0 |
| 158 | |
| 159 | elif self.is_split_op(): |
| 160 | ifm_idx = 0 |
| 161 | ofm_idx = 0 |
| 162 | if self.type == "Split": |
| 163 | ifm_idx = 1 |
| 164 | |
| 165 | elif self.is_concat_op(): |
| 166 | ifms, _ = self.get_concat_inputs_axis() |
| 167 | ifm_idx = self.inputs.index(ifms[0]) |
| 168 | if len(ifms) > 1: |
| 169 | ifm2_idx = self.inputs.index(ifms[1]) |
| 170 | ofm_idx = 0 |
| 171 | |
| 172 | return ifm_idx, ifm2_idx, weight_idx, bias_idx, ofm_idx |
| 173 | |
| 174 | def get_ifm_ifm2_weights_ofm(self): |
| 175 | ifm_tensor = None |
| 176 | ifm2_tensor = None |
| 177 | weight_tensor = None |
| 178 | ofm_tensor = None |
| 179 | |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 180 | 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] | 181 | if ifm_idx != -1: |
| 182 | ifm_tensor = self.inputs[ifm_idx] |
| 183 | if ifm2_idx != -1: |
| 184 | ifm2_tensor = self.inputs[ifm2_idx] |
| 185 | if weight_idx != -1: |
| 186 | weight_tensor = self.inputs[weight_idx] |
| 187 | if ofm_idx != -1: |
| 188 | ofm_tensor = self.outputs[ofm_idx] |
| 189 | |
| 190 | return ifm_tensor, ifm2_tensor, weight_tensor, ofm_tensor |
| 191 | |
| 192 | def get_ifm_weights_biases_ofm(self): |
| 193 | ifm_tensor = None |
| 194 | weight_tensor = None |
| 195 | bias_tensor = None |
| 196 | ofm_tensor = None |
| 197 | |
| 198 | ifm_idx, _, weight_idx, bias_idx, ofm_idx = self.get_ifm_ifm2_weight_bias_ofm_indices() |
| 199 | if ifm_idx != -1: |
| 200 | ifm_tensor = self.inputs[ifm_idx] |
| 201 | if weight_idx != -1: |
| 202 | weight_tensor = self.inputs[weight_idx] |
| 203 | if bias_idx != -1: |
| 204 | bias_tensor = self.inputs[bias_idx] |
| 205 | if ofm_idx != -1: |
| 206 | ofm_tensor = self.outputs[ofm_idx] |
| 207 | |
| 208 | return ifm_tensor, weight_tensor, bias_tensor, ofm_tensor |
| 209 | |
Jacob Bohlin | 49d9212 | 2020-08-19 14:36:46 +0200 | [diff] [blame] | 210 | def get_ifm_ifm2_weights_biases_ofm(self): |
| 211 | ifm_tensor = None |
| 212 | ifm2_tensor = None |
| 213 | weight_tensor = None |
| 214 | bias_tensor = None |
| 215 | ofm_tensor = None |
| 216 | |
| 217 | ifm_idx, ifm2_idx, weight_idx, bias_idx, ofm_idx = self.get_ifm_ifm2_weight_bias_ofm_indices() |
| 218 | if ifm_idx != -1: |
| 219 | ifm_tensor = self.inputs[ifm_idx] |
| 220 | if ifm2_idx != -1: |
| 221 | ifm2_tensor = self.inputs[ifm2_idx] |
| 222 | if weight_idx != -1: |
| 223 | weight_tensor = self.inputs[weight_idx] |
| 224 | if bias_idx != -1: |
| 225 | bias_tensor = self.inputs[bias_idx] |
| 226 | if ofm_idx != -1: |
| 227 | ofm_tensor = self.outputs[ofm_idx] |
| 228 | |
| 229 | return ifm_tensor, ifm2_tensor, weight_tensor, bias_tensor, ofm_tensor |
| 230 | |
Louis Verhaard | 98a3499 | 2020-09-01 10:39:04 +0200 | [diff] [blame] | 231 | def get_ofm(self): |
| 232 | _, _, _, ofm = self.get_ifm_ifm2_weights_ofm() |
| 233 | return ofm |
| 234 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 235 | def is_concat_op(self): |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 236 | return self.type in ("Concat", "ConcatV2", "QuantizedConcat", "ConcatTFLite", "PackReshaped") |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 237 | |
| 238 | def get_concat_inputs_axis(self): |
| 239 | assert self.is_concat_op() |
| 240 | |
| 241 | if self.type == "ConcatV2": |
| 242 | axis_tensor = self.inputs[-1] |
| 243 | inputs = self.inputs[:-1] |
| 244 | elif self.type == "Concat": |
| 245 | axis_tensor = self.inputs[0] |
| 246 | inputs = self.inputs[1:] |
| 247 | elif self.type == "QuantizedConcat": |
| 248 | axis_tensor = self.inputs[0] |
| 249 | inputs = self.inputs[1:] |
| 250 | inputs = inputs[: len(inputs) // 3] # Skip min/max |
| 251 | |
| 252 | if self.type == "ConcatTFLite": |
| 253 | inputs = self.inputs |
| 254 | axis = self.attrs["axis"] |
| 255 | elif self.type == "PackReshaped": |
| 256 | # Requires fixup_pack_input to be called before this point |
| 257 | inputs = self.inputs |
| 258 | axis = self.attrs["axis"] |
| 259 | assert len(self.inputs) == self.attrs["values_count"] |
| 260 | else: |
| 261 | assert len(axis_tensor.ops) == 1 and axis_tensor.ops[0].type == "Const" |
| 262 | axis = int(axis_tensor.values) |
| 263 | |
| 264 | return inputs, axis |
| 265 | |
Louis Verhaard | b2fb212 | 2020-06-04 15:51:24 +0200 | [diff] [blame] | 266 | def get_dilation_h_w(self): |
| 267 | _, dilation_h, dilation_w, _ = self.attrs.get("dilation", (1, 1, 1, 1)) |
| 268 | return dilation_h, dilation_w |
| 269 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 270 | def is_split_op(self): |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 271 | return self.type in ("Split", "SplitV", "StridedSlice", "Slice", "UnpackReshaped") |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 272 | |
| 273 | def get_split_inputs_axis(self): |
| 274 | assert self.is_split_op() |
| 275 | |
| 276 | offset_start = None |
| 277 | offset_end = None |
| 278 | axis = None |
| 279 | if self.type == "Split": |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 280 | num_splits = self.attrs.get("num_splits") |
| 281 | axis_tens = self.inputs[0] |
| 282 | assert len(axis_tens.ops) == 1 and axis_tens.ops[0].type == "Const" |
| 283 | axis = int(axis_tens.values) |
| 284 | input_tens = self.inputs[1] |
| 285 | outputs = self.outputs |
| 286 | assert num_splits == len(outputs) |
| 287 | |
Louis Verhaard | 9b8fa12 | 2020-05-15 13:41:13 +0200 | [diff] [blame] | 288 | elif self.type == "SplitV": |
Charles Xu | 53d4752 | 2020-05-04 11:32:05 +0200 | [diff] [blame] | 289 | num_splits = self.attrs.get("num_splits") |
| 290 | input_tens = self.inputs[0] |
| 291 | size_tens = self.inputs[1] |
| 292 | assert len(size_tens.ops) == 1 and size_tens.ops[0].type == "Const" |
| 293 | sizes = size_tens.values |
Patrik Gustavsson | 271ddc3 | 2020-09-01 09:15:27 +0200 | [diff] [blame] | 294 | |
Charles Xu | 53d4752 | 2020-05-04 11:32:05 +0200 | [diff] [blame] | 295 | axis_tens = self.inputs[2] |
| 296 | assert len(axis_tens.ops) == 1 and axis_tens.ops[0].type == "Const" |
| 297 | axis = int(axis_tens.values) |
Patrik Gustavsson | 271ddc3 | 2020-09-01 09:15:27 +0200 | [diff] [blame] | 298 | |
| 299 | for idx, size in enumerate(sizes): |
| 300 | # One but only one size might be set to -1, indicating that size should be inferred |
| 301 | if size == -1: |
| 302 | sizes[idx] = input_tens.shape[axis] - (sum(sizes) + 1) |
| 303 | break |
| 304 | |
Charles Xu | 53d4752 | 2020-05-04 11:32:05 +0200 | [diff] [blame] | 305 | outputs = self.outputs |
| 306 | assert num_splits == len(outputs) |
| 307 | assert sum(sizes) == input_tens.shape[axis] |
| 308 | |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 309 | elif self.type == "Slice": |
| 310 | input_tens, begin_tens, size_tens = self.inputs |
| 311 | outputs = self.outputs |
| 312 | offset_start = [0] * len(input_tens.shape) |
| 313 | offset_end = [0] * len(input_tens.shape) |
| 314 | |
| 315 | for idx in range(len(begin_tens.values)): |
| 316 | # Check if the op should slice in dimension idx |
| 317 | if size_tens.values[idx] != input_tens.shape[idx]: |
| 318 | offset_start[idx] = begin_tens.values[idx] |
| 319 | offset_end[idx] = size_tens.values[idx] + offset_start[idx] |
| 320 | |
| 321 | elif self.type == "StridedSlice": |
| 322 | input_tens, begin_tens, end_tens, strides_tens = self.inputs |
| 323 | outputs = self.outputs |
| 324 | out_tens = outputs[0] |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 325 | |
| 326 | # Extract masks |
| 327 | begin_mask = self.attrs["begin_mask"] |
| 328 | ellipsis_mask = self.attrs["ellipsis_mask"] |
| 329 | end_mask = self.attrs["end_mask"] |
| 330 | new_axis_mask = self.attrs["new_axis_mask"] |
| 331 | shrink_axis_mask = self.attrs["shrink_axis_mask"] |
Patrik Gustavsson | cf72890 | 2020-04-30 08:57:23 +0200 | [diff] [blame] | 332 | |
| 333 | # 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] | 334 | # may have the attribute modified and handled in the graph optimization phase. |
Patrik Gustavsson | cf72890 | 2020-04-30 08:57:23 +0200 | [diff] [blame] | 335 | assert shrink_axis_mask == new_axis_mask == ellipsis_mask == 0 |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 336 | assert len(input_tens.shape) == len(out_tens.shape) |
Louis Verhaard | fa2f92a | 2020-09-21 11:56:18 +0200 | [diff] [blame^] | 337 | offset_start = get_slice_offsets(input_tens.shape, begin_tens, begin_mask, is_begin=True) |
| 338 | offset_end = get_slice_offsets(input_tens.shape, end_tens, end_mask, is_begin=False) |
Tim Hall | 79d07d2 | 2020-04-27 18:20:16 +0100 | [diff] [blame] | 339 | elif self.type == "UnpackReshaped": |
| 340 | # Requires fixup_unpack_output to be called before this point |
| 341 | input_tens = self.inputs[0] |
| 342 | outputs = self.outputs |
| 343 | axis = self.attrs["axis"] |
| 344 | num_splits = self.attrs["num"] |
| 345 | # Number of outputs have to equal the value of the dimension to unpack |
| 346 | assert num_splits == len(outputs) == input_tens.shape[axis] |
| 347 | else: |
| 348 | assert False |
| 349 | |
| 350 | return input_tens, outputs, axis, offset_start, offset_end |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 351 | |
| 352 | def set_activation_lut(self, lut_tensor): |
Fredrik Svedberg | a0c3624 | 2020-06-03 15:43:31 +0200 | [diff] [blame] | 353 | self.attrs["fused_activation_function"] = "LUT" |
| 354 | self.activation_lut = lut_tensor |
Michael McGeagh | c5b549b | 2020-08-07 11:54:28 +0100 | [diff] [blame] | 355 | self.add_input_tensor(lut_tensor) |
Michael McGeagh | 5778ffd | 2020-08-06 17:31:02 +0100 | [diff] [blame] | 356 | |
| 357 | def add_input_tensor(self, tens): |
| 358 | self.inputs.append(tens) |
| 359 | if self not in tens.consumer_list: |
| 360 | tens.consumer_list.append(self) |
| 361 | |
Jacob Bohlin | 67e0d8f | 2020-08-20 10:53:02 +0200 | [diff] [blame] | 362 | def set_input_tensor(self, tens, idx): |
| 363 | tens_to_remove = self.inputs[idx] |
| 364 | if tens_to_remove in tens.consumer_list: |
| 365 | tens.consumer_list.remove(tens_to_remove) |
| 366 | |
| 367 | self.inputs[idx] = tens |
| 368 | if self not in tens.consumer_list: |
| 369 | tens.consumer_list.append(self) |
| 370 | |
Michael McGeagh | 5778ffd | 2020-08-06 17:31:02 +0100 | [diff] [blame] | 371 | def set_output_tensor(self, tens): |
| 372 | tens.ops = [self] |
| 373 | self.outputs = [tens] |
Jacob Bohlin | a41cd4d | 2020-08-26 18:21:28 +0200 | [diff] [blame] | 374 | |
| 375 | def needs_bias(self): |
| 376 | return self.type in ( |
| 377 | "Conv2DBiasAct", |
| 378 | "DepthwiseConv2dBiasAct", |
| 379 | "Conv2DBackpropInputSwitchedBias", |
| 380 | "FullyConnectedAct", |
| 381 | ) |
Louis Verhaard | 98a3499 | 2020-09-01 10:39:04 +0200 | [diff] [blame] | 382 | |
| 383 | def get_output_quantization(self): |
| 384 | return self.attrs.get("forced_output_quantization", self.get_ofm().quantization) |