| # | |
| # Copyright (c) 2021 Intel Corporation | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| version: 1.0 | |
| model: # mandatory. used to specify model specific information. | |
| name: mp_handpose | |
| framework: onnxrt_qlinearops # mandatory. supported values are tensorflow, pytorch, pytorch_ipex, onnxrt_integer, onnxrt_qlinear or mxnet; allow new framework backend extension. | |
| quantization: # optional. tuning constraints on model-wise for advance user to reduce tuning space. | |
| approach: post_training_static_quant # optional. default value is post_training_static_quant. | |
| calibration: | |
| dataloader: | |
| batch_size: 1 | |
| dataset: | |
| dummy: | |
| shape: [1, 256, 256, 3] | |
| low: -1.0 | |
| high: 1.0 | |
| dtype: float32 | |
| label: True | |
| model_wise: # optional. tuning constraints on model-wise for advance user to reduce tuning space. | |
| weight: | |
| granularity: per_tensor | |
| scheme: asym | |
| dtype: int8 | |
| algorithm: minmax | |
| activation: | |
| granularity: per_tensor | |
| scheme: asym | |
| dtype: int8 | |
| algorithm: minmax | |
| tuning: | |
| accuracy_criterion: | |
| relative: 0.02 # optional. default value is relative, other value is absolute. this example allows relative accuracy loss: 1%. | |
| exit_policy: | |
| timeout: 0 # optional. tuning timeout (seconds). default value is 0 which means early stop. combine with max_trials field to decide when to exit. | |
| random_seed: 9527 # optional. random seed for deterministic tuning. | |