From 53442e9ac04a2aebd00cc48015fa0b5b2c325285 Mon Sep 17 00:00:00 2001 From: chenzhuo Date: Fri, 20 Aug 2021 09:54:15 +0800 Subject: [PATCH] add post training quantization of yolov3_resnet18 --- .../ascend310_quant_infer/run_quant_infer.sh | 7 +- .../ascend310_quant_infer/export_bin.py | 4 +- .../ascend310_quant_infer/post_quant.py | 4 +- .../ascend310_quant_infer/run_quant_infer.sh | 7 +- .../ascend310_quant_infer/run_quant_infer.sh | 7 +- .../ascend310_quant_infer/run_quant_infer.sh | 7 +- .../ascend310_quant_infer/run_quant_infer.sh | 7 +- .../ascend310_quant_infer/run_quant_infer.sh | 4 +- .../official/cv/yolov3_resnet18/README.md | 39 ++ .../official/cv/yolov3_resnet18/README_CN.md | 39 ++ .../ascend310_quant_infer/acc.py | 51 +++ .../ascend310_quant_infer/export_bin.py | 80 +++++ .../ascend310_quant_infer/inc/model_process.h | 112 ++++++ .../inc/sample_process.h | 58 +++ .../ascend310_quant_infer/inc/utils.h | 52 +++ .../ascend310_quant_infer/post_quant.py | 101 ++++++ .../ascend310_quant_infer/run_quant_infer.sh | 105 ++++++ .../ascend310_quant_infer/src/CMakeLists.txt | 43 +++ .../ascend310_quant_infer/src/acl.json | 1 + .../ascend310_quant_infer/src/build.sh | 55 +++ .../ascend310_quant_infer/src/main.cpp | 42 +++ .../src/model_process.cpp | 339 ++++++++++++++++++ .../src/sample_process.cpp | 263 ++++++++++++++ .../ascend310_quant_infer/src/utils.cpp | 113 ++++++ .../ascend310_quant_infer/utils.py | 176 +++++++++ 25 files changed, 1697 insertions(+), 19 deletions(-) create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/acc.py create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/export_bin.py create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/model_process.h create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/sample_process.h create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/utils.h create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/post_quant.py create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/run_quant_infer.sh create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/CMakeLists.txt create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/acl.json create mode 100755 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/build.sh create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/main.cpp create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/model_process.cpp create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/sample_process.cpp create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/utils.cpp create mode 100644 model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/utils.py diff --git a/model_zoo/official/cv/crnn/ascend310_quant_infer/run_quant_infer.sh b/model_zoo/official/cv/crnn/ascend310_quant_infer/run_quant_infer.sh index 024deeb5edf..571fd95cde2 100644 --- a/model_zoo/official/cv/crnn/ascend310_quant_infer/run_quant_infer.sh +++ b/model_zoo/official/cv/crnn/ascend310_quant_infer/run_quant_infer.sh @@ -43,9 +43,10 @@ if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then export PYTHONPATH=${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:$PYTHONPATH + export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH + export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe + export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH export ASCEND_OPP_PATH=$ASCEND_HOME/opp fi diff --git a/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/export_bin.py b/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/export_bin.py index 65f76b33716..ad29f345534 100644 --- a/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/export_bin.py +++ b/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/export_bin.py @@ -102,7 +102,9 @@ def generate_data(): batch_msk_lst = [] shape_lst = [] for i, line in enumerate(img_lst): - img_path, msk_path = line.strip().split(" ") + ori_img_path, ori_msk_path = line.strip().split(" ") + img_path = "VOCdevkit" + ori_img_path.split("VOCdevkit")[1] + msk_path = "VOCdevkit" + ori_msk_path.split("VOCdevkit")[1] img_path = os.path.join(args.data_root, img_path) msk_path = os.path.join(args.data_root, msk_path) org_width, org_height = get_img_size(img_path) diff --git a/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/post_quant.py b/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/post_quant.py index 26f9c8dd1ed..125d65741d5 100644 --- a/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/post_quant.py +++ b/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/post_quant.py @@ -87,7 +87,9 @@ def generate_batch_data(): # evaluate batch_img_lst = [] - img_path, _ = img_lst[0].strip().split(" ") + ori_img_path, _ = img_lst[0].strip().split(" ") + img_path = "VOCdevkit" + ori_img_path.split("VOCdevkit")[1] + img_path = os.path.join(args.data_root, img_path) img_ = cv2.imread(img_path) batch_img_lst.append(img_) diff --git a/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/run_quant_infer.sh b/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/run_quant_infer.sh index c8ecb7a8444..9b224a77772 100644 --- a/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/run_quant_infer.sh +++ b/model_zoo/official/cv/deeplabv3/ascend310_quant_infer/run_quant_infer.sh @@ -45,9 +45,10 @@ if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then export PYTHONPATH=${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:$PYTHONPATH + export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH + export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe + export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH export ASCEND_OPP_PATH=$ASCEND_HOME/opp fi diff --git a/model_zoo/official/cv/maskrcnn/ascend310_quant_infer/run_quant_infer.sh b/model_zoo/official/cv/maskrcnn/ascend310_quant_infer/run_quant_infer.sh index 934f0194b84..a13307e0aa3 100644 --- a/model_zoo/official/cv/maskrcnn/ascend310_quant_infer/run_quant_infer.sh +++ b/model_zoo/official/cv/maskrcnn/ascend310_quant_infer/run_quant_infer.sh @@ -45,9 +45,10 @@ if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then export PYTHONPATH=${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:$PYTHONPATH + export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH + export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe + export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH export ASCEND_OPP_PATH=$ASCEND_HOME/opp fi diff --git a/model_zoo/official/cv/unet/ascend310_quant_infer/run_quant_infer.sh b/model_zoo/official/cv/unet/ascend310_quant_infer/run_quant_infer.sh index a2a1bf66c8d..af7c30afee2 100644 --- a/model_zoo/official/cv/unet/ascend310_quant_infer/run_quant_infer.sh +++ b/model_zoo/official/cv/unet/ascend310_quant_infer/run_quant_infer.sh @@ -43,9 +43,10 @@ if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then export PYTHONPATH=${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:$PYTHONPATH + export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH + export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe + export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH export ASCEND_OPP_PATH=$ASCEND_HOME/opp fi diff --git a/model_zoo/official/cv/vgg16/ascend310_quant_infer/run_quant_infer.sh b/model_zoo/official/cv/vgg16/ascend310_quant_infer/run_quant_infer.sh index 31bba45de8e..88359d30332 100644 --- a/model_zoo/official/cv/vgg16/ascend310_quant_infer/run_quant_infer.sh +++ b/model_zoo/official/cv/vgg16/ascend310_quant_infer/run_quant_infer.sh @@ -43,9 +43,10 @@ if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then export PYTHONPATH=${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:$PYTHONPATH + export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH + export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe + export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH export ASCEND_OPP_PATH=$ASCEND_HOME/opp fi diff --git a/model_zoo/official/cv/yolov3_darknet53/ascend310_quant_infer/run_quant_infer.sh b/model_zoo/official/cv/yolov3_darknet53/ascend310_quant_infer/run_quant_infer.sh index 919d6d67e9f..cf89a607331 100644 --- a/model_zoo/official/cv/yolov3_darknet53/ascend310_quant_infer/run_quant_infer.sh +++ b/model_zoo/official/cv/yolov3_darknet53/ascend310_quant_infer/run_quant_infer.sh @@ -48,8 +48,8 @@ if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then export PYTHONPATH=${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/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$PATH - export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$LD_LIBRARY_PATH + export PATH=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH export ASCEND_OPP_PATH=$ASCEND_HOME/opp diff --git a/model_zoo/official/cv/yolov3_resnet18/README.md b/model_zoo/official/cv/yolov3_resnet18/README.md index d8cda40a34d..c3d57d5a8a8 100644 --- a/model_zoo/official/cv/yolov3_resnet18/README.md +++ b/model_zoo/official/cv/yolov3_resnet18/README.md @@ -17,6 +17,7 @@ - [Inference Process](#inference-process) - [Usage](#usage) - [result](#result) + - [Post Training Quantization](#post-training-quantization) - [Model Description](#model-description) - [Performance](#performance) - [Evaluation Performance](#evaluation-performance) @@ -358,6 +359,44 @@ Inference result is saved in current path, you can find result in acc.log file. class 1 precision is 85.34%, recall is 79.13% ``` +## [Post Training Quantization](#contents) + +Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization. +Note the precision and recall values are results of two-classification(person and face) used our own annotations with COCO2017 dataset. +Note quantization-related config file utils.py is located in the directory ascend310_quant_infer. + +1. Generate data of .bin format required for AIR model inference at Ascend310 platform. + +```shell +python export_bin.py --image_dir [COCO DATA PATH] --eval_mindrecord_dir [MINDRECORD PATH] --anno_path [LABEL PATH] +``` + +Note that image_dir is set as the parent directory of COCO dataset. + +2. Export quantized AIR model. + +Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to [official website](https://www.hiascend.com/software/cann/community). + +```shell +python post_quant.py --image_dir [COCO DATA PATH] --eval_mindrecord_dir [MINDRECORD PATH] --anno_path [LABEL PATH] --ckpt_file [CKPT_PATH] +``` + +The quantized AIR file will be stored as "./results/yolov3_resnet_quant.air". + +3. Implement inference at Ascend310 platform. + +```shell +# Ascend310 quant inference +bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [SHAPE_PATH] [ANNOTATION_PATH] +``` + +Inference result is saved in current path, you can find result like this in acc.log file. + +```bash +class 0 precision is 91.34%, recall is 64.92% +class 1 precision is 94.61%, recall is 64.07% +``` + # [Model Description](#contents) ## [Performance](#contents) diff --git a/model_zoo/official/cv/yolov3_resnet18/README_CN.md b/model_zoo/official/cv/yolov3_resnet18/README_CN.md index 8634afd3a7f..9fb7f9e1df6 100644 --- a/model_zoo/official/cv/yolov3_resnet18/README_CN.md +++ b/model_zoo/official/cv/yolov3_resnet18/README_CN.md @@ -19,6 +19,7 @@ - [推理过程](#推理过程) - [用法](#用法) - [结果](#结果) + - [训练后量化推理](#训练后量化推理) - [模型描述](#模型描述) - [性能](#性能) - [评估性能](#评估性能) @@ -355,6 +356,44 @@ bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID] class 1 precision is 85.34%, recall is 79.13% ``` +## [训练后量化推理](#contents) + +训练后量化推理的相关执行脚本文件在"ascend310_quant_infer"目录下，依次执行以下步骤实现训练后量化推理。 +注意精度和召回值是使用我们自己的标注和COCO2017的两种分类（人与脸）的结果。 +注意训练后量化端测推理有关的文件utils.py位于ascend310_quant_infer目录下。 + +1、生成Ascend310平台AIR模型推理需要的.bin格式数据。 + +```shell +python export_bin.py --image_dir [COCO DATA PATH] --eval_mindrecord_dir [MINDRECORD PATH] --ann_file [ANNOTATION PATH] +``` + +注意image_dir设置成COCO数据集的上级目录。 + +2、导出训练后量化的AIR格式模型。 + +导出训练后量化模型需要配套的量化工具包，参考[官方地址](https://www.hiascend.com/software/cann/community) + +```shell +python post_quant.py --image_dir [COCO DATA PATH] --eval_mindrecord_dir [MINDRECORD PATH] --ckpt_file [CKPT_PATH] +``` + +导出的模型会存储在./result/yolov3_resnet_quant.air。 + +3、在Ascend310执行推理量化模型。 + +```shell +# Ascend310 inference +bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [SHAPE_PATH] [ANNOTATION_PATH] +``` + +推理结果保存在脚本执行的当前路径，可以在acc.log中看到精度计算结果。 + +```bash +class 0 precision is 91.34%, recall is 64.92% +class 1 precision is 94.61%, recall is 64.07% +``` + # 模型描述 ## 性能 diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/acc.py b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/acc.py new file mode 100644 index 00000000000..da1990b0db5 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/acc.py @@ -0,0 +1,51 @@ +# 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. +# ============================================================================ +"""post process for 310 inference""" +import os +import argparse +import numpy as np +from utils import metrics + + +parser = argparse.ArgumentParser("yolov3_resnet18 quant postprocess") +parser.add_argument("--anno_path", type=str, required=True, help="path to annotation.npy") +parser.add_argument("--result_path", type=str, required=True, help="path to inference results.") +parser.add_argument("--batch_size", type=int, default=1, help="batch size of data.") +parser.add_argument("--num_classes", type=int, default=2, help="number of classed to detect.") + +args, _ = parser.parse_known_args() + + +def calculate_acc(): + """ Calculate accuracy of yolov3_resnet18 inference""" + ann = np.load(args.anno_path, allow_pickle=True) + pred_data = [] + prefix = "Yolov3-resnet18_coco_bs_" + str(args.batch_size) + "_" + for i in range(len(ann)): + result0 = os.path.join(args.result_path, prefix + str(i) + "_output_0.bin") + result1 = os.path.join(args.result_path, prefix + str(i) + "_output_1.bin") + output0 = np.fromfile(result0, np.float32).reshape(args.batch_size, 13860, 4) + output1 = np.fromfile(result1, np.float32).reshape(args.batch_size, 13860, 2) + for batch_idx in range(args.batch_size): + pred_data.append({"boxes": output0[batch_idx], + "box_scores": output1[batch_idx], + "annotation": ann[i]}) + precisions, recalls = metrics(pred_data) + for j in range(args.num_classes): + print("class {} precision is {:.2f}%, recall is {:.2f}%".format(j, precisions[j] * 100, recalls[j] * 100)) + + +if __name__ == '__main__': + calculate_acc() diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/export_bin.py b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/export_bin.py new file mode 100644 index 00000000000..b1950a668d2 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/export_bin.py @@ -0,0 +1,80 @@ +# 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. +# ============================================================================ +"""generate data and label needed for AIR model inference""" +import os +import sys +import numpy as np +from mindspore import context + + +context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") + + +def generate_data(dataset_path): + """ + Generate data and label needed for AIR model inference at Ascend310 platform. + """ + ds = create_yolo_dataset(dataset_path, is_training=False) + cur_dir = os.getcwd() + "/data" + img_folder = cur_dir + "/00_image" + if not os.path.exists(img_folder): + os.makedirs(img_folder) + shape_folder = cur_dir + "/01_image_shape" + if not os.path.exists(shape_folder): + os.makedirs(shape_folder) + total = ds.get_dataset_size() + ann_list = [] + print("\n========================================\n") + print("total images num: ", total) + print("Processing, please wait a moment.") + prefix = "Yolov3-resnet18_coco_bs_1_" + for i, data in enumerate(ds.create_dict_iterator(output_numpy=True, num_epochs=1)): + image_np = data['image'] + image_shape = data['image_shape'] + annotation = data['annotation'] + file_name = prefix + str(i) + ".bin" + image_path = os.path.join(img_folder, file_name) + image_np.tofile(image_path) + shape_path = os.path.join(shape_folder, file_name) + image_shape.tofile(shape_path) + + ann_list.append(annotation) + ann_file = np.array(ann_list) + np.save(os.path.join(cur_dir, "annotation_list.npy"), ann_file) + + +if __name__ == "__main__": + sys.path.append("..") + from src.dataset import create_yolo_dataset, data_to_mindrecord_byte_image + from model_utils.config import config as default_config + + if not os.path.isdir(default_config.eval_mindrecord_dir): + os.makedirs(default_config.eval_mindrecord_dir) + + yolo_prefix = "yolo.mindrecord" + mindrecord_file = os.path.join(default_config.eval_mindrecord_dir, yolo_prefix + "0") + if not os.path.exists(mindrecord_file): + if os.path.isdir(default_config.image_dir) and os.path.exists(default_config.anno_path): + print("Create Mindrecord") + data_to_mindrecord_byte_image(default_config.image_dir, + default_config.anno_path, + default_config.eval_mindrecord_dir, + prefix=yolo_prefix, + file_num=8) + print("Create Mindrecord Done, at {}".format(default_config.eval_mindrecord_dir)) + else: + print("image_dir or anno_path not exits") + + generate_data(mindrecord_file) diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/model_process.h b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/model_process.h new file mode 100644 index 00000000000..3706b955b97 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/model_process.h @@ -0,0 +1,112 @@ +/** + * 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. + */ + +#pragma once +#include +#include +#include "../inc/utils.h" +#include "acl/acl.h" + +/** +* ModelProcess +*/ +class ModelProcess { + public: + /** + * @brief Constructor + */ + ModelProcess(); + + /** + * @brief Destructor + */ + ~ModelProcess(); + + /** + * @brief load model from file with mem + * @param [in] modelPath: model path + * @return result + */ + Result LoadModelFromFileWithMem(const char *modelPath); + + /** + * @brief unload model + */ + void Unload(); + + /** + * @brief create model desc + * @return result + */ + Result CreateDesc(); + + /** + * @brief destroy desc + */ + void DestroyDesc(); + + /** + * @brief create model input + * @param [in] inputDataBuffer: input buffer + * @param [in] bufferSize: input buffer size + * @return result + */ + Result CreateInput(const std::vector &inputDataBuffer, const std::vector &bufferSize); + + /** + * @brief destroy input resource + */ + void DestroyInput(); + + /** + * @brief create output buffer + * @return result + */ + Result CreateOutput(); + + /** + * @brief destroy output resource + */ + void DestroyOutput(); + + /** + * @brief model execute + * @return result + */ + Result Execute(); + + /** + * @brief dump model output result to file + */ + void DumpModelOutputResult(char *output_name); + + /** + * @brief get model output result + */ + void OutputModelResult(); + + private: + uint32_t modelId_; + size_t modelMemSize_; + size_t modelWeightSize_; + void *modelMemPtr_; + void *modelWeightPtr_; + bool loadFlag_; // model load flag + aclmdlDesc *modelDesc_; + aclmdlDataset *input_; + aclmdlDataset *output_; +}; + diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/sample_process.h b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/sample_process.h new file mode 100644 index 00000000000..4e28dff95b2 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/sample_process.h @@ -0,0 +1,58 @@ +/** + * 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. + */ + +#pragma once +#include +#include +#include "../inc/utils.h" +#include "acl/acl.h" + +/** +* SampleProcess +*/ +class SampleProcess { + public: + /** + * @brief Constructor + */ + SampleProcess(); + + /** + * @brief Destructor + */ + ~SampleProcess(); + + /** + * @brief init reousce + * @return result + */ + Result InitResource(); + + /** + * @brief sample process + * @return result + */ + Result Process(char *om_path, char *input_folder, char *shape_folder); + + void GetAllFiles(std::string path, std::vector *files); + + private: + void DestroyResource(); + + int32_t deviceId_; + aclrtContext context_; + aclrtStream stream_; +}; diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/utils.h b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/utils.h new file mode 100644 index 00000000000..3ae2a571b8e --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/inc/utils.h @@ -0,0 +1,52 @@ +/** + * 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. + */ + +#pragma once +#include +#include + +#define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO] " fmt "\n", ##args) +#define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN] " fmt "\n", ##args) +#define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args) + +typedef enum Result { + SUCCESS = 0, + FAILED = 1 +} Result; + +/** +* Utils +*/ +class Utils { + public: + /** + * @brief create device buffer of file + * @param [in] fileName: file name + * @param [out] fileSize: size of file + * @return device buffer of file + */ + static void *GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize); + + /** + * @brief create buffer of file + * @param [in] fileName: file name + * @param [out] fileSize: size of file + * @return buffer of pic + */ + static void* ReadBinFile(std::string fileName, uint32_t *fileSize); +}; + +#pragma once diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/post_quant.py b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/post_quant.py new file mode 100644 index 00000000000..45f0ad637e2 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/post_quant.py @@ -0,0 +1,101 @@ +# 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. +# ============================================================================ +"""do post training quantization for Ascend310""" +import os +import sys +import numpy as np + +from amct_mindspore.quantize_tool import create_quant_config +from amct_mindspore.quantize_tool import quantize_model +from amct_mindspore.quantize_tool import save_model +import mindspore as ms +from mindspore import context, Tensor +from mindspore.train.serialization import load_checkpoint, load_param_into_net + + +context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") + + +def quant_yolov3_resnet(network, dataset, input_data): + """ + Export post training quantization model of AIR format. + + Args: + network: the origin network for inference. + dataset: the data for inference. + input_data: the data used for constructing network. The shape and format of input data should be the same as + actual data for inference. + """ + + # step2: create the quant config json file + create_quant_config("./config.json", network, *input_data) + + # step3: do some network modification and return the modified network + calibration_network = quantize_model("./config.json", network, *input_data) + calibration_network.set_train(False) + + # step4: perform the evaluation of network to do activation calibration + for data in dataset.create_dict_iterator(num_epochs=1): + _ = calibration_network(data["image"], data["image_shape"]) + + # step5: export the air file + save_model("results/yolov3_resnet_quant", calibration_network, *input_data) + print("[INFO] the quantized AIR file has been stored at: \n {}".format("results/yolov3_resnet_quant.air")) + + +def export_yolov3_resnet(): + """ prepare for quantization of yolov3_resnet """ + cfg = ConfigYOLOV3ResNet18() + net = yolov3_resnet18(cfg) + eval_net = YoloWithEval(net, cfg) + param_dict = load_checkpoint(default_config.ckpt_file) + load_param_into_net(eval_net, param_dict) + eval_net.set_train(False) + + default_config.export_batch_size = 1 + shape = [default_config.export_batch_size, 3] + cfg.img_shape + input_data = Tensor(np.zeros(shape), ms.float32) + input_shape = Tensor(np.zeros([1, 2]), ms.float32) + inputs = (input_data, input_shape) + + if not os.path.isdir(default_config.eval_mindrecord_dir): + os.makedirs(default_config.eval_mindrecord_dir) + + yolo_prefix = "yolo.mindrecord" + mindrecord_file = os.path.join(default_config.eval_mindrecord_dir, yolo_prefix + "0") + if not os.path.exists(mindrecord_file): + if os.path.isdir(default_config.image_dir) and os.path.exists(default_config.anno_path): + print("Create Mindrecord") + data_to_mindrecord_byte_image(default_config.image_dir, + default_config.anno_path, + default_config.eval_mindrecord_dir, + prefix=yolo_prefix, + file_num=8) + print("Create Mindrecord Done, at {}".format(default_config.eval_mindrecord_dir)) + else: + print("image_dir or anno_path not exits") + datasets = create_yolo_dataset(mindrecord_file, is_training=False) + ds = datasets.take(1) + quant_yolov3_resnet(eval_net, ds, inputs) + + +if __name__ == "__main__": + sys.path.append("..") + from src.yolov3 import yolov3_resnet18, YoloWithEval + from src.config import ConfigYOLOV3ResNet18 + from src.dataset import create_yolo_dataset, data_to_mindrecord_byte_image + from model_utils.config import config as default_config + + export_yolov3_resnet() diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/run_quant_infer.sh b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/run_quant_infer.sh new file mode 100644 index 00000000000..e2b7bc8f9b7 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/run_quant_infer.sh @@ -0,0 +1,105 @@ +#!/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 [ $# -lt 4 ]; then + echo "Usage: bash run_quant_infer.sh [AIR_PATH] [DATA_PATH] [SHAPE_PATH] [ANNOTATION_PATH]" + echo "Example: bash run_quant_infer.sh ./yolov3_resnet_quant.air ./00_image ./01_image_shape ./annotation_list.npy" +exit 1 +fi + +get_real_path(){ + if [ "${1:0:1}" == "/" ]; then + echo "$1" + else + echo "$(realpath -m $PWD/$1)" + fi +} +model=$(get_real_path $1) +data_path=$(get_real_path $2) +shape_path=$(get_real_path $3) +annotation_path=$(get_real_path $4) + +echo "air name: "$model +echo "dataset path: "$data_path +echo "shape path: "$shape_path +echo "annotation path: "$annotation_path + +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=$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH + export LD_LIBRARY_PATH=/usr/local/lib:$ASCEND_HOME/fwkacllib/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/atc/lib64:$LD_LIBRARY_PATH + export TBE_IMPL_PATH=$ASCEND_HOME/opp/op_impl/built-in/ai_core/tbe + export PYTHONPATH=${TBE_IMPL_PATH}:$PYTHONPATH + export ASCEND_OPP_PATH=$ASCEND_HOME/opp +fi + +function air_to_om() +{ + atc --input_format=NCHW --framework=1 --model=$model --output=yolov3_resnet_quant --soc_version=Ascend310 &> atc.log +} + +function compile_app() +{ + bash ./src/build.sh &> build.log +} + +function infer() +{ + if [ -d result ]; then + rm -rf ./result + fi + mkdir result + ./out/main ./yolov3_resnet_quant.om $data_path $shape_path &> infer.log +} + +function cal_acc() +{ + python3.7 ./acc.py --result_path=./result --anno_path=$annotation_path &> acc.log +} + +echo "start atc================================================" +air_to_om +if [ $? -ne 0 ]; then + echo "air to om code failed" + exit 1 +fi + +echo "start compile============================================" +compile_app +if [ $? -ne 0 ]; then + echo "compile app code failed" + exit 1 +fi + +echo "start infer==============================================" +infer +if [ $? -ne 0 ]; then + echo " execute inference failed" + exit 1 +fi + +echo "start calculate acc======================================" +cal_acc +if [ $? -ne 0 ]; then + echo "calculate accuracy failed" + exit 1 +fi \ No newline at end of file diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/CMakeLists.txt b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/CMakeLists.txt new file mode 100644 index 00000000000..655026d7d91 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/CMakeLists.txt @@ -0,0 +1,43 @@ +# Copyright (c) Huawei Technologies Co., Ltd. 2021. All rights reserved. + +# CMake lowest version requirement +cmake_minimum_required(VERSION 3.5.1) +# project information +project(InferClassification) +# Check environment variable +if(NOT DEFINED ENV{ASCEND_HOME}) + message(FATAL_ERROR "please define environment variable:ASCEND_HOME") +endif() + +# Compile options +add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0) +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined") + +# Skip build rpath +set(CMAKE_SKIP_BUILD_RPATH True) + +# Set output directory +set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/) +set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SRC_ROOT}/../out) + +# Set include directory and library directory +set(FWKACL_LIB_DIR $ENV{ASCEND_HOME}/fwkacllib) +set(ACL_LIB_DIR $ENV{ASCEND_HOME}/acllib) +set(ATLAS_ACL_LIB_DIR $ENV{ASCEND_HOME}/ascend-toolkit/latest/acllib) + +# Header path +include_directories(${ACL_LIB_DIR}/include/) +include_directories(${FWKACL_LIB_DIR}/include/) +include_directories(${ATLAS_ACL_LIB_DIR}/include/) +include_directories(${PROJECT_SRC_ROOT}/../inc) + +# add host lib path +link_directories(${ACL_LIB_DIR} ${FWKACL_LIB_DIR}) +find_library(acl libascendcl.so ${ACL_LIB_DIR}/lib64 ${FWKACL_LIB_DIR}/lib64 ${ATLAS_ACL_LIB_DIR}/lib64) + +add_executable(main utils.cpp + sample_process.cpp + model_process.cpp + main.cpp) + +target_link_libraries(main ${acl} gflags pthread) diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/acl.json b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/acl.json new file mode 100644 index 00000000000..0967ef424bc --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/acl.json @@ -0,0 +1 @@ +{} diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/build.sh b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/build.sh new file mode 100755 index 00000000000..b5979b68e60 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/build.sh @@ -0,0 +1,55 @@ +#!/bin/bash +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +path_cur=$(cd "`dirname $0`" || exit; pwd) + +function preparePath() { + rm -rf $1 + mkdir -p $1 + cd $1 || exit +} + +function buildA300() { + if [ ! "${ARCH_PATTERN}" ]; then + # set ARCH_PATTERN to acllib when it was not specified by user + export ARCH_PATTERN=acllib + echo "ARCH_PATTERN is set to the default value: ${ARCH_PATTERN}" + else + echo "ARCH_PATTERN is set to ${ARCH_PATTERN} by user, reset it to ${ARCH_PATTERN}/acllib" + export ARCH_PATTERN=${ARCH_PATTERN}/acllib + fi + + path_build=$path_cur/build + preparePath $path_build + cmake .. + make -j + ret=$? + cd .. + return ${ret} +} + +# set ASCEND_VERSION to ascend-toolkit/latest when it was not specified by user +if [ ! "${ASCEND_VERSION}" ]; then + export ASCEND_VERSION=ascend-toolkit/latest + echo "Set ASCEND_VERSION to the default value: ${ASCEND_VERSION}" +else + echo "ASCEND_VERSION is set to ${ASCEND_VERSION} by user" +fi + +buildA300 + +if [ $? -ne 0 ]; then + exit 1 +fi diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/main.cpp b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/main.cpp new file mode 100644 index 00000000000..5fb6bad7044 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/main.cpp @@ -0,0 +1,42 @@ +/** + * 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 +#include "../inc/sample_process.h" +#include "../inc/utils.h" +bool g_is_device = false; + +int main(int argc, char **argv) { + if (argc != 4) { + ERROR_LOG("usage:./main path_of_om path_of_inputFolder path_of_shapeFolder"); + return FAILED; + } + SampleProcess processSample; + Result ret = processSample.InitResource(); + if (ret != SUCCESS) { + ERROR_LOG("sample init resource failed"); + return FAILED; + } + + ret = processSample.Process(argv[1], argv[2], argv[3]); + if (ret != SUCCESS) { + ERROR_LOG("sample process failed"); + return FAILED; + } + + INFO_LOG("execute sample success"); + return SUCCESS; +} diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/model_process.cpp b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/model_process.cpp new file mode 100644 index 00000000000..bc12f3fac13 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/model_process.cpp @@ -0,0 +1,339 @@ +/** + * 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 "../inc/model_process.h" +#include +#include +#include +#include +#include "../inc/utils.h" +extern bool g_is_device; + +ModelProcess::ModelProcess() :modelId_(0), modelMemSize_(0), modelWeightSize_(0), modelMemPtr_(nullptr), +modelWeightPtr_(nullptr), loadFlag_(false), modelDesc_(nullptr), input_(nullptr), output_(nullptr) { +} + +ModelProcess::~ModelProcess() { + Unload(); + DestroyDesc(); + DestroyInput(); + DestroyOutput(); +} + +Result ModelProcess::LoadModelFromFileWithMem(const char *modelPath) { + if (loadFlag_) { + ERROR_LOG("has already loaded a model"); + return FAILED; + } + + aclError ret = aclmdlQuerySize(modelPath, &modelMemSize_, &modelWeightSize_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("query model failed, model file is %s", modelPath); + return FAILED; + } + + ret = aclrtMalloc(&modelMemPtr_, modelMemSize_, ACL_MEM_MALLOC_HUGE_FIRST); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("malloc buffer for mem failed, require size is %zu", modelMemSize_); + return FAILED; + } + + ret = aclrtMalloc(&modelWeightPtr_, modelWeightSize_, ACL_MEM_MALLOC_HUGE_FIRST); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("malloc buffer for weight failed, require size is %zu", modelWeightSize_); + return FAILED; + } + + ret = aclmdlLoadFromFileWithMem(modelPath, &modelId_, modelMemPtr_, + modelMemSize_, modelWeightPtr_, modelWeightSize_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("load model from file failed, model file is %s", modelPath); + return FAILED; + } + + loadFlag_ = true; + INFO_LOG("load model %s success", modelPath); + return SUCCESS; +} + +Result ModelProcess::CreateDesc() { + modelDesc_ = aclmdlCreateDesc(); + if (modelDesc_ == nullptr) { + ERROR_LOG("create model description failed"); + return FAILED; + } + + aclError ret = aclmdlGetDesc(modelDesc_, modelId_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("get model description failed"); + return FAILED; + } + + INFO_LOG("create model description success"); + + return SUCCESS; +} + +void ModelProcess::DestroyDesc() { + if (modelDesc_ != nullptr) { + (void)aclmdlDestroyDesc(modelDesc_); + modelDesc_ = nullptr; + } +} + +Result ModelProcess::CreateInput(const std::vector &inputDataBuffer, const std::vector &bufferSize) { + input_ = aclmdlCreateDataset(); + if (input_ == nullptr) { + ERROR_LOG("can't create dataset, create input failed"); + return FAILED; + } + for (size_t i = 0; i < inputDataBuffer.size(); ++i) { + aclDataBuffer* inputData = aclCreateDataBuffer(inputDataBuffer[i], bufferSize[i]); + if (inputData == nullptr) { + ERROR_LOG("can't create data buffer, create input failed"); + return FAILED; + } + + aclError ret = aclmdlAddDatasetBuffer(input_, inputData); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("add input dataset buffer failed"); + aclDestroyDataBuffer(inputData); + inputData = nullptr; + return FAILED; + } + } + return SUCCESS; +} + +void ModelProcess::DestroyInput() { + if (input_ == nullptr) { + return; + } + + for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(input_); ++i) { + aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(input_, i); + aclDestroyDataBuffer(dataBuffer); + } + aclmdlDestroyDataset(input_); + input_ = nullptr; +} + +Result ModelProcess::CreateOutput() { + if (modelDesc_ == nullptr) { + ERROR_LOG("no model description, create output failed"); + return FAILED; + } + + output_ = aclmdlCreateDataset(); + if (output_ == nullptr) { + ERROR_LOG("can't create dataset, create output failed"); + return FAILED; + } + + size_t outputSize = aclmdlGetNumOutputs(modelDesc_); + for (size_t i = 0; i < outputSize; ++i) { + size_t buffer_size = aclmdlGetOutputSizeByIndex(modelDesc_, i); + + void *outputBuffer = nullptr; + aclError ret = aclrtMalloc(&outputBuffer, buffer_size, ACL_MEM_MALLOC_NORMAL_ONLY); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("can't malloc buffer, size is %zu, create output failed", buffer_size); + return FAILED; + } + + aclDataBuffer* outputData = aclCreateDataBuffer(outputBuffer, buffer_size); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("can't create data buffer, create output failed"); + aclrtFree(outputBuffer); + return FAILED; + } + + ret = aclmdlAddDatasetBuffer(output_, outputData); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("can't add data buffer, create output failed"); + aclrtFree(outputBuffer); + aclDestroyDataBuffer(outputData); + return FAILED; + } + } + + INFO_LOG("create model output success"); + return SUCCESS; +} + +void ModelProcess::DumpModelOutputResult(char *output_name) { + size_t outputNum = aclmdlGetDatasetNumBuffers(output_); + + for (size_t i = 0; i < outputNum; ++i) { + std::stringstream ss; + ss << "result/" << output_name << "_output_" << i << ".bin"; + std::string outputFileName = ss.str(); + FILE *outputFile = fopen(outputFileName.c_str(), "wb"); + if (outputFile) { + aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i); + void* data = aclGetDataBufferAddr(dataBuffer); + uint32_t len = aclGetDataBufferSizeV2(dataBuffer); + + void* outHostData = NULL; + aclError ret = ACL_ERROR_NONE; + if (!g_is_device) { + ret = aclrtMallocHost(&outHostData, len); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret); + return; + } + + ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret); + (void)aclrtFreeHost(outHostData); + return; + } + + fwrite(outHostData, len, sizeof(char), outputFile); + + ret = aclrtFreeHost(outHostData); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret); + return; + } + } else { + fwrite(data, len, sizeof(char), outputFile); + } + fclose(outputFile); + outputFile = nullptr; + } else { + ERROR_LOG("create output file [%s] failed", outputFileName.c_str()); + return; + } + } + + INFO_LOG("dump data success"); + return; +} + +void ModelProcess::OutputModelResult() { + for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) { + aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i); + void* data = aclGetDataBufferAddr(dataBuffer); + uint32_t len = aclGetDataBufferSizeV2(dataBuffer); + + void *outHostData = NULL; + aclError ret = ACL_ERROR_NONE; + float *outData = NULL; + if (!g_is_device) { + ret = aclrtMallocHost(&outHostData, len); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("aclrtMallocHost failed, ret[%d]", ret); + return; + } + + ret = aclrtMemcpy(outHostData, len, data, len, ACL_MEMCPY_DEVICE_TO_HOST); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("aclrtMemcpy failed, ret[%d]", ret); + return; + } + + outData = reinterpret_cast(outHostData); + } else { + outData = reinterpret_cast(data); + } + std::map > resultMap; + for (unsigned int j = 0; j < len / sizeof(float); ++j) { + resultMap[*outData] = j; + outData++; + } + + int cnt = 0; + for (auto it = resultMap.begin(); it != resultMap.end(); ++it) { + // print top 5 + if (++cnt > 5) { + break; + } + + INFO_LOG("top %d: index[%d] value[%lf]", cnt, it->second, it->first); + } + if (!g_is_device) { + ret = aclrtFreeHost(outHostData); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("aclrtFreeHost failed, ret[%d]", ret); + return; + } + } + } + + INFO_LOG("output data success"); + return; +} + +void ModelProcess::DestroyOutput() { + if (output_ == nullptr) { + return; + } + + for (size_t i = 0; i < aclmdlGetDatasetNumBuffers(output_); ++i) { + aclDataBuffer* dataBuffer = aclmdlGetDatasetBuffer(output_, i); + void* data = aclGetDataBufferAddr(dataBuffer); + (void)aclrtFree(data); + (void)aclDestroyDataBuffer(dataBuffer); + } + + (void)aclmdlDestroyDataset(output_); + output_ = nullptr; +} + +Result ModelProcess::Execute() { + aclError ret = aclmdlExecute(modelId_, input_, output_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("execute model failed, modelId is %u", modelId_); + return FAILED; + } + + INFO_LOG("model execute success"); + return SUCCESS; +} + +void ModelProcess::Unload() { + if (!loadFlag_) { + WARN_LOG("no model had been loaded, unload failed"); + return; + } + + aclError ret = aclmdlUnload(modelId_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("unload model failed, modelId is %u", modelId_); + } + + if (modelDesc_ != nullptr) { + (void)aclmdlDestroyDesc(modelDesc_); + modelDesc_ = nullptr; + } + + if (modelMemPtr_ != nullptr) { + aclrtFree(modelMemPtr_); + modelMemPtr_ = nullptr; + modelMemSize_ = 0; + } + + if (modelWeightPtr_ != nullptr) { + aclrtFree(modelWeightPtr_); + modelWeightPtr_ = nullptr; + modelWeightSize_ = 0; + } + + loadFlag_ = false; + INFO_LOG("unload model success, modelId is %u", modelId_); +} diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/sample_process.cpp b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/sample_process.cpp new file mode 100644 index 00000000000..e84a0becffc --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/sample_process.cpp @@ -0,0 +1,263 @@ +/** + * 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 "../inc/sample_process.h" +#include +#include +#include +#include +#include +#include +#include "../inc/model_process.h" +#include "acl/acl.h" +#include "../inc/utils.h" +extern bool g_is_device; +using std::string; +using std::vector; + +SampleProcess::SampleProcess() :deviceId_(0), context_(nullptr), stream_(nullptr) { +} + +SampleProcess::~SampleProcess() { + DestroyResource(); +} + +Result SampleProcess::InitResource() { + // ACL init + + const char *aclConfigPath = "./src/acl.json"; + aclError ret = aclInit(aclConfigPath); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("acl init failed"); + return FAILED; + } + INFO_LOG("acl init success"); + + // open device + ret = aclrtSetDevice(deviceId_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("acl open device %d failed", deviceId_); + return FAILED; + } + INFO_LOG("open device %d success", deviceId_); + + // create context (set current) + ret = aclrtCreateContext(&context_, deviceId_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("acl create context failed"); + return FAILED; + } + INFO_LOG("create context success"); + + // create stream + ret = aclrtCreateStream(&stream_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("acl create stream failed"); + return FAILED; + } + INFO_LOG("create stream success"); + + // get run mode + aclrtRunMode runMode; + ret = aclrtGetRunMode(&runMode); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("acl get run mode failed"); + return FAILED; + } + g_is_device = (runMode == ACL_DEVICE); + INFO_LOG("get run mode success"); + return SUCCESS; +} + +void SampleProcess::GetAllFiles(std::string path, std::vector *files) { + DIR *pDir = NULL; + struct dirent* ptr; + if (!(pDir = opendir(path.c_str()))) { + return; + } + while ((ptr = readdir(pDir)) != 0) { + if (strcmp(ptr->d_name, ".") != 0 && strcmp(ptr->d_name, "..") != 0) { + files->push_back(path + "/" + ptr->d_name); + } + } + closedir(pDir); +} + +Result SampleProcess::Process(char *om_path, char *input_folder, char *shape_folder) { + // model init + double second_to_millisecond = 1000; + double second_to_microsecond = 1000000; + + double whole_cost_time = 0.0; + struct timeval start_global = {0}; + struct timeval end_global = {0}; + double startTimeMs_global = 0.0; + double endTimeMs_global = 0.0; + + gettimeofday(&start_global, nullptr); + + ModelProcess processModel; + const char* omModelPath = om_path; + + Result ret = processModel.LoadModelFromFileWithMem(omModelPath); + if (ret != SUCCESS) { + ERROR_LOG("execute LoadModelFromFileWithMem failed"); + return FAILED; + } + + ret = processModel.CreateDesc(); + if (ret != SUCCESS) { + ERROR_LOG("execute CreateDesc failed"); + return FAILED; + } + + ret = processModel.CreateOutput(); + if (ret != SUCCESS) { + ERROR_LOG("execute CreateOutput failed"); + return FAILED; + } + + std::vector testFile; + GetAllFiles(input_folder, &testFile); + std::vector shapeFile; + GetAllFiles(shape_folder, &shapeFile); + + if (testFile.size() !=shapeFile.size()) { + ERROR_LOG("number of data files is not equal to shape file"); + } + + double model_cost_time = 0.0; + double edge_to_edge_model_cost_time = 0.0; + + for (size_t index = 0; index < testFile.size(); ++index) { + INFO_LOG("start to process data file:%s", testFile[index].c_str()); + INFO_LOG("start to process shape file:%s", shapeFile[index].c_str()); + // model process + + struct timeval time_init = {0}; + double timeval_init = 0.0; + gettimeofday(&time_init, nullptr); + timeval_init = (time_init.tv_sec * second_to_microsecond + time_init.tv_usec) / second_to_millisecond; + + uint32_t devBufferSize; + void *picDevBuffer = Utils::GetDeviceBufferOfFile(testFile[index], &devBufferSize); + if (picDevBuffer == nullptr) { + ERROR_LOG("get pic device buffer failed, index is %zu", index); + return FAILED; + } + + uint32_t devBufferShapeSize; + void *shapeDevBuffer = Utils::GetDeviceBufferOfFile(shapeFile[index], &devBufferShapeSize); + if (shapeDevBuffer == nullptr) { + ERROR_LOG("get shape device buffer failed, index is %zu", index); + return FAILED; + } + + std::vector inputBuffers({picDevBuffer, shapeDevBuffer}); + std::vector inputSizes({devBufferSize, devBufferShapeSize}); + + ret = processModel.CreateInput(inputBuffers, inputSizes); + if (ret != SUCCESS) { + ERROR_LOG("execute CreateInput failed"); + aclrtFree(picDevBuffer); + return FAILED; + } + + struct timeval start = {0}; + struct timeval end = {0}; + gettimeofday(&start, nullptr); + double startTimeMs = (start.tv_sec * second_to_microsecond + start.tv_usec) / second_to_millisecond; + + ret = processModel.Execute(); + + gettimeofday(&end, nullptr); + double endTimeMs = (end.tv_sec * second_to_microsecond + end.tv_usec) / second_to_millisecond; + + double cost_time = endTimeMs - startTimeMs; + INFO_LOG("model infer time: %lf ms", cost_time); + + model_cost_time += cost_time; + + double edge_to_edge_cost_time = endTimeMs - timeval_init; + edge_to_edge_model_cost_time += edge_to_edge_cost_time; + + if (ret != SUCCESS) { + ERROR_LOG("execute inference failed"); + aclrtFree(picDevBuffer); + return FAILED; + } + + int pos = testFile[index].find_last_of('/'); + std::string name = testFile[index].substr(pos+1); + std::string outputname = name.substr(0, name.rfind(".")); + + // dump output result to file in the current directory + processModel.DumpModelOutputResult(const_cast(outputname.c_str())); + + // release model input buffer + aclrtFree(picDevBuffer); + processModel.DestroyInput(); + } + double test_file_size = testFile.size(); + INFO_LOG("infer dataset size:%lf", test_file_size); + + gettimeofday(&end_global, nullptr); + startTimeMs_global = (start_global.tv_sec * second_to_microsecond + start_global.tv_usec) / second_to_millisecond; + endTimeMs_global = (end_global.tv_sec * second_to_microsecond + end_global.tv_usec) / second_to_millisecond; + whole_cost_time = (endTimeMs_global - startTimeMs_global) / test_file_size; + + model_cost_time /= test_file_size; + INFO_LOG("model cost time per sample: %lf ms", model_cost_time); + edge_to_edge_model_cost_time /= test_file_size; + INFO_LOG("edge-to-edge model cost time per sample:%lf ms", edge_to_edge_model_cost_time); + INFO_LOG("whole cost time per sample: %lf ms", whole_cost_time); + + return SUCCESS; +} + +void SampleProcess::DestroyResource() { + aclError ret; + if (stream_ != nullptr) { + ret = aclrtDestroyStream(stream_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("destroy stream failed"); + } + stream_ = nullptr; + } + INFO_LOG("end to destroy stream"); + + if (context_ != nullptr) { + ret = aclrtDestroyContext(context_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("destroy context failed"); + } + context_ = nullptr; + } + INFO_LOG("end to destroy context"); + + ret = aclrtResetDevice(deviceId_); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("reset device failed"); + } + INFO_LOG("end to reset device is %d", deviceId_); + + ret = aclFinalize(); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("finalize acl failed"); + } + INFO_LOG("end to finalize acl"); +} + diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/utils.cpp b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/utils.cpp new file mode 100644 index 00000000000..d9208c8cfd9 --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/src/utils.cpp @@ -0,0 +1,113 @@ +/** + * 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 "../inc/utils.h" +#include +#include +#include +#include +#include "acl/acl.h" + +extern bool g_is_device; + +void* Utils::ReadBinFile(std::string fileName, uint32_t *fileSize) { + struct stat sBuf; + int fileStatus = stat(fileName.data(), &sBuf); + if (fileStatus == -1) { + ERROR_LOG("failed to get file"); + return nullptr; + } + if (S_ISREG(sBuf.st_mode) == 0) { + ERROR_LOG("%s is not a file, please enter a file", fileName.c_str()); + return nullptr; + } + + std::ifstream binFile(fileName, std::ifstream::binary); + if (binFile.is_open() == false) { + ERROR_LOG("open file %s failed", fileName.c_str()); + return nullptr; + } + + binFile.seekg(0, binFile.end); + uint32_t binFileBufferLen = binFile.tellg(); + if (binFileBufferLen == 0) { + ERROR_LOG("binfile is empty, filename is %s", fileName.c_str()); + binFile.close(); + return nullptr; + } + + binFile.seekg(0, binFile.beg); + + void* binFileBufferData = nullptr; + aclError ret = ACL_ERROR_NONE; + if (!g_is_device) { + ret = aclrtMallocHost(&binFileBufferData, binFileBufferLen); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("malloc for binFileBufferData failed"); + binFile.close(); + return nullptr; + } + if (binFileBufferData == nullptr) { + ERROR_LOG("malloc binFileBufferData failed"); + binFile.close(); + return nullptr; + } + } else { + ret = aclrtMalloc(&binFileBufferData, binFileBufferLen, ACL_MEM_MALLOC_NORMAL_ONLY); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("malloc device buffer failed. size is %u", binFileBufferLen); + binFile.close(); + return nullptr; + } + } + binFile.read(static_cast(binFileBufferData), binFileBufferLen); + binFile.close(); + *fileSize = binFileBufferLen; + return binFileBufferData; +} + +void* Utils::GetDeviceBufferOfFile(std::string fileName, uint32_t *fileSize) { + uint32_t inputHostBuffSize = 0; + void* inputHostBuff = Utils::ReadBinFile(fileName, &inputHostBuffSize); + if (inputHostBuff == nullptr) { + return nullptr; + } + if (!g_is_device) { + void *inBufferDev = nullptr; + uint32_t inBufferSize = inputHostBuffSize; + aclError ret = aclrtMalloc(&inBufferDev, inBufferSize, ACL_MEM_MALLOC_NORMAL_ONLY); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("malloc device buffer failed. size is %u", inBufferSize); + aclrtFreeHost(inputHostBuff); + return nullptr; + } + + ret = aclrtMemcpy(inBufferDev, inBufferSize, inputHostBuff, inputHostBuffSize, ACL_MEMCPY_HOST_TO_DEVICE); + if (ret != ACL_ERROR_NONE) { + ERROR_LOG("memcpy failed. device buffer size is %u, input host buffer size is %u", + inBufferSize, inputHostBuffSize); + aclrtFree(inBufferDev); + aclrtFreeHost(inputHostBuff); + return nullptr; + } + aclrtFreeHost(inputHostBuff); + *fileSize = inBufferSize; + return inBufferDev; + } else { + *fileSize = inputHostBuffSize; + return inputHostBuff; + } +} diff --git a/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/utils.py b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/utils.py new file mode 100644 index 00000000000..f2d78f31b2f --- /dev/null +++ b/model_zoo/official/cv/yolov3_resnet18/ascend310_quant_infer/utils.py @@ -0,0 +1,176 @@ +# Copyright 2020 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. +# ============================================================================ +"""metrics utils""" + +import numpy as np + + +def calc_iou(bbox_pred, bbox_ground): + """Calculate iou of predicted bbox and ground truth.""" + x1 = bbox_pred[0] + y1 = bbox_pred[1] + width1 = bbox_pred[2] - bbox_pred[0] + height1 = bbox_pred[3] - bbox_pred[1] + + x2 = bbox_ground[0] + y2 = bbox_ground[1] + width2 = bbox_ground[2] - bbox_ground[0] + height2 = bbox_ground[3] - bbox_ground[1] + + endx = max(x1 + width1, x2 + width2) + startx = min(x1, x2) + width = width1 + width2 - (endx - startx) + + endy = max(y1 + height1, y2 + height2) + starty = min(y1, y2) + height = height1 + height2 - (endy - starty) + + if width <= 0 or height <= 0: + iou = 0 + else: + area = width * height + area1 = width1 * height1 + area2 = width2 * height2 + iou = area * 1. / (area1 + area2 - area) + + return iou + + +def apply_nms(all_boxes, all_scores, thres, max_boxes): + """Apply NMS to bboxes.""" + x1 = all_boxes[:, 0] + y1 = all_boxes[:, 1] + x2 = all_boxes[:, 2] + y2 = all_boxes[:, 3] + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + + order = all_scores.argsort()[::-1] + keep = [] + + while order.size > 0: + i = order[0] + keep.append(i) + + if len(keep) >= max_boxes: + break + + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thres)[0] + + order = order[inds + 1] + return keep + + +class ConfigYOLOV3ResNet18: + """ + Config parameters for YOLOv3. + + Examples: + ConfigYoloV3ResNet18. + """ + def __init__(self): + self.img_shape = [352, 640] + self.feature_shape = [32, 3, 352, 640] + self.num_classes = 2 + self.nms_max_num = 50 + + self.backbone_input_shape = [64, 64, 128, 256] + self.backbone_shape = [64, 128, 256, 512] + self.backbone_layers = [2, 2, 2, 2] + self.backbone_stride = [1, 2, 2, 2] + self.ignore_threshold = 0.5 + self.obj_threshold = 0.3 + self.nms_threshold = 0.4 + self.anchor_scales = [(10, 13), + (16, 30), + (33, 23), + (30, 61), + (62, 45), + (59, 119), + (116, 90), + (156, 198), + (163, 326)] + self.out_channel = int(len(self.anchor_scales) / 3 * (self.num_classes + 5)) + + +def metrics(pred_data): + """Calculate precision and recall of predicted bboxes.""" + config = ConfigYOLOV3ResNet18() + num_classes = config.num_classes + count_corrects = [1e-6 for _ in range(num_classes)] + count_grounds = [1e-6 for _ in range(num_classes)] + count_preds = [1e-6 for _ in range(num_classes)] + + for i, sample in enumerate(pred_data): + gt_anno = sample["annotation"] + box_scores = sample['box_scores'] + boxes = sample['boxes'] + mask = box_scores >= config.obj_threshold + boxes_ = [] + scores_ = [] + classes_ = [] + max_boxes = config.nms_max_num + for c in range(num_classes): + class_boxes = np.reshape(boxes, [-1, 4])[np.reshape(mask[:, c], [-1])] + class_box_scores = np.reshape(box_scores[:, c], [-1])[np.reshape(mask[:, c], [-1])] + nms_index = apply_nms(class_boxes, class_box_scores, config.nms_threshold, max_boxes) + class_boxes = class_boxes[nms_index] + class_box_scores = class_box_scores[nms_index] + classes = np.ones_like(class_box_scores, 'int32') * c + boxes_.append(class_boxes) + scores_.append(class_box_scores) + classes_.append(classes) + + boxes = np.concatenate(boxes_, axis=0) + classes = np.concatenate(classes_, axis=0) + + # metric + count_correct = [1e-6 for _ in range(num_classes)] + count_ground = [1e-6 for _ in range(num_classes)] + count_pred = [1e-6 for _ in range(num_classes)] + + for anno in gt_anno: + count_ground[anno[4]] += 1 + + for box_index, box in enumerate(boxes): + bbox_pred = [box[1], box[0], box[3], box[2]] + count_pred[classes[box_index]] += 1 + + for anno in gt_anno: + class_ground = anno[4] + + if classes[box_index] == class_ground: + iou = calc_iou(bbox_pred, anno) + if iou >= 0.5: + count_correct[class_ground] += 1 + break + + count_corrects = [count_corrects[i] + count_correct[i] for i in range(num_classes)] + count_preds = [count_preds[i] + count_pred[i] for i in range(num_classes)] + count_grounds = [count_grounds[i] + count_ground[i] for i in range(num_classes)] + + precision = np.array([count_corrects[ix] / count_preds[ix] for ix in range(num_classes)]) + recall = np.array([count_corrects[ix] / count_grounds[ix] for ix in range(num_classes)]) + return precision, recall