forked from PaddlePaddle/Paddle
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[inference]add hard_swish dynamic plugin (PaddlePaddle#35214)
- Loading branch information
1 parent
b8bb16f
commit d85ef46
Showing
4 changed files
with
313 additions
and
12 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
117 changes: 117 additions & 0 deletions
117
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_swish.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,117 @@ | ||
| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # 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. | ||
|
|
||
| from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons | ||
| from program_config import TensorConfig, ProgramConfig | ||
| import numpy as np | ||
| import paddle.inference as paddle_infer | ||
| from functools import partial | ||
| from typing import Optional, List, Callable, Dict, Any, Set | ||
| import unittest | ||
|
|
||
|
|
||
| class TrtConvertHardSwishTest(TrtLayerAutoScanTest): | ||
| def is_program_valid(self, program_config: ProgramConfig) -> bool: | ||
| inputs = program_config.inputs | ||
| weights = program_config.weights | ||
| attrs = [ | ||
| program_config.ops[i].attrs | ||
| for i in range(len(program_config.ops)) | ||
| ] | ||
|
|
||
| if attrs[0]['threshold'] <= 0 or attrs[0]['scale'] <= 0: | ||
| return False | ||
|
|
||
| return True | ||
|
|
||
| def sample_program_configs(self): | ||
| def generate_input1(attrs: List[Dict[str, Any]]): | ||
| return np.ones([1, 3, 64, 64]).astype(np.float32) | ||
|
|
||
| for threshold in [6.0, 7.0, 100.0, 0.0, -1.0]: | ||
| for scale in [5.0, 6.0, 7.0, -1.0, 0.0, 100.0]: | ||
| for offset in [3.0, 4.0, 5.0, -1.0, 0.0, 100.0]: | ||
| dics = [{ | ||
| "threshold": threshold, | ||
| "scale": scale, | ||
| "offset": offset | ||
| }] | ||
|
|
||
| ops_config = [{ | ||
| "op_type": "hard_swish", | ||
| "op_inputs": { | ||
| "X": ["input_data"] | ||
| }, | ||
| "op_outputs": { | ||
| "Out": ["hard_swish_output_data"] | ||
| }, | ||
| "op_attrs": dics[0] | ||
| }] | ||
| ops = self.generate_op_config(ops_config) | ||
|
|
||
| program_config = ProgramConfig( | ||
| ops=ops, | ||
| weights={}, | ||
| inputs={ | ||
| "input_data": TensorConfig(data_gen=partial( | ||
| generate_input1, dics)) | ||
| }, | ||
| outputs=["hard_swish_output_data"]) | ||
|
|
||
| yield program_config | ||
|
|
||
| def sample_predictor_configs( | ||
| self, program_config) -> (paddle_infer.Config, List[int], float): | ||
| def generate_dynamic_shape(attrs): | ||
| self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]} | ||
| self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]} | ||
| self.dynamic_shape.opt_input_shape = {"input_data": [1, 3, 64, 64]} | ||
|
|
||
| def clear_dynamic_shape(): | ||
| self.dynamic_shape.min_input_shape = {} | ||
| self.dynamic_shape.max_input_shape = {} | ||
| self.dynamic_shape.opt_input_shape = {} | ||
|
|
||
| def generate_trt_nodes_num(attrs, dynamic_shape): | ||
| return 1, 2 | ||
|
|
||
| attrs = [ | ||
| program_config.ops[i].attrs | ||
| for i in range(len(program_config.ops)) | ||
| ] | ||
|
|
||
| # for static_shape | ||
| clear_dynamic_shape() | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Float32 | ||
| yield self.create_inference_config(), generate_trt_nodes_num( | ||
| attrs, False), 1e-5 | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Half | ||
| yield self.create_inference_config(), generate_trt_nodes_num( | ||
| attrs, False), (1e-5, 1e-5) | ||
|
|
||
| # for dynamic_shape | ||
| generate_dynamic_shape(attrs) | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Float32 | ||
| yield self.create_inference_config(), generate_trt_nodes_num(attrs, | ||
| True), 1e-5 | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Half | ||
| yield self.create_inference_config(), generate_trt_nodes_num( | ||
| attrs, True), (1e-5, 1e-5) | ||
|
|
||
| def test(self): | ||
| self.run_test() | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| unittest.main() |