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- #!/usr/bin/env python3
-
- # Copyright (c) 2021 CINN 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 sys
- import time
- import unittest
-
- import numpy as np
-
- from paddle import base
- from paddle.cinn.common import DefaultHostTarget, DefaultNVGPUTarget
- from paddle.cinn.frontend import Interpreter
-
- enable_gpu = sys.argv.pop()
- model_dir = sys.argv.pop()
- print("enable_gpu is : ", enable_gpu)
- print("model_dir is : ", model_dir)
-
-
- class TestLoadResnet50Model(unittest.TestCase):
- def setUp(self):
- if enable_gpu == "ON":
- self.target = DefaultNVGPUTarget()
- else:
- self.target = DefaultHostTarget()
- self.model_dir = model_dir
- self.x_shape = [1, 3, 224, 224]
- self.target_tensor = 'save_infer_model/scale_0.tmp_1'
- self.input_tensor = 'inputs'
-
- def get_paddle_inference_result(self, model_dir, data):
- config = base.core.AnalysisConfig(
- model_dir + '/__model__', model_dir + '/params'
- )
- config.disable_gpu()
- config.switch_ir_optim(False)
- self.paddle_predictor = base.core.create_paddle_predictor(config)
- data = base.core.PaddleTensor(data)
- results = self.paddle_predictor.run([data])
- get_tensor = self.paddle_predictor.get_output_tensor(
- self.target_tensor
- ).copy_to_cpu()
- return get_tensor
-
- def apply_test(self):
- start = time.time()
- x_data = np.random.random(self.x_shape).astype("float32")
- self.executor = Interpreter([self.input_tensor], [self.x_shape])
- print("self.mode_dir is:", self.model_dir)
- # True means load combined model
- self.executor.load_paddle_model(
- self.model_dir, self.target, True, "resnet50"
- )
- prog = self.executor.get_program()
- # print program
- print("resnet50 program is:\n")
- print(prog)
- end1 = time.time()
- print("load_paddle_model time is: %.3f sec" % (end1 - start))
- a_t = self.executor.get_tensor(self.input_tensor)
- a_t.from_numpy(x_data, self.target)
- out = self.executor.get_tensor(self.target_tensor)
- out.from_numpy(np.zeros(out.shape(), dtype='float32'), self.target)
- for i in range(10):
- self.executor.run()
-
- repeat = 10
- end4 = time.perf_counter()
- for i in range(repeat):
- self.executor.run()
- end5 = time.perf_counter()
- print(
- "Repeat %d times, average Executor.run() time is: %.3f ms"
- % (repeat, (end5 - end4) * 1000 / repeat)
- )
-
- a_t.from_numpy(x_data, self.target)
- out.from_numpy(np.zeros(out.shape(), dtype='float32'), self.target)
- self.executor.run()
-
- out = out.numpy(self.target)
- target_result = self.get_paddle_inference_result(self.model_dir, x_data)
-
- print("result in test_model: \n")
- out = out.reshape(-1)
- target_result = target_result.reshape(-1)
- for i in range(0, min(out.shape[0], 200)):
- if np.abs(out[i] - target_result[i]) > 1e-3:
- print(
- "Error! ",
- i,
- "-th data has diff with target data:\n",
- out[i],
- " vs: ",
- target_result[i],
- ". Diff is: ",
- out[i] - target_result[i],
- )
- np.testing.assert_allclose(out, target_result, atol=1e-3)
-
- def test_model(self):
- self.apply_test()
-
-
- if __name__ == "__main__":
- unittest.main()
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