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120 changes: 120 additions & 0 deletions
120
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shape.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 TrtConvertSumTest(TrtLayerAutoScanTest): | ||
|
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| def is_program_valid(self, program_config: ProgramConfig) -> bool: | ||
| return True | ||
|
|
||
| def sample_program_configs(self): | ||
|
|
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| def generate_input1(batch): | ||
| if self.dims == 4: | ||
| return np.ones([batch, 3, 24, 24]).astype(np.float32) | ||
| elif self.dims == 3: | ||
| return np.ones([batch, 3, 24]).astype(np.float32) | ||
| elif self.dims == 2: | ||
| return np.ones([batch, 24]).astype(np.float32) | ||
| elif self.dims == 1: | ||
| return np.ones([24]).astype(np.float32) | ||
|
|
||
| for dims in [1, 2, 3, 4]: | ||
| for batch in [1, 4]: | ||
| self.dims = dims | ||
| ops_config = [{ | ||
| "op_type": "shape", | ||
| "op_inputs": { | ||
| "Input": ["input1"] | ||
| }, | ||
| "op_outputs": { | ||
| "Out": ["output"] | ||
| }, | ||
| "op_attrs": {} | ||
| }] | ||
| ops = self.generate_op_config(ops_config) | ||
| program_config = ProgramConfig( | ||
| ops=ops, | ||
| weights={}, | ||
| inputs={ | ||
| "input1": | ||
| TensorConfig(data_gen=partial(generate_input1, batch)) | ||
| }, | ||
| outputs=["output"]) | ||
|
|
||
| yield program_config | ||
|
|
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| def sample_predictor_configs( | ||
| self, program_config) -> (paddle_infer.Config, List[int], float): | ||
|
|
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| def generate_dynamic_shape(): | ||
| if self.dims == 4: | ||
| self.dynamic_shape.min_input_shape = {"input1": [1, 3, 24, 24]} | ||
| self.dynamic_shape.max_input_shape = {"input1": [4, 3, 48, 48]} | ||
| self.dynamic_shape.opt_input_shape = {"input1": [1, 3, 24, 24]} | ||
| elif self.dims == 3: | ||
| self.dynamic_shape.min_input_shape = {"input1": [1, 3, 24]} | ||
| self.dynamic_shape.max_input_shape = {"input1": [4, 3, 48]} | ||
| self.dynamic_shape.opt_input_shape = {"input1": [1, 3, 24]} | ||
| elif self.dims == 2: | ||
| self.dynamic_shape.min_input_shape = {"input1": [1, 24]} | ||
| self.dynamic_shape.max_input_shape = {"input1": [4, 48]} | ||
| self.dynamic_shape.opt_input_shape = {"input1": [1, 24]} | ||
| elif self.dims == 1: | ||
| self.dynamic_shape.min_input_shape = {"input1": [24]} | ||
| self.dynamic_shape.max_input_shape = {"input1": [48]} | ||
| self.dynamic_shape.opt_input_shape = { | ||
| "input1": [24], | ||
| } | ||
|
|
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| def generate_trt_nodes_num(dynamic_shape): | ||
| if (not dynamic_shape): | ||
| return 0, 3 | ||
| return 1, 2 | ||
|
|
||
| def clear_dynamic_shape(): | ||
| self.dynamic_shape.min_input_shape = {} | ||
| self.dynamic_shape.max_input_shape = {} | ||
| self.dynamic_shape.opt_input_shape = {} | ||
|
|
||
| # for static_shape | ||
| clear_dynamic_shape() | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Float32 | ||
| yield self.create_inference_config(), generate_trt_nodes_num( | ||
| False), 1e-5 | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Half | ||
| yield self.create_inference_config(), generate_trt_nodes_num( | ||
| False), 1e-5 | ||
|
|
||
| # for dynamic_shape | ||
| generate_dynamic_shape() | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Float32 | ||
| yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5 | ||
| self.trt_param.precision = paddle_infer.PrecisionType.Half | ||
| yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5 | ||
|
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| def test(self): | ||
| self.run_test() | ||
|
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|
|
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| if __name__ == "__main__": | ||
| unittest.main() |
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