wehub-resource-sync
113 lines
3.8 KiB
Python
Executable File
113 lines
3.8 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright (c) 2021 CINN 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 sys
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import time
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import unittest
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import numpy as np
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from paddle import base
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from paddle.cinn.common import DefaultHostTarget, DefaultNVGPUTarget
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from paddle.cinn.frontend import Interpreter
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enable_gpu = sys.argv.pop()
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model_dir = sys.argv.pop()
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class TestLoadEfficientNetModel(unittest.TestCase):
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def setUp(self):
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if enable_gpu == "ON":
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self.target = DefaultNVGPUTarget()
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else:
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self.target = DefaultHostTarget()
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self.model_dir = model_dir
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self.x_shape = [1, 3, 224, 224]
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self.target_tensor = 'save_infer_model/scale_0'
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self.input_tensor = 'image'
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def get_paddle_inference_result(self, model_dir, data):
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config = base.core.AnalysisConfig(
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model_dir + '/__model__', model_dir + '/params'
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)
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config.disable_gpu()
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config.switch_ir_optim(False)
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self.paddle_predictor = base.core.create_paddle_predictor(config)
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data = base.core.PaddleTensor(data)
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results = self.paddle_predictor.run([data])
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get_tensor = self.paddle_predictor.get_output_tensor(
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self.target_tensor
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).copy_to_cpu()
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return get_tensor
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def apply_test(self):
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start = time.time()
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x_data = np.random.random(self.x_shape).astype("float32")
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self.executor = Interpreter([self.input_tensor], [self.x_shape])
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print("self.mode_dir is:", self.model_dir)
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# True means load combined model
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self.executor.load_paddle_model(self.model_dir, self.target, True)
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end1 = time.time()
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print("load_paddle_model time is: %.3f sec" % (end1 - start))
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a_t = self.executor.get_tensor(self.input_tensor)
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a_t.from_numpy(x_data, self.target)
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out = self.executor.get_tensor(self.target_tensor)
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out.from_numpy(np.zeros(out.shape(), dtype='float32'), self.target)
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for i in range(10):
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self.executor.run()
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repeat = 10
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end4 = time.perf_counter()
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for i in range(repeat):
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self.executor.run()
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end5 = time.perf_counter()
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print(
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f"Repeat {repeat} times, average Executor.run() time is: {(end5 - end4) * 1000 / repeat:.3f} ms"
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)
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a_t.from_numpy(x_data, self.target)
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out.from_numpy(np.zeros(out.shape(), dtype='float32'), self.target)
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self.executor.run()
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out = out.numpy(self.target)
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target_result = self.get_paddle_inference_result(self.model_dir, x_data)
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print("result in test_model: \n")
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out = out.reshape(-1)
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target_result = target_result.reshape(-1)
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for i in range(0, min(out.shape[0], 200)):
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if np.abs(out[i] - target_result[i]) > 1e-3:
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print(
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"Error! ",
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i,
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"-th data has diff with target data:\n",
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out[i],
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" vs: ",
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target_result[i],
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". Diff is: ",
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out[i] - target_result[i],
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)
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np.testing.assert_allclose(out, target_result, atol=1e-3)
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def test_model(self):
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self.apply_test()
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# self.target.arch = Target.NVGPUArch()
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# self.apply_test()
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if __name__ == "__main__":
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unittest.main()
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