forked from mindspore-Ecosystem/mindspore
yolov5 add 310 infer
This commit is contained in:
chenweitao_295
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@ -14,12 +14,15 @@
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- [Evaluation](#testing)
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- [Evaluation Process](#evaluation-process)
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- [Evaluation](#evaluation)
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- [Convert Process](#convert-process)
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- [Convert](#convert)
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- [Inference Process](#inference-process)
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- [Export MindIR](#export-mindir)
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- [Infer on Ascend310](#infer-on-ascend310)
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- [result](#result)
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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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- [Inference Performance](#inference-performance)
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- [310 Inference Performance](#310-inference-performance)
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- [ModelZoo Homepage](#modelzoo-homepage)
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# [YOLOv5 Description](#contents)
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@ -125,9 +128,11 @@ sh run_eval.sh dataset/xxx checkpoint/xxx.ckpt
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└─yolov5
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├─README.md
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├─mindspore_hub_conf.md # config for mindspore hub
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├─ascend310_infer # application for 310 inference
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├─scripts
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├─run_standalone_train.sh # launch standalone training(1p) in ascend
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├─run_distribute_train.sh # launch distributed training(8p) in ascend
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├─run_infer_310.sh # launch 310 inference in ascend
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└─run_eval.sh # launch evaluating in ascend
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├─src
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├─__init__.py # python init file
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@ -142,9 +147,9 @@ sh run_eval.sh dataset/xxx checkpoint/xxx.ckpt
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├─util.py # util function
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├─yolo.py # yolov5 network
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├─yolo_dataset.py # create dataset for YOLOV5
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├─eval.py # evaluate val results
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├─export.py # convert mindspore model to air model
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├─postprocess.py # postprocess script
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└─train.py # train net
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```
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@ -306,14 +311,51 @@ The above python command will run in the background. You can view the results th
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674
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```
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## [Convert Process](#contents)
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## [Inference process](#contents)
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### Convert
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### Export MindIR
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If you want to infer the network on Ascend 310, you should convert the model to AIR:
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```shell
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python export.py --ckpt_file [CKPT_PATH] --file_format [EXPORT_FORMAT] --batch_size [BATCH_SIZE]
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```
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```python
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python export.py [BATCH_SIZE] [PRETRAINED_BACKBONE]
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The ckpt_file parameter is required,
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`EXPORT_FORMAT` should be in ["AIR", "MINDIR"].Current model only support CPU MODE.
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`BATCH_SIZE` current batch_size can only be set to 1.
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### Infer on Ascend310
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Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
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Current batch_size can only be set to 1.
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```shell
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# Ascend310 inference
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bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DVPP] [DEVICE_ID]
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```
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- `ANN_FILE` annotations file path.
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- `DVPP` is mandatory, and must choose from ["DVPP", "CPU"], it's case-insensitive. Current model only support CPU MODE.
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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 like this in acc.log file.
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```bash
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# acc.log
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=============coco 310 infer reulst=========
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.369
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.571
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.398
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.216
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.301
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.502
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.558
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.388
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.617
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677
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```
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# [Model Description](#contents)
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@ -355,6 +397,20 @@ YOLOv5 on 5K images(The annotation and data format must be the same as coco val2
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| Accuracy | map=36.8~37.2%(shape=640) |
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| Model for inference | 58M (.ckpt file) |
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### 310 Inference Performance
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| Parameters | Ascend |
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| ------------------- | ---------------------------------------- |
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| Model Version | YOLOv5s |
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| Resource | Ascend 310; CentOS 3.10 |
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| Uploaded Date | 07/06/2021 (month/day/year) |
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| MindSpore Version | 1.2.0 |
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| Dataset | Coco2017 5K images |
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| batch_size | 1 |
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| outputs | box position and sorces, and probability |
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| Accuracy | Accuracy=0.71654 |
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| Model for inference | 58M(.ckpt file) |
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# [Description of Random Situation](#contents)
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In dataset.py, we set the seed inside ```create_dataset``` function.
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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 +1,23 @@
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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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mkdir out
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fi
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cd out || exit
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cmake .. \
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-DMINDSPORE_PATH="`pip 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 +1,213 @@
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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 <math.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/dataset/transforms.h"
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#include "include/dataset/vision_ascend.h"
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#include "include/dataset/execute.h"
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#include "include/dataset/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::ModelType;
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using mindspore::GraphCell;
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using mindspore::kSuccess;
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using mindspore::MSTensor;
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using mindspore::DataType;
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using mindspore::dataset::Execute;
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using mindspore::dataset::InterpolationMode;
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using mindspore::dataset::TensorTransform;
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using mindspore::dataset::vision::Resize;
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using mindspore::dataset::vision::HWC2CHW;
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using mindspore::dataset::vision::Normalize;
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using mindspore::dataset::vision::Decode;
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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_int32(image_height, 640, "image height");
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DEFINE_int32(image_width, 640, "image width");
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int TransFormImg(MSTensor *input, MSTensor *output) {
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void *imgput;
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void *imgoutput;
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float *address_img;
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float *address;
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imgput = input->MutableData();
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imgoutput = output->MutableData();
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address_img = static_cast<float *>(imgput);
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address = static_cast<float *>(imgoutput);
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int new_height = static_cast<int>(FLAGS_image_height) / 2;
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int new_width = static_cast<int>(FLAGS_image_width) / 2;
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std::vector<int64_t> input_shape = input->Shape();
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int channel = static_cast<int> (input_shape[0]);
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int new_channel = channel * 4; // concatenate image
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int outIdx = 0;
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int imgIdx = 0;
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for (int new_c = 0; new_c < new_channel; new_c++) {
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int c = new_c % channel;
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for (int new_h = 0; new_h < new_height; new_h++) {
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for (int new_w = 0; new_w < new_width; new_w++) {
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if (new_c < channel) {
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outIdx = new_c * new_height * new_width + new_h * new_width + new_w;
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imgIdx = c * static_cast<int>(FLAGS_image_height) * static_cast<int>(FLAGS_image_width) +
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new_h * 2 * static_cast<int>(FLAGS_image_width) + new_w * 2;
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} else if (channel <= new_c && new_c < channel * 2) {
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outIdx = new_c * new_height * new_width + new_h * new_width + new_w;
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imgIdx = c * static_cast<int>(FLAGS_image_height) * static_cast<int>(FLAGS_image_width) +
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static_cast<int>((new_h + 0.5) * 2 * static_cast<int>(FLAGS_image_width)) + new_w * 2;
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} else if (channel * 2 <= new_c && new_c< channel * 3) {
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outIdx = new_c * new_height * new_width + new_h * new_width + new_w;
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imgIdx = c * static_cast<int>(FLAGS_image_height) * static_cast<int>(FLAGS_image_width) +
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new_h * 2 * static_cast<int>(FLAGS_image_width) + static_cast<int>((new_w + 0.5) * 2);
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} else if (channel * 3 <= new_c && new_c< channel * 4) {
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outIdx = new_c * new_height * new_width + new_h * new_width + new_w;
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imgIdx = c * static_cast<int>(FLAGS_image_height) * static_cast<int>(FLAGS_image_width) +
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static_cast<int>((new_h + 0.5) * 2 * static_cast<int>(FLAGS_image_width)) +
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static_cast<int>((new_w + 0.5) * 2);
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} else {
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std::cout << "new channels Out of range." << std::endl;
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return 1;
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}
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address[outIdx] = address_img[imgIdx];
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}
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}
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}
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return 0;
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}
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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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ascend310->SetPrecisionMode("allow_fp32_to_fp16");
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ascend310->SetOpSelectImplMode("high_precision");
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ascend310->SetBufferOptimizeMode("off_optimize");
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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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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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auto all_files = GetAllFiles(FLAGS_dataset_path);
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std::map<double, double> costTime_map;
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size_t size = all_files.size();
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std::shared_ptr<TensorTransform> decode(new Decode());
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auto resize = Resize({FLAGS_image_height, FLAGS_image_width});
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auto normalize = Normalize({123.675, 116.28, 103.53}, {58.395, 57.120, 57.375});
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auto hwc2chw = HWC2CHW();
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Execute composeDecode({decode});
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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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auto imgDecode = MSTensor();
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auto img = MSTensor();
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composeDecode(ReadFileToTensor(all_files[i]), &imgDecode);
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std::vector<int64_t> shape = imgDecode.Shape();
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if ((static_cast<int> (shape[0]) < static_cast<int>(FLAGS_image_height)) &&
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(static_cast<int> (shape[1]) < static_cast<int>(FLAGS_image_width))) {
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resize = Resize({FLAGS_image_height, FLAGS_image_width}, InterpolationMode::kCubic);
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} else if ((static_cast<int> (shape[0]) > static_cast<int>(FLAGS_image_height)) &&
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(static_cast<int> (shape[1]) > static_cast<int>(FLAGS_image_width))) {
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resize = Resize({FLAGS_image_height, FLAGS_image_width}, InterpolationMode::kNearestNeighbour);
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} else {
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resize = Resize({FLAGS_image_height, FLAGS_image_width}, InterpolationMode::kLinear);
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}
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if ((sizeof(shape)/sizeof(shape[0])) <= 2) {
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std::cout << "image channels is not 3." << std::endl;
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return 1;
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}
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Execute transform({resize, normalize, hwc2chw});
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transform(imgDecode, &img);
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size_t buffer_size = img.DataSize();
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std::vector<int64_t> img_shape = img.Shape();
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mindspore::MSTensor buffer("output", mindspore::DataType::kNumberTypeFloat32,
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{static_cast<int64_t>(img_shape[0] * 4), static_cast<int64_t>(FLAGS_image_height) / 2,
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static_cast<int64_t>(FLAGS_image_width) / 2},
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nullptr, buffer_size);
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TransFormImg(&img, &buffer);
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std::vector<MSTensor> model_inputs = model.GetInputs();
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inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
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buffer.Data().get(), buffer.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;
|
||||
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 +1,130 @@
|
|||
/**
|
||||
* 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 <fstream>
|
||||
#include <algorithm>
|
||||
#include <iostream>
|
||||
|
||||
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;
|
||||
}
|
||||
|
|
@ -18,8 +18,8 @@ import numpy as np
|
|||
import mindspore
|
||||
from mindspore import context, Tensor
|
||||
from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
|
||||
|
||||
from src.yolo import YOLOV5s
|
||||
from src.config import ConfigYOLOV5
|
||||
from src.yolo import YOLOV5s_Infer
|
||||
|
||||
parser = argparse.ArgumentParser(description='yolov5 export')
|
||||
parser.add_argument("--device_id", type=int, default=0, help="Device id")
|
||||
|
|
@ -27,7 +27,7 @@ parser.add_argument("--batch_size", type=int, default=1, help="batch size")
|
|||
parser.add_argument("--testing_shape", type=int, default=640, help="test shape")
|
||||
parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
|
||||
parser.add_argument("--file_name", type=str, default="yolov5", help="output file name.")
|
||||
parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
|
||||
parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default='AIR', help='file format')
|
||||
parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
|
||||
help="device target")
|
||||
args = parser.parse_args()
|
||||
|
|
@ -37,14 +37,16 @@ if args.device_target == "Ascend":
|
|||
context.set_context(device_id=args.device_id)
|
||||
|
||||
if __name__ == "__main__":
|
||||
ts_shape = args.testing_shape
|
||||
config = ConfigYOLOV5()
|
||||
if args.testing_shape:
|
||||
config.test_img_shape = [int(args.testing_shape), int(args.testing_shape)]
|
||||
ts_shape = config.test_img_shape[0]
|
||||
|
||||
network = YOLOV5s(is_training=False)
|
||||
network.set_train(False)
|
||||
network = YOLOV5s_Infer(config.test_img_shape)
|
||||
|
||||
param_dict = load_checkpoint(args.ckpt_file)
|
||||
load_param_into_net(network, param_dict)
|
||||
|
||||
input_data = Tensor(np.zeros([args.batch_size, 3, ts_shape, ts_shape]), mindspore.float32)
|
||||
input_data = Tensor(np.zeros([args.batch_size, 12, int(ts_shape/2), int(ts_shape/2)]), mindspore.float32)
|
||||
|
||||
export(network, input_data, file_name=args.file_name, file_format=args.file_format)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,328 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""YoloV5 310 infer."""
|
||||
import os
|
||||
import sys
|
||||
import argparse
|
||||
import datetime
|
||||
import time
|
||||
import ast
|
||||
from collections import defaultdict
|
||||
import numpy as np
|
||||
from pycocotools.coco import COCO
|
||||
from pycocotools.cocoeval import COCOeval
|
||||
from src.logger import get_logger
|
||||
|
||||
parser = argparse.ArgumentParser('yolov5 postprocess')
|
||||
|
||||
# dataset related
|
||||
parser.add_argument('--per_batch_size', default=1, type=int, help='batch size for per gpu')
|
||||
|
||||
# logging related
|
||||
parser.add_argument('--log_path', type=str, default='outputs/', help='checkpoint save location')
|
||||
|
||||
# detect_related
|
||||
parser.add_argument('--nms_thresh', type=float, default=0.6, help='threshold for NMS')
|
||||
parser.add_argument('--ann_file', 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('--dataset_path', type=str, default='', help='path of image dataset')
|
||||
parser.add_argument('--result_files', type=str, default='./result_Files', help='path to 310 infer result path')
|
||||
parser.add_argument('--multi_label', type=ast.literal_eval, default=True, help='whether to use multi label')
|
||||
parser.add_argument('--multi_label_thresh', type=float, default=0.1, help='threshold to throw low quality boxes')
|
||||
|
||||
args, _ = parser.parse_known_args()
|
||||
|
||||
|
||||
class Redirct:
|
||||
def __init__(self):
|
||||
self.content = ""
|
||||
|
||||
def write(self, content):
|
||||
self.content += content
|
||||
|
||||
def flush(self):
|
||||
self.content = ""
|
||||
|
||||
|
||||
class DetectionEngine:
|
||||
"""Detection engine."""
|
||||
|
||||
def __init__(self, args_detection):
|
||||
self.ignore_threshold = args_detection.ignore_threshold
|
||||
self.labels = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat',
|
||||
'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat',
|
||||
'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',
|
||||
'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
|
||||
'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
|
||||
'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
|
||||
'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
|
||||
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book',
|
||||
'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
|
||||
self.num_classes = len(self.labels)
|
||||
self.results = {}
|
||||
self.file_path = ''
|
||||
self.save_prefix = args_detection.outputs_dir
|
||||
self.ann_file = args_detection.ann_file
|
||||
self._coco = COCO(self.ann_file)
|
||||
self._img_ids = list(sorted(self._coco.imgs.keys()))
|
||||
self.det_boxes = []
|
||||
self.nms_thresh = args_detection.nms_thresh
|
||||
self.multi_label = args_detection.multi_label
|
||||
self.multi_label_thresh = args_detection.multi_label_thresh
|
||||
# self.coco_catids = self._coco.getCatIds()
|
||||
self.coco_catIds = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27,
|
||||
28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53,
|
||||
54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80,
|
||||
81, 82, 84, 85, 86, 87, 88, 89, 90]
|
||||
|
||||
def do_nms_for_results(self):
|
||||
"""Get result boxes."""
|
||||
# np.save('/opt/disk1/hjc/yolov5_positive_policy/result.npy', self.results)
|
||||
for image_id in self.results:
|
||||
for clsi in self.results[image_id]:
|
||||
dets = self.results[image_id][clsi]
|
||||
dets = np.array(dets)
|
||||
keep_index = self._diou_nms(dets, thresh=self.nms_thresh)
|
||||
|
||||
keep_box = [{'image_id': int(image_id),
|
||||
'category_id': int(clsi),
|
||||
'bbox': list(dets[i][:4].astype(float)),
|
||||
'score': dets[i][4].astype(float)}
|
||||
for i in keep_index]
|
||||
self.det_boxes.extend(keep_box)
|
||||
|
||||
def _nms(self, predicts, threshold):
|
||||
"""Calculate NMS."""
|
||||
# convert xywh -> xmin ymin xmax ymax
|
||||
x1 = predicts[:, 0]
|
||||
y1 = predicts[:, 1]
|
||||
x2 = x1 + predicts[:, 2]
|
||||
y2 = y1 + predicts[:, 3]
|
||||
scores = predicts[:, 4]
|
||||
|
||||
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
|
||||
order = scores.argsort()[::-1]
|
||||
|
||||
reserved_boxes = []
|
||||
while order.size > 0:
|
||||
i = order[0]
|
||||
reserved_boxes.append(i)
|
||||
max_x1 = np.maximum(x1[i], x1[order[1:]])
|
||||
max_y1 = np.maximum(y1[i], y1[order[1:]])
|
||||
min_x2 = np.minimum(x2[i], x2[order[1:]])
|
||||
min_y2 = np.minimum(y2[i], y2[order[1:]])
|
||||
|
||||
intersect_w = np.maximum(0.0, min_x2 - max_x1 + 1)
|
||||
intersect_h = np.maximum(0.0, min_y2 - max_y1 + 1)
|
||||
intersect_area = intersect_w * intersect_h
|
||||
ovr = intersect_area / (areas[i] + areas[order[1:]] - intersect_area)
|
||||
|
||||
indexes = np.where(ovr <= threshold)[0]
|
||||
order = order[indexes + 1]
|
||||
return reserved_boxes
|
||||
|
||||
def _diou_nms(self, dets, thresh=0.5):
|
||||
"""
|
||||
convert xywh -> xmin ymin xmax ymax
|
||||
"""
|
||||
x1 = dets[:, 0]
|
||||
y1 = dets[:, 1]
|
||||
x2 = x1 + dets[:, 2]
|
||||
y2 = y1 + dets[:, 3]
|
||||
scores = dets[:, 4]
|
||||
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
|
||||
order = scores.argsort()[::-1]
|
||||
keep = []
|
||||
while order.size > 0:
|
||||
i = order[0]
|
||||
keep.append(i)
|
||||
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)
|
||||
center_x1 = (x1[i] + x2[i]) / 2
|
||||
center_x2 = (x1[order[1:]] + x2[order[1:]]) / 2
|
||||
center_y1 = (y1[i] + y2[i]) / 2
|
||||
center_y2 = (y1[order[1:]] + y2[order[1:]]) / 2
|
||||
inter_diag = (center_x2 - center_x1) ** 2 + (center_y2 - center_y1) ** 2
|
||||
out_max_x = np.maximum(x2[i], x2[order[1:]])
|
||||
out_max_y = np.maximum(y2[i], y2[order[1:]])
|
||||
out_min_x = np.minimum(x1[i], x1[order[1:]])
|
||||
out_min_y = np.minimum(y1[i], y1[order[1:]])
|
||||
outer_diag = (out_max_x - out_min_x) ** 2 + (out_max_y - out_min_y) ** 2
|
||||
diou = ovr - inter_diag / outer_diag
|
||||
diou = np.clip(diou, -1, 1)
|
||||
inds = np.where(diou <= thresh)[0]
|
||||
order = order[inds + 1]
|
||||
return keep
|
||||
|
||||
def write_result(self):
|
||||
"""Save result to file."""
|
||||
import json
|
||||
t = datetime.datetime.now().strftime('_%Y_%m_%d_%H_%M_%S')
|
||||
try:
|
||||
self.file_path = self.save_prefix + '/predict' + t + '.json'
|
||||
f = open(self.file_path, 'w')
|
||||
json.dump(self.det_boxes, f)
|
||||
except IOError as e:
|
||||
raise RuntimeError("Unable to open json file to dump. What(): {}".format(str(e)))
|
||||
else:
|
||||
f.close()
|
||||
return self.file_path
|
||||
|
||||
def get_eval_result(self):
|
||||
"""Get eval result."""
|
||||
coco_gt = COCO(self.ann_file)
|
||||
coco_dt = coco_gt.loadRes(self.file_path)
|
||||
coco_eval = COCOeval(coco_gt, coco_dt, 'bbox')
|
||||
coco_eval.evaluate()
|
||||
coco_eval.accumulate()
|
||||
rdct = Redirct()
|
||||
stdout = sys.stdout
|
||||
sys.stdout = rdct
|
||||
coco_eval.summarize()
|
||||
sys.stdout = stdout
|
||||
return rdct.content
|
||||
|
||||
def detect(self, outputs, batch, img_shape, image_id):
|
||||
"""Detect boxes."""
|
||||
outputs_num = len(outputs)
|
||||
# output [|32, 52, 52, 3, 85| ]
|
||||
for batch_id in range(batch):
|
||||
for out_id in range(outputs_num):
|
||||
# 32, 52, 52, 3, 85
|
||||
out_item = outputs[out_id]
|
||||
# 52, 52, 3, 85
|
||||
out_item_single = out_item[batch_id, :]
|
||||
# get number of items in one head, [B, gx, gy, anchors, 5+80]
|
||||
dimensions = out_item_single.shape[:-1]
|
||||
out_num = 1
|
||||
for d in dimensions:
|
||||
out_num *= d
|
||||
ori_w, ori_h = img_shape[batch_id]
|
||||
img_id = int(image_id[batch_id])
|
||||
x = out_item_single[..., 0] * ori_w
|
||||
y = out_item_single[..., 1] * ori_h
|
||||
w = out_item_single[..., 2] * ori_w
|
||||
h = out_item_single[..., 3] * ori_h
|
||||
|
||||
conf = out_item_single[..., 4:5]
|
||||
cls_emb = out_item_single[..., 5:]
|
||||
cls_argmax = np.expand_dims(np.argmax(cls_emb, axis=-1), axis=-1)
|
||||
x = x.reshape(-1)
|
||||
y = y.reshape(-1)
|
||||
w = w.reshape(-1)
|
||||
h = h.reshape(-1)
|
||||
x_top_left = x - w / 2.
|
||||
y_top_left = y - h / 2.
|
||||
cls_emb = cls_emb.reshape(-1, self.num_classes)
|
||||
if self.multi_label:
|
||||
conf = conf.reshape(-1, 1)
|
||||
# create all False
|
||||
confidence = cls_emb * conf
|
||||
flag = cls_emb > self.multi_label_thresh
|
||||
flag = flag.nonzero()
|
||||
for index in range(len(flag[0])):
|
||||
i = flag[0][index]
|
||||
j = flag[1][index]
|
||||
confi = confidence[i][j]
|
||||
if confi < self.ignore_threshold:
|
||||
continue
|
||||
if img_id not in self.results:
|
||||
self.results[img_id] = defaultdict(list)
|
||||
x_lefti = max(0, x_top_left[i])
|
||||
y_lefti = max(0, y_top_left[i])
|
||||
wi = min(w[i], ori_w)
|
||||
hi = min(h[i], ori_h)
|
||||
clsi = j
|
||||
# transform catId to match coco
|
||||
coco_clsi = self.coco_catIds[clsi]
|
||||
self.results[img_id][coco_clsi].append([x_lefti, y_lefti, wi, hi, confi])
|
||||
else:
|
||||
cls_argmax = np.expand_dims(np.argmax(cls_emb, axis=-1), axis=-1)
|
||||
conf = conf.reshape(-1)
|
||||
cls_argmax = cls_argmax.reshape(-1)
|
||||
|
||||
# create all False
|
||||
flag = np.random.random(cls_emb.shape) > sys.maxsize
|
||||
for i in range(flag.shape[0]):
|
||||
c = cls_argmax[i]
|
||||
flag[i, c] = True
|
||||
confidence = cls_emb[flag] * conf
|
||||
|
||||
for x_lefti, y_lefti, wi, hi, confi, clsi in zip(x_top_left, y_top_left, w, h, confidence,
|
||||
cls_argmax):
|
||||
if confi < self.ignore_threshold:
|
||||
continue
|
||||
if img_id not in self.results:
|
||||
self.results[img_id] = defaultdict(list)
|
||||
x_lefti = max(0, x_lefti)
|
||||
y_lefti = max(0, y_lefti)
|
||||
wi = min(wi, ori_w)
|
||||
hi = min(hi, ori_h)
|
||||
# transform catId to match coco
|
||||
coco_clsi = self.coco_catids[clsi]
|
||||
self.results[img_id][coco_clsi].append([x_lefti, y_lefti, wi, hi, confi])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
start_time = time.time()
|
||||
|
||||
args.outputs_dir = os.path.join(args.log_path,
|
||||
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
|
||||
args.logger = get_logger(args.outputs_dir, 0)
|
||||
|
||||
# init detection engine
|
||||
detection = DetectionEngine(args)
|
||||
|
||||
coco = COCO(args.ann_file)
|
||||
result_path = args.result_files
|
||||
|
||||
files = os.listdir(args.dataset_path)
|
||||
|
||||
for file in files:
|
||||
img_ids_name = file.split('.')[0]
|
||||
img_id_ = int(np.squeeze(img_ids_name))
|
||||
imgIds = coco.getImgIds(imgIds=[img_id_])
|
||||
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
|
||||
image_shape = ((img['width'], img['height']),)
|
||||
img_id_ = (np.squeeze(img_ids_name),)
|
||||
|
||||
result_path_0 = os.path.join(result_path, img_ids_name + "_0.bin")
|
||||
result_path_1 = os.path.join(result_path, img_ids_name + "_1.bin")
|
||||
result_path_2 = os.path.join(result_path, img_ids_name + "_2.bin")
|
||||
|
||||
output_small = np.fromfile(result_path_0, dtype=np.float32).reshape(1, 20, 20, 3, 85)
|
||||
output_me = np.fromfile(result_path_1, dtype=np.float32).reshape(1, 40, 40, 3, 85)
|
||||
output_big = np.fromfile(result_path_2, dtype=np.float32).reshape(1, 80, 80, 3, 85)
|
||||
|
||||
detection.detect([output_small, output_me, output_big], args.per_batch_size, image_shape, img_id_)
|
||||
|
||||
args.logger.info('Calculating mAP...')
|
||||
detection.do_nms_for_results()
|
||||
result_file_path = detection.write_result()
|
||||
args.logger.info('result file path: {}'.format(result_file_path))
|
||||
eval_result = detection.get_eval_result()
|
||||
|
||||
cost_time = time.time() - start_time
|
||||
args.logger.info('\n=============coco 310 infer reulst=========\n' + eval_result)
|
||||
args.logger.info('testing cost time {:.2f}h'.format(cost_time / 3600.))
|
||||
|
|
@ -0,0 +1,108 @@
|
|||
#!/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 || $# -gt 5 ]]; then
|
||||
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DVPP] [DEVICE_ID]
|
||||
DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
|
||||
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
get_real_path(){
|
||||
if [ "${1:0:1}" == "/" ]; then
|
||||
echo "$1"
|
||||
else
|
||||
echo "$(realpath -m $PWD/$1)"
|
||||
fi
|
||||
}
|
||||
model=$(get_real_path $1)
|
||||
data_path=$(get_real_path $2)
|
||||
ann_file=$(get_real_path $3)
|
||||
DVPP=${4^^}
|
||||
|
||||
device_id=0
|
||||
if [ $# == 5 ]; then
|
||||
device_id=$5
|
||||
fi
|
||||
|
||||
echo "mindir name: "$model
|
||||
echo "dataset path: "$data_path
|
||||
echo "ann file: "$ann_file
|
||||
echo "image process mode: "$DVPP
|
||||
echo "device id: "$device_id
|
||||
|
||||
export ASCEND_HOME=/usr/local/Ascend/
|
||||
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$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=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
|
||||
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
|
||||
else
|
||||
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
|
||||
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/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/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
|
||||
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
|
||||
fi
|
||||
|
||||
function compile_app()
|
||||
{
|
||||
cd ../ascend310_infer || exit
|
||||
bash build.sh &> build.log
|
||||
}
|
||||
|
||||
function infer()
|
||||
{
|
||||
cd - || exit
|
||||
if [ -d result_Files ]; then
|
||||
rm -rf ./result_Files
|
||||
fi
|
||||
if [ -d time_Result ]; then
|
||||
rm -rf ./time_Result
|
||||
fi
|
||||
mkdir result_Files
|
||||
mkdir time_Result
|
||||
if [ "$DVPP" == "DVPP" ];then
|
||||
echo "Only support CPU mode"
|
||||
exit 1
|
||||
elif [ "$DVPP" == "CPU" ]; then
|
||||
../ascend310_infer/out/main --mindir_path=$model --dataset_path=$data_path --device_id=$device_id --image_height=640 --image_width=640 &> infer.log
|
||||
else
|
||||
echo "image process mode must be in [DVPP|CPU]"
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
function cal_acc()
|
||||
{
|
||||
python3.7 ../postprocess.py --result_files=./result_Files --dataset_path=$data_path --ann_file=$ann_file &> 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
|
||||
|
|
@ -35,14 +35,14 @@ class YOLOv5(nn.Cell):
|
|||
self.out_channel = out_channel
|
||||
self.backbone = backbone
|
||||
|
||||
self.conv1 = Conv(512, 256, k=1, s=1)#10
|
||||
self.C31 = C3(512, 256, n=1, shortcut=False)#11
|
||||
self.conv1 = Conv(512, 256, k=1, s=1) # 10
|
||||
self.C31 = C3(512, 256, n=1, shortcut=False) # 11
|
||||
self.conv2 = Conv(256, 128, k=1, s=1)
|
||||
self.C32 = C3(256, 128, n=1, shortcut=False)#13
|
||||
self.C32 = C3(256, 128, n=1, shortcut=False) # 13
|
||||
self.conv3 = Conv(128, 128, k=3, s=2)
|
||||
self.C33 = C3(256, 256, n=1, shortcut=False)#15
|
||||
self.C33 = C3(256, 256, n=1, shortcut=False) # 15
|
||||
self.conv4 = Conv(256, 256, k=3, s=2)
|
||||
self.C34 = C3(512, 512, n=1, shortcut=False)#17
|
||||
self.C34 = C3(512, 512, n=1, shortcut=False) # 17
|
||||
|
||||
self.backblock1 = YoloBlock(128, 255)
|
||||
self.backblock2 = YoloBlock(256, 255)
|
||||
|
|
@ -62,24 +62,24 @@ class YOLOv5(nn.Cell):
|
|||
|
||||
backbone4, backbone6, backbone9 = self.backbone(x)
|
||||
|
||||
cv1 = self.conv1(backbone9)#10
|
||||
cv1 = self.conv1(backbone9) # 10
|
||||
ups1 = P.ResizeNearestNeighbor((img_hight / 16, img_width / 16))(cv1)
|
||||
concat1 = self.concat((ups1, backbone6))
|
||||
bcsp1 = self.C31(concat1)#13
|
||||
bcsp1 = self.C31(concat1) # 13
|
||||
cv2 = self.conv2(bcsp1)
|
||||
ups2 = P.ResizeNearestNeighbor((img_hight / 8, img_width / 8))(cv2)#15
|
||||
ups2 = P.ResizeNearestNeighbor((img_hight / 8, img_width / 8))(cv2) # 15
|
||||
concat2 = self.concat((ups2, backbone4))
|
||||
bcsp2 = self.C32(concat2)#17
|
||||
bcsp2 = self.C32(concat2) # 17
|
||||
cv3 = self.conv3(bcsp2)
|
||||
|
||||
concat3 = self.concat((cv3, cv2))
|
||||
bcsp3 = self.C33(concat3)#20
|
||||
bcsp3 = self.C33(concat3) # 20
|
||||
cv4 = self.conv4(bcsp3)
|
||||
concat4 = self.concat((cv4, cv1))
|
||||
bcsp4 = self.C34(concat4)#23
|
||||
small_object_output = self.backblock1(bcsp2) # h/8, w/8
|
||||
medium_object_output = self.backblock2(bcsp3) # h/16, w/16
|
||||
big_object_output = self.backblock3(bcsp4) # h/32, w/32
|
||||
bcsp4 = self.C34(concat4) # 23
|
||||
small_object_output = self.backblock1(bcsp2) # h/8, w/8
|
||||
medium_object_output = self.backblock2(bcsp3) # h/16, w/16
|
||||
big_object_output = self.backblock3(bcsp4) # h/32, w/32
|
||||
return small_object_output, medium_object_output, big_object_output
|
||||
|
||||
|
||||
|
|
@ -98,6 +98,7 @@ class YoloBlock(nn.Cell):
|
|||
YoloBlock(12, 255)
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, in_channels, out_channels):
|
||||
super(YoloBlock, self).__init__()
|
||||
|
||||
|
|
@ -145,7 +146,7 @@ class DetectionBlock(nn.Cell):
|
|||
raise KeyError("Invalid scale value for DetectionBlock")
|
||||
self.anchors = Tensor([self.config.anchor_scales[i] for i in idx], ms.float32)
|
||||
self.num_anchors_per_scale = 3
|
||||
self.num_attrib = 4+1+self.config.num_classes
|
||||
self.num_attrib = 4 + 1 + self.config.num_classes
|
||||
self.lambda_coord = 1
|
||||
|
||||
self.sigmoid = nn.Sigmoid()
|
||||
|
|
@ -199,6 +200,7 @@ class DetectionBlock(nn.Cell):
|
|||
|
||||
class Iou(nn.Cell):
|
||||
"""Calculate the iou of boxes"""
|
||||
|
||||
def __init__(self):
|
||||
super(Iou, self).__init__()
|
||||
self.min = P.Minimum()
|
||||
|
|
@ -212,8 +214,8 @@ class Iou(nn.Cell):
|
|||
"""
|
||||
box1_xy = box1[:, :, :, :, :, :2]
|
||||
box1_wh = box1[:, :, :, :, :, 2:4]
|
||||
box1_mins = box1_xy - box1_wh / F.scalar_to_array(2.0) # topLeft
|
||||
box1_maxs = box1_xy + box1_wh / F.scalar_to_array(2.0) # rightDown
|
||||
box1_mins = box1_xy - box1_wh / F.scalar_to_array(2.0) # topLeft
|
||||
box1_maxs = box1_xy + box1_wh / F.scalar_to_array(2.0) # rightDown
|
||||
|
||||
box2_xy = box2[:, :, :, :, :, :2]
|
||||
box2_wh = box2[:, :, :, :, :, 2:4]
|
||||
|
|
@ -237,6 +239,7 @@ class YoloLossBlock(nn.Cell):
|
|||
"""
|
||||
Loss block cell of YOLOV5 network.
|
||||
"""
|
||||
|
||||
def __init__(self, scale, config=ConfigYOLOV5()):
|
||||
super(YoloLossBlock, self).__init__()
|
||||
self.config = config
|
||||
|
|
@ -356,8 +359,23 @@ class YOLOV5s(nn.Cell):
|
|||
return output_big, output_me, output_small
|
||||
|
||||
|
||||
class YOLOV5s_Infer(nn.Cell):
|
||||
"""
|
||||
YOLOV5 Infer.
|
||||
"""
|
||||
|
||||
def __init__(self, inputshape):
|
||||
super(YOLOV5s_Infer, self).__init__()
|
||||
self.network = YOLOV5s(is_training=False)
|
||||
self.inputshape = inputshape
|
||||
|
||||
def construct(self, x):
|
||||
return self.network(x, self.inputshape)
|
||||
|
||||
|
||||
class YoloWithLossCell(nn.Cell):
|
||||
"""YOLOV5 loss."""
|
||||
|
||||
def __init__(self, network):
|
||||
super(YoloWithLossCell, self).__init__()
|
||||
self.yolo_network = network
|
||||
|
|
@ -367,7 +385,6 @@ class YoloWithLossCell(nn.Cell):
|
|||
self.loss_small = YoloLossBlock('s', self.config)
|
||||
self.tenser_to_array = P.TupleToArray()
|
||||
|
||||
|
||||
def construct(self, x, y_true_0, y_true_1, y_true_2, gt_0, gt_1, gt_2, input_shape):
|
||||
input_shape = F.shape(x)[2:4]
|
||||
input_shape = F.cast(self.tenser_to_array(input_shape) * 2, ms.float32)
|
||||
|
|
@ -381,6 +398,7 @@ class YoloWithLossCell(nn.Cell):
|
|||
|
||||
class TrainingWrapper(nn.Cell):
|
||||
"""Training wrapper."""
|
||||
|
||||
def __init__(self, network, optimizer, sens=1.0):
|
||||
super(TrainingWrapper, self).__init__(auto_prefix=False)
|
||||
self.network = network
|
||||
|
|
@ -414,6 +432,7 @@ class TrainingWrapper(nn.Cell):
|
|||
|
||||
class Giou(nn.Cell):
|
||||
"""Calculating giou"""
|
||||
|
||||
def __init__(self):
|
||||
super(Giou, self).__init__()
|
||||
self.cast = P.Cast()
|
||||
|
|
@ -449,6 +468,7 @@ class Giou(nn.Cell):
|
|||
giou = C.clip_by_value(giou, -1.0, 1.0)
|
||||
return giou
|
||||
|
||||
|
||||
def xywh2x1y1x2y2(box_xywh):
|
||||
boxes_x1 = box_xywh[..., 0:1] - box_xywh[..., 2:3] / 2
|
||||
boxes_y1 = box_xywh[..., 1:2] - box_xywh[..., 3:4] / 2
|
||||
|
|
|
|||
Loading…
Reference in New Issue