forked from mindspore-Ecosystem/mindspore
!15481 yolov3_darknet53 & resnet18 310 inference
From: @zhangxiaoxiao16 Reviewed-by: @c_34,@liangchenghui Signed-off-by: @c_34
This commit is contained in:
Gitee
commit
fb1bd143d0
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@ -14,6 +14,8 @@
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- [Distributed Training](#distributed-training)
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- [Evaluation Process](#evaluation-process)
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- [Evaluation](#evaluation)
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- [Export MindIR](#export-mindir)
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- [Inference Process](#inference-process)
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- [Model Description](#model-description)
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- [Performance](#performance)
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- [Evaluation Performance](#evaluation-performance)
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@ -331,6 +333,52 @@ This the standard format from `pycocotools`, you can refer to [cocodataset](http
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551
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```
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### [Export MindIR](#contents)
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Currently, batchsize can only set to 1.
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```shell
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python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
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```
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The ckpt_file parameter is required,
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`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
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### [Inference Process](#contents)
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#### Usage
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Before performing inference, the air file must bu exported by export.py.
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Current batch_Size can only be set to 1. Because the DVPP hardware is used for processing, the picture must comply with the JPEG encoding format, Otherwise, an error will be reported.
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```shell
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# Ascend310 inference
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bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
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```
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`DEVICE_ID` is optional, default value is 0.
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#### result
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Inference result is saved in current path, you can find result in acc.log file.
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```eval log
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# acc.log
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=============coco eval reulst=========
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.528
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.322
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.259
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.398
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.423
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.442
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551
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```
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## [Model Description](#contents)
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### [Performance](#contents)
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@ -16,6 +16,10 @@
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- [分布式训练](#分布式训练)
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- [评估过程](#评估过程)
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- [评估](#评估)
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- [导出mindir模型](#导出mindir模型)
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- [推理过程](#推理过程)
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- [用法](#用法-2)
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- [结果](#结果-2)
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- [模型描述](#模型描述)
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- [性能](#性能)
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- [评估性能](#评估性能)
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@ -334,6 +338,49 @@ sh run_eval.sh dataset/coco2014/ checkpoint/0-319_102400.ckpt
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551
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```
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## 导出mindir模型
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```shell
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python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
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```
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参数`ckpt_file` 是必需的，`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。
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## 推理过程
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### 用法
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在执行推理之前，需要通过export.py导出mindir文件。
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目前仅可处理batch_Size为1，由于使用了DVPP硬件进行图片处理，因此图片必须满足JPEG编码格式，否则将会报错。
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```shell
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# Ascend310 推理
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bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
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```
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`DEVICE_ID` 可选，默认值为 0。
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### 结果
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推理结果保存在当前路径，可在acc.log中看到最终精度结果。
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```eval log
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# acc.log
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=============coco eval reulst=========
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.311
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.528
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.322
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.127
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.259
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.398
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.423
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.224
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.442
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551
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```
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# 模型描述
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## 性能
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@ -0,0 +1,14 @@
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cmake_minimum_required(VERSION 3.14.1)
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project(Ascend310Infer)
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add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
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set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
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option(MINDSPORE_PATH "mindspore install path" "")
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include_directories(${MINDSPORE_PATH})
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include_directories(${MINDSPORE_PATH}/include)
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include_directories(${PROJECT_SRC_ROOT})
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find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
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file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
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add_executable(main src/main.cc src/utils.cc)
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target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
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@ -0,0 +1,26 @@
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aipp_op {
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aipp_mode : static
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input_format : YUV420SP_U8
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related_input_rank : 0
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csc_switch : true
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rbuv_swap_switch : false
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matrix_r0c0 : 256
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matrix_r0c1 : 0
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matrix_r0c2 : 359
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matrix_r1c0 : 256
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matrix_r1c1 : -88
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matrix_r1c2 : -183
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matrix_r2c0 : 256
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matrix_r2c1 : 454
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matrix_r2c2 : 0
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input_bias_0 : 0
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input_bias_1 : 128
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input_bias_2 : 128
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mean_chn_0 : 124
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mean_chn_1 : 117
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mean_chn_2 : 104
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var_reci_chn_0 : 0.0171247538316637
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var_reci_chn_1 : 0.0175070028011204
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var_reci_chn_2 : 0.0174291938997821
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}
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@ -0,0 +1,29 @@
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#!/bin/bash
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# Copyright 2021 Huawei Technologies Co., Ltd
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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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# ============================================================================
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if [ -d out ]; then
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rm -rf out
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fi
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mkdir out
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cd out || exit
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if [ -f "Makefile" ]; then
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make clean
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fi
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cmake .. \
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-DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
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make
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@ -0,0 +1,32 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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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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*/
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#ifndef MINDSPORE_INFERENCE_UTILS_H_
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#define MINDSPORE_INFERENCE_UTILS_H_
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#include <sys/stat.h>
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#include <dirent.h>
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#include <vector>
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#include <string>
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#include <memory>
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#include "include/api/types.h"
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std::vector<std::string> GetAllFiles(std::string_view dirName);
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DIR *OpenDir(std::string_view dirName);
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std::string RealPath(std::string_view path);
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mindspore::MSTensor ReadFileToTensor(const std::string &file);
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int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
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#endif
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@ -0,0 +1,136 @@
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/**
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* Copyright 2021 Huawei Technologies Co., Ltd
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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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*/
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#include <sys/time.h>
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#include <gflags/gflags.h>
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#include <dirent.h>
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#include <iostream>
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#include <string>
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#include <algorithm>
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#include <iosfwd>
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#include <vector>
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#include <fstream>
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#include <sstream>
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#include "include/api/model.h"
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#include "include/api/context.h"
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#include "include/api/types.h"
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#include "include/api/serialization.h"
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#include "include/minddata/dataset/include/vision_ascend.h"
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#include "include/minddata/dataset/include/execute.h"
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#include "include/minddata/dataset/include/vision.h"
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#include "inc/utils.h"
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using mindspore::Context;
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using mindspore::Serialization;
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using mindspore::Model;
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using mindspore::Status;
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using mindspore::MSTensor;
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using mindspore::dataset::Execute;
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using mindspore::ModelType;
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using mindspore::GraphCell;
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using mindspore::kSuccess;
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using mindspore::dataset::vision::DvppDecodeResizeJpeg;
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DEFINE_string(mindir_path, "", "mindir path");
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DEFINE_string(dataset_path, ".", "dataset path");
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DEFINE_int32(device_id, 0, "device id");
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DEFINE_string(aipp_path, "./aipp.cfg", "aipp path");
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int main(int argc, char **argv) {
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gflags::ParseCommandLineFlags(&argc, &argv, true);
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if (RealPath(FLAGS_mindir_path).empty()) {
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std::cout << "Invalid mindir" << std::endl;
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return 1;
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}
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auto context = std::make_shared<Context>();
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auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
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ascend310->SetDeviceID(FLAGS_device_id);
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context->MutableDeviceInfo().push_back(ascend310);
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mindspore::Graph graph;
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Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
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ascend310->SetInsertOpConfigPath(FLAGS_aipp_path);
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Model model;
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Status ret = model.Build(GraphCell(graph), context);
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if (ret != kSuccess) {
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std::cout << "ERROR: Build failed." << std::endl;
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return 1;
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}
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std::vector<MSTensor> model_inputs = model.GetInputs();
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if (model_inputs.empty()) {
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std::cout << "Invalid model, inputs is empty." << std::endl;
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return 1;
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}
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auto all_files = GetAllFiles(FLAGS_dataset_path);
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if (all_files.empty()) {
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std::cout << "ERROR: no input data." << std::endl;
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return 1;
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}
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std::map<double, double> costTime_map;
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size_t size = all_files.size();
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for (size_t i = 0; i < size; ++i) {
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struct timeval start = {0};
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struct timeval end = {0};
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double startTimeMs;
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double endTimeMs;
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std::vector<MSTensor> inputs;
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std::vector<MSTensor> outputs;
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std::cout << "Start predict input files:" << all_files[i] << std::endl;
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Execute resize_op(std::shared_ptr<DvppDecodeResizeJpeg>(new DvppDecodeResizeJpeg({416, 416})));
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auto imgDvpp = std::make_shared<MSTensor>();
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resize_op(ReadFileToTensor(all_files[i]), imgDvpp.get());
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inputs.emplace_back(imgDvpp->Name(), imgDvpp->DataType(), imgDvpp->Shape(),
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imgDvpp->Data().get(), imgDvpp->DataSize());
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gettimeofday(&start, nullptr);
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ret = model.Predict(inputs, &outputs);
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gettimeofday(&end, nullptr);
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if (ret != kSuccess) {
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std::cout << "Predict " << all_files[i] << " failed." << std::endl;
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return 1;
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}
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startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
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endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
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costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
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WriteResult(all_files[i], outputs);
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}
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double average = 0.0;
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int inferCount = 0;
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for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
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double diff = 0.0;
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diff = iter->second - iter->first;
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average += diff;
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inferCount++;
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}
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average = average / inferCount;
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std::stringstream timeCost;
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timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
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std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
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std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
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std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
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fileStream << timeCost.str();
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fileStream.close();
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costTime_map.clear();
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return 0;
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}
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@ -0,0 +1,129 @@
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/**
|
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* Copyright 2021 Huawei Technologies Co., Ltd
|
||||
*
|
||||
* 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.
|
||||
*/
|
||||
|
||||
#include <fstream>
|
||||
#include <algorithm>
|
||||
#include <iostream>
|
||||
#include "inc/utils.h"
|
||||
|
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using mindspore::MSTensor;
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using mindspore::DataType;
|
||||
|
||||
std::vector<std::string> GetAllFiles(std::string_view dirName) {
|
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struct dirent *filename;
|
||||
DIR *dir = OpenDir(dirName);
|
||||
if (dir == nullptr) {
|
||||
return {};
|
||||
}
|
||||
std::vector<std::string> res;
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while ((filename = readdir(dir)) != nullptr) {
|
||||
std::string dName = std::string(filename->d_name);
|
||||
if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
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continue;
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}
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res.emplace_back(std::string(dirName) + "/" + filename->d_name);
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}
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std::sort(res.begin(), res.end());
|
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for (auto &f : res) {
|
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std::cout << "image file: " << f << std::endl;
|
||||
}
|
||||
return res;
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||||
}
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||||
|
||||
int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
|
||||
std::string homePath = "./result_Files";
|
||||
for (size_t i = 0; i < outputs.size(); ++i) {
|
||||
size_t outputSize;
|
||||
std::shared_ptr<const void> netOutput;
|
||||
netOutput = outputs[i].Data();
|
||||
outputSize = outputs[i].DataSize();
|
||||
int pos = imageFile.rfind('/');
|
||||
std::string fileName(imageFile, pos + 1);
|
||||
fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
|
||||
std::string outFileName = homePath + "/" + fileName;
|
||||
FILE * outputFile = fopen(outFileName.c_str(), "wb");
|
||||
fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
|
||||
fclose(outputFile);
|
||||
outputFile = nullptr;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
mindspore::MSTensor ReadFileToTensor(const std::string &file) {
|
||||
if (file.empty()) {
|
||||
std::cout << "Pointer file is nullptr" << std::endl;
|
||||
return mindspore::MSTensor();
|
||||
}
|
||||
|
||||
std::ifstream ifs(file);
|
||||
if (!ifs.good()) {
|
||||
std::cout << "File: " << file << " is not exist" << std::endl;
|
||||
return mindspore::MSTensor();
|
||||
}
|
||||
|
||||
if (!ifs.is_open()) {
|
||||
std::cout << "File: " << file << "open failed" << std::endl;
|
||||
return mindspore::MSTensor();
|
||||
}
|
||||
|
||||
ifs.seekg(0, std::ios::end);
|
||||
size_t size = ifs.tellg();
|
||||
mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
|
||||
|
||||
ifs.seekg(0, std::ios::beg);
|
||||
ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
|
||||
ifs.close();
|
||||
|
||||
return buffer;
|
||||
}
|
||||
|
||||
|
||||
DIR *OpenDir(std::string_view dirName) {
|
||||
if (dirName.empty()) {
|
||||
std::cout << " dirName is null ! " << std::endl;
|
||||
return nullptr;
|
||||
}
|
||||
std::string realPath = RealPath(dirName);
|
||||
struct stat s;
|
||||
lstat(realPath.c_str(), &s);
|
||||
if (!S_ISDIR(s.st_mode)) {
|
||||
std::cout << "dirName is not a valid directory !" << std::endl;
|
||||
return nullptr;
|
||||
}
|
||||
DIR *dir;
|
||||
dir = opendir(realPath.c_str());
|
||||
if (dir == nullptr) {
|
||||
std::cout << "Can not open dir " << dirName << std::endl;
|
||||
return nullptr;
|
||||
}
|
||||
std::cout << "Successfully opened the dir " << dirName << std::endl;
|
||||
return dir;
|
||||
}
|
||||
|
||||
std::string RealPath(std::string_view path) {
|
||||
char realPathMem[PATH_MAX] = {0};
|
||||
char *realPathRet = nullptr;
|
||||
realPathRet = realpath(path.data(), realPathMem);
|
||||
|
||||
if (realPathRet == nullptr) {
|
||||
std::cout << "File: " << path << " is not exist.";
|
||||
return "";
|
||||
}
|
||||
|
||||
std::string realPath(realPathMem);
|
||||
std::cout << path << " realpath is: " << realPath << std::endl;
|
||||
return realPath;
|
||||
}
|
||||
|
|
@ -0,0 +1,63 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""YoloV3 postprocess."""
|
||||
import os
|
||||
import argparse
|
||||
import datetime
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
from eval import DetectionEngine
|
||||
|
||||
def get_img_size(file_name):
|
||||
img = Image.open(file_name)
|
||||
return img.size
|
||||
|
||||
parser = argparse.ArgumentParser('YoloV3 postprocess')
|
||||
parser.add_argument('--result_path', type=str, required=True, help='result files path.')
|
||||
parser.add_argument('--img_path', type=str, required=True, help='train data dir.')
|
||||
parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu')
|
||||
parser.add_argument('--nms_thresh', type=float, default=0.5, help='threshold for NMS')
|
||||
parser.add_argument('--annFile', type=str, default='', help='path to annotation')
|
||||
parser.add_argument('--ignore_threshold', type=float, default=0.001, help='threshold to throw low quality boxes')
|
||||
parser.add_argument('--log_path', type=str, default='outputs/', help='inference result save location')
|
||||
|
||||
args, _ = parser.parse_known_args()
|
||||
|
||||
if __name__ == "__main__":
|
||||
args.outputs_dir = os.path.join(args.log_path,
|
||||
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
|
||||
if not os.path.exists(args.outputs_dir):
|
||||
os.makedirs(args.outputs_dir)
|
||||
|
||||
detection = DetectionEngine(args)
|
||||
bs = args.per_batch_size
|
||||
|
||||
f_list = os.listdir(args.img_path)
|
||||
for f in f_list:
|
||||
image_size = get_img_size(os.path.join(args.img_path, f))
|
||||
f = f.split('.')[0]
|
||||
output_big = np.fromfile(os.path.join(args.result_path, f + '_0.bin'), np.float32).reshape(bs, 13, 13, 3, 85)
|
||||
output_me = np.fromfile(os.path.join(args.result_path, f + '_1.bin'), np.float32).reshape(bs, 26, 26, 3, 85)
|
||||
output_small = np.fromfile(os.path.join(args.result_path, f + '_2.bin'), np.float32).reshape(bs, 52, 52, 3, 85)
|
||||
image_id = [int(f.split('_')[-1])]
|
||||
image_shape = [[image_size[0], image_size[1]]]
|
||||
|
||||
detection.detect([output_small, output_me, output_big], bs, image_shape, image_id)
|
||||
|
||||
detection.do_nms_for_results()
|
||||
result_file_path = detection.write_result()
|
||||
eval_result = detection.get_eval_result()
|
||||
|
||||
print('\n=============coco eval result=========\n' + eval_result)
|
||||
|
|
@ -0,0 +1,100 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
if [[ $# -lt 3 || $# -gt 4 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
|
||||
DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
data_path=$(get_real_path $2)
|
||||
anno_path=$(get_real_path $3)
|
||||
|
||||
device_id=0
|
||||
if [ $# == 4 ]; then
|
||||
device_id=$4
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
echo "dataset path: "$data_path
|
||||
echo "annotation path: "$anno_path
|
||||
echo "device id: "$device_id
|
||||
|
||||
export ASCEND_HOME=/usr/local/Ascend/
|
||||
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||
export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
|
||||
../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id --aipp_path=../ascend310_infer/aipp.cfg &> infer.log
|
||||
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path --annFile=$anno_path &> acc.log
|
||||
}
|
||||
|
||||
compile_app
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "compile app code failed"
|
||||
exit 1
|
||||
fi
|
||||
infer
|
||||
if [ $? -ne 0 ]; then
|
||||
echo " execute inference failed"
|
||||
exit 1
|
||||
fi
|
||||
cal_acc
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "calculate accuracy failed"
|
||||
exit 1
|
||||
fi
|
||||
|
|
@ -12,6 +12,8 @@
|
|||
- [Training](#training)
|
||||
- [Evaluation Process](#evaluation-process)
|
||||
- [Evaluation](#evaluation)
|
||||
- [Export MindIR](#export-mindir)
|
||||
- [Inference Process](#inference-process)
|
||||
- [Model Description](#model-description)
|
||||
- [Performance](#performance)
|
||||
- [Evaluation Performance](#evaluation-performance)
|
||||
|
|
@ -193,6 +195,40 @@ You will get the precision and recall value of each class:
|
|||
|
||||
Note the precision and recall values are results of two-classification(person and face) used our own annotations with coco2017.
|
||||
|
||||
## [Export MindIR](#contents)
|
||||
|
||||
Currently, batchsize can only set to 1.
|
||||
|
||||
```shell
|
||||
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
||||
```
|
||||
|
||||
The ckpt_file parameter is required,
|
||||
`EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
|
||||
|
||||
## [Inference Process](#contents)
|
||||
|
||||
### Usage
|
||||
|
||||
Before performing inference, the mindir file must be exported by export.py.
|
||||
Current batch_Size can only be set to 1. Images to be processed needs to be copied to the to-be-processed folder based on the annotation file.
|
||||
|
||||
```shell
|
||||
# Ascend310 inference
|
||||
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
|
||||
```
|
||||
|
||||
`DEVICE_ID` is optional, default value is 0.
|
||||
|
||||
### result
|
||||
|
||||
Inference result is saved in current path, you can find result in acc.log file.
|
||||
|
||||
```bash
|
||||
class 0 precision is 88.18%, recall is 66.00%
|
||||
class 1 precision is 85.34%, recall is 79.13%
|
||||
```
|
||||
|
||||
# [Model Description](#contents)
|
||||
|
||||
## [Performance](#contents)
|
||||
|
|
|
|||
|
|
@ -15,6 +15,10 @@
|
|||
- [Ascend上训练](#ascend上训练)
|
||||
- [评估过程](#评估过程)
|
||||
- [Ascend评估](#ascend评估)
|
||||
- [导出mindir模型](#导出mindir模型)
|
||||
- [推理过程](#推理过程)
|
||||
- [用法](#用法-2)
|
||||
- [结果](#结果-2)
|
||||
- [模型描述](#模型描述)
|
||||
- [性能](#性能)
|
||||
- [评估性能](#评估性能)
|
||||
|
|
@ -194,6 +198,37 @@ YOLOv3整体网络架构如下：
|
|||
|
||||
注意精度和召回值是使用我们自己的标注和COCO 2017的两种分类（人与脸）的结果。
|
||||
|
||||
## 导出mindir模型
|
||||
|
||||
```shell
|
||||
python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
|
||||
```
|
||||
|
||||
参数`ckpt_file` 是必需的，`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中进行选择。
|
||||
|
||||
## 推理过程
|
||||
|
||||
### 用法
|
||||
|
||||
在执行推理之前，需要通过export.py导出mindir文件。
|
||||
目前仅可处理batch_Size为1，且图片需要根据关联的标签文件导出至待处理文件夹。
|
||||
|
||||
```shell
|
||||
# Ascend310 推理
|
||||
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
|
||||
```
|
||||
|
||||
`DEVICE_ID` 可选，默认值为 0。
|
||||
|
||||
### 结果
|
||||
|
||||
推理结果保存在当前路径，可在acc.log中看到最终精度结果。
|
||||
|
||||
```bash
|
||||
class 0 precision is 88.18%, recall is 66.00%
|
||||
class 1 precision is 85.34%, recall is 79.13%
|
||||
```
|
||||
|
||||
# 模型描述
|
||||
|
||||
## 性能
|
||||
|
|
|
|||
|
|
@ -0,0 +1,14 @@
|
|||
cmake_minimum_required(VERSION 3.14.1)
|
||||
project(Ascend310Infer)
|
||||
add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
|
||||
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
|
||||
set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
|
||||
option(MINDSPORE_PATH "mindspore install path" "")
|
||||
include_directories(${MINDSPORE_PATH})
|
||||
include_directories(${MINDSPORE_PATH}/include)
|
||||
include_directories(${PROJECT_SRC_ROOT})
|
||||
find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
|
||||
file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
|
||||
|
||||
add_executable(main src/main.cc src/utils.cc)
|
||||
target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)
|
||||
|
|
@ -0,0 +1,29 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
if [ -d out ]; then
|
||||
rm -rf out
|
||||
fi
|
||||
|
||||
mkdir out
|
||||
cd out || exit
|
||||
|
||||
if [ -f "Makefile" ]; then
|
||||
make clean
|
||||
fi
|
||||
|
||||
cmake .. \
|
||||
-DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
|
||||
make
|
||||
|
|
@ -0,0 +1,32 @@
|
|||
/**
|
||||
* Copyright 2021 Huawei Technologies Co., Ltd
|
||||
*
|
||||
* 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.
|
||||
*/
|
||||
|
||||
#ifndef MINDSPORE_INFERENCE_UTILS_H_
|
||||
#define MINDSPORE_INFERENCE_UTILS_H_
|
||||
|
||||
#include <sys/stat.h>
|
||||
#include <dirent.h>
|
||||
#include <vector>
|
||||
#include <string>
|
||||
#include <memory>
|
||||
#include "include/api/types.h"
|
||||
|
||||
std::vector<std::string> GetAllFiles(std::string_view dirName);
|
||||
DIR *OpenDir(std::string_view dirName);
|
||||
std::string RealPath(std::string_view path);
|
||||
mindspore::MSTensor ReadFileToTensor(const std::string &file);
|
||||
int WriteResult(const std::string& imageFile, const std::vector<mindspore::MSTensor> &outputs);
|
||||
#endif
|
||||
|
|
@ -0,0 +1,201 @@
|
|||
/**
|
||||
* Copyright 2021 Huawei Technologies Co., Ltd
|
||||
*
|
||||
* 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.
|
||||
*/
|
||||
#include <sys/time.h>
|
||||
#include <gflags/gflags.h>
|
||||
#include <dirent.h>
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
#include <algorithm>
|
||||
#include <iosfwd>
|
||||
#include <vector>
|
||||
#include <fstream>
|
||||
#include <sstream>
|
||||
|
||||
#include "include/api/model.h"
|
||||
#include "include/api/context.h"
|
||||
#include "include/api/types.h"
|
||||
#include "include/api/serialization.h"
|
||||
#include "include/minddata/dataset/include/execute.h"
|
||||
#include "include/minddata/dataset/include/vision.h"
|
||||
#include "inc/utils.h"
|
||||
|
||||
using mindspore::Context;
|
||||
using mindspore::Serialization;
|
||||
using mindspore::Model;
|
||||
using mindspore::Status;
|
||||
using mindspore::ModelType;
|
||||
using mindspore::GraphCell;
|
||||
using mindspore::kSuccess;
|
||||
using mindspore::MSTensor;
|
||||
using mindspore::DataType;
|
||||
using mindspore::dataset::Execute;
|
||||
using mindspore::dataset::TensorTransform;
|
||||
using mindspore::dataset::vision::Resize;
|
||||
using mindspore::dataset::vision::Pad;
|
||||
using mindspore::dataset::vision::HWC2CHW;
|
||||
using mindspore::dataset::vision::Normalize;
|
||||
using mindspore::dataset::vision::Decode;
|
||||
using mindspore::dataset::InterpolationMode;
|
||||
|
||||
DEFINE_string(mindir_path, "", "mindir path");
|
||||
DEFINE_string(dataset_path, ".", "dataset path");
|
||||
DEFINE_int32(device_id, 0, "device id");
|
||||
|
||||
int PadImage(const MSTensor &input, MSTensor *output) {
|
||||
std::shared_ptr<TensorTransform> normalize(new Normalize({0, 0, 0},
|
||||
{255, 255, 255}));
|
||||
Execute composeNormalize({normalize});
|
||||
std::vector<int64_t> shape = input.Shape();
|
||||
auto imgResize = MSTensor();
|
||||
auto imgPad = MSTensor();
|
||||
const int IMAGEWIDTH = 352;
|
||||
const int IMAGEHEIGHT = 640;
|
||||
float widthScale, heightScale;
|
||||
widthScale = static_cast<float>(IMAGEWIDTH) / shape[0];
|
||||
heightScale = static_cast<float>(IMAGEHEIGHT) / shape[1];
|
||||
int widthSize, heightSize;
|
||||
if (widthScale < heightScale) {
|
||||
widthSize = shape[0]*widthScale;
|
||||
heightSize = shape[1]*widthScale;
|
||||
} else {
|
||||
widthSize = shape[0]*heightScale;
|
||||
heightSize = shape[1]*heightScale;
|
||||
}
|
||||
std::shared_ptr<TensorTransform> resize(new Resize({widthSize, heightSize}, InterpolationMode::kArea));
|
||||
Execute composeResize({resize});
|
||||
Status ret = composeResize(input, &imgResize);
|
||||
if (ret != kSuccess) {
|
||||
std::cout << "ERROR: Resize failed." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
int padH = IMAGEHEIGHT - heightSize;
|
||||
int padW = IMAGEWIDTH - widthSize;
|
||||
int padHH = padH / 2;
|
||||
int padWH = padW / 2;
|
||||
std::shared_ptr<TensorTransform> pad(new Pad({padHH, padWH, (padH - padHH), (padW - padWH)}, {128}));
|
||||
Execute composePad({pad});
|
||||
ret = composePad(imgResize, &imgPad);
|
||||
if (ret != kSuccess) {
|
||||
std::cout << "ERROR: Pad failed." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
ret = composeNormalize(imgPad, output);
|
||||
if (ret != kSuccess) {
|
||||
std::cout << "ERROR: Normalize failed." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
int main(int argc, char **argv) {
|
||||
gflags::ParseCommandLineFlags(&argc, &argv, true);
|
||||
if (RealPath(FLAGS_mindir_path).empty()) {
|
||||
std::cout << "Invalid mindir" << std::endl;
|
||||
return 1;
|
||||
}
|
||||
|
||||
auto context = std::make_shared<Context>();
|
||||
auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
|
||||
ascend310->SetDeviceID(FLAGS_device_id);
|
||||
context->MutableDeviceInfo().push_back(ascend310);
|
||||
mindspore::Graph graph;
|
||||
Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
|
||||
|
||||
Model model;
|
||||
Status ret = model.Build(GraphCell(graph), context);
|
||||
if (ret != kSuccess) {
|
||||
std::cout << "ERROR: Build failed." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
std::vector<MSTensor> model_inputs = model.GetInputs();
|
||||
if (model_inputs.empty()) {
|
||||
std::cout << "Invalid model, inputs is empty." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
auto all_files = GetAllFiles(FLAGS_dataset_path);
|
||||
if (all_files.empty()) {
|
||||
std::cout << "ERROR: no input data." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
|
||||
std::map<double, double> costTime_map;
|
||||
size_t size = all_files.size();
|
||||
std::shared_ptr<TensorTransform> decode(new Decode());
|
||||
Execute composeDecode({decode});
|
||||
std::shared_ptr<TensorTransform> hwc2chw(new HWC2CHW());
|
||||
Execute composeTranspose({hwc2chw});
|
||||
|
||||
for (size_t i = 0; i < size; ++i) {
|
||||
struct timeval start = {0};
|
||||
struct timeval end = {0};
|
||||
double startTimeMs;
|
||||
double endTimeMs;
|
||||
std::vector<MSTensor> inputs;
|
||||
std::vector<MSTensor> outputs;
|
||||
std::cout << "Start predict input files:" << all_files[i] << std::endl;
|
||||
auto imgDecode = MSTensor();
|
||||
auto image = ReadFileToTensor(all_files[i]);
|
||||
ret = composeDecode(image, &imgDecode);
|
||||
if (ret != kSuccess) {
|
||||
std::cout << "ERROR: Decode failed." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
auto imgPad = MSTensor();
|
||||
PadImage(imgDecode, &imgPad);
|
||||
auto img = MSTensor();
|
||||
composeTranspose(imgPad, &img);
|
||||
float imgInfo[2];
|
||||
imgInfo[0] = imgDecode.Shape()[0];
|
||||
imgInfo[1] = imgDecode.Shape()[1];
|
||||
MSTensor imgShape("imgShape", DataType::kNumberTypeFloat32, std::vector<int64_t>{1, 2}, imgInfo, 8);
|
||||
|
||||
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
|
||||
img.Data().get(), img.DataSize());
|
||||
inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(),
|
||||
imgShape.Data().get(), imgShape.DataSize());
|
||||
|
||||
gettimeofday(&start, nullptr);
|
||||
ret = model.Predict(inputs, &outputs);
|
||||
gettimeofday(&end, nullptr);
|
||||
if (ret != kSuccess) {
|
||||
std::cout << "Predict " << all_files[i] << " failed." << std::endl;
|
||||
return 1;
|
||||
}
|
||||
startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
|
||||
endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
|
||||
costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
|
||||
WriteResult(all_files[i], outputs);
|
||||
}
|
||||
double average = 0.0;
|
||||
int inferCount = 0;
|
||||
|
||||
for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
|
||||
double diff = 0.0;
|
||||
diff = iter->second - iter->first;
|
||||
average += diff;
|
||||
inferCount++;
|
||||
}
|
||||
average = average / inferCount;
|
||||
std::stringstream timeCost;
|
||||
timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
|
||||
std::cout << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
|
||||
std::string fileName = "./time_Result" + std::string("/test_perform_static.txt");
|
||||
std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
|
||||
fileStream << timeCost.str();
|
||||
fileStream.close();
|
||||
costTime_map.clear();
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -0,0 +1,129 @@
|
|||
/**
|
||||
* Copyright 2021 Huawei Technologies Co., Ltd
|
||||
*
|
||||
* 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.
|
||||
*/
|
||||
|
||||
#include <fstream>
|
||||
#include <algorithm>
|
||||
#include <iostream>
|
||||
#include "inc/utils.h"
|
||||
|
||||
using mindspore::MSTensor;
|
||||
using mindspore::DataType;
|
||||
|
||||
std::vector<std::string> GetAllFiles(std::string_view dirName) {
|
||||
struct dirent *filename;
|
||||
DIR *dir = OpenDir(dirName);
|
||||
if (dir == nullptr) {
|
||||
return {};
|
||||
}
|
||||
std::vector<std::string> res;
|
||||
while ((filename = readdir(dir)) != nullptr) {
|
||||
std::string dName = std::string(filename->d_name);
|
||||
if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
|
||||
continue;
|
||||
}
|
||||
res.emplace_back(std::string(dirName) + "/" + filename->d_name);
|
||||
}
|
||||
std::sort(res.begin(), res.end());
|
||||
for (auto &f : res) {
|
||||
std::cout << "image file: " << f << std::endl;
|
||||
}
|
||||
return res;
|
||||
}
|
||||
|
||||
int WriteResult(const std::string& imageFile, const std::vector<MSTensor> &outputs) {
|
||||
std::string homePath = "./result_Files";
|
||||
for (size_t i = 0; i < outputs.size(); ++i) {
|
||||
size_t outputSize;
|
||||
std::shared_ptr<const void> netOutput;
|
||||
netOutput = outputs[i].Data();
|
||||
outputSize = outputs[i].DataSize();
|
||||
int pos = imageFile.rfind('/');
|
||||
std::string fileName(imageFile, pos + 1);
|
||||
fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
|
||||
std::string outFileName = homePath + "/" + fileName;
|
||||
FILE * outputFile = fopen(outFileName.c_str(), "wb");
|
||||
fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
|
||||
fclose(outputFile);
|
||||
outputFile = nullptr;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
mindspore::MSTensor ReadFileToTensor(const std::string &file) {
|
||||
if (file.empty()) {
|
||||
std::cout << "Pointer file is nullptr" << std::endl;
|
||||
return mindspore::MSTensor();
|
||||
}
|
||||
|
||||
std::ifstream ifs(file);
|
||||
if (!ifs.good()) {
|
||||
std::cout << "File: " << file << " is not exist" << std::endl;
|
||||
return mindspore::MSTensor();
|
||||
}
|
||||
|
||||
if (!ifs.is_open()) {
|
||||
std::cout << "File: " << file << "open failed" << std::endl;
|
||||
return mindspore::MSTensor();
|
||||
}
|
||||
|
||||
ifs.seekg(0, std::ios::end);
|
||||
size_t size = ifs.tellg();
|
||||
mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
|
||||
|
||||
ifs.seekg(0, std::ios::beg);
|
||||
ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
|
||||
ifs.close();
|
||||
|
||||
return buffer;
|
||||
}
|
||||
|
||||
|
||||
DIR *OpenDir(std::string_view dirName) {
|
||||
if (dirName.empty()) {
|
||||
std::cout << " dirName is null ! " << std::endl;
|
||||
return nullptr;
|
||||
}
|
||||
std::string realPath = RealPath(dirName);
|
||||
struct stat s;
|
||||
lstat(realPath.c_str(), &s);
|
||||
if (!S_ISDIR(s.st_mode)) {
|
||||
std::cout << "dirName is not a valid directory !" << std::endl;
|
||||
return nullptr;
|
||||
}
|
||||
DIR *dir;
|
||||
dir = opendir(realPath.c_str());
|
||||
if (dir == nullptr) {
|
||||
std::cout << "Can not open dir " << dirName << std::endl;
|
||||
return nullptr;
|
||||
}
|
||||
std::cout << "Successfully opened the dir " << dirName << std::endl;
|
||||
return dir;
|
||||
}
|
||||
|
||||
std::string RealPath(std::string_view path) {
|
||||
char realPathMem[PATH_MAX] = {0};
|
||||
char *realPathRet = nullptr;
|
||||
realPathRet = realpath(path.data(), realPathMem);
|
||||
|
||||
if (realPathRet == nullptr) {
|
||||
std::cout << "File: " << path << " is not exist.";
|
||||
return "";
|
||||
}
|
||||
|
||||
std::string realPath(realPathMem);
|
||||
std::cout << path << " realpath is: " << realPath << std::endl;
|
||||
return realPath;
|
||||
}
|
||||
|
|
@ -19,7 +19,7 @@ import mindspore as ms
|
|||
from mindspore import context, Tensor
|
||||
from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
|
||||
|
||||
from src.yolov3 import yolov3_resnet18
|
||||
from src.yolov3 import yolov3_resnet18, YoloWithEval
|
||||
from src.config import ConfigYOLOV3ResNet18
|
||||
|
||||
parser = argparse.ArgumentParser(description='yolov3_resnet18 export')
|
||||
|
|
@ -38,14 +38,17 @@ if args.device_target == "Ascend":
|
|||
|
||||
if __name__ == "__main__":
|
||||
config = ConfigYOLOV3ResNet18()
|
||||
network = yolov3_resnet18(config)
|
||||
net = yolov3_resnet18(config)
|
||||
eval_net = YoloWithEval(net, config)
|
||||
|
||||
param_dict = load_checkpoint(args.ckpt_file)
|
||||
load_param_into_net(network, param_dict)
|
||||
load_param_into_net(eval_net, param_dict)
|
||||
|
||||
network.set_train(False)
|
||||
eval_net.set_train(False)
|
||||
|
||||
shape = [args.batch_size, 3] + config.img_shape
|
||||
input_data = Tensor(np.zeros(shape), ms.float32)
|
||||
input_shape = Tensor(np.zeros([1, 2]), ms.float32)
|
||||
inputs = (input_data, input_shape)
|
||||
|
||||
export(network, input_data, file_name=args.file_name, file_format=args.file_format)
|
||||
export(eval_net, *inputs, file_name=args.file_name, file_format=args.file_format)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,60 @@
|
|||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
"""Postprocess for yolov3-resnet18"""
|
||||
import os
|
||||
import argparse
|
||||
import numpy as np
|
||||
from src.config import ConfigYOLOV3ResNet18
|
||||
from src.utils import metrics
|
||||
|
||||
parser = argparse.ArgumentParser(description='Yolov3 postprocess')
|
||||
parser.add_argument("--batchsize", type=int, default=1, help="batchsize.")
|
||||
parser.add_argument("--anno_path", type=str, required=True, help="Annotation path.")
|
||||
parser.add_argument("--result_path", type=str, required=True, help="result files path.")
|
||||
args = parser.parse_args()
|
||||
|
||||
if __name__ == '__main__':
|
||||
config = ConfigYOLOV3ResNet18()
|
||||
batchsize = args.batchsize
|
||||
|
||||
anno_dict = {}
|
||||
for line in open(args.anno_path):
|
||||
line_list = line.split(' ')
|
||||
line_list[0] = line_list[0].split('/')[-1]
|
||||
anno_dict[line_list[0]] = line_list[1:]
|
||||
|
||||
pred_data = []
|
||||
for key in anno_dict:
|
||||
result0 = os.path.join(args.result_path, key.split('.')[0] + '_0.bin')
|
||||
result1 = os.path.join(args.result_path, key.split('.')[0] + '_1.bin')
|
||||
output0 = np.fromfile(result0, np.float32).reshape(batchsize, 13860, 4)
|
||||
output1 = np.fromfile(result1, np.float32).reshape(batchsize, 13860, 2)
|
||||
|
||||
anno_list = []
|
||||
for v in anno_dict[key]:
|
||||
v_list = v.split(',')
|
||||
anno_list.append(v_list)
|
||||
annotation = np.array(anno_list, np.int64)
|
||||
|
||||
for batch_idx in range(batchsize):
|
||||
pred_data.append({"boxes": output0[batch_idx],
|
||||
"box_scores": output1[batch_idx],
|
||||
"annotation": annotation})
|
||||
|
||||
precisions, recalls = metrics(pred_data)
|
||||
print("\n========================================\n")
|
||||
for i in range(config.num_classes):
|
||||
print("class {} precision is {:.2f}%, recall is {:.2f}%".format(i, precisions[i] * 100, recalls[i] * 100))
|
||||
|
|
@ -0,0 +1,98 @@
|
|||
#!/bin/bash
|
||||
# Copyright 2021 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
|
||||
if [[ $# -lt 3 || $# -gt 4 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
data_path=$(get_real_path $2)
|
||||
anno_path=$(get_real_path $3)
|
||||
|
||||
device_id=0
|
||||
if [ $# == 4 ]; then
|
||||
device_id=$4
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
echo "dataset path: "$data_path
|
||||
echo "annotation path: "$anno_path
|
||||
echo "device id: "$device_id
|
||||
|
||||
export ASCEND_HOME=/usr/local/Ascend/
|
||||
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||
export PATH=$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
|
||||
export PYTHONPATH=$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id &> infer.log
|
||||
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --result_path=./result_Files --anno_path=$anno_path &> acc.log
|
||||
}
|
||||
|
||||
compile_app
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "compile app code failed"
|
||||
exit 1
|
||||
fi
|
||||
infer
|
||||
if [ $? -ne 0 ]; then
|
||||
echo " execute inference failed"
|
||||
exit 1
|
||||
fi
|
||||
cal_acc
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "calculate accuracy failed"
|
||||
exit 1
|
||||
fi
|
||||
Loading…
Reference in New Issue