amend yolov5 and centerface Readme

2021-07-05 22:12:06 +08:00 · 2021-07-05 22:12:06 +08:00 · f84cf543d7
parent 1790db17dd
commit f84cf543d7
3 changed files with 71 additions and 14 deletions
--- a/model_zoo/official/cv/centerface/README.md
+++ b/model_zoo/official/cv/centerface/README.md
@ -648,11 +648,11 @@ The ckpt_file parameter is required,
 ### Infer on Ascend310

 Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
-Need to install OpenCV, You can download it from [OpenCV](https://opencv.org/).  
+Need to install OpenCV(Version >= 4.0), You can download it from [OpenCV](https://opencv.org/).

 ```shell
 # Ascend310 inference
-bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [SAVE_PATH] [LABEL_PATH] [DVPP] [DEVICE_ID]
 ```

 - `DVPP` is mandatory, and must choose from ["DVPP", "CPU"], it's case-insensitive. Only support CPU mode .
@ -715,7 +715,7 @@ CenterFace on 3.2K images(The annotation and data format must be the same as wid

 | Parameters          | CenterFace                      |
 | ------------------- | --------------------------- |
-| Model Version       | CNNCTC                      |
+| Model Version       | CenterFace                      |
 | Resource            | Ascend 310; CentOS 3.10     |
 | Uploaded Date       | 23/06/2021 (month/day/year) |
 | MindSpore Version   | 1.2.0                       |
--- a/model_zoo/official/cv/centerface/scripts/run_infer_310.sh
+++ b/model_zoo/official/cv/centerface/scripts/run_infer_310.sh
@ -15,7 +15,7 @@
 # ============================================================================

 if [[ $# -lt 5 || $# -gt 6 ]]; then
-    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [SAVE_PATH] [LABEL_FILE] [DVPP] [DEVICE_ID]
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [SAVE_PATH] [LABEL_PATH] [DVPP] [DEVICE_ID]
    DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive. Current only support CPU mode.
    DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
 exit 1
@ -31,7 +31,7 @@ get_real_path(){
 model=$(get_real_path $1)
 data_path=$(get_real_path $2)
 save_path=$(get_real_path $3)
-label_file=$(get_real_path $4)
+label_path=$(get_real_path $4)
 DVPP=${5^^}
 device_id=0
 if [ $# == 6 ]; then
@ -41,7 +41,7 @@ fi
 echo "mindir name: "$model
 echo "dataset path: "$data_path
 echo "save path: "$save_path
-echo "label file: "$label_file
+echo "label path: "$label_path
 echo "image process mode: "$DVPP
 echo "device id: "$device_id

@ -101,7 +101,7 @@ function cal_ap()
    if [ -d ${save_path} ]; then
        rm -rf ${save_path}
    fi
-    python3.7 ../postprocess.py --result_path=./result_Files --label_file=$label_file --meta_file=./dataset/meta --save_path=$save_path &> ap.log &
+    python3.7 ../postprocess.py --result_path=./result_Files --label_path=$label_path --meta_path=./dataset/meta --save_path=$save_path &> ap.log &
 }

 preprocess_data
--- a/model_zoo/official/cv/yolov5/README_CN.md
+++ b/model_zoo/official/cv/yolov5/README_CN.md
@ -14,12 +14,15 @@
        - [测试](#测试)
    - [评估过程](#评估过程)
        - [评估](#评估)
-    - [转换过程](#转换过程)
-        - [转换](#转换)
+    - [推理过程](#推理过程)
+        - [导出MindIR](#导出mindir)
+        - [在Ascend310执行推理](#在ascend310执行推理)
+        - [结果](#结果)
 - [模型说明](#模型说明)
    - [性能](#性能)
        - [评估性能](#评估性能)
        - [推理性能](#推理性能)
+        - [310推理性能](#310推理性能)
 - [ModelZoo主页](#modelzoo主页)

 # [YOLOv5描述](#目录)
@ -125,9 +128,11 @@ sh run_eval.sh dataset/xxx checkpoint/xxx.ckpt
 ©¸©¤yolov5
  ©À©¤README.md
  ©À©¤mindspore_hub_conf.md             # Mindspore Hub配置
+  ©À©¤ascend310_infer                   # 用于310推理
  ©À©¤scripts
    ©À©¤run_standalone_train.sh         # 在Ascend中启动单机训练（1卡）
    ©À©¤run_distribute_train.sh         # 在Ascend中启动分布式训练（8卡）
+    ©À©¤run_infer_310.sh                # 在Ascend中启动310推理
    ©¸©¤run_eval.sh                     # 在Ascend中启动评估
  ©À©¤src
    ©À©¤__init__.py                     # Python初始化文件
@ -145,6 +150,8 @@ sh run_eval.sh dataset/xxx checkpoint/xxx.ckpt

  ©À©¤eval.py                           # 评估验证结果
  ©À©¤export.py                         # 将MindSpore模型转换为AIR模型
+  ©À©¤preprocess.py                     # 310推理前处理脚本
+  ©À©¤postprocess.py                    # 310推理后处理脚本
  ©¸©¤train.py                          # 训练网络
 ```

@ -306,14 +313,49 @@ sh run_eval.sh dataset/coco2017 checkpoint/yolov5.ckpt
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.674
 ```

-## [转换过程](#目录)
+## [推理过程](#目录)

-### 转换
+### 导出MindIR

-如果您想推断Ascend 310上的网络，则应将模型转换为AIR：
+```shell
+python export.py --ckpt_file [CKPT_PATH] --file_format [EXPORT_FORMAT] --batch_size [BATCH_SIZE]
+```

-```python
-python export.py [BATCH_SIZE] [PRETRAINED_BACKBONE]
+参数ckpt_file为必填项，
+`EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中选择。
+`BATCH_SIZE` 目前仅支持batch_size为1的推理。
+
+### 在Ascend310执行推理
+
+在执行推理前，mindir文件必须通过`export.py`脚本导出。以下展示了使用mindir模型执行推理的示例。
+
+```shell
+# Ascend310 inference
+bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANN_FILE] [DVPP] [DEVICE_ID]
+```
+
+- `ANN_FILE` Annotations 文件路径。
+- `DVPP` 为必填项，需要在["DVPP", "CPU"]选择，大小写均可。目前仅支持CPU算子推理。
+- `DEVICE_ID` 可选，默认值为0。
+
+### 结果
+
+推理结果保存在脚本执行的当前路径，你可以在acc.log中看到以下精度计算结果。
+
+```bash
+=============coco 310 infer reulst=========
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.369
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.571
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.398
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.216
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.421
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.487
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.301
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.502
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.558
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.388
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.617
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.677
 ```

 # [模型说明](#目录)
@ -355,6 +397,21 @@ YOLOv5应用于5000张图像上（标注和数据格式必须与COCO val 2017相
 |精度|map=36.8~37.2%（shape=640）|
 |推理模型| 58M（.ckpt文件）|

+### 310推理性能
+
+YOLOv5应用于5000张图像上（标注和数据格式必须与COCO val 2017相同）
+
+|参数| YOLOv5s |
+| -------------------------- | ----------------------------------------------------------- |
+| 资源                   | Ascend 310；CPU 2.60GHz，192核；内存：755G             |
+|上传日期| 2021年06月28日 |
+| MindSpore版本 | 1.2.0 |
+|数据集|5000张图像|
+|批处理大小|1|
+|输出|边框位置和分数，以及概率|
+|精度|map=36.9%（shape=640）|
+|推理模型| 58M（.ckpt文件）|
+
 # [随机情况说明](#目录)

 在dataset.py中，我们设置了“create_dataset”函数内的种子。