wehub-resource-sync
154 lines
4.4 KiB
Python
154 lines
4.4 KiB
Python
# Copyright (c) 2019 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 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 test_post_training_quantization_mobilenetv1 import (
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TestPostTrainingQuantization,
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val,
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)
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import paddle
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paddle.enable_static()
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class TestPostTrainingForResnet50(TestPostTrainingQuantization):
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def test_post_training_resnet50(self):
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model = "ResNet-50"
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algo = "min_max"
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round_type = "round"
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data_urls = [
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'http://paddle-inference-dist.bj.bcebos.com/int8/resnet50_int8_model_combined.tar.gz'
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]
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data_md5s = ['db212fd4e9edc83381aef4533107e60c']
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quantizable_op_type = ["conv2d", "mul"]
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is_full_quantize = False
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is_use_cache_file = False
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is_optimize_model = False
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diff_threshold = 0.025
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self.run_test(
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model,
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'model.pdmodel',
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'model.pdiparams',
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algo,
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round_type,
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data_urls,
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data_md5s,
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"model",
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quantizable_op_type,
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is_full_quantize,
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is_use_cache_file,
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is_optimize_model,
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diff_threshold,
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)
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def run_program(
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self,
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model_path,
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model_filename,
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params_filename,
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batch_size,
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infer_iterations,
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):
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image_shape = [3, 224, 224]
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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[
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infer_program,
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feed_dict,
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fetch_targets,
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] = paddle.static.load_inference_model(
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model_path,
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exe,
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model_filename=model_filename,
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params_filename=params_filename,
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)
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val_reader = paddle.batch(val(), batch_size)
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iterations = infer_iterations
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test_info = []
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cnt = 0
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periods = []
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for batch_id, data in enumerate(val_reader()):
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image = np.array([x[0].reshape(image_shape) for x in data]).astype(
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"float32"
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)
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label = np.array([x[1] for x in data]).astype("int64")
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label = label.reshape([-1, 1])
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t1 = time.time()
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_, acc1, _ = exe.run(
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infer_program,
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feed={feed_dict[0]: image, feed_dict[1]: label},
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fetch_list=fetch_targets,
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)
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t2 = time.time()
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period = t2 - t1
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periods.append(period)
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test_info.append(np.mean(acc1) * len(data))
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cnt += len(data)
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if (batch_id + 1) % 100 == 0:
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print(f"{batch_id + 1} images,")
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sys.stdout.flush()
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if (batch_id + 1) == iterations:
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break
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throughput = cnt / np.sum(periods)
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latency = np.average(periods)
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acc1 = np.sum(test_info) / cnt
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return (throughput, latency, acc1, feed_dict)
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class TestPostTrainingForResnet50ONNXFormat(TestPostTrainingForResnet50):
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def test_post_training_resnet50(self):
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model = "ResNet-50"
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algo = "min_max"
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round_type = "round"
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data_urls = [
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'http://paddle-inference-dist.bj.bcebos.com/int8/resnet50_int8_model_combined.tar.gz'
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]
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data_md5s = ['db212fd4e9edc83381aef4533107e60c']
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quantizable_op_type = ["conv2d", "mul"]
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is_full_quantize = False
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is_use_cache_file = False
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is_optimize_model = False
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diff_threshold = 0.025
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onnx_format = True
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self.run_test(
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model,
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'model.pdmodel',
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'model.pdiparams',
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algo,
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round_type,
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data_urls,
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data_md5s,
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"model",
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quantizable_op_type,
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is_full_quantize,
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is_use_cache_file,
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is_optimize_model,
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diff_threshold,
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onnx_format=onnx_format,
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)
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if __name__ == '__main__':
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unittest.main()
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