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60 lines
2.5 KiB
60 lines
2.5 KiB
# copyright (c) 2018 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 sys
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from test_calibration_resnet50 import TestCalibration
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class TestCalibrationForMobilenetv1(TestCalibration):
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def download_model(self):
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# mobilenetv1 fp32 data
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data_urls = [
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'http://paddle-inference-dist.bj.bcebos.com/int8/mobilenetv1_int8_model.tar.gz'
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]
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data_md5s = ['13892b0716d26443a8cdea15b3c6438b']
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self.model_cache_folder = self.download_data(data_urls, data_md5s,
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"mobilenetv1_fp32")
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self.model = "MobileNet-V1"
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self.algo = "KL"
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def test_calibration(self):
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self.download_model()
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print("Start FP32 inference for {0} on {1} images ...").format(
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self.model, self.infer_iterations * self.batch_size)
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(fp32_throughput, fp32_latency,
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fp32_acc1) = self.run_program(self.model_cache_folder + "/model")
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print("Start INT8 calibration for {0} on {1} images ...").format(
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self.model, self.sample_iterations * self.batch_size)
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self.run_program(
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self.model_cache_folder + "/model", True, algo=self.algo)
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print("Start INT8 inference for {0} on {1} images ...").format(
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self.model, self.infer_iterations * self.batch_size)
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(int8_throughput, int8_latency,
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int8_acc1) = self.run_program(self.int8_model)
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delta_value = fp32_acc1 - int8_acc1
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self.assertLess(delta_value, 0.01)
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print(
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"FP32 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
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format(self.model, self.batch_size, fp32_throughput, fp32_latency,
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fp32_acc1))
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print(
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"INT8 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
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format(self.model, self.batch_size, int8_throughput, int8_latency,
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int8_acc1))
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sys.stdout.flush()
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if __name__ == '__main__':
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unittest.main()
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