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117
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_swish.py
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| # 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 | ||
|
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|
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| 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 | ||
|
|
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| return True | ||
|
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| 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 | ||
| }] | ||
|
|
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| 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"]) | ||
|
|
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| yield program_config | ||
|
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| 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 = {} | ||
|
|
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| 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)) | ||
| ] | ||
|
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| # 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-2 | ||
|
|
||
| # 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-2 | ||
|
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| def test(self): | ||
| self.run_test() | ||
|
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| if __name__ == "__main__": | ||
| unittest.main() |