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add inference api:exp_specify_tensorrt_subgraph_precision (PaddlePadd…
…le#62402) add inference api:exp_specify_tensorrt_subgraph_precision (PaddlePaddle#62402)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,144 @@ | ||
| # Copyright (c) 2024 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. | ||
|
|
||
| import os | ||
| import shutil | ||
| import tempfile | ||
| import unittest | ||
|
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| import numpy as np | ||
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| import paddle | ||
| from paddle import nn, static | ||
| from paddle.inference import Config, PrecisionType, create_predictor | ||
|
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| paddle.enable_static() | ||
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|
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| class SimpleNet(nn.Layer): | ||
| def __init__(self): | ||
| super().__init__() | ||
| self.conv1 = nn.Conv2D( | ||
| in_channels=4, | ||
| out_channels=4, | ||
| kernel_size=3, | ||
| stride=2, | ||
| padding=0, | ||
| ) | ||
| self.relu1 = nn.ReLU() | ||
| self.conv2 = nn.Conv2D( | ||
| in_channels=4, | ||
| out_channels=2, | ||
| kernel_size=3, | ||
| stride=2, | ||
| padding=0, | ||
| ) | ||
| self.relu2 = nn.ReLU() | ||
| self.conv3 = nn.Conv2D( | ||
| in_channels=2, | ||
| out_channels=1, | ||
| kernel_size=3, | ||
| stride=2, | ||
| padding=0, | ||
| ) | ||
| self.relu3 = nn.ReLU() | ||
| self.flatten = nn.Flatten() | ||
| self.fc = nn.Linear(729, 10) | ||
| self.softmax = nn.Softmax() | ||
|
|
||
| def forward(self, x): | ||
| x = self.conv1(x) | ||
| x = self.relu1(x) | ||
| x = self.conv2(x) | ||
| x = self.relu2(x) | ||
| x = self.conv3(x) | ||
| x = self.relu3(x) | ||
| x = self.flatten(x) | ||
| x = self.fc(x) | ||
| x = self.softmax(x) | ||
| return x | ||
|
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|
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| class TestTRTOptimizationLevel(unittest.TestCase): | ||
| def setUp(self): | ||
| self.place = paddle.CUDAPlace(0) | ||
| self.temp_dir = tempfile.TemporaryDirectory() | ||
| self.path = os.path.join(self.temp_dir.name, 'optimization_level', '') | ||
| self.model_prefix = self.path + 'infer_model' | ||
|
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| def tearDown(self): | ||
| shutil.rmtree(self.path) | ||
|
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| def build_model(self): | ||
| image = static.data( | ||
| name='img', shape=[None, 4, 224, 224], dtype='float32' | ||
| ) | ||
| predict = SimpleNet()(image) | ||
| exe = paddle.static.Executor(self.place) | ||
| exe.run(paddle.static.default_startup_program()) | ||
| paddle.static.save_inference_model( | ||
| self.model_prefix, [image], [predict], exe | ||
| ) | ||
|
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| def init_predictor(self): | ||
| config = Config( | ||
| self.model_prefix + '.pdmodel', self.model_prefix + '.pdiparams' | ||
| ) | ||
| config.enable_use_gpu(256, 0, PrecisionType.Float32) | ||
| config.exp_disable_tensorrt_ops(["relu_1.tmp_0"]) | ||
| config.enable_tensorrt_engine( | ||
| workspace_size=1 << 30, | ||
| max_batch_size=1, | ||
| min_subgraph_size=3, | ||
| precision_mode=PrecisionType.Float32, | ||
| use_static=False, | ||
| use_calib_mode=False, | ||
| ) | ||
|
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| config.exp_specify_tensorrt_subgraph_precision( | ||
| ["conv2d_1.w_0"], [""], ["conv2d_2.w_0"] | ||
| ) | ||
|
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| config.enable_memory_optim() | ||
| # config.disable_glog_info() | ||
| config.set_tensorrt_optimization_level(0) | ||
| self.assertEqual(config.tensorrt_optimization_level(), 0) | ||
| predictor = create_predictor(config) | ||
| return predictor | ||
|
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| def infer(self, predictor, img): | ||
| input_names = predictor.get_input_names() | ||
| for i, name in enumerate(input_names): | ||
| input_tensor = predictor.get_input_handle(name) | ||
| input_tensor.reshape(img[i].shape) | ||
| input_tensor.copy_from_cpu(img[i].copy()) | ||
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| predictor.run() | ||
| results = [] | ||
| output_names = predictor.get_output_names() | ||
| for i, name in enumerate(output_names): | ||
| output_tensor = predictor.get_output_handle(name) | ||
| output_data = output_tensor.copy_to_cpu() | ||
| results.append(output_data) | ||
| return results | ||
|
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| def test_optimization_level(self): | ||
| self.build_model() | ||
| predictor = self.init_predictor() | ||
| img = np.ones((1, 4, 224, 224), dtype=np.float32) | ||
| results = self.infer(predictor, img=[img]) | ||
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| if __name__ == '__main__': | ||
| unittest.main() |