wehub-resource-sync
118 lines
3.9 KiB
Python
118 lines
3.9 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from inference_pass_test import InferencePassTest
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import paddle
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from paddle.framework import core
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from paddle.inference import Config, create_predictor
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# -------------------------- TestNet --------------------------
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# x
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# / \
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# conv2d \ x
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# | \ IR/Pass / \
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# batch_norm conv2d ——————> tensorrt_engine conv2d
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# | / \ /
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# relu / elemenwise_add
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# \ / |
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# elemenwise_add y
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# |
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# y
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# -------------------------------------------------------------
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class TestNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.conv1 = paddle.nn.Conv2D(3, 6, kernel_size=3, bias_attr=False)
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self.bn1 = paddle.nn.BatchNorm2D(6)
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self.relu = paddle.nn.ReLU()
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self.conv2 = paddle.nn.Conv2D(3, 6, kernel_size=3, bias_attr=False)
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def forward(self, x):
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x1 = self.conv1(x)
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x1 = self.bn1(x1)
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x1 = self.relu(x1)
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x2 = self.conv2(x)
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y = paddle.add(x1, x2)
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return y
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class UseOptimizedModel(InferencePassTest):
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def setUp(self):
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paddle.disable_static()
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self.test_model = TestNet()
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self.input_data = (np.ones([1, 3, 32, 32])).astype('float32')
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self.path_prefix = "inference_test_models/use_optimized_model_test"
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self.cache_dir = "inference_test_models/cache"
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paddle.jit.save(
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self.test_model,
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self.path_prefix,
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input_spec=[
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paddle.static.InputSpec(shape=[1, 3, 32, 32], dtype='float32')
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],
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)
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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out_origin_model = self.inference()
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out_optimized_model = self.inference()
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np.testing.assert_allclose(
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out_origin_model, out_optimized_model, rtol=1e-5, atol=1e-2
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)
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def inference(self):
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# Config
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config = Config(
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self.path_prefix + ".json", self.path_prefix + ".pdiparams"
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)
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config.enable_use_gpu(100, 0)
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config.enable_tensorrt_engine(
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workspace_size=1 << 30,
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max_batch_size=1,
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min_subgraph_size=1,
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precision_mode=paddle.inference.PrecisionType.Float32,
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use_static=True,
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use_calib_mode=False,
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)
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config.enable_tuned_tensorrt_dynamic_shape()
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config.exp_disable_tensorrt_ops(["elementwise_add"])
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config.set_optim_cache_dir(self.cache_dir)
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config.use_optimized_model(True)
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# predictor
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predictor = create_predictor(config)
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# inference
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input_tensor = predictor.get_input_handle(
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predictor.get_input_names()[0]
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)
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input_tensor.reshape(self.input_data.shape)
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input_tensor.copy_from_cpu(self.input_data.copy())
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predictor.run()
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output_tensor = predictor.get_output_handle(
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predictor.get_output_names()[0]
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)
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out = output_tensor.copy_to_cpu()
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out = np.array(out).flatten()
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return out
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if __name__ == "__main__":
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unittest.main()
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