diff --git a/research/cv/ArtTrack/README_CN.md b/research/cv/ArtTrack/README_CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..176aa2f1fd2cd4885edbb0fe345bf0691065c48f
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+# 目录
+
+- [目录](#目录)
+- [ArtTrack描述](#arttrack描述)
+- [模型架构](#模型架构)
+- [数据集](#数据集)
+- [环境要求](#环境要求)
+- [快速入门](#快速入门)
+ - [MPII数据集](#mpii数据集)
+ - [单卡](#单卡)
+ - [多卡](#多卡)
+ - [推理与评估](#推理与评估)
+- [脚本说明](#脚本说明)
+ - [脚本及样例代码](#脚本及样例代码)
+ - [脚本参数](#脚本参数)
+ - [训练过程](#训练过程)
+ - [MPII数据集](#mpii数据集-1)
+ - [环境](#环境)
+ - [训练](#训练)
+ - [单卡](#单卡-1)
+ - [多卡](#多卡-1)
+ - [推理与评估](#推理与评估-1)
+ - [评估过程](#评估过程)
+ - [MPII数据集](#mpii数据集-2)
+ - [评估](#评估)
+ - [结果](#结果)
+- [模型描述](#模型描述)
+ - [性能](#性能)
+ - [评估性能](#评估性能)
+ - [MPII上的ArtTrack](#mpii上的arttrack)
+- [ModelZoo主页](#modelzoo主页)
+
+# ArtTrack描述
+
+ArtTrack是2017年提出的基于卷积网络的多人跟踪模型。现实的场景十分复杂，通常包括快速运动，外观和服装的大幅度变异，以及人与人之间的遮挡等因素。为了利用可用的图像信息，ArtTrack使用ResNet端到端地将身体关节和特定人的联系起来。并将这些关联合并到一个框架中，在时间维度上把关节（人）关联起来。为了提高视频推理的效率，采用了基于局部组合优化的快速推理方法，通过建立一个稀疏模型，使变量之间的连接数量保持在最小。
+
+[论文](https://arxiv.org/abs/1612.01465) ： Insafutdinov E, Andriluka M, Pishchulin L, et al. Arttrack: Articulated
+multi-person tracking in the wild[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.
+2017: 6457-6465.
+
+# 模型架构
+
+ArtTrack的总体网络架构如下：
+[链接](https://arxiv.org/abs/1612.01465)
+
+# 数据集
+
+使用的数据集：
+
+- [MPII](http://human-pose.mpi-inf.mpg.de/)
+ MPII人体姿态数据集是一种用于评估关节式人体姿态估计的基准。该数据集包含大约25000张图像，其中包含超过40000名带有标注的人体关节的人。这些图像是根据人类日常活动的分类系统收集的。总的来说，该数据集覆盖410个人类活动，每个图像都提供一个活动标签。
+
+解压后的MPII如图所示：
+
+```text
+mpii
+├── images
+│ ├── 099890470.jpg
+│ ├── 099894296.jpg
+│ ├── 099914957.jpg
+│ ├── 099920730.jpg
+│ ├── 099927653.jpg
+│ └── ....
+└── mpii_human_pose_v1_u12_1.mat
+```
+
+# 环境要求
+
+- 硬件（GPU）
+ - 使用GPU训练模型。
+- 框架
+ - [MindSpore](https://www.mindspore.cn/install/en)
+- 如需查看详情，请参见如下资源：
+ - [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
+ - [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
+
+# 快速入门
+
+通过官方网站安装MindSpore后，您可以按照如下步骤进行训练和评估：
+
+## MPII数据集
+
+### 单卡
+
+```bash
+# 用法 bash scripts/run_train_single_gpu.sh TARGET CONFIG_PATH [OPTION] ...
+bash scripts/run_train_single_gpu.sh mpii_single config/mpii_train_single_gpu.yaml
+```
+
+### 多卡
+
+```bash
+# 用法 bash scripts/run_train_multiple_gpu.sh TARGET CONFIG_PATH CUDA_VISIBLE_DEVICES DEVICE_NUM [OPTION] ...
+bash scripts/run_train_multiple_gpu.sh mpii_single config/mpii_train_multiple_gpu.yaml "0,1,2,3,4,5,6,7" 8
+```
+
+### 推理与评估
+
+```bash
+# 推理与评估
+# 用法 bash scripts/eval.sh TARGET CKPT_PATH OUTPUT_PATH
+# 根据实际情况替换ckpt文件
+bash scripts/eval.sh mpii_single ckpt/rand_0/arttrack-1_356.ckpt out/prediction.mat
+
+# 只推理
+python eval.py mpii_single --config config/mpii_eval.yaml --option "load_ckpt=ckpt/rank_0/arttrack-1_356.ckpt" --output "out/prediction.mat"
+
+# 只评估
+python eval.py mpii_single --config config/mpii_eval.yaml --accuracy --prediction "out/prediction.mat"
+```
+
+# 脚本说明
+
+## 脚本及样例代码
+
+```text
+ArtTrack
+├── config
+│ ├── coco_pairwise.yaml # coco数据集pairwise配置
+│ ├── coco_eval.yaml # coco数据集推理配置
+│ ├── coco_train_multiple_gpu.yaml # coco数据集多卡训练配置
+│ ├── coco_train_single_gpu.yaml # coco数据集单卡训练配置
+│ ├── mpii_eval.yaml # mpii数据集推理配置
+│ ├── mpii_train_multiple_gpu.yaml # mpii数据集多卡训练配置
+│ ├── mpii_train_single_gpu.yaml # mpii数据集单卡训练配置
+│ └── tf2ms.json # tf模型参数转mindspord映射表
+├── environment.yaml # conda环境
+├── log.yaml # log配置
+├── patch
+│ ├── 0001-fix-lib.patch # tf代码补丁
+│ ├── 0002-pybind11.patch # tf代码补丁
+│ └── 0003-split-dataset.patch # tf代码补丁
+├── preprocess.py # 预处理脚本
+├── README_CN.md # 模型相关说明
+├── requirements.txt # 依赖列表
+├── scripts
+│ ├── download.sh # 下载预训练和数据集
+│ ├── eval.sh # 推理与评估
+│ ├── prepare.sh # 环境预处理
+│ ├── run_train_multiple_gpu.sh # 多卡训练
+│ └── run_train_single_gpu.sh # 单卡训练
+├── src
+│ ├── __init__.py
+│ ├── tool
+│ │ ├── __init__.py
+│ │ ├── preprocess
+│ │ │ ├── __init__.py
+│ │ │ ├── crop.py # mpii图片放缩
+│ │ │ ├── mat2json.py # 由mat索引转为json
+│ │ │ ├── pairwise_stats.py # 生成pairwise
+│ │ │ ├── parts.json
+│ │ │ ├── preprocess_single.py # mpii 预处理
+│ │ │ ├── split.py # 数据集划分
+│ │ │ ├── tf2ms.py # tf ckpt转为mindspore模型
+│ │ │ └── utils.py # 预处理工具
+│ │ ├── eval
+│ │ │ ├── __init__.py
+│ │ │ ├── coco.py # coco评估
+│ │ │ ├── pck.py # mpii评估
+│ │ │ └── multiple.py # coco推理
+│ │ └── decorator.py
+│ ├── config.py # 配置相关
+│ ├── dataset
+│ │ ├── __init__.py
+│ │ ├── coco.py # coco数据集
+│ │ ├── mpii.py # mpii数据集
+│ │ ├── pose.py # 数据集加载
+│ │ └── util.py # 数据集工具
+│ ├── log.py # 日志配置
+│ ├── model
+│ │ ├── losses.py # 自定义loss
+│ │ ├── pose.py # 模型
+│ │ ├── predict.py # 推理
+│ │ └── resnet
+│ │ ├── __init__.py
+│ │ ├── resnet.py # ResNet
+│ │ └── util.py # ResNet工具
+│ ├── multiperson
+│ │ ├── detections.py # 目标发现
+│ │ ├── predict.py # 推理
+│ │ └── visualize.py # 可视化
+│ └── args_util.py # 命令行工具
+├── eval.py # 推理与评估
+└── train.py # 训练工具
+```
+
+## 脚本参数
+
+```yaml
+# 数据集
+dataset:
+ path: out/train_index_dataset.json
+ type: mpii_raw
+ parallel: 1
+ # need about 13G GPU memory
+ batch_size: 16
+ mirror: true
+ padding: yes
+ shuffle: yes
+
+# mindspore context
+context:
+ # GRAPH
+ # mode: 0
+
+ # PYNATIVE
+ mode: 1
+ device_target: GPU
+
+# mindspore parallel context
+# 若该字段存在使用数据并行
+parallel_context:
+ parallel_mode: data_parallel
+
+# 训练轮次
+epoch: 25
+# 是否是训练
+train: yes
+
+# 关键点数
+num_joints: 14
+
+# 数据集中关键点编号，与all_joints_names对应
+all_joints: [ [ 0, 5 ], [ 1, 4 ], [ 2, 3 ], [ 6, 11 ], [ 7, 10 ], [ 8, 9 ], [ 12 ], [ 13 ] ]
+# 关键点名称
+all_joints_names: [ 'ankle', 'knee', 'hip', 'wrist', 'elbow', 'shoulder', 'chin', 'forehead' ]
+
+# 评估阈值
+pck_threshold: 2
+
+# 数据集处理时热力图阈值
+pos_dist_thresh: 17
+# 全局放缩
+global_scale: 0.8452830189
+
+# 是否使用location refinement
+location_refinement: true
+# location refinement是否使用huber loss
+locref_huber_loss: true
+# huber loss权重
+locref_loss_weight: 0.05
+locref_stdev: 7.2801
+
+# 是否使用intermediate supervision
+intermediate_supervision: no
+# intermediate supervision 在第3个block的层数
+intermediate_supervision_layer: 12
+# intermediate supervision 的in channel
+intermediate_supervision_input: 1024
+
+# 限制图片大小
+max_input_size: 600
+
+# 学习率
+multi_step:
+ - [ 0.05,0.2,0.02,0.01 ]
+ - [ 500,2700,4600,6500 ]
+```
+
+更多配置细节请参考`config`目录下配置文件。
+
+## 训练过程
+
+### MPII数据集
+
+#### 环境
+
+```bash
+# conda 环境
+conda env create -n art-track python=3.7.5
+conda activate art-track
+pip install -r requeirments.txt
+
+# 下载ResNet101预训练模型
+bash scripts/download.sh pretrained_resnet101
+
+# 安装依赖，转换模型
+bash scripts/prepare.sh env
+
+# 下载mpii数据集
+bash scripts/download.sh dataset_mpii
+
+# mpii数据集预处理
+bash scripts/prepare.sh mpii
+```
+
+#### 训练
+
+##### 单卡
+
+```bash
+# 用法 bash scripts/run_train_single_gpu.sh TARGET CONFIG_PATH [OPTION] ...
+bash scripts/run_train_single_gpu.sh mpii_single config/mpii_train_single_gpu.yaml
+```
+
+##### 多卡
+
+```bash
+# 用法 bash scripts/run_train_multiple_gpu.sh TARGET CONFIG_PATH CUDA_VISIBLE_DEVICES DEVICE_NUM [OPTION] ...
+bash scripts/run_train_multiple_gpu.sh mpii_single config/mpii_train_multiple_gpu.yaml "0,1,2,3,4,5,6,7" 8
+```
+
+#### 推理与评估
+
+```bash
+# 推理与评估
+# 用法 bash scripts/eval.sh TARGET CKPT_PATH OUTPUT_PATH
+# 根据实际情况替换ckpt文件
+bash scripts/eval.sh mpii_single ckpt/rand_0/arttrack-1_356.ckpt out/prediction.mat
+
+# 推理
+python eval.py mpii_single --config config/mpii_eval.yaml --option "load_ckpt=ckpt/rank_0/arttrack-1_356.ckpt" --output "out/prediction.mat"
+
+# 评估
+python eval.py mpii_single --config config/mpii_eval.yaml --accuracy --prediction "out/prediction.mat"
+```
+
+## 评估过程
+
+### MPII数据集
+
+#### 评估
+
+```bash
+# 推理与评估
+# 用法 bash scripts/eval.sh TARGET CKPT_PATH OUTPUT_PATH
+# 根据实际情况替换ckpt文件
+bash scripts/eval.sh mpii_single ckpt/rand_0/arttrack-1_356.ckpt out/prediction.mat
+
+# 只推理
+python eval.py mpii_single --config config/mpii_eval.yaml --option "load_ckpt=ckpt/rank_0/arttrack-1_356.ckpt" --output "out/prediction.mat"
+
+# 只评估
+python eval.py mpii_single --config config/mpii_eval.yaml --accuracy --prediction "out/prediction.mat"
+```
+
+#### 结果
+
+```text
+& ankle & knee & hip & wrist & elbow & shoulder & chin & forehead & total
+& 66.0 & 70.9 & 74.7 & 65.5 & 72.4 & 83.4 & 87.0 & 84.2 & 74.1
+```
+
+# 模型描述
+
+## 性能
+
+### 评估性能
+
+#### MPII上的ArtTrack
+
+| 参数 | GPU V100 |
+| ------------- | --------------------------------------------------------------------------- |
+| 模型版本 | ArtTrack |
+| 资源 | Telsa GPU V100 |
+| 上传日期 | 2022-01-11 |
+| MindSpore版本 | 1.5.0 |
+| 数据集 | MPII |
+| 训练参数 | epoch=25, steps per epoch=356, batch_size=16 |
+| 优化器 | SGD |
+| 损失函数 | SigmoidCrossEntropyWithLogits |
+| 输出 | 关键点坐标 |
+| 损失 | 0.016214851 |
+| 速度 | 1292.854毫秒/步(8卡) |
+| 总时长 | 3.2小时 |
+| 微调检查点 | 496M (.ckpt文件) |
+| 脚本 | [链接](https://gitee.com/mindspore/models/tree/master/research/cv/ArtTrack) |
+
+# ModelZoo主页
+
+请浏览官网[主页](https://gitee.com/mindspore/models)。
diff --git a/research/cv/ArtTrack/config/coco_eval.yaml b/research/cv/ArtTrack/config/coco_eval.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e53c52d35efa755d68ce242de18b8b07f90c28c9
--- /dev/null
+++ b/research/cv/ArtTrack/config/coco_eval.yaml
@@ -0,0 +1,45 @@
+dataset:
+ path: ./coco
+ type: coco
+ parallel: 1
+ phase: val2014
+ ann: person_keypoints # 'image_info' or 'person_keypoints'
+load_ckpt: out/tf2ms.ckpt
+coco_only_images_with_people: true
+
+context:
+ device_target: GPU
+ mode: 1
+
+all_joints: [ [ 0 ], [ 2, 1 ], [ 4, 3 ], [ 6, 5 ], [ 8, 7 ], [ 10, 9 ], [ 12, 11 ], [ 14, 13 ], [ 16, 15 ] ]
+all_joints_names: [ "nose", 'eye', 'ear', 'shoulder', 'elbow', 'hand', 'hip', 'knee', 'foot' ]
+num_joints: 17
+
+global_scale: 1.0
+pos_dist_thresh: 17
+
+location_refinement: true
+locref_huber_loss: true
+locref_loss_weight: 0.05
+locref_stdev: 7.2801
+
+pairwise_predict: true
+pairwise_huber_loss: true
+pairwise_loss_weight: 0.05
+pairwise_stats_fn: out/pairwise_stats.mat
+pairwise_model_dir: out/pairwise
+
+intermediate_supervision: true
+intermediate_supervision_layer: 12
+
+max_input_size: 650
+multi_step:
+ - [ 0.005,0.02,0.002,0.0005,0.0002,0.00005 ]
+ - [ 10000,450000,750000,1050000,1550000,1800000 ]
+display_iters: 20
+save_iters: 200000
+
+nms_radius: 5.0
+det_min_score: 0.2
+use_gt_segm: yes
+gt_segm_output: out/predictions_with_segm.json
diff --git a/research/cv/ArtTrack/config/coco_pairwise.yaml b/research/cv/ArtTrack/config/coco_pairwise.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9d31899c999629c20af945f7a6dce464edc3b6e2
--- /dev/null
+++ b/research/cv/ArtTrack/config/coco_pairwise.yaml
@@ -0,0 +1,30 @@
+dataset:
+ path: ./coco
+ type: coco
+ parallel: 1
+ phase: train2014
+ ann: person_keypoints # 'image_info' or 'person_keypoints'
+
+coco_only_images_with_people: true
+
+all_joints: [ [ 0 ], [ 2, 1 ], [ 4, 3 ], [ 6, 5 ], [ 8, 7 ], [ 10, 9 ], [ 12, 11 ], [ 14, 13 ], [ 16, 15 ] ]
+all_joints_names: [ "nose", 'eye', 'ear', 'shoulder', 'elbow', 'hand', 'hip', 'knee', 'foot' ]
+num_joints: 17
+global_scale: 1.0
+pos_dist_thresh: 17
+
+
+location_refinement: true
+locref_huber_loss: true
+locref_loss_weight: 0.05
+locref_stdev: 7.2801
+
+pairwise_predict: no
+pairwise_huber_loss: true
+pairwise_loss_weight: 0.05
+pairwise_stats_fn: out/pairwise_stats.mat
+
+intermediate_supervision: true
+intermediate_supervision_layer: 12
+
+max_input_size: 650
diff --git a/research/cv/ArtTrack/config/coco_train_multiple_gpu.yaml b/research/cv/ArtTrack/config/coco_train_multiple_gpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fb7b1ba00b2db8e2bf2404b2c15771049a5ac075
--- /dev/null
+++ b/research/cv/ArtTrack/config/coco_train_multiple_gpu.yaml
@@ -0,0 +1,50 @@
+dataset:
+ path: ./coco
+ type: coco
+ parallel: 4
+ # need about 24G GPU memory
+ batch_size: 16
+ phase: train2014
+ ann: person_keypoints # 'image_info' or 'person_keypoints'
+ padding: yes
+ mirror: yes
+load_ckpt: out/pretrained_resnet101.ckpt
+epoch: 25
+train: yes
+
+context:
+ # GRAPH
+ # mode: 0
+
+ # PYNATIVE
+ mode: 1
+ device_target: GPU
+
+parallel_context:
+ parallel_mode: data_parallel
+
+coco_only_images_with_people: true
+all_joints: [ [ 0 ], [ 2, 1 ], [ 4, 3 ], [ 6, 5 ], [ 8, 7 ], [ 10, 9 ], [ 12, 11 ], [ 14, 13 ], [ 16, 15 ] ]
+all_joints_names: [ "nose", 'eye', 'ear', 'shoulder', 'elbow', 'hand', 'hip', 'knee', 'foot' ]
+num_joints: 17
+global_scale: 1.0
+pos_dist_thresh: 17
+
+location_refinement: true
+locref_huber_loss: true
+locref_loss_weight: 0.05
+locref_stdev: 7.2801
+
+pairwise_predict: yes
+pairwise_huber_loss: true
+pairwise_loss_weight: 0.05
+pairwise_stats_fn: out/pairwise_stats.mat
+
+intermediate_supervision: true
+intermediate_supervision_layer: 12
+intermediate_supervision_input: 1024
+
+max_input_size: 650
+multi_step:
+ - [ 0.05,0.2,0.02,0.005,0.002,0.0005 ]
+ - [ 32,1400,2500,3200,5000, 6000 ]
\ No newline at end of file
diff --git a/research/cv/ArtTrack/config/coco_train_single_gpu.yaml b/research/cv/ArtTrack/config/coco_train_single_gpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1c6b7c28240fad85d83975fe53e39d12cd2bf08c
--- /dev/null
+++ b/research/cv/ArtTrack/config/coco_train_single_gpu.yaml
@@ -0,0 +1,48 @@
+dataset:
+ path: ./coco
+ type: coco
+ parallel: 4
+ # need about 24G GPU memory
+ batch_size: 16
+ phase: train2014
+ ann: person_keypoints # 'image_info' or 'person_keypoints'
+ padding: yes
+ mirror: yes
+load_ckpt: out/pretrained_resnet101.ckpt
+epoch: 25
+train: yes
+
+context:
+ # GRAPH
+ # mode: 0
+
+ # PYNATIVE
+ mode: 1
+ device_target: GPU
+coco_only_images_with_people: true
+all_joints: [ [ 0 ], [ 2, 1 ], [ 4, 3 ], [ 6, 5 ], [ 8, 7 ], [ 10, 9 ], [ 12, 11 ], [ 14, 13 ], [ 16, 15 ] ]
+all_joints_names: [ "nose", 'eye', 'ear', 'shoulder', 'elbow', 'hand', 'hip', 'knee', 'foot' ]
+num_joints: 17
+global_scale: 1.0
+pos_dist_thresh: 17
+#scale_jitter_lo: 0.85
+scale_jitter_up: 1.15
+
+location_refinement: true
+locref_huber_loss: true
+locref_loss_weight: 0.05
+locref_stdev: 7.2801
+
+pairwise_predict: yes
+pairwise_huber_loss: true
+pairwise_loss_weight: 0.05
+pairwise_stats_fn: out/pairwise_stats.mat
+
+intermediate_supervision: true
+intermediate_supervision_layer: 12
+intermediate_supervision_input: 1024
+
+max_input_size: 650
+multi_step:
+ - [ 0.05,0.2,0.02,0.005,0.002,0.0005 ]
+ - [ 500,22500,37500,52500,77500,90000 ]
\ No newline at end of file
diff --git a/research/cv/ArtTrack/config/mpii_eval.yaml b/research/cv/ArtTrack/config/mpii_eval.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..16117f7ccdfe40d67c859a8c4ccde7ee86a0e14b
--- /dev/null
+++ b/research/cv/ArtTrack/config/mpii_eval.yaml
@@ -0,0 +1,32 @@
+dataset:
+ path: out/test_index_dataset.json
+ type: mpii_raw
+ parallel: 1
+ batch_size: 1
+ mirror: no
+ padding: no
+ shuffle: no
+train: no
+output: out/prediction.mat
+pck_threshold: 0.5
+
+num_joints: 14
+all_joints: [ [ 0, 5 ], [ 1, 4 ], [ 2, 3 ], [ 6, 11 ], [ 7, 10 ], [ 8, 9 ], [ 12 ], [ 13 ] ]
+all_joints_names: [ 'ankle', 'knee', 'hip', 'wrist', 'elbow', 'shoulder', 'chin', 'forehead' ]
+
+pos_dist_thresh: 17
+global_scale: 0.8452830189
+
+location_refinement: true
+locref_huber_loss: true
+locref_loss_weight: 0.05
+locref_stdev: 7.2801
+
+intermediate_supervision: no
+intermediate_supervision_layer: 12
+
+max_input_size: 600
+multi_step:
+ - [ 0.005,0.02,0.002,0.001 ]
+ - [ 10000,430000,730000,1030000 ]
+
diff --git a/research/cv/ArtTrack/config/mpii_train_multiple_gpu.yaml b/research/cv/ArtTrack/config/mpii_train_multiple_gpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..05298eced807a82ec08a437cb4b7bce8c4eeb910
--- /dev/null
+++ b/research/cv/ArtTrack/config/mpii_train_multiple_gpu.yaml
@@ -0,0 +1,48 @@
+dataset:
+ path: out/train_index_dataset.json
+ type: mpii_raw
+ parallel: 1
+ # need about 13G GPU memory
+ batch_size: 16
+ mirror: true
+ padding: yes
+ shuffle: yes
+load_ckpt: out/pretrained_resnet101.ckpt
+context:
+ # GRAPH
+ # mode: 0
+
+ # PYNATIVE
+ mode: 1
+ device_target: GPU
+
+parallel_context:
+ parallel_mode: data_parallel
+
+epoch: 25
+train: yes
+
+num_joints: 14
+all_joints: [ [ 0, 5 ], [ 1, 4 ], [ 2, 3 ], [ 6, 11 ], [ 7, 10 ], [ 8, 9 ], [ 12 ], [ 13 ] ]
+all_joints_names: [ 'ankle', 'knee', 'hip', 'wrist', 'elbow', 'shoulder', 'chin', 'forehead' ]
+
+pck_threshold: 2
+
+
+pos_dist_thresh: 17
+global_scale: 0.8452830189
+
+location_refinement: true
+locref_huber_loss: true
+locref_loss_weight: 0.05
+locref_stdev: 7.2801
+
+intermediate_supervision: no
+intermediate_supervision_layer: 12
+intermediate_supervision_input: 1024
+
+max_input_size: 600
+multi_step:
+ - [ 0.05,0.2,0.02,0.01 ]
+ - [ 500,2700,4600,6500 ]
+
diff --git a/research/cv/ArtTrack/config/mpii_train_single_gpu.yaml b/research/cv/ArtTrack/config/mpii_train_single_gpu.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6536b3a35a476472b6ef4b1497c4f4d50c87b819
--- /dev/null
+++ b/research/cv/ArtTrack/config/mpii_train_single_gpu.yaml
@@ -0,0 +1,46 @@
+dataset:
+ path: out/train_index_dataset.json
+ type: mpii_raw
+ parallel: 1
+ # need about 13G GPU memory
+ batch_size: 16
+ mirror: true
+ padding: yes
+ shuffle: yes
+load_ckpt: out/pretrained_resnet101.ckpt
+context:
+ # GRAPH
+ # mode: 0
+
+ # PYNATIVE
+ mode: 1
+ device_target: GPU
+
+
+epoch: 25
+train: yes
+
+num_joints: 14
+all_joints: [ [ 0, 5 ], [ 1, 4 ], [ 2, 3 ], [ 6, 11 ], [ 7, 10 ], [ 8, 9 ], [ 12 ], [ 13 ] ]
+all_joints_names: [ 'ankle', 'knee', 'hip', 'wrist', 'elbow', 'shoulder', 'chin', 'forehead' ]
+
+pck_threshold: 2
+
+
+pos_dist_thresh: 17
+global_scale: 0.8452830189
+
+location_refinement: true
+locref_huber_loss: true
+locref_loss_weight: 0.05
+locref_stdev: 7.2801
+
+intermediate_supervision: no
+intermediate_supervision_layer: 12
+intermediate_supervision_input: 1024
+
+max_input_size: 600
+multi_step:
+ - [ 0.05,0.2,0.02,0.01 ]
+ - [ 500,21500,36500,51500 ]
+
diff --git a/research/cv/ArtTrack/config/tf2ms.json b/research/cv/ArtTrack/config/tf2ms.json
new file mode 100644
index 0000000000000000000000000000000000000000..cecf7ecbee026d97235486e2c5829905b181a125
--- /dev/null
+++ b/research/cv/ArtTrack/config/tf2ms.json
@@ -0,0 +1,530 @@
+{
+ "pose/intermediate_supervision/block4/biases": "net.part_pred_interm.conv2d_transpose.bias",
+ "pose/intermediate_supervision/block4/weights": "net.part_pred_interm.conv2d_transpose.weight",
+ "pose/locref_pred/block4/biases": "net.locref.conv2d_transpose.bias",
+ "pose/locref_pred/block4/weights": "net.locref.conv2d_transpose.weight",
+ "pose/pairwise_pred/block4/biases": "net.pairwise_pred.conv2d_transpose.bias",
+ "pose/pairwise_pred/block4/weights": "net.pairwise_pred.conv2d_transpose.weight",
+ "pose/part_pred/block4/biases": "net.part_pred.conv2d_transpose.bias",
+ "pose/part_pred/block4/weights": "net.part_pred.conv2d_transpose.weight",
+ "resnet_v1_101/conv1/BatchNorm/gamma": "net.resnet101.conv2d_same.conv2d.1.batchnorm.gamma",
+ "resnet_v1_101/conv1/BatchNorm/beta": "net.resnet101.conv2d_same.conv2d.1.batchnorm.beta",
+ "resnet_v1_101/conv1/BatchNorm/moving_mean": "net.resnet101.conv2d_same.conv2d.1.batchnorm.moving_mean",
+ "resnet_v1_101/conv1/BatchNorm/moving_variance": "net.resnet101.conv2d_same.conv2d.1.batchnorm.moving_variance",
+ "resnet_v1_101/conv1/weights": "net.resnet101.conv2d_same.conv2d.1.conv.weight",
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+ "resnet_v1_101/block1/unit_1/bottleneck_v1/conv1/weights": "net.resnet101.layer.blocks_cell.0.units_cells.0.conv2d1.conv.weight",
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+ "resnet_v1_101/block1/unit_1/bottleneck_v1/conv3/weights": "net.resnet101.layer.blocks_cell.0.units_cells.0.conv2d3.conv.weight",
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+ "resnet_v1_101/block3/unit_22/bottleneck_v1/conv2/BatchNorm/beta": "net.resnet101.layer.blocks_cell.2.units_cells.21.conv2d2.conv2d.batchnorm.beta",
+ "resnet_v1_101/block3/unit_22/bottleneck_v1/conv2/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.2.units_cells.21.conv2d2.conv2d.batchnorm.gamma",
+ "resnet_v1_101/block3/unit_22/bottleneck_v1/conv2/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.2.units_cells.21.conv2d2.conv2d.batchnorm.moving_mean",
+ "resnet_v1_101/block3/unit_22/bottleneck_v1/conv2/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.2.units_cells.21.conv2d2.conv2d.batchnorm.moving_variance",
+ "resnet_v1_101/block3/unit_22/bottleneck_v1/conv2/weights": "net.resnet101.layer.blocks_cell.2.units_cells.21.conv2d2.conv2d.conv.weight",
+ "resnet_v1_101/block3/unit_22/bottleneck_v1/conv3/BatchNorm/beta": "net.resnet101.layer.blocks_cell.2.units_cells.21.conv2d3.batchnorm.beta",
+ "resnet_v1_101/block3/unit_22/bottleneck_v1/conv3/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.2.units_cells.21.conv2d3.batchnorm.gamma",
+ "resnet_v1_101/block3/unit_22/bottleneck_v1/conv3/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.2.units_cells.21.conv2d3.batchnorm.moving_mean",
+ "resnet_v1_101/block3/unit_22/bottleneck_v1/conv3/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.2.units_cells.21.conv2d3.batchnorm.moving_variance",
+ "resnet_v1_101/block3/unit_22/bottleneck_v1/conv3/weights": "net.resnet101.layer.blocks_cell.2.units_cells.21.conv2d3.conv.weight",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv1/BatchNorm/beta": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d1.batchnorm.beta",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv1/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d1.batchnorm.gamma",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv1/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d1.batchnorm.moving_mean",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv1/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d1.batchnorm.moving_variance",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv1/weights": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d1.conv.weight",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv2/BatchNorm/beta": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d2.conv2d.batchnorm.beta",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv2/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d2.conv2d.batchnorm.gamma",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv2/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d2.conv2d.batchnorm.moving_mean",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv2/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d2.conv2d.batchnorm.moving_variance",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv2/weights": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d2.conv2d.conv.weight",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv3/BatchNorm/beta": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d3.batchnorm.beta",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv3/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d3.batchnorm.gamma",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv3/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d3.batchnorm.moving_mean",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv3/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d3.batchnorm.moving_variance",
+ "resnet_v1_101/block3/unit_23/bottleneck_v1/conv3/weights": "net.resnet101.layer.blocks_cell.2.units_cells.22.conv2d3.conv.weight",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv1/BatchNorm/beta": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d1.batchnorm.beta",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv1/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d1.batchnorm.gamma",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv1/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d1.batchnorm.moving_mean",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv1/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d1.batchnorm.moving_variance",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv1/weights": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d1.conv.weight",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv2/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d2.conv2d.batchnorm.moving_variance",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv2/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d2.conv2d.batchnorm.moving_mean",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv2/BatchNorm/beta": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d2.conv2d.batchnorm.beta",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv2/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d2.conv2d.batchnorm.gamma",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv2/weights": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d2.conv2d.conv.weight",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv3/BatchNorm/beta": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d3.batchnorm.beta",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv3/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d3.batchnorm.gamma",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv3/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d3.batchnorm.moving_mean",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv3/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d3.batchnorm.moving_variance",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/conv3/weights": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d3.conv.weight",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/shortcut/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d_shortcut.batchnorm.gamma",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/shortcut/BatchNorm/beta": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d_shortcut.batchnorm.beta",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/shortcut/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d_shortcut.batchnorm.moving_mean",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/shortcut/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d_shortcut.batchnorm.moving_variance",
+ "resnet_v1_101/block4/unit_1/bottleneck_v1/shortcut/weights": "net.resnet101.layer.blocks_cell.3.units_cells.0.conv2d_shortcut.conv.weight",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv1/BatchNorm/beta": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d1.batchnorm.beta",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv1/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d1.batchnorm.gamma",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv1/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d1.batchnorm.moving_mean",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv1/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d1.batchnorm.moving_variance",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv1/weights": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d1.conv.weight",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv2/BatchNorm/beta": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d2.conv2d.batchnorm.beta",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv2/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d2.conv2d.batchnorm.gamma",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv2/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d2.conv2d.batchnorm.moving_mean",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv2/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d2.conv2d.batchnorm.moving_variance",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv2/weights": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d2.conv2d.conv.weight",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv3/BatchNorm/beta": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d3.batchnorm.beta",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv3/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d3.batchnorm.gamma",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv3/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d3.batchnorm.moving_mean",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv3/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d3.batchnorm.moving_variance",
+ "resnet_v1_101/block4/unit_2/bottleneck_v1/conv3/weights": "net.resnet101.layer.blocks_cell.3.units_cells.1.conv2d3.conv.weight",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv1/BatchNorm/beta": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d1.batchnorm.beta",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv1/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d1.batchnorm.gamma",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv1/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d1.batchnorm.moving_mean",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv1/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d1.batchnorm.moving_variance",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv1/weights": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d1.conv.weight",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/BatchNorm/beta": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d2.conv2d.batchnorm.beta",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d2.conv2d.batchnorm.gamma",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d2.conv2d.batchnorm.moving_mean",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d2.conv2d.batchnorm.moving_variance",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv2/weights": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d2.conv2d.conv.weight",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv3/BatchNorm/beta": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d3.batchnorm.beta",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv3/BatchNorm/gamma": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d3.batchnorm.gamma",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv3/BatchNorm/moving_mean": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d3.batchnorm.moving_mean",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv3/BatchNorm/moving_variance": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d3.batchnorm.moving_variance",
+ "resnet_v1_101/block4/unit_3/bottleneck_v1/conv3/weights": "net.resnet101.layer.blocks_cell.3.units_cells.2.conv2d3.conv.weight"
+}
diff --git a/research/cv/ArtTrack/eval.py b/research/cv/ArtTrack/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..f01457818bf81bd1262afba3255d6d0bf53a9a6f
--- /dev/null
+++ b/research/cv/ArtTrack/eval.py
@@ -0,0 +1,173 @@
+# Copyright 2022 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.
+# ============================================================================
+import os
+import sys
+
+import matplotlib.pyplot as plt
+import mindspore as ms
+from mindspore import context as ctx
+import numpy as np
+from scipy import io as sio
+
+from src.args_util import command, create_arg_parser, TARGET_COCO_MULTI, TARGET_MPII_SINGLE
+from src.dataset.mpii import MPII
+from src.dataset.pose import Batch
+from src.log import log
+from src.model.pose import PoseNet, PoseNetTest
+from src.model.predict import argmax_pose_predict, extract_cnn_output
+from src.multiperson.visualize import show_heatmaps
+from src.tool.decorator import process_cfg
+from src.tool.eval.pck import enclosing_rect, print_results, rect_size
+
+
+@command
+def test(parser, args, cfg):
+ if args.target == TARGET_MPII_SINGLE:
+ if args.accuracy:
+ eval_mpii(cfg, args.prediction or args.output)
+ return
+ predict_mpii(cfg, args.visual, args.cache, args.output)
+ elif args.target == TARGET_COCO_MULTI:
+ if args.accuracy:
+ from src.tool.eval.coco import eval_coco
+ eval_coco(cfg, args.prediction)
+ return
+ from src.tool.eval.multiple import test as multiple_test
+ multiple_test(cfg, args.cache, args.visual, args.dev,
+ args.score_maps_cached, args.graph, args.output, args.range_num, args.range_index)
+ else:
+ parser.print_help()
+
+
+@process_cfg
+def predict_mpii(cfg=None, visual=False, cache=False, output=None):
+ """
+ entry for predicting single mpii
+ Args:
+ cfg: config
+ visual: if True, visualize prediction
+ cache: if True, cache score map
+ output: path to output
+ """
+ cfg.train = False
+ ctx.set_context(**cfg.context)
+ out_dir = cfg.scoremap_dir
+ if cache:
+ if not os.path.exists(out_dir):
+ os.makedirs(out_dir)
+ dataset = MPII(cfg)
+ dataset.set_mirror(False)
+
+ net = PoseNet(cfg=cfg)
+ test_net = PoseNetTest(net, cfg)
+
+ if hasattr(cfg, 'load_ckpt') and os.path.exists(cfg.load_ckpt):
+ ms.load_checkpoint(cfg.load_ckpt, net=test_net)
+
+ num_images = len(dataset)
+ predictions = np.zeros((num_images,), dtype=np.object)
+
+ for i in range(num_images):
+ log.info('processing image %s/%s', i, num_images - 1)
+ batch = dataset.get_item(i)
+ o = test_net(
+ ms.Tensor(np.expand_dims(batch[Batch.inputs], axis=0),
+ dtype=ms.dtype.float32),
+ )
+ out = o[0].transpose([0, 2, 3, 1]).asnumpy()
+ locref = o[1].transpose([0, 2, 3, 1]).asnumpy() if o[1] is not None else None
+ pairwise_pred = o[2].transpose([0, 2, 3, 1]).asnumpy() if o[2] is not None else None
+ scmap, locref, _ = extract_cnn_output(out, locref, pairwise_pred,
+ cfg)
+ pose = argmax_pose_predict(scmap, locref, cfg.stride)
+
+ pose_refscale = np.copy(pose)
+ pose_refscale[:, 0:2] /= cfg.global_scale
+ predictions[i] = pose_refscale
+
+ if visual:
+ img = np.transpose(np.squeeze(batch[Batch.inputs]).astype('uint8'), [1, 2, 0])
+ show_heatmaps(cfg, img, scmap, pose)
+ plt.waitforbuttonpress(timeout=1)
+ plt.close()
+
+ if cache:
+ base = os.path.basename(batch[Batch.data_item].im_path).decode()
+ raw_name = os.path.splitext(base)[0]
+ out_fn = os.path.join(out_dir, raw_name + '.mat')
+ sio.savemat(out_fn, mdict={'scoremaps': scmap.astype('float32')})
+
+ out_fn = os.path.join(out_dir, raw_name + '_locreg' + '.mat')
+ if cfg.location_refinement:
+ sio.savemat(out_fn, mdict={'locreg_pred': locref.astype('float32')})
+
+ sio.savemat(output or cfg.output, mdict={'joints': predictions})
+
+
+@process_cfg
+def eval_mpii(cfg=None, prediction=None):
+ """
+ eval mpii entry
+ """
+ dataset = MPII(cfg)
+ filename = prediction or cfg.output
+ pred = sio.loadmat(filename)
+
+ joints = pred['joints']
+ pck_ratio_thresh = cfg.pck_threshold
+
+ num_joints = cfg.num_joints
+ num_images = joints.shape[1]
+
+ pred_joints = np.zeros((num_images, num_joints, 2))
+ gt_joints = np.zeros((num_images, num_joints, 2))
+ pck_thresh = np.zeros((num_images, 1))
+ gt_present_joints = np.zeros((num_images, num_joints))
+
+ for k in range(num_images):
+ pred = joints[0, k]
+ gt = dataset.data[k].joints[0]
+ if gt.shape[0] == 0:
+ continue
+ gt_joint_ids = gt[:, 0].astype('int32')
+ rect = enclosing_rect(gt[:, 1:3])
+ pck_thresh[k] = pck_ratio_thresh * np.amax(rect_size(rect))
+
+ gt_present_joints[k, gt_joint_ids] = 1
+ gt_joints[k, gt_joint_ids, :] = gt[:, 1:3]
+ pred_joints[k, :, :] = pred[:, 0:2]
+
+ dists = np.sqrt(np.sum((pred_joints - gt_joints) ** 2, axis=2))
+ correct = dists <= pck_thresh
+
+ num_all = np.sum(gt_present_joints, axis=0)
+
+ num_correct = np.zeros((num_joints,))
+ for j_id in range(num_joints):
+ num_correct[j_id] = np.sum(correct[gt_present_joints[:, j_id] == 1, j_id], axis=0)
+
+ pck = num_correct / num_all * 100.0
+
+ print_results(pck, cfg)
+
+
+def main():
+ parser = create_arg_parser()['eval']
+ args = parser.parse_args(sys.argv[1:])
+ test(parser, args)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/research/cv/ArtTrack/log.yaml b/research/cv/ArtTrack/log.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e2d9f1e9ea4354c054fb1148dc39913d9469652f
--- /dev/null
+++ b/research/cv/ArtTrack/log.yaml
@@ -0,0 +1,40 @@
+version: 1
+disable_existing_loggers: False
+
+formatters:
+ simple:
+ format: "%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(threadName)s - %(message)s"
+handlers:
+ console:
+ class: logging.StreamHandler
+ level: DEBUG
+ formatter: simple
+ stream: ext://sys.stdout
+
+ debug_file_handler:
+ class: logging.handlers.RotatingFileHandler
+ level: DEBUG
+ formatter: simple
+ filename: ./log/debug.log
+ maxBytes: 10485760 # 10MB
+ backupCount: 20
+ encoding: utf8
+ delay: yes
+
+ error_file_handler:
+ class: logging.handlers.RotatingFileHandler
+ level: ERROR
+ formatter: simple
+ filename: ./log/error.log
+ maxBytes: 10485760 # 10MB
+ backupCount: 20
+ encoding: utf8
+ delay: yes
+loggers:
+ ArtTrack:
+ level: INFO
+ handlers: [ console ]
+ propagate: False
+root:
+ level: INFO
+ handlers: [ console ]
diff --git a/research/cv/ArtTrack/patch/0001-fix-lib.patch b/research/cv/ArtTrack/patch/0001-fix-lib.patch
new file mode 100644
index 0000000000000000000000000000000000000000..fe713cea0dd8ee4b6afb9016f709d5c8fc5ac69c
--- /dev/null
+++ b/research/cv/ArtTrack/patch/0001-fix-lib.patch
@@ -0,0 +1,59 @@
+From d4cef68e789363498e4035b7ca47e64f3ebef6b6 Mon Sep 17 00:00:00 2001
+From: yiguangzheng <78542727@qq.com>
+Date: Fri, 5 Nov 2021 12:55:06 +0800
+Subject: [PATCH 1/2] fix lib
+
+---
+ .../andres/graph/multicut-lifted/greedy-additive.hxx | 7 +++++++
+ lib/multicut_cython/solve_nl_lmp.hxx | 3 +++
+ lib/nms_cython/include/nms_scoremap.hxx | 2 ++
+ 3 files changed, 12 insertions(+)
+
+diff --git a/lib/multicut_cython/include/andres/graph/multicut-lifted/greedy-additive.hxx b/lib/multicut_cython/include/andres/graph/multicut-lifted/greedy-additive.hxx
+index 03df92b..3efbda1 100755
+--- a/lib/multicut_cython/include/andres/graph/multicut-lifted/greedy-additive.hxx
++++ b/lib/multicut_cython/include/andres/graph/multicut-lifted/greedy-additive.hxx
+@@ -8,6 +8,13 @@
+ #include <algorithm>
+ #include <map>
+ #include <queue>
++/*
++GCC 11 started to explicitly require including <limits>, <memory>, <utility>, <thread>
++according to https://www.gnu.org/software/gcc/gcc-11/porting_to.html#header-dep-changes
++
++https://stackoverflow.com/questions/4798936/numeric-limits-was-not-declared-in-this-scope-no-matching-function-for-call-t
++*/
++#include <limits>
+
+ #include "andres/partition.hxx"
+
+diff --git a/lib/multicut_cython/solve_nl_lmp.hxx b/lib/multicut_cython/solve_nl_lmp.hxx
+index 4113d89..2b18aaa 100644
+--- a/lib/multicut_cython/solve_nl_lmp.hxx
++++ b/lib/multicut_cython/solve_nl_lmp.hxx
+@@ -9,6 +9,9 @@
+
+ #include <iostream>
+
++#include <array>
++
++
+ using namespace std;
+
+ template <typename T>
+diff --git a/lib/nms_cython/include/nms_scoremap.hxx b/lib/nms_cython/include/nms_scoremap.hxx
+index f0bdb67..1128728 100755
+--- a/lib/nms_cython/include/nms_scoremap.hxx
++++ b/lib/nms_cython/include/nms_scoremap.hxx
+@@ -8,6 +8,8 @@
+
+ #include <iostream>
+
++#include <array>
++
+ // namespace bp = boost::python;
+
+ using namespace std;
+--
+2.32.0.windows.1
+
diff --git a/research/cv/ArtTrack/patch/0002-pybind11.patch b/research/cv/ArtTrack/patch/0002-pybind11.patch
new file mode 100644
index 0000000000000000000000000000000000000000..77c08875757f650738959e1fc34fc2757e7fbd91
--- /dev/null
+++ b/research/cv/ArtTrack/patch/0002-pybind11.patch
@@ -0,0 +1,165 @@
+From dd3e9d7203a49b25c3acc199c314794dd998f48b Mon Sep 17 00:00:00 2001
+From: yiguangzheng <78542727@qq.com>
+Date: Sat, 6 Nov 2021 14:37:26 +0800
+Subject: [PATCH 2/2] pybind11
+
+---
+ lib/multicut_cython/multicut.cxx | 45 +++++++++++++++++++++++++++
+ lib/multicut_cython/setup_pybind11.py | 13 ++++++++
+ lib/nms_cython/nms_grid.cxx | 38 ++++++++++++++++++++++
+ lib/nms_cython/setup_pybind11.py | 22 +++++++++++++
+ 4 files changed, 118 insertions(+)
+ create mode 100644 lib/multicut_cython/multicut.cxx
+ create mode 100644 lib/multicut_cython/setup_pybind11.py
+ create mode 100644 lib/nms_cython/nms_grid.cxx
+ create mode 100644 lib/nms_cython/setup_pybind11.py
+
+diff --git a/lib/multicut_cython/multicut.cxx b/lib/multicut_cython/multicut.cxx
+new file mode 100644
+index 0000000..03d52af
+--- /dev/null
++++ b/lib/multicut_cython/multicut.cxx
+@@ -0,0 +1,45 @@
++#include <pybind11/pybind11.h>
++#include <pybind11/numpy.h>
++#include <iostream>
++#include "solve_nl_lmp.hxx"
++
++namespace py = pybind11;
++
++py::array_t<uint64_t> solve_nl_lmp(py::array_t<double>& unary_array,
++ py::array_t<uint16_t>& pwidx_array,
++ py::array_t<double>& pw_array,
++ bool is_sparse_graph,
++ bool solver_type,
++ bool do_suppression,
++ bool logit_in_solver
++ ) {
++ py::buffer_info unary_array_buf = unary_array.request();
++ py::buffer_info pwidx_array_buf = pwidx_array.request();
++ py::buffer_info pw_array_buf = pw_array.request();
++
++ if (unary_array_buf.ndim != 2 || pwidx_array_buf.ndim != 2 || pw_array_buf.ndim != 2)
++ {
++ throw std::runtime_error("numpy.ndarray dims must be 2!");
++ }
++ //申请空间
++ auto result = py::array_t<uint64_t>(unary_array_buf.shape[0] * 2);
++ result.resize(py::array::ShapeContainer({unary_array_buf.shape[0], 2}));
++ py::buffer_info result_buf = result.request();
++
++ //获取numpy.ndarray 数据指针
++ double* unary_array_ptr = static_cast<double*>(unary_array_buf.ptr);
++ uint16_t* pwidx_array_ptr = static_cast<uint16_t*>(pwidx_array_buf.ptr);
++ double* pw_array_ptr = static_cast<double*>(pw_array_buf.ptr);
++ uint64_t* result_ptr = static_cast<uint64_t*>(result_buf.ptr);
++
++ solve_nl_lmp_cpp(unary_array_ptr, unary_array_buf.shape[0], unary_array_buf.shape[1],
++ pwidx_array_ptr, pwidx_array_buf.shape[0], pwidx_array_buf.shape[1],
++ pw_array_ptr, pw_array_buf.shape[0], pw_array_buf.shape[1],
++ is_sparse_graph, solver_type, do_suppression, logit_in_solver,
++ result_ptr);
++
++ return result;
++}
++PYBIND11_MODULE(multicut, m) {
++ m.def("solve_nl_lmp", &solve_nl_lmp);
++}
+\ No newline at end of file
+diff --git a/lib/multicut_cython/setup_pybind11.py b/lib/multicut_cython/setup_pybind11.py
+new file mode 100644
+index 0000000..31bb337
+--- /dev/null
++++ b/lib/multicut_cython/setup_pybind11.py
+@@ -0,0 +1,13 @@
++from setuptools import setup, Extension
++import pybind11
++functions_module = Extension(
++ name='multicut',
++ sources=['multicut.cxx','src/nl-lmp.cxx'],
++ language="c++",
++ include_dirs=[pybind11.get_include(),'.', 'include', 'src'],
++ extra_compile_args=['-std=c++11','-O3','-fPIC', '-DHAVE_CPP11_INITIALIZER_LISTS'],
++ extra_link_args=['-std=c++11', '-L./']
++)
++
++
++setup(name = 'multicut', ext_modules=[functions_module])
+\ No newline at end of file
+diff --git a/lib/nms_cython/nms_grid.cxx b/lib/nms_cython/nms_grid.cxx
+new file mode 100644
+index 0000000..563cebd
+--- /dev/null
++++ b/lib/nms_cython/nms_grid.cxx
+@@ -0,0 +1,38 @@
++#include <pybind11/pybind11.h>
++#include <pybind11/numpy.h>
++#include <pybind11/stl.h>
++#include <iostream>
++#include "nms_scoremap.hxx"
++
++namespace py = pybind11;
++py::list nms_grid(py::array_t<float> &scoremap,
++ py::array_t<uint8_t> &grid,
++ double prob_thresh)
++{
++ py::buffer_info scoremap_buf = scoremap.request();
++ py::buffer_info grid_buf = grid.request();
++
++ if (scoremap_buf.ndim != 2 || grid_buf.ndim != 2)
++ {
++ throw std::runtime_error("numpy.ndarray dims must be 2!");
++ }
++ int W = scoremap_buf.shape[1];
++ int H = scoremap_buf.shape[0];
++
++ int grid_W = grid_buf.shape[1];
++ int grid_H = grid_buf.shape[0];
++
++ auto scoremap_ptr = static_cast<float *>(scoremap_buf.ptr);
++ auto grid_ptr = static_cast<uint8_t *>(grid_buf.ptr);
++
++ auto v = nms_grid_cpp(scoremap_ptr, H, W,
++ grid_ptr, grid_H, grid_W,
++ prob_thresh);
++ py::list result = py::cast(v);
++
++ return result;
++}
++PYBIND11_MODULE(nms_grid, m)
++{
++ m.def("nms_grid", &nms_grid);
++}
+\ No newline at end of file
+diff --git a/lib/nms_cython/setup_pybind11.py b/lib/nms_cython/setup_pybind11.py
+new file mode 100644
+index 0000000..9dd7bcb
+--- /dev/null
++++ b/lib/nms_cython/setup_pybind11.py
+@@ -0,0 +1,22 @@
++from setuptools import setup, Extension
++import pybind11
++functions_module = Extension(
++ name='nms_grid',
++ sources=['nms_grid.cxx'],
++ language="c++",
++ include_dirs=[pybind11.get_include(),'.', 'include'],
++ extra_compile_args=['-DILOUSESTL','-DIL_STD','-std=c++11','-O3','-fPIC', '-DHAVE_CPP11_INITIALIZER_LISTS'],
++ extra_link_args=['-std=c++11', '-L./']
++)
++
++# extensions = [
++# Extension(
++# 'nms_grid', ['nms_grid.pyx'],
++# language="c++",
++# include_dirs=[np.get_include(), '.','include'],
++# extra_compile_args=['-DILOUSESTL','-DIL_STD','-std=c++11','-O3'],
++# extra_link_args=['-std=c++11']
++# )
++# ]
++
++setup(name = 'nms_grid', ext_modules=[functions_module])
+\ No newline at end of file
+--
+2.32.0.windows.1
+
diff --git a/research/cv/ArtTrack/patch/0003-split-dataset.patch b/research/cv/ArtTrack/patch/0003-split-dataset.patch
new file mode 100644
index 0000000000000000000000000000000000000000..062f13fed2236cf44f75ea046a2b41f87f5790d4
--- /dev/null
+++ b/research/cv/ArtTrack/patch/0003-split-dataset.patch
@@ -0,0 +1,1177 @@
+From d72df7b178b87ccc4bf8b0726993cf5a2757050f Mon Sep 17 00:00:00 2001
+From: yiguangzheng <78542727@qq.com>
+Date: Mon, 29 Nov 2021 10:48:55 +0800
+Subject: [PATCH] split dataset
+
+---
+ matlab/mpii/split_dataset.m | 35 +++++++++++++++++++++++++++++++++++
+ matlab/mpii/test_index.mat | Bin 0 -> 11578 bytes
+ matlab/mpii/train_index.mat | Bin 0 -> 45585 bytes
+ 3 files changed, 35 insertions(+)
+ create mode 100644 matlab/mpii/split_dataset.m
+ create mode 100644 matlab/mpii/test_index.mat
+ create mode 100644 matlab/mpii/train_index.mat
+
+diff --git a/matlab/mpii/split_dataset.m b/matlab/mpii/split_dataset.m
+new file mode 100644
+index 0000000..ed0d8ce
+--- /dev/null
++++ b/matlab/mpii/split_dataset.m
+@@ -0,0 +1,35 @@
++function split_dataset(dataset_path, test_ratio, train_dataset, test_dataset,save_train_index,save_test_index)
++if (nargin <= 2)
++ train_dataset = '';
++ test_dataset = '';
++ save_train_index = '';
++ save_test_index = '';
++end
++ load(dataset_path, 'dataset');
++ origin_dataset = dataset;
++ rp = randperm(length(dataset));
++ test_amount = floor(length(dataset) * test_ratio);
++ train_amount = length(dataset) - test_amount;
++ if (isempty(train_dataset))
++ train_index = rp(1:train_amount);
++ else
++ load(train_dataset,'train_index');
++ end
++ if (isempty(test_dataset))
++ test_index = rp((train_amount+1):length(dataset));
++ else
++ load(test_dataset,'test_index');
++ end
++ train_dataset = dataset(train_index);
++ test_dataset = dataset(test_index);
++ dataset = train_dataset;
++ save([dataset_path,'-train'], 'dataset');
++ dataset = test_dataset;
++ save([dataset_path,'-test'],'dataset')
++ if(~isempty(save_train_index))
++ save(save_train_index,'train_index')
++ end
++ if(~isempty(save_test_index))
++ save(save_test_index,'test_index')
++ end
++end
+\ No newline at end of file
+diff --git a/matlab/mpii/test_index.mat b/matlab/mpii/test_index.mat
+new file mode 100644
+index 0000000000000000000000000000000000000000..8d3c5d5096ef12b0270fb3965360251b8ad0c9ea
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+uSo)l{v9^%aZk?1h?0%A}=Jz$esK%fYf`96pvcI=4tvgWx0Q?`{eymS91ucF6
+
+literal 0
+HcmV?d00001
+
+--
+2.32.0.windows.1
+
diff --git a/research/cv/ArtTrack/preprocess.py b/research/cv/ArtTrack/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4e4c8d3d4b42b9da358114cbd9ba8d341698026
--- /dev/null
+++ b/research/cv/ArtTrack/preprocess.py
@@ -0,0 +1,58 @@
+# Copyright 2022 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.
+# ============================================================================
+import os
+import sys
+
+from src.args_util import command, create_arg_parser, TARGET_MAT2JSON, TARGET_MPII_SINGLE, TARGET_PAIRWISE, TARGET_TF2MS
+
+
+@command
+def preprocess(parser, args, cfg):
+ if args.target == TARGET_MPII_SINGLE:
+ a = (args.dataset_dir, args.dataset_name, args.save_dir, args.image_dir)
+ from src.tool.preprocess.preprocess_single import preprocess_single
+ preprocess_single(*a)
+
+ if args.split:
+ if args.save_dir is not None:
+ _dir = args.save_dir
+ elif args.dataset_dir is not None:
+ _dir = os.path.join(args.dataset_dir, 'cropped')
+ else:
+ _dir = './cropped'
+ from src.tool.preprocess.split import split
+ split(os.path.join(_dir, 'dataset.json'), _dir)
+ elif args.target == TARGET_TF2MS:
+ a = (args.checkpoint, args.output, args.map)
+ from src.tool.preprocess.tf2ms import tf2ms
+ tf2ms(*a)
+ elif args.target == TARGET_MAT2JSON:
+ from src.tool.preprocess.mat2json import mat2json
+ mat2json(args.index_mat, args.name, args.dataset_json, args.output_dir, args.index_offset, args.stdout)
+ elif args.target == TARGET_PAIRWISE:
+ from src.tool.preprocess.pairwise_stats import pairwise_stats
+ pairwise_stats(cfg)
+ else:
+ parser.print_help()
+
+
+def main():
+ parser = create_arg_parser()['preprocess']
+ args = parser.parse_args(sys.argv[1:])
+ preprocess(parser, args)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/research/cv/ArtTrack/requirements.txt b/research/cv/ArtTrack/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..41bb617e4598d820c2e241dd1e5244b996e9f07b
--- /dev/null
+++ b/research/cv/ArtTrack/requirements.txt
@@ -0,0 +1,14 @@
+numpy~=1.21.2
+matplotlib~=3.4.3
+pillow~=8.3.2
+scipy~=1.7.0
+imageio~=2.9.0
+pyyaml~=5.4.1
+easydict~=1.9
+pycocotools~=2.0.2
+pybind11~=2.7.0
+psutil~=5.8.0
+munkres~=1.1.4
+cython~=0.29.24
+tensorflow~=1.15
+mindspore-gpu
diff --git a/research/cv/ArtTrack/scripts/download.sh b/research/cv/ArtTrack/scripts/download.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7292d5af6f589ae88cd8bb6f85c48d562b10081d
--- /dev/null
+++ b/research/cv/ArtTrack/scripts/download.sh
@@ -0,0 +1,108 @@
+#!/usr/bin/env bash
+# Copyright 2022 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.
+# ============================================================================
+
+script_dir=$(cd "$(dirname "$0")" || exit;pwd)
+base_dir=$(cd "$script_dir"/.. || exit;pwd)
+if command -v curl > /dev/null; then
+ DOWNLOADER="curl -L -O"
+else
+ DOWNLOADER="wget"
+fi
+
+function download_dataset_mpii() {
+ mkdir -p "$base_dir"/mpii
+ cd "$base_dir"/mpii || exit
+ echo 'downloading mpii dataset'
+ if [ ! -f mpii_human_pose_v1_u12_1.mat ] && [ ! -f mpii_human_pose_v1_u12_2.zip ]
+ then
+ $DOWNLOADER https://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1_u12_2.zip
+ fi
+ if [ ! -d images ] && [ ! -f mpii_human_pose_v1.tar.gz ]
+ then
+ $DOWNLOADER https://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1.tar.gz
+ fi
+ echo 'extract dataset'
+ if [ ! -f mpii_human_pose_v1_u12_1.mat ]
+ then
+ unzip mpii_human_pose_v1_u12_2.zip
+ ln -s mpii_human_pose_v1_u12_2/mpii_human_pose_v1_u12_1.mat mpii_human_pose_v1_u12_1.mat
+ fi
+ if [ ! -d images ]
+ then
+ tar xf mpii_human_pose_v1.tar.gz
+ fi
+}
+
+function download_pretrained_resnet101() {
+ mkdir -p $base_dir/out
+ cd $base_dir/out || exit
+ $DOWNLOADER http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz
+ tar xf resnet_v1_101_2016_08_28.tar.gz
+}
+
+function download_dataset_coco() {
+ mkdir -p "$base_dir"/coco
+ cd "$base_dir"/coco || exit
+ COCO_SITE="http://images.cocodataset.org"
+ echo 'downloading coco dataset'
+ if [ ! -d images/train2014 ] && [ ! -f train2014.zip ]
+ then
+ $DOWNLOADER "$COCO_SITE/zips/train2014.zip"
+ fi
+ if [ ! -d images/val2014 ] && [ ! -f val2014.zip ]
+ then
+ $DOWNLOADER "$COCO_SITE/zips/val2014.zip"
+ fi
+ if [ ! -d annotations/annotations_trainval2014 ] && [ ! -f annotations_trainval2014.zip ]
+ then
+ $DOWNLOADER "$COCO_SITE/annotations/annotations_trainval2014.zip"
+ fi
+
+ echo 'extract dataset'
+ if [ ! -d annotations ]
+ then
+ unzip annotations_trainval2014.zip
+ fi
+ mkdir -p images
+ if [ ! -d images/train2014 ]
+ then
+ cd "$base_dir"/coco/images || exit
+ unzip ../train2014.zip
+ fi
+ if [ ! -d images/val2014 ]
+ then
+ cd "$base_dir"/coco/images || exit
+ unzip ../val2014.zip
+ fi
+
+}
+
+case $1 in
+dataset_mpii)
+ download_dataset_mpii
+ ;;
+dataset_coco)
+ download_dataset_coco
+ ;;
+pretrained_resnet101)
+ download_pretrained_resnet101
+ ;;
+*)
+ echo "Please run the script as: "
+ echo "bash scripts/download.sh TARGET."
+ echo "TARGET: dataset_mpii, dataset_coco, pretrained_resnet101"
+ echo "For example: bash scripts/download.sh dataset_mpii"
+esac
diff --git a/research/cv/ArtTrack/scripts/eval.sh b/research/cv/ArtTrack/scripts/eval.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cf4e1a5db05f1d67ca3f95dde9d04926284bfde2
--- /dev/null
+++ b/research/cv/ArtTrack/scripts/eval.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+if [ $# -lt 3 ]
+then
+ echo "Please run the script as: "
+ echo "bash scripts/eval.sh TARGET CKPT_PATH PREDICTION_PATH"
+ echo "TARGET: mpii_single, coco_multi"
+ echo "For example: bash scripts/eval.sh mpii_single ./ckpt_0/arttrack.ckpt out/prediction.mat"
+exit 1
+fi
+python eval.py "$1" --config config/mpii_eval.yaml --option "load_ckpt=$2" --output "$3"
+python eval.py "$1" --config config/mpii_eval.yaml --accuracy --prediction "$3"
\ No newline at end of file
diff --git a/research/cv/ArtTrack/scripts/prepare.sh b/research/cv/ArtTrack/scripts/prepare.sh
new file mode 100644
index 0000000000000000000000000000000000000000..871bcc842f7f3b76b36c8ed01f5f415fac292d0e
--- /dev/null
+++ b/research/cv/ArtTrack/scripts/prepare.sh
@@ -0,0 +1,90 @@
+#!/usr/bin/env bash
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+script_dir=$(cd "$(dirname "$0")" || exit;pwd)
+base_dir=$(cd "$script_dir/.." || exit;pwd)
+
+function prepare_env() {
+ mkdir -p "$base_dir"/out
+ cd "$base_dir"/out || exit
+ echo 'clone tensorflow version code'
+ if [ ! -d pose-tensorflow ]
+ then
+ git clone https://github.com/eldar/pose-tensorflow.git
+ fi
+ cd pose-tensorflow || exit
+ git am "$base_dir"/patch/*
+ cd lib/multicut_cython || exit
+ python setup_pybind11.py install
+ cd ../nms_cython || exit
+ python setup_pybind11.py install
+
+ cd "$base_dir" || exit
+ echo 'convert pretrained resnet101 checkpoint for mindspore'
+ python preprocess.py tf2ms --checkpoint out/resnet_v1_101.ckpt --map config/tf2ms.json --output out/pretrained_resnet101.ckpt
+}
+
+function prepare_mpii() {
+ cd "$base_dir" || exit
+ echo 'preprocess dataset'
+ python preprocess.py mpii_single --dataset-dir mpii --dataset-name mpii_human_pose_v1_u12_1
+ echo 'split dataset'
+ python preprocess.py mat2json --index-mat out/pose-tensorflow/matlab/mpii/test_index.mat --name test_index \
+ --dataset-json mpii/cropped/dataset.json \
+ --output-dir out
+
+ python preprocess.py mat2json --index-mat out/pose-tensorflow/matlab/mpii/train_index.mat --name train_index \
+ --dataset-json mpii/cropped/dataset.json \
+ --output-dir out
+
+ echo 'prepare mpii successfully.'
+}
+
+function prepare_coco() {
+ if [ ! -f out/pairwise_stats.mat ]
+ then
+ python preprocess.py pairwise --config config/coco_pairwise.yaml
+ fi
+
+ if [ ! -d out/pairwise ]
+ then
+ cd "$base_dir"/out || exit
+ $DOWNLOADER https://datasets.d2.mpi-inf.mpg.de/deepercut-models-tensorflow/pairwise_coco.tar.gz
+ tar xvzf pairwise_coco.tar.gz
+ fi
+
+ echo 'prepare coco successfully.'
+
+}
+
+
+case $1 in
+env)
+ prepare_env
+ ;;
+mpii)
+ prepare_mpii
+ ;;
+coco)
+ prepare_coco
+ ;;
+*)
+ echo "Please run the script as: "
+ echo "bash scripts/prepare.sh [TARGET]."
+ echo "TARGET: env, mpii, coco"
+ echo "For example: bash scripts/prepare.sh env"
+ ;;
+esac
\ No newline at end of file
diff --git a/research/cv/ArtTrack/scripts/run_train_multiple_gpu.sh b/research/cv/ArtTrack/scripts/run_train_multiple_gpu.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0b1c26936e6180a6429f841f9bd84e655857520a
--- /dev/null
+++ b/research/cv/ArtTrack/scripts/run_train_multiple_gpu.sh
@@ -0,0 +1,37 @@
+#!/usr/bin/env bash
+# Copyright 2022 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 [ $# -lt 4 ]
+then
+ echo "Please run the script as: "
+ echo "bash scripts/run_train_single_gpu.sh TARGET CONFIG_PATH CUDA_VISIBLE_DEVICES DEVICE_NUM [OPTION] ..."
+ echo "TARGET: mpii_single"
+ echo "For example: bash scripts/run_train_multiple_gpu.sh mpii_single config/mpii_train_multiple_gpu.yaml \"0,1,2,3,4,5,6,7\" 8 \"dataset.path=./out/train_index_dataset.json\""
+exit 1
+fi
+set -e
+index=0
+OPTIONS=''
+for arg in "$@"
+do
+ if [ $index -ge 4 ]
+ then
+ OPTIONS="$OPTIONS --option $arg"
+ fi
+ let index+=1
+done
+export CUDA_VISIBLE_DEVICES=$3
+echo "$CUDA_VISIBLE_DEVICES"
+mpirun -n "$4" python train.py "$1" --config "$2" $OPTIONS | tee "mpii_train_multiple_gpu-`(date +%Y-%m-%d_%H%M%S)`.log"
diff --git a/research/cv/ArtTrack/scripts/run_train_single_gpu.sh b/research/cv/ArtTrack/scripts/run_train_single_gpu.sh
new file mode 100644
index 0000000000000000000000000000000000000000..a55d156323800c27a7a9373950e59e359511a08f
--- /dev/null
+++ b/research/cv/ArtTrack/scripts/run_train_single_gpu.sh
@@ -0,0 +1,35 @@
+#!/usr/bin/env bash
+# Copyright 2022 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 [ $# -lt 2 ]
+then
+ echo "Please run the script as: "
+ echo "bash scripts/run_train_single_gpu.sh TARGET CONFIG_PATH [OPTION] ..."
+ echo "TARGET: mpii_single"
+ echo "For example: bash scripts/run_train_single_gpu.sh mpii_single config/mpii_train_single_gpu.yaml \"dataset.path=./out/train_index_dataset.json\""
+exit 1
+fi
+set -e
+index=0
+OPTIONS=''
+for arg in "$@"
+do
+ if [ $index -ge 2 ]
+ then
+ OPTIONS="$OPTIONS --option $arg"
+ fi
+ let index+=1
+done
+python train.py "$1" --config "$2" $OPTIONS | tee "mpii_train_single_gpu-`(date +%Y-%m-%d_%H%M%S)`.log"
diff --git a/research/cv/ArtTrack/src/__init__.py b/research/cv/ArtTrack/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/research/cv/ArtTrack/src/args_util.py b/research/cv/ArtTrack/src/args_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..cacf33e91320c76ccd5acc0fe497de607f84f02c
--- /dev/null
+++ b/research/cv/ArtTrack/src/args_util.py
@@ -0,0 +1,166 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import argparse
+import re
+from typing import List
+
+from src.config import load_config, merge_a_into_b
+from src.log import setup_log
+
+TARGET_MPII_SINGLE = 'mpii_single'
+TARGET_COCO_MULTI = 'coco_multi'
+TARGET_TF2MS = 'tf2ms'
+TARGET_MAT2JSON = 'mat2json'
+TARGET_PAIRWISE = 'pairwise'
+
+
+def to_number_or_str(value: str):
+ if value is None:
+ return None
+ try:
+ if re.match(r"^([-+])?\d+$", value) is not None:
+ return int(value)
+
+ return float(value)
+ except ValueError:
+ return value
+
+
+def compose_option(option: List[str]):
+ result = dict()
+ for o in option:
+ kv = o.split('=', 1)
+ key = kv[0]
+ value = kv[1] if len(kv) == 2 else None
+ keys = key.split('.')
+ cursor = result
+ for k in keys[:-1]:
+ last_cursor = cursor
+ cursor = cursor.get(k, None)
+ if cursor is None:
+ cursor = dict()
+ last_cursor[k] = cursor
+ cursor[keys[-1]] = to_number_or_str(value)
+ return result
+
+
+def setup_config(args):
+ cfg = None
+ if args.config is not None:
+ cfg = load_config(args.config)
+ if args.option:
+ option = compose_option(args.option)
+ if cfg is not None:
+ merge_a_into_b(option, cfg)
+ return cfg
+
+
+def command(func):
+ def _command(p, a):
+ if a.log:
+ setup_log(a.log)
+ return func(p, a, setup_config(a))
+
+ return _command
+
+
+def join_targets(targets):
+ return ', '.join(targets)
+
+
+def create_arg_parser():
+ common_parser = argparse.ArgumentParser(add_help=False)
+ common_parser.add_argument('-c', '--config', type=str, nargs='?')
+ common_parser.add_argument('--log', default="log.yaml", type=str, nargs='?', help="log config file")
+ common_parser.add_argument('--option', type=str, nargs='+', help="extra option will override config file. "
+ "example: context.device_target=GPU")
+
+ parser = argparse.ArgumentParser(description='tool')
+ subparsers = parser.add_subparsers(metavar="COMMAND", dest='command')
+ # preprocess
+ parser_pre = subparsers.add_parser(
+ 'preprocess', aliases=['pre'], help='preprocess', parents=[common_parser])
+ pre_targets = [TARGET_MPII_SINGLE, TARGET_TF2MS, TARGET_MAT2JSON, TARGET_PAIRWISE]
+ parser_pre.add_argument('target', metavar='TARGET',
+ choices=pre_targets,
+ help='option choices: %s' % join_targets(pre_targets), nargs='?')
+ # preprocess single
+ pre_group_single = parser_pre.add_argument_group(TARGET_MPII_SINGLE)
+ pre_group_single.add_argument('--dataset-dir', default='.', type=str, nargs='?')
+ pre_group_single.add_argument('--dataset-name', default='mpii_human_pose_v1_u12_1', type=str, nargs='?')
+ pre_group_single.add_argument('--save-dir', default=None, type=str, nargs='?')
+ pre_group_single.add_argument('--image-dir', default=None, type=str, nargs='?')
+ pre_group_single.add_argument('--split', default=False, action='store_true',
+ help="split dataset to train and eval.")
+ pre_group_single.add_argument('--eval-ratio', default=0.2, type=float, nargs='?')
+ # preprocess tf2ms
+ pre_group_tf2ms = parser_pre.add_argument_group(TARGET_TF2MS)
+ pre_group_tf2ms.add_argument('--checkpoint', type=str, nargs='?', help='path to tf parameter')
+ pre_group_tf2ms.add_argument('--output', default='out/tf2ms.ckpt', type=str, nargs='?')
+ pre_group_tf2ms.add_argument('--map', default='config/tf2ms.json', type=str, nargs='?')
+
+ pre_group_mat2json = parser_pre.add_argument_group(TARGET_MAT2JSON)
+ pre_group_mat2json.add_argument('--index-mat', type=str, nargs='?', help='mat format index file path')
+ pre_group_mat2json.add_argument('--name', type=str, nargs='?', help='field name in mat format index file. '
+ 'this option is also used as a filename '
+ 'for output')
+ pre_group_mat2json.add_argument('--dataset-json', type=str, nargs='?', help='json format dataset file path. '
+ 'output dataset related to index, '
+ 'if this option appears')
+ pre_group_mat2json.add_argument('--output-dir', type=str, nargs='?', help='all output will in this dir.')
+ pre_group_mat2json.add_argument('--index-offset', type=int, nargs='?', default=-1)
+ pre_group_mat2json.add_argument('--stdout', action='store_true', default=False)
+
+ # train
+ parser_train = subparsers.add_parser(
+ 'train', help='train', parents=[common_parser])
+ train_targets = [TARGET_MPII_SINGLE, TARGET_COCO_MULTI]
+ parser_train.add_argument('target', metavar='TARGET', choices=train_targets,
+ help='option choices: %s' % join_targets(train_targets), nargs='?')
+ # eval
+ parser_test = subparsers.add_parser(
+ 'eval', help='eval', parents=[common_parser])
+ test_targets = [TARGET_MPII_SINGLE, TARGET_COCO_MULTI]
+ test_group_single = parser_test.add_argument_group(TARGET_MPII_SINGLE)
+ test_group_single.add_argument('target', metavar='TARGET', choices=test_targets,
+ help='option choices: %s' % join_targets(test_targets), nargs='?')
+ test_group_single.add_argument('--visual', default=False, action='store_true',
+ help='visualize result')
+ test_group_single.add_argument('--cache', default=False, action='store_true',
+ help='cache score map')
+ test_group_single.add_argument('--accuracy', default=False, action='store_true',
+ help='only calculate accuracy')
+ test_group_single.add_argument('--output', type=str, nargs='?', help="path to save prediction result")
+ test_group_single.add_argument('--prediction', type=str, nargs='?', help='prediction path for accuracy. '
+ 'or use yaml config, '
+ 'single:output multi:gt_segm_output')
+ test_group_multi = parser_test.add_argument_group(TARGET_COCO_MULTI)
+ test_group_multi.add_argument('--dev', default=False, action='store_true',
+ help='development mode')
+ test_group_multi.add_argument('--graph', default=False, action='store_true',
+ help='eval graph')
+ test_group_multi.add_argument('--score-maps-cached', default=False, action='store_true',
+ help='use cached score map in yaml config cached_scoremaps')
+ test_group_multi.add_argument('--range-num', type=int, nargs='?',
+ help='range number. split dataset to this number')
+ test_group_multi.add_argument('--range-index', type=int, nargs='?',
+ help='range index. start 0. only eval this range index')
+ parsers = {
+ 'preprocess': parser_pre,
+ 'train': parser_train,
+ 'eval': parser_test
+ }
+ return parsers
diff --git a/research/cv/ArtTrack/src/config.py b/research/cv/ArtTrack/src/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..0843c31d88eaab1919ec32b51aa99f7db42275e4
--- /dev/null
+++ b/research/cv/ArtTrack/src/config.py
@@ -0,0 +1,100 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import yaml
+from easydict import EasyDict as edict
+
+
+def get_default_config():
+ _cfg = edict()
+ _cfg.context = edict({"mode": 1, "device_target": "GPU"})
+ _cfg.parallel_context = None
+ _cfg.stride = 8.0
+ _cfg.weigh_only_present_joints = False
+ _cfg.mean_pixel = [123.68, 116.779, 103.939]
+ _cfg.global_scale = 1.0
+ _cfg.location_refinement = False
+ _cfg.locref_stdev = 7.2801
+ _cfg.locref_loss_weight = 1.0
+ _cfg.locref_huber_loss = True
+ _cfg.intermediate_supervision = False
+ _cfg.intermediate_supervision_layer = 12
+ _cfg.intermediate_supervision_input = 1024
+ _cfg.mirror = False
+ _cfg.crop = False
+ _cfg.crop_pad = 0
+ _cfg.scoremap_dir = "out/eval"
+ _cfg.dataset = edict({"path": "", "type": "", "parallel": 1, "batch_size": 1,
+ "shuffle": False, "mirror": False, "padding": False})
+ _cfg.use_gt_segm = False
+ _cfg.sparse_graph = []
+ _cfg.pairwise_stats_collect = False
+ _cfg.pairwise_stats_fn = "pairwise_stats.mat"
+ _cfg.pairwise_predict = False
+ _cfg.pairwise_huber_loss = True
+ _cfg.pairwise_loss_weight = 1.0
+ _cfg.tensorflow_pairwise_order = True
+ return _cfg
+
+
+cfg = get_default_config()
+
+
+def merge_a_into_b(a, b):
+ """Merge config dictionary a into config dictionary b, clobbering the
+ options in b whenever they are also specified in a.
+ """
+ if not isinstance(a, edict) and not isinstance(a, dict):
+ return
+
+ for k, v in a.items():
+ # a must specify keys that are in b
+ # if k not in b:
+ # raise KeyError('{} is not a valid config key'.format(k))
+
+ # recursively merge dicts
+ if isinstance(v, (edict, dict)):
+ try:
+ item = b.get(k, None)
+ if item is None:
+ item = edict()
+ b[k] = item
+ merge_a_into_b(a[k], b[k])
+ except Exception:
+ print('Error under config key: {}'.format(k))
+ raise
+ else:
+ b[k] = v
+
+
+def load_config(filename):
+ """Load a config from file filename and merge it into the default options.
+ """
+ with open(filename, 'r', encoding='utf-8') as f:
+ yaml_cfg = edict(yaml.load(f, Loader=yaml.FullLoader))
+
+ merge_a_into_b(yaml_cfg, cfg)
+
+ return cfg
+
+
+def check_config(c=None):
+ if c is None:
+ return load_config()
+ return c
+
+
+if __name__ == "__main__":
+ print(load_config())
diff --git a/research/cv/ArtTrack/src/dataset/__init__.py b/research/cv/ArtTrack/src/dataset/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cc45ec5a33c9576f744df56bb3c5b73705aeac9
--- /dev/null
+++ b/research/cv/ArtTrack/src/dataset/__init__.py
@@ -0,0 +1,18 @@
+# Copyright 2022 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.
+# ============================================================================
+
+from .util import DATASET_TYPE_MPII_MR, DATASET_TYPE_MPII_RAW, DATASET_TYPE_COCO
+from .mpii import MPII
+from .coco import MSCOCO
diff --git a/research/cv/ArtTrack/src/dataset/coco.py b/research/cv/ArtTrack/src/dataset/coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..8aa462863b7cecaa23e6ddd8d1b51fe67596a85c
--- /dev/null
+++ b/research/cv/ArtTrack/src/dataset/coco.py
@@ -0,0 +1,180 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import json
+import os
+
+import imageio as io
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+from pycocotools import mask as mask_utils
+from pycocotools.coco import COCO
+
+from src.dataset.pose import Batch, DataItem, PoseDataset
+
+
+def get_gt_visibilities(in_file, visibilities):
+ """
+ get ground truth visibilities
+ """
+ with open(in_file) as data_file:
+ data = json.load(data_file)
+
+ for person_id in range(len(data)):
+ keypoints = data[person_id]["keypoints"]
+ keypoints = [visibilities[person_id][i // 3] if i % 3 == 2 else int(keypoints[i]) for i in
+ range(len(keypoints))]
+ data[person_id]["keypoints"] = keypoints
+
+ with open(in_file, 'w') as data_file:
+ json.dump(data, data_file)
+
+
+class MSCOCO(PoseDataset):
+
+ def load_dataset(self):
+ dataset = self.cfg.dataset.path
+ dataset_phase = self.cfg.dataset.phase
+ dataset_ann = self.cfg.dataset.ann
+
+ # initialize COCO api
+ ann_file = '%s/annotations/%s_%s.json' % (dataset, dataset_ann, dataset_phase)
+ self.coco = COCO(ann_file)
+
+ img_ids = self.coco.getImgIds()
+
+ data = []
+
+ # loop through each image
+ for imgId in img_ids:
+ item = DataItem()
+
+ img = self.coco.loadImgs(imgId)[0]
+ item.im_path = "%s/images/%s/%s" % (dataset, dataset_phase, img["file_name"])
+ item.im_size = [3, img["height"], img["width"]]
+ item.coco_id = imgId
+ item.scale = self.get_scale()
+ if not self.is_valid_size(item.im_size, item.scale):
+ continue
+ ann_ids = self.coco.getAnnIds(imgIds=img['id'], iscrowd=False)
+ anns = self.coco.loadAnns(ann_ids)
+
+ all_person_key_points = []
+ masked_persons_rle = []
+ visible_persons_rle = []
+ all_visibilities = []
+
+ # Consider only images with people
+ has_people = len(anns)
+ if not has_people and self.cfg.coco_only_images_with_people:
+ continue
+
+ for ann in anns: # loop through each person
+ person_key_points = []
+ visibilities = []
+ if ann["num_keypoints"] != 0:
+ for i in range(self.cfg.num_joints):
+ x_coord = ann["keypoints"][3 * i]
+ y_coord = ann["keypoints"][3 * i + 1]
+ visibility = ann["keypoints"][3 * i + 2]
+ visibilities.append(visibility)
+ if visibility != 0: # i.e. if labeled
+ person_key_points.append([i, x_coord, y_coord])
+ all_person_key_points.append(np.array(person_key_points))
+ visible_persons_rle.append(mask_utils.decode(self.coco.annToRLE(ann)))
+ all_visibilities.append(visibilities)
+ if ann["num_keypoints"] == 0:
+ masked_persons_rle.append(self.coco.annToRLE(ann))
+
+ item.joints = np.array(all_person_key_points)
+ item.im_neg_mask = mask_utils.merge(masked_persons_rle)
+ if self.cfg.use_gt_segm:
+ item.gt_segm = np.moveaxis(np.array(visible_persons_rle), 0, -1)
+ item.visibilities = all_visibilities
+ data.append(item)
+
+ self.has_gt = self.cfg.dataset != "image_info"
+ return data
+
+ def compute_score_map_weights(self, scmap_shape, joint_id, data_item):
+ size = scmap_shape[0:2]
+ scmask = np.ones(size)
+ m = mask_utils.decode(data_item.im_neg_mask)
+ if m.size:
+ img = Image.fromarray(m)
+ img = img.resize((size[1], size[0]))
+ scmask = 1.0 - np.array(img)
+ scmask = np.stack([scmask] * self.cfg.num_joints, axis=-1)
+ return scmask
+
+ def get_pose_segments(self):
+ return [[0, 1], [0, 2], [1, 3], [2, 4], [5, 7], [6, 8], [7, 9], [8, 10], [11, 13], [12, 14], [13, 15], [14, 16]]
+
+ def visualize_coco(self, coco_img_results, visibilities):
+ """
+ visualize coco
+ """
+ in_file = "tmp.json"
+ with open(in_file, 'w') as outfile:
+ json.dump(coco_img_results, outfile)
+ get_gt_visibilities(in_file, visibilities)
+
+ # initialize coco_pred api
+ coco_pred = self.coco.loadRes(in_file)
+ os.remove(in_file)
+
+ img_ids = [coco_img_results[0]["image_id"]]
+
+ for imgId in img_ids:
+ img = coco_pred.loadImgs(imgId)[0]
+ im_path = "%s/images/%s/%s" % (self.cfg.dataset.path, self.cfg.dataset.phase, img["file_name"])
+ I = io.imread(im_path)
+
+ fig = plt.figure()
+ a = fig.add_subplot(2, 2, 1)
+ plt.imshow(I)
+ a.set_title('Initial Image')
+
+ a = fig.add_subplot(2, 2, 2)
+ plt.imshow(I)
+ a.set_title('Predicted Keypoints')
+ ann_ids = coco_pred.getAnnIds(imgIds=img['id'])
+ anns = coco_pred.loadAnns(ann_ids)
+ coco_pred.showAnns(anns)
+
+ a = fig.add_subplot(2, 2, 3)
+ plt.imshow(I)
+ a.set_title('GT Keypoints')
+ ann_ids = self.coco.getAnnIds(imgIds=img['id'])
+ anns = self.coco.loadAnns(ann_ids)
+ self.coco.showAnns(anns)
+
+ plt.show()
+
+ def __getitem__(self, item):
+ batch = self.get_item(item)
+ res = (
+ batch.get(Batch.inputs, None),
+ batch.get(Batch.part_score_targets, None),
+ batch.get(Batch.part_score_weights, None),
+ batch.get(Batch.locref_targets, None),
+ batch.get(Batch.locref_mask, None),
+ batch.get(Batch.pairwise_targets, None),
+ batch.get(Batch.pairwise_mask, None),
+ )
+ if not self.cfg.train:
+ res = (batch[Batch.data_item].im_path,) + res
+ return res
diff --git a/research/cv/ArtTrack/src/dataset/mpii.py b/research/cv/ArtTrack/src/dataset/mpii.py
new file mode 100644
index 0000000000000000000000000000000000000000..59dd16e6d73195cb4a3d42e0eee92b7612e8c1cf
--- /dev/null
+++ b/research/cv/ArtTrack/src/dataset/mpii.py
@@ -0,0 +1,26 @@
+# Copyright 2022 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.
+# ============================================================================
+
+from src.dataset.pose import PoseDataset
+
+
+class MPII(PoseDataset):
+
+ def mirror_joint_coords(self, joints, image_width):
+ joints[:, 1] = image_width - joints[:, 1]
+ return joints
+
+ def get_pose_segments(self):
+ return [[0, 1], [1, 2], [3, 4], [4, 5], [6, 7], [7, 8], [9, 10], [10, 11], [12, 13]]
diff --git a/research/cv/ArtTrack/src/dataset/pose.py b/research/cv/ArtTrack/src/dataset/pose.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5420d51bbb5c84111544ba0d98f2a00bb1e3651
--- /dev/null
+++ b/research/cv/ArtTrack/src/dataset/pose.py
@@ -0,0 +1,532 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import json
+import os
+import random as rand
+from enum import Enum
+
+import numpy as np
+from numpy import array as arr, concatenate as cat
+import scipy.io as sio
+from imageio import imread
+from PIL import Image
+
+from src import dataset
+from src.log import log as logging
+
+
+class Batch(Enum):
+ inputs = 0
+ part_score_targets = 1
+ part_score_weights = 2
+ locref_targets = 3
+ locref_mask = 4
+ pairwise_targets = 5
+ pairwise_mask = 6
+ data_item = 7
+
+
+def mirror_joints_map(all_joints, num_joints):
+ """
+ mirror joints
+ Args:
+ all_joints: joints
+ num_joints: number of joints
+ """
+ res = np.arange(num_joints)
+ symmetric_joints = [p for p in all_joints if len(p) == 2]
+ for pair in symmetric_joints:
+ res[pair[0]] = pair[1]
+ res[pair[1]] = pair[0]
+ return res
+
+
+def extend_crop(crop, crop_pad, image_size):
+ """
+ extend crop
+ """
+ crop[0] = max(crop[0] - crop_pad, 0)
+ crop[1] = max(crop[1] - crop_pad, 0)
+ crop[2] = min(crop[2] + crop_pad, image_size[2] - 1)
+ crop[3] = min(crop[3] + crop_pad, image_size[1] - 1)
+ return crop
+
+
+def data_to_input(data):
+ """
+ transpose data to (C,H,W)
+ """
+ return np.transpose(data, [2, 0, 1]).astype(np.float32)
+
+
+def get_pairwise_index(j_id, j_id_end, num_joints):
+ """
+ get pairwise index
+ """
+ return (num_joints - 1) * j_id + j_id_end - int(j_id < j_id_end)
+
+
+def collect_pairwise_stats(joint_id, coords):
+ """
+ collect pairwise stats
+ """
+ pairwise_stats = {}
+ for person_id in range(len(coords)):
+ num_joints = len(joint_id[person_id])
+ for k_start in range(num_joints):
+ j_id_start = joint_id[person_id][k_start]
+ joint_pt = coords[person_id][k_start, :]
+ j_x_start = np.asscalar(joint_pt[0])
+ j_y_start = np.asscalar(joint_pt[1])
+ for k_end in range(num_joints):
+ if k_start != k_end:
+ j_id_end = joint_id[person_id][k_end]
+ joint_pt = coords[person_id][k_end, :]
+ j_x_end = np.asscalar(joint_pt[0])
+ j_y_end = np.asscalar(joint_pt[1])
+ if (j_id_start, j_id_end) not in pairwise_stats:
+ pairwise_stats[(j_id_start, j_id_end)] = []
+ pairwise_stats[(j_id_start, j_id_end)].append([j_x_end - j_x_start, j_y_end - j_y_start])
+ return pairwise_stats
+
+
+def load_pairwise_stats(cfg):
+ """
+ load pairwise stats
+ """
+ mat_stats = sio.loadmat(cfg.pairwise_stats_fn)
+ pairwise_stats = {}
+ for _id in range(len(mat_stats['graph'])):
+ pair = tuple(mat_stats['graph'][_id])
+ pairwise_stats[pair] = {"mean": mat_stats['means'][_id], "std": mat_stats['std_devs'][_id]}
+ for pair in pairwise_stats:
+ pairwise_stats[pair]["mean"] *= cfg.global_scale
+ pairwise_stats[pair]["std"] *= cfg.global_scale
+ return pairwise_stats
+
+
+class DataItem:
+ pass
+
+
+# noinspection PyAttributeOutsideInit
+class PoseDataset:
+ """
+ basic dataset
+ """
+
+ def __init__(self, cfg):
+ self.cfg = cfg
+ if cfg.dataset.type in [dataset.DATASET_TYPE_MPII_RAW,
+ dataset.DATASET_TYPE_COCO]:
+ self.data = self.load_dataset() if cfg.dataset else []
+ else:
+ self.data = []
+ return
+ self.num_images = len(self.data)
+ self.set_mirror(cfg.dataset.mirror)
+ self.set_pairwise_stats_collect(cfg.pairwise_stats_collect)
+ if self.cfg.pairwise_predict:
+ self.pairwise_stats = load_pairwise_stats(self.cfg)
+
+ def load_dataset(self):
+ """
+ load dataset
+ """
+ cfg = self.cfg
+ file_name = cfg.dataset.path
+ # Load Matlab file dataset annotation
+ with open(file_name, 'r') as f:
+ mlab = f.read()
+ mlab = json.loads(mlab)
+
+ data = []
+ has_gt = True
+
+ for i, sample in enumerate(mlab):
+
+ item = DataItem()
+ item.image_id = i
+ item.im_path = os.path.expanduser(sample['image'])
+ item.im_size = np.array(sample['size'], dtype=np.int32)
+ item.scale = self.get_scale()
+ if not self.is_valid_size(item.im_size, item.scale):
+ continue
+ if sample.get('joints', None) is not None:
+ joints = np.array(sample['joints'][0])
+ joint_id = joints[:, 0]
+ # make sure joint ids are 0-indexed
+ if joint_id.size != 0:
+ assert (joint_id < cfg.num_joints).any()
+ joints[:, 0] = joint_id
+ item.joints = [joints]
+ else:
+ has_gt = False
+ if cfg.crop:
+ crop = sample[3][0] - 1
+ item.crop = extend_crop(crop, cfg.crop_pad, item.im_size)
+ data.append(item)
+
+ self.has_gt = has_gt
+ return data
+
+ def set_mirror(self, mirror):
+ """
+ setup mirror
+ """
+ self.mirror = mirror
+ if mirror:
+ image_indices = np.arange(self.num_images * 2)
+ self.mirrored = image_indices >= self.num_images
+ image_indices[self.mirrored] = image_indices[self.mirrored] - self.num_images
+ self.image_indices = image_indices
+ self.symmetric_joints = mirror_joints_map(self.cfg.all_joints, self.cfg.num_joints)
+ else:
+ # assert not self.cfg.mirror
+ self.image_indices = np.arange(self.num_images)
+ self.mirrored = [False] * self.num_images
+
+ def set_pairwise_stats_collect(self, pairwise_stats_collect):
+ """
+ setup pairwise stats collect
+ """
+ self.pairwise_stats_collect = pairwise_stats_collect
+ if self.pairwise_stats_collect:
+ assert self.get_scale() == 1.0
+
+ def mirror_joint_coords(self, joints, image_width):
+ """
+ mirror joint coords
+ horizontally flip the x-coordinate, keep y unchanged
+ """
+ # horizontally flip the x-coordinate, keep y unchanged
+ joints[:, 1] = image_width - joints[:, 1] - 1
+ return joints
+
+ def mirror_joints(self, joints, symmetric_joints, image_width):
+ """
+ mirror joints
+ """
+ # joint ids are 0 indexed
+ res = np.copy(joints)
+ res = self.mirror_joint_coords(res, image_width)
+ # swap the joint_id for a symmetric one
+ joint_id = joints[:, 0].astype(int)
+ res[:, 0] = symmetric_joints[joint_id]
+ return res
+
+ def num_training_samples(self):
+ """
+ the number of training samples
+ """
+ num = self.num_images
+ if self.mirror:
+ num *= 2
+ return num
+
+ def __len__(self):
+ """
+ the number of training samples
+ """
+ return self.num_training_samples()
+
+ def __getitem__(self, item):
+ """
+ get a sample
+ """
+ batch = self.get_item(item)
+ res = (
+ batch.get(Batch.inputs, None),
+ batch.get(Batch.part_score_targets, None),
+ batch.get(Batch.part_score_weights, None),
+ batch.get(Batch.locref_targets, None),
+ batch.get(Batch.locref_mask, None),
+ )
+ if not self.cfg.train:
+ res = (batch[Batch.data_item].im_path,) + res
+ return res
+
+ def get_idx_mirror(self, idx):
+ """
+ get real index and mirror
+ """
+ img_idx = self.image_indices[idx]
+ mirror = self.cfg.dataset.mirror and self.mirrored[idx]
+
+ return img_idx, mirror
+
+ def get_training_sample(self, img_idx):
+ """
+ get sample
+ """
+ return self.data[img_idx]
+
+ def get_scale(self):
+ """
+ get scale
+ """
+ cfg = self.cfg
+ scale = cfg.global_scale
+ if hasattr(cfg, 'scale_jitter_lo') and hasattr(cfg, 'scale_jitter_up'):
+ scale_jitter = rand.uniform(cfg.scale_jitter_lo, cfg.scale_jitter_up)
+ scale *= scale_jitter
+ return scale
+
+ def get_item(self, item):
+ """
+ get item
+ """
+ imidx, mirror = self.get_idx_mirror(item)
+ data_item = self.get_training_sample(imidx)
+ scale = data_item.scale
+
+ return self.make_sample(data_item, scale, mirror)
+
+ def is_valid_size(self, image_size, scale):
+ """
+ check image size
+ """
+ im_width = image_size[2]
+ im_height = image_size[1]
+
+ min_input_size = 100
+ if im_height < min_input_size or im_width < min_input_size:
+ return False
+
+ if hasattr(self.cfg, 'max_input_size'):
+ max_input_size = self.cfg.max_input_size
+ input_width = im_width * scale
+ input_height = im_height * scale
+ if input_height > max_input_size or input_width > max_input_size:
+ return False
+
+ return True
+
+ def make_sample(self, data_item, scale, mirror):
+ """
+ make sample
+ """
+ im_file = data_item.im_path
+ logging.debug('image %s', im_file)
+ logging.debug('mirror %r', mirror)
+ image = imread(im_file, pilmode='RGB')
+
+ if self.has_gt:
+ joints = np.copy(data_item.joints)
+
+ if self.cfg.crop:
+ crop = data_item.crop
+ image = image[crop[1]:crop[3] + 1, crop[0]:crop[2] + 1, :]
+ if self.has_gt:
+ joints[:, 1:3] -= crop[0:2].astype(joints.dtype)
+ image = Image.fromarray(image)
+ if self.cfg.dataset.padding:
+ new_image = Image.new("RGB", (self.cfg.max_input_size, self.cfg.max_input_size), (0, 0, 0))
+ new_image.paste(image)
+ image = new_image
+ img = np.array(image.resize(
+ (int(image.size[0] * scale), int(image.size[1] * scale))) if scale != 1 else image)
+ image = np.array(image)
+ scaled_img_size = arr(img.shape[0:2])
+
+ if mirror:
+ img = np.fliplr(img)
+
+ batch = {Batch.inputs: img}
+
+ if self.has_gt:
+ stride = self.cfg.stride
+
+ if mirror:
+ joints = [self.mirror_joints(person_joints, self.symmetric_joints, image.shape[1]) for person_joints in
+ joints]
+
+ sm_size = np.ceil(scaled_img_size / (stride * 2)).astype(int) * 2
+
+ scaled_joints = [person_joints[:, 1:3] * scale for person_joints in joints]
+
+ joint_id = [person_joints[:, 0].astype(int) for person_joints in joints]
+ batch = self.compute_targets_and_weights(joint_id, scaled_joints, data_item, sm_size, scale, batch)
+
+ if self.pairwise_stats_collect:
+ data_item.pairwise_stats = collect_pairwise_stats(joint_id, scaled_joints)
+
+ batch = {key: data_to_input(data) for (key, data) in batch.items()}
+
+ batch[Batch.data_item] = data_item
+
+ return batch
+
+ def set_locref(self, locref_map, locref_mask, locref_scale, i, j, j_id, dx, dy):
+ """
+ set location ref
+ """
+ locref_mask[j, i, j_id * 2 + 0] = 1
+ locref_mask[j, i, j_id * 2 + 1] = 1
+ locref_map[j, i, j_id * 2 + 0] = dx * locref_scale
+ locref_map[j, i, j_id * 2 + 1] = dy * locref_scale
+
+ def set_pairwise_map(self, pairwise_map, pairwise_mask, i, j, j_id, j_id_end, coords, pt_x, pt_y, person_id, k_end):
+ """
+ set pairwise map
+ """
+ num_joints = self.cfg.num_joints
+ joint_pt = coords[person_id][k_end, :]
+ j_x_end = np.asscalar(joint_pt[0])
+ j_y_end = np.asscalar(joint_pt[1])
+ pair_id = get_pairwise_index(j_id, j_id_end, num_joints)
+ stats = self.pairwise_stats[(j_id, j_id_end)]
+ dx = j_x_end - pt_x
+ dy = j_y_end - pt_y
+ pairwise_mask[j, i, pair_id * 2 + 0] = 1
+ pairwise_mask[j, i, pair_id * 2 + 1] = 1
+ pairwise_map[j, i, pair_id * 2 + 0] = (dx - stats["mean"][0]) / stats["std"][0]
+ pairwise_map[j, i, pair_id * 2 + 1] = (dy - stats["mean"][1]) / stats["std"][1]
+
+ def compute_targets_and_weights(self, joint_id, coords, data_item, size, scale, batch):
+ """
+ compute targets and weights
+ """
+ stride = self.cfg.stride
+ dist_thresh = self.cfg.pos_dist_thresh * scale
+ num_joints = self.cfg.num_joints
+ half_stride = stride / 2
+ scmap = np.zeros(cat([size, arr([num_joints])]))
+
+ locref_shape = cat([size, arr([num_joints * 2])])
+ locref_mask = np.zeros(locref_shape)
+ locref_map = np.zeros(locref_shape)
+
+ pairwise_shape = cat([size, arr([num_joints * (num_joints - 1) * 2])])
+ pairwise_mask = np.zeros(pairwise_shape)
+ pairwise_map = np.zeros(pairwise_shape)
+
+ dist_thresh_sq = dist_thresh ** 2
+
+ width = size[1]
+ height = size[0]
+
+ self.loop_person(joint_id, coords, half_stride, stride, dist_thresh, width,
+ height, dist_thresh_sq,
+ locref_map,
+ scmap,
+ locref_mask, pairwise_map, pairwise_mask)
+ scmap_weights = self.compute_score_map_weights(scmap.shape, joint_id, data_item)
+
+ # Update batch
+ batch.update({
+ Batch.part_score_targets: scmap,
+ Batch.part_score_weights: scmap_weights
+ })
+ if self.cfg.location_refinement:
+ batch.update({
+ Batch.locref_targets: locref_map,
+ Batch.locref_mask: locref_mask
+ })
+ if self.cfg.pairwise_predict:
+ batch.update({
+ Batch.pairwise_targets: pairwise_map,
+ Batch.pairwise_mask: pairwise_mask
+ })
+
+ return batch
+
+ def loop_person(self, joint_id, coords, half_stride, stride, dist_thresh, width,
+ height, dist_thresh_sq,
+ locref_map,
+ scmap,
+ locref_mask, pairwise_map, pairwise_mask):
+ for person_id in range(len(coords)):
+ self.loop_joint(joint_id, person_id, coords, half_stride, stride, dist_thresh, width,
+ height, dist_thresh_sq,
+ locref_map,
+ scmap,
+ locref_mask, pairwise_map, pairwise_mask)
+
+ def loop_joint(self, joint_id, person_id, coords, half_stride, stride, dist_thresh, width,
+ height, dist_thresh_sq,
+ locref_map,
+ scmap,
+ locref_mask, pairwise_map, pairwise_mask):
+ for k, j_id in enumerate(joint_id[person_id]):
+ joint_pt = coords[person_id][k, :]
+ j_x = np.asscalar(joint_pt[0])
+ j_y = np.asscalar(joint_pt[1])
+
+ # don't loop over entire heatmap, but just relevant locations
+ j_x_sm = round((j_x - half_stride) / stride)
+ j_y_sm = round((j_y - half_stride) / stride)
+ min_x = round(max(j_x_sm - dist_thresh - 1, 0))
+ max_x = round(min(j_x_sm + dist_thresh + 1, width - 1))
+ min_y = round(max(j_y_sm - dist_thresh - 1, 0))
+ max_y = round(min(j_y_sm + dist_thresh + 1, height - 1))
+
+ self.loop_area(stride, half_stride, min_x, max_x, min_y, max_y, j_x, j_y, dist_thresh_sq, locref_map,
+ j_id,
+ scmap,
+ locref_mask, joint_id, person_id, pairwise_map, pairwise_mask, coords, k)
+
+ def loop_area(self, stride, half_stride, min_x, max_x, min_y, max_y, j_x, j_y, dist_thresh_sq, locref_map, j_id,
+ scmap,
+ locref_mask, joint_id, person_id, pairwise_map, pairwise_mask, coords, k):
+ for j in range(min_y, max_y + 1): # range(height):
+ pt_y = j * stride + half_stride
+ for i in range(min_x, max_x + 1): # range(width):
+ # pt = arr([i*stride+half_stride, j*stride+half_stride])
+ # diff = joint_pt - pt
+ # The code above is too slow in python
+ pt_x = i * stride + half_stride
+ # print(la.norm(diff))
+
+ self.set_score_map(pt_x, pt_y, j_x, j_y, dist_thresh_sq, locref_map, i, j, j_id, scmap,
+ locref_mask, joint_id, person_id, pairwise_map, pairwise_mask, coords, k)
+
+ def set_score_map(self, pt_x, pt_y, j_x, j_y, dist_thresh_sq, locref_map, i, j, j_id, scmap, locref_mask,
+ joint_id,
+ person_id, pairwise_map, pairwise_mask, coords, k):
+
+ dx = j_x - pt_x
+ dy = j_y - pt_y
+ dist = dx ** 2 + dy ** 2
+
+ if dist <= dist_thresh_sq:
+ dist = dx ** 2 + dy ** 2
+ locref_scale = 1.0 / self.cfg.locref_stdev
+ current_normalized_dist = dist * locref_scale ** 2
+ prev_normalized_dist = locref_map[j, i, j_id * 2 + 0] ** 2 + locref_map[j, i, j_id * 2 + 1] ** 2
+ update_scores = (scmap[j, i, j_id] == 0) or prev_normalized_dist > current_normalized_dist
+ if self.cfg.location_refinement and update_scores:
+ self.set_locref(locref_map, locref_mask, locref_scale, i, j, j_id, dx, dy)
+ if self.cfg.pairwise_predict and update_scores:
+ for k_end, j_id_end in enumerate(joint_id[person_id]):
+ if k != k_end:
+ self.set_pairwise_map(pairwise_map, pairwise_mask, i, j, j_id, j_id_end,
+ coords, pt_x, pt_y, person_id, k_end)
+ scmap[j, i, j_id] = 1
+
+ def compute_score_map_weights(self, scmap_shape, joint_id, data_item):
+ """
+ compute score map weights
+ """
+ cfg = self.cfg
+ if cfg.weigh_only_present_joints:
+ weights = np.zeros(scmap_shape)
+ for person_joint_id in joint_id:
+ for j_id in person_joint_id:
+ weights[:, :, j_id] = 1.0
+ else:
+ weights = np.ones(scmap_shape)
+ return weights
diff --git a/research/cv/ArtTrack/src/dataset/util.py b/research/cv/ArtTrack/src/dataset/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..0724fab32eebce07e30738b7b7b2f18b254d1b67
--- /dev/null
+++ b/research/cv/ArtTrack/src/dataset/util.py
@@ -0,0 +1,79 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import psutil
+from mindspore import dataset as ds
+
+DATASET_TYPE_MPII_RAW = 'mpii_raw'
+DATASET_TYPE_MPII_MR = 'mpii_mr'
+DATASET_TYPE_COCO = 'coco'
+
+
+def _parallel(num):
+ """
+ get parallel
+ """
+ if num is None or num < 0:
+ return max(1, int(psutil.cpu_count() / 2))
+ return num
+
+
+def create_dataset(dataset_type, dataset, shuffle=False, batch_size=1, parallel=None, train=False, num_shards=None,
+ rank_id=None):
+ """
+ create dataset
+ Args:
+ dataset_type: dataset type
+ dataset: path to dataset
+ shuffle: shuffle
+ batch_size: batch size
+ parallel: if None, cpu_count / 2
+ train: train mode
+ num_shards: shards for distributed training
+ rank_id: distributed id
+ """
+ ds.config.set_enable_shared_mem(False)
+ if dataset_type == DATASET_TYPE_MPII_RAW:
+ columns = [
+ "inputs",
+ "part_score_targets",
+ "part_score_weights",
+ "locref_targets",
+ "locref_mask",
+ ]
+ if not train:
+ columns = ['im_path'] + columns
+ dataset = ds.GeneratorDataset(source=dataset, column_names=columns, shuffle=shuffle,
+ num_parallel_workers=_parallel(parallel), num_shards=num_shards,
+ shard_id=rank_id)
+ elif dataset_type == DATASET_TYPE_COCO:
+ columns = [
+ "inputs",
+ "part_score_targets",
+ "part_score_weights",
+ "locref_targets",
+ "locref_mask",
+ "pairwise_targets",
+ "pairwise_mask",
+ ]
+ if not train:
+ columns = ['im_path'] + columns
+ dataset = ds.GeneratorDataset(source=dataset, column_names=columns, shuffle=shuffle,
+ num_parallel_workers=_parallel(parallel), num_shards=num_shards,
+ shard_id=rank_id)
+ if dataset is not None:
+ dataset = dataset.batch(batch_size)
+
+ return dataset
diff --git a/research/cv/ArtTrack/src/log.py b/research/cv/ArtTrack/src/log.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb7f5c1ebcdf8c8e5c0c741f03b0a3ccda718b56
--- /dev/null
+++ b/research/cv/ArtTrack/src/log.py
@@ -0,0 +1,30 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import logging.config
+import logging.handlers
+
+import yaml
+
+LOG_FORMAT = "%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s"
+sh = logging.StreamHandler()
+logging.basicConfig(level=logging.INFO, format=LOG_FORMAT, handlers=[sh])
+log = logging.getLogger('ArtTrack')
+
+
+def setup_log(file=None):
+ with open(file=file or 'log.yaml', mode='r', encoding="utf-8") as f:
+ logging_yaml = yaml.load(stream=f, Loader=yaml.FullLoader)
+ logging.config.dictConfig(config=logging_yaml)
diff --git a/research/cv/ArtTrack/src/model/losses.py b/research/cv/ArtTrack/src/model/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..10df5c7e6009ca67751b71d2f5172e1bd1774375
--- /dev/null
+++ b/research/cv/ArtTrack/src/model/losses.py
@@ -0,0 +1,55 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import mindspore.numpy as np
+from mindspore import nn
+from mindspore.ops import functional as F
+from mindspore.ops.operations import Abs
+
+
+class HuberLossWithWeight(nn.LossBase):
+ """
+ huber loss
+ """
+
+ def __init__(self):
+ super(HuberLossWithWeight, self).__init__()
+ self.abs = Abs()
+
+ def construct(self, predictons, labels, weight=1.0, k=1.0):
+ diff = predictons - labels
+ abs_diff = self.abs(diff)
+ k = np.array(k)
+ losses = np.where(abs_diff < k, 0.5 * np.square(diff), k * abs_diff - 0.5 * k ** 2)
+ return self.get_loss(losses, weight)
+
+
+class MSELossWithWeight(nn.LossBase):
+ """
+ mse loss
+ """
+
+ def construct(self, base, target, weight=1.0):
+ x = F.square(base - target)
+ return self.get_loss(x, weight)
+
+
+class WeightLoss(nn.LossBase):
+ """
+ weight loss
+ """
+
+ def construct(self, loss, weight=1.0):
+ return self.get_loss(loss, weight)
diff --git a/research/cv/ArtTrack/src/model/pose.py b/research/cv/ArtTrack/src/model/pose.py
new file mode 100644
index 0000000000000000000000000000000000000000..42c6c9af269cb4df61580f152e1e8f93f2ba5b43
--- /dev/null
+++ b/research/cv/ArtTrack/src/model/pose.py
@@ -0,0 +1,188 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import mindspore as ms
+import mindspore.numpy as np
+from mindspore import nn
+from mindspore.ops import stop_gradient
+from src.model.losses import HuberLossWithWeight, MSELossWithWeight, WeightLoss
+from src.model.resnet import util
+
+
+class PredictionLayer(nn.Cell):
+ """
+ prediction layer
+ """
+
+ def __init__(self, cfg, name, in_channels, out_channels):
+ """
+ Args:
+ cfg: net config
+ name: prediction name
+ in_channels: in channels
+ out_channels: out channels
+ """
+ super(PredictionLayer, self).__init__()
+ self.cfg = cfg
+ self.name = name
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.conv2d_transpose = nn.Conv2dTranspose(in_channels=self.in_channels,
+ out_channels=self.out_channels, kernel_size=3, stride=2,
+ bias_init='normal', has_bias=True)
+
+ def construct(self, x):
+ return self.conv2d_transpose(x)
+
+
+class PoseNet(nn.Cell):
+ """
+ pose net
+ """
+
+ def __init__(self, cfg):
+ """
+ Args:
+ cfg: net config
+ """
+ super(PoseNet, self).__init__()
+ self.cfg = cfg
+ self.resnet101 = util.resnet_101(3, output_stride=16, global_pool=False)
+ self.resnet101.set_train(False)
+ self.resnet101.set_grad(requires_grad=False)
+ self.mean = np.array(self.cfg.mean_pixel)
+ self.mean = self.mean.reshape([1, 3, 1, 1])
+
+ self.part_pred = PredictionLayer(self.cfg, 'part_pred', 2048, self.cfg.num_joints)
+ if cfg.location_refinement:
+ self.locref = PredictionLayer(cfg, 'locref_pred', 2048, cfg.num_joints * 2)
+ if cfg.pairwise_predict:
+ self.pairwise_pred = PredictionLayer(cfg, 'pairwise_pred', 2048, cfg.num_joints * (cfg.num_joints - 1) * 2)
+ if cfg.intermediate_supervision:
+ self.part_pred_interm = PredictionLayer(cfg, 'part_pred_interm', cfg.intermediate_supervision_input,
+ self.cfg.num_joints)
+
+ def get_im_centered(self, inputs):
+ return inputs - self.mean
+
+ def construct(self, inputs):
+ im_centered = self.get_im_centered(inputs)
+ net, intermediate = self.resnet101(im_centered)
+ net = stop_gradient(net)
+ intermediate = stop_gradient(intermediate)
+ return net, intermediate
+
+
+class PoseNetTest(nn.Cell):
+ """
+ pose net eval
+ """
+
+ def __init__(self, net, cfg):
+ """
+ Args:
+ net: pose net
+ cfg: net config
+ """
+ super(PoseNetTest, self).__init__()
+ self.net = net
+ self.cfg = cfg
+ self.location_refinement = cfg.location_refinement
+ self.pairwise_predict = cfg.pairwise_predict
+ self.sigmoid = nn.Sigmoid()
+
+ def construct(self, *inputs):
+ features, _ = self.net(inputs[0])
+ out = self.net.part_pred(features)
+ pairwise_pred = None
+ locref = None
+ if self.pairwise_predict:
+ pairwise_pred = self.net.pairwise_pred(features)
+ if self.location_refinement:
+ locref = self.net.locref(features)
+
+ return self.sigmoid(out), pairwise_pred, locref
+
+
+class PoseNetBaseLoss(nn.Cell):
+ """
+ pose net base loss
+ """
+
+ def __init__(self, net, cfg):
+ super(PoseNetBaseLoss, self).__init__()
+ self.net = net
+ self.cfg = cfg
+
+
+class PoseNetTotalLoss(PoseNetBaseLoss):
+ """
+ pose net total loss
+ """
+
+ def __init__(self, net, cfg):
+ """
+ Args:
+ net: pose net
+ cfg: net config
+ """
+ super(PoseNetTotalLoss, self).__init__(net, cfg)
+ self.part_score_weights = 1.0
+ self.sce = ms.ops.SigmoidCrossEntropyWithLogits()
+ self.weight_loss = WeightLoss()
+ self.pairwise_loss_func = HuberLossWithWeight() if self.cfg.pairwise_huber_loss else MSELossWithWeight()
+ self.locref_loss_func = HuberLossWithWeight() if self.cfg.locref_huber_loss else MSELossWithWeight()
+ self.location_refinement = cfg.location_refinement
+ self.pairwise_predict = cfg.pairwise_predict
+ self.intermediate_supervision = cfg.intermediate_supervision
+ self.locref_loss_weight = cfg.locref_loss_weight
+
+ def construct(self, inputs, part_score_targets, part_score_weights,
+ locref_targets, locref_mask,
+ pairwise_targets=None, pairwise_mask=None
+ ):
+ """
+ Args:
+ inputs: input images
+ part_score_targets: part score targets
+ part_score_weights: part score weights
+ locref_targets: location reference targets
+ locref_mask: location reference mask
+ pairwise_targets: pairwise targets
+ pairwise_mask: pairwise mask
+ Return:
+ total loss
+ """
+ features, intermediate = self.net(inputs)
+ total_loss = self.sce(self.net.part_pred(features), part_score_targets)
+ total_loss = self.weight_loss(total_loss, part_score_weights)
+
+ if self.intermediate_supervision:
+ part_loss_interm = self.sce(self.net.part_pred_interm(intermediate), part_score_targets)
+ part_loss_interm = self.weight_loss(part_loss_interm, part_score_weights)
+ total_loss = total_loss + part_loss_interm
+
+ if self.location_refinement:
+ locref_pred = self.net.locref(features)
+ locref_loss = self.locref_loss_weight * self.locref_loss_func(locref_pred, locref_targets, locref_mask)
+ total_loss = total_loss + locref_loss
+
+ if self.pairwise_predict:
+ pairwise_pred = self.net.pairwise_pred(features)
+ pairwise_loss = self.cfg.pairwise_loss_weight * self.pairwise_loss_func(pairwise_pred, pairwise_targets,
+ pairwise_mask)
+ total_loss = total_loss + pairwise_loss
+
+ return total_loss
diff --git a/research/cv/ArtTrack/src/model/predict.py b/research/cv/ArtTrack/src/model/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..97a30dc67cf795f255f522390f418ca59c62b7b3
--- /dev/null
+++ b/research/cv/ArtTrack/src/model/predict.py
@@ -0,0 +1,151 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import numpy as np
+import scipy.io
+
+
+def extract_cnn_output(scmap, locref_target=None, pairwise_pred=None, cfg=None, pairwise_stats=None):
+ """
+ extract cnn output
+
+ Args:
+ scmap: score map
+ locref_target: location reference target
+ pairwise_pred: pairwise prediction
+ cfg: net config
+ pairwise_stats: pairwise stats
+ Return:
+ (score map, location reference, pairwise difference)
+ """
+ scmap = np.squeeze(scmap)
+ locref = None
+ pairwise_diff = None
+ if cfg.location_refinement and locref_target is not None:
+ locref = np.squeeze(locref_target)
+ shape = locref.shape
+ locref = np.reshape(locref, (shape[0], shape[1], -1, 2))
+ locref *= cfg.locref_stdev
+ if cfg.pairwise_predict and pairwise_pred is not None:
+ pairwise_diff = np.squeeze(pairwise_pred)
+ shape = pairwise_diff.shape
+ pairwise_diff = np.reshape(pairwise_diff, (shape[0], shape[1], -1, 2))
+ num_joints = cfg.num_joints
+ for pair in pairwise_stats:
+ pair_id = (num_joints - 1) * pair[0] + pair[1] - int(pair[0] < pair[1])
+ pairwise_diff[:, :, pair_id, 0] *= pairwise_stats[pair]["std"][0]
+ pairwise_diff[:, :, pair_id, 0] += pairwise_stats[pair]["mean"][0]
+ pairwise_diff[:, :, pair_id, 1] *= pairwise_stats[pair]["std"][1]
+ pairwise_diff[:, :, pair_id, 1] += pairwise_stats[pair]["mean"][1]
+ return scmap, locref, pairwise_diff
+
+
+def argmax_pose_predict(scmap, offmat, stride):
+ """Combine scoremat and offsets to the final pose."""
+ num_joints = scmap.shape[2]
+ pose = []
+ for joint_idx in range(num_joints):
+ maxloc = np.unravel_index(np.argmax(scmap[:, :, joint_idx]),
+ scmap[:, :, joint_idx].shape)
+ offset = np.array(offmat[maxloc][joint_idx])[::-1] if offmat is not None else 0
+ pos_f8 = (np.array(maxloc).astype('float') * stride + 0.5 * stride + offset)
+ pose.append(np.hstack((pos_f8[::-1],
+ [scmap[maxloc][joint_idx]])))
+ return np.array(pose)
+
+
+def argmax_arrows_predict(scmap, offmat, pairwise_diff, stride):
+ """
+ argmax arrows prediction
+ """
+ num_joints = scmap.shape[2]
+ arrows = {}
+ for joint_idx in range(num_joints):
+ maxloc = np.unravel_index(np.argmax(scmap[:, :, joint_idx]),
+ scmap[:, :, joint_idx].shape)
+ offset = np.array(offmat[maxloc][joint_idx])[::-1] if offmat is not None else 0
+ pos_f8 = (np.array(maxloc).astype('float') * stride + 0.5 * stride +
+ offset)[::-1]
+ for joint_idx_end in range(num_joints):
+ if joint_idx_end != joint_idx:
+ pair_id = (num_joints - 1) * joint_idx + joint_idx_end - int(joint_idx < joint_idx_end)
+ difference = np.array(pairwise_diff[maxloc][pair_id])[::-1] if pairwise_diff is not None else 0
+ pos_f8_end = (np.array(maxloc).astype('float') * stride + 0.5 * stride + difference)[::-1]
+ arrows[(joint_idx, joint_idx_end)] = (pos_f8, pos_f8_end)
+
+ return arrows
+
+
+def multi_dim_argmax(arr):
+ """ Getting argmax over the first 2 axes """
+ m, n, i, j = arr.shape
+ arr = np.reshape(arr, (m * n, i, j))
+ return np.unravel_index(np.argmax(arr, 0), (m, n))
+
+
+def interweave_matrices(x, y, z):
+ """Combine matrices by concatenating their cols: x.col(1),y.col(1),z.col(1) ... x.col(n),y.col(n),z.col(n) """
+ num_joints = x.shape[1]
+ id_x = (np.arange(0, num_joints, 0.5) + 1).astype('int')
+ id_y = (np.arange(0, num_joints, 0.5) + 0.5).astype('int')
+ id_z = (np.arange(0, num_joints, 0.5) + 0).astype('int')
+ x = np.insert(x, id_x, 0, axis=1)
+ y = np.insert(y, id_y, 0, axis=1)
+ z = np.insert(z, id_z, 0, axis=1)
+ return x + y + z
+
+
+def pose_predict_with_gt_segm(scmap, offmat, stride, gt_segm, coco_id):
+ """Generate all poses in an image using segmentations"""
+ if gt_segm.size == 0:
+ img_keypoints = []
+ else:
+ num_persons = gt_segm.shape[2]
+ num_joints = scmap.shape[2]
+ init_w = gt_segm.shape[1]
+ init_h = gt_segm.shape[0]
+
+ upsized_w = scmap.shape[1] * stride
+ upsized_h = scmap.shape[0] * stride
+
+ diff_w_l = int((upsized_w - init_w) // 2)
+ diff_w_r = int(upsized_w - init_w - diff_w_l)
+
+ diff_h_u = int((upsized_h - init_h) // 2)
+ diff_h_d = int(upsized_h - init_h - diff_h_u)
+
+ padded_gt_segm = np.pad(gt_segm, ((diff_h_u, diff_h_d), (diff_w_l, diff_w_r), (0, 0)), 'constant')
+ upsized_scmap = scipy.ndimage.zoom(scmap, (stride, stride, 1))
+
+ person_joint_scmap = padded_gt_segm[:, :, :, np.newaxis] * upsized_scmap[:, :, np.newaxis, :]
+ upsized_maxloc = multi_dim_argmax(person_joint_scmap)
+ maxloc_0 = (upsized_maxloc[0] // stride).astype(int)
+ maxloc_1 = (upsized_maxloc[1] // stride).astype(int)
+ indices = np.array([np.arange(num_joints)] * num_persons)
+ offset = np.moveaxis(offmat[(maxloc_0, maxloc_1, indices)][:, :, ::-1], -1, 0) if offmat is not None else 0
+ pos_f8 = (np.array((maxloc_0, maxloc_1)).astype('float') * stride + 0.5 * stride + offset)
+ v = scmap[(maxloc_0, maxloc_1, indices)]
+ img_keypoints = (interweave_matrices(pos_f8[1].astype('int'), pos_f8[0].astype('int'), v)).tolist()
+
+ coco_img_results = []
+ for person_keypoints in img_keypoints:
+ person_result = {}
+ person_result["image_id"] = coco_id
+ person_result["category_id"] = 1
+ person_result["keypoints"] = person_keypoints
+ person_result["score"] = 1
+ coco_img_results.append(person_result)
+
+ return coco_img_results
diff --git a/research/cv/ArtTrack/src/model/resnet/__init__.py b/research/cv/ArtTrack/src/model/resnet/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/research/cv/ArtTrack/src/model/resnet/resnet.py b/research/cv/ArtTrack/src/model/resnet/resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..94bb77f88e82e39c6605354ca6d95286f333065d
--- /dev/null
+++ b/research/cv/ArtTrack/src/model/resnet/resnet.py
@@ -0,0 +1,314 @@
+# Copyright 2022 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.
+# ============================================================================
+
+from mindspore import nn
+from mindspore import ops
+
+
+class Subsample(nn.Cell):
+ """Subsamples the input along the spatial dimensions.
+ Args:
+ factor: The subsampling factor.
+ Returns:
+ output: A `Tensor` of size [batch, channels, height_out, width_out] with the
+ input, either intact (if factor == 1) or subsampled (if factor > 1).
+ """
+
+ def __init__(self, factor):
+ super(Subsample, self).__init__()
+ self.factor = factor
+ self.max_pool2d = nn.MaxPool2d(1, self.factor)
+
+ def construct(self, inputs):
+ if self.factor == 1:
+ return inputs
+
+ return self.max_pool2d(inputs)
+
+
+class Conv2dSame(nn.Cell):
+ """
+ stride 2-D convolution with 'SAME' padding.
+ When stride > 1, then we do explicit zero-padding, followed by conv2d with
+ 'VALID' padding.
+ """
+
+ def __init__(self, in_channels, out_channels, kernel_size, stride, rate=1):
+ """
+ Args:
+ in_channels: in channels
+ out_channels: out channels
+ kernel_size: An int with the kernel_size of the filters.
+ stride: An integer, the output stride.
+ rate: An integer, rate for atrous convolution.
+ """
+ super(Conv2dSame, self).__init__()
+ self.in_channels = in_channels
+ self.num_outputs = out_channels
+ self.kernel_size = kernel_size
+ self.stride = stride
+ self.rate = rate
+
+ self.kernel_size_effective = self.kernel_size + (self.kernel_size - 1) * (self.rate - 1)
+ self.pad_total = self.kernel_size_effective - 1
+ self.pad_beg = self.pad_total // 2
+ self.pad_end = self.pad_total - self.pad_beg
+
+ if self.stride == 1:
+ self.conv2d = nn.Conv2dBnAct(in_channels, self.num_outputs, self.kernel_size,
+ stride=self.stride, dilation=self.rate, pad_mode='same', has_bn=True,
+ activation="relu")
+ else:
+ self.pad = nn.Pad(((0, 0), (0, 0), (self.pad_beg, self.pad_end), (self.pad_beg, self.pad_end)))
+ self.conv2d = nn.SequentialCell([self.pad, nn.Conv2dBnAct(in_channels, self.num_outputs, self.kernel_size,
+ stride=self.stride, dilation=self.rate,
+ pad_mode='valid', has_bn=True,
+ activation="relu")])
+
+ def construct(self, inputs):
+ """
+ Args:
+ inputs: A 4-D tensor of size [batch, channels, height_out, width_out].
+ Returns:
+ output: A 4-D tensor of size [batch, channels, height_out, width_out] with
+ the convolution output.
+ """
+ return self.conv2d(inputs)
+
+
+class Bottleneck(nn.Cell):
+ """
+ Bottleneck residual unit variant with BN after convolutions.
+ """
+ expansion = 4
+
+ def __init__(self,
+ in_channel,
+ out_channel,
+ stride,
+ rate=1):
+ """
+ Args:
+ in_channel: in channel.
+ out_channel: out channel.
+ stride: The ResNet unit's stride. Determines the amount of downsampling of
+ the units output compared to its input.
+ rate: An integer, rate for atrous convolution.
+ """
+ super(Bottleneck, self).__init__()
+ self.in_channel = in_channel
+ self.out_channel = out_channel
+ self.depth = out_channel // 4
+ self.stride = stride
+ self.rate = rate
+ if self.out_channel == self.in_channel:
+ self.conv2d_shortcut = Subsample(self.stride)
+ else:
+ self.conv2d_shortcut = nn.Conv2dBnAct(self.in_channel, self.out_channel, 1, stride=self.stride, has_bn=True)
+
+ self.relu = nn.ReLU()
+
+ self.conv2d1 = nn.Conv2dBnAct(self.in_channel, self.depth, 1, stride=1, activation="relu", has_bn=True)
+ self.conv2d2 = Conv2dSame(self.depth, self.depth, 3, self.stride, self.rate)
+ self.conv2d3 = nn.Conv2dBnAct(self.depth, self.out_channel, 1, stride=1, has_bn=True)
+
+ def construct(self, inputs):
+ """
+ Args:
+ inputs: A tensor of size [batch, channels, height, width].
+ Returns:
+ The ResNet unit's output.
+ """
+ shortcut = self.conv2d_shortcut(inputs)
+
+ residual = self.conv2d1(inputs)
+ residual = self.conv2d2(residual)
+ residual = self.conv2d3(residual)
+
+ output = self.relu(shortcut + residual)
+ return output
+
+
+class Block(nn.Cell):
+ """
+ Resnet block consisted of Bottlenecks
+ """
+
+ def __init__(self, units, intermediate=None):
+ """
+ Args:
+ units: Bottlenecks
+ intermediate: if is not None, the result of corresponding Bottlenecks will be returned
+ as a intermediate value
+ """
+ super(Block, self).__init__()
+ self.units = units
+ self.units_cells = nn.CellList(units)
+ self.intermediate = intermediate
+
+ def construct(self, feature, intermediate=None):
+ """
+ Args:
+ feature: feature
+ intermediate: intermediate
+
+ Return:
+ (feature, ) or (feature, intermediate)
+ """
+ out = feature
+ out_intermediate = intermediate
+ for i, cell in enumerate(self.units_cells, 1):
+ out = cell(out)
+ if self.intermediate is not None and self.intermediate == i:
+ out_intermediate = out
+ if out_intermediate is None:
+ r = (out,)
+ else:
+ r = (out, out_intermediate)
+ return r
+
+
+class Layer(nn.Cell):
+ """
+ Resnet Layer consisted of Block.
+ """
+ current_stride = 1
+ rate = 1
+
+ def __init__(self, in_channel, blocks, output_stride=None, unit_class=Bottleneck, block_class=Block):
+ """
+ Args:
+ in_channel: in channel
+ blocks: blocks config. should be generated by _make_block
+ output_stride: If None, then the output will be computed at the nominal
+ network stride. If output_stride is not None, it specifies the requested
+ ratio of input to output spatial resolution.
+ unit_class: class of unit. options: Bottleneck
+ block_class: class of block. options: Block
+ """
+ super(Layer, self).__init__()
+ self.unit_class = unit_class
+ self.block_class = block_class
+ self.blocks = blocks
+ self.output_stride = output_stride
+ self.in_channel = in_channel
+ self.blocks_cell = []
+ self.last_out_channel = self.in_channel
+ self.intermediate_block = 3
+ self.intermediate_unit = 12
+ for i, block in enumerate(blocks, 1):
+ units = []
+ for _, unit in enumerate(block, 1):
+ if self.output_stride is not None and self.current_stride > self.output_stride:
+ raise ValueError('The target output_stride cannot be reached.')
+
+ if self.output_stride is not None and self.current_stride == self.output_stride:
+ net = self.unit_class(rate=self.rate, in_channel=self.last_out_channel, **dict(unit, stride=1))
+ self.last_out_channel = unit['out_channel']
+ self.rate *= unit.get('stride', 1)
+
+ else:
+ net = self.unit_class(rate=1, in_channel=self.last_out_channel, **unit)
+ self.last_out_channel = unit['out_channel']
+ self.current_stride *= unit.get('stride', 1)
+ units.append(net)
+ self.blocks_cell.append(
+ self.block_class(units, None if i != self.intermediate_block else self.intermediate_unit))
+ if self.output_stride is not None and self.current_stride != self.output_stride:
+ raise ValueError('The target output_stride cannot be reached.')
+ self.blocks_cell = nn.CellList(self.blocks_cell)
+ self.out_channel = self.last_out_channel
+
+ def construct(self, inputs):
+ """
+ Args:
+ inputs: feature
+ Return:
+ block result
+ """
+ out = (inputs, None)
+ for cell in self.blocks_cell:
+ out = cell(*out)
+ return out
+
+
+class ResNet(nn.Cell):
+ """
+ Resnet
+ """
+
+ def __init__(self,
+ blocks,
+ in_channels,
+ num_classes=None,
+ global_pool=True,
+ output_stride=None,
+ include_root_block=True,
+ ):
+ """
+ Args:
+ blocks: blocks config. should be generated by _make_block
+ in_channels: in channels
+ num_classes: the number of classes
+ global_pool: If True, we perform global average pooling before computing the
+ logits. Set to True for image classification, False for dense prediction.
+ output_stride: If None, then the output will be computed at the nominal
+ network stride. If output_stride is not None, it specifies the requested
+ ratio of input to output spatial resolution.
+ include_root_block: include_root_block: If True, include the initial convolution followed by
+ max-pooling, if False excludes it.
+ """
+ super(ResNet, self).__init__()
+ self.blocks = blocks
+ self.in_channels = in_channels
+ self.num_classes = num_classes
+ self.global_pool = global_pool
+ self.output_stride = output_stride
+ self.include_root_block = include_root_block
+
+ self.conv2d_same = Conv2dSame(self.in_channels, 64, 7, 2)
+ self.max_pool2d = nn.MaxPool2d(3, 2, pad_mode='same')
+
+ self.reduce_mean = ops.ReduceMean(True)
+
+ if self.include_root_block:
+ if self.output_stride is not None:
+ if self.output_stride % 4 != 0:
+ raise ValueError('The output_stride needs to be a multiple of 4.')
+ self.output_stride /= 4
+ self.layer = Layer(64, self.blocks, self.output_stride)
+ else:
+ self.layer = Layer(self.in_channels, self.blocks, self.output_stride)
+ if self.num_classes is not None:
+ self.conv2d1 = nn.Conv2d(self.blocks[-1][-1]['out_channel'], self.num_classes, 1)
+
+ def construct(self, inputs):
+ """
+ Args:
+ inputs: inputs
+ Return:
+ (result, intermediate)
+ """
+ net = inputs
+ if self.include_root_block:
+ net = self.conv2d_same(net)
+ net = self.max_pool2d(net)
+ net, intermediate = self.layer(net)
+ if self.global_pool:
+ # Global average pooling.
+ net = self.reduce_mean(net, [2, 3])
+ if self.num_classes is not None:
+ net = self.conv2d1(net)
+ return net, intermediate
diff --git a/research/cv/ArtTrack/src/model/resnet/util.py b/research/cv/ArtTrack/src/model/resnet/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..120a601785a9a542626aab155afc1ab39c7b7a0c
--- /dev/null
+++ b/research/cv/ArtTrack/src/model/resnet/util.py
@@ -0,0 +1,41 @@
+# Copyright 2022 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.
+# ============================================================================
+
+from src.model.resnet import resnet
+
+
+def _make_blocks(out_channel, stride, num_block):
+ """
+ make blocks config
+ """
+ result = []
+ for ch, s, num in zip(out_channel, stride, num_block):
+ result.append([{
+ 'out_channel': ch,
+ 'stride': 1
+ }] * (num - 1) + [{
+ 'out_channel': ch,
+ 'stride': s
+ }])
+ return result
+
+
+def resnet_101(in_channels, **kwargs):
+ """
+ get resnet 101
+ """
+ blocks = _make_blocks([256, 512, 1024, 2048], [2, 2, 2, 1], [3, 4, 23, 3])
+ net = resnet.ResNet(blocks, in_channels, **kwargs)
+ return net
diff --git a/research/cv/ArtTrack/src/multiperson/detections.py b/research/cv/ArtTrack/src/multiperson/detections.py
new file mode 100644
index 0000000000000000000000000000000000000000..957fc0855f325abe71cfa89a612534855f6eac6f
--- /dev/null
+++ b/research/cv/ArtTrack/src/multiperson/detections.py
@@ -0,0 +1,94 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import math
+from collections import namedtuple
+
+import numpy as np
+from nms_grid import nms_grid
+
+Detections = namedtuple('Detections', ['coord', 'coord_grid', 'conf', 'pairwise'])
+
+
+def pos_from_grid_raw(cfg, gridpos):
+ """
+ position from grid raw
+ """
+ return gridpos * cfg.stride + 0.5 * cfg.stride
+
+
+def pos_from_gridpos_offset(cfg, gridpos, pred_offset):
+ """
+ position from grid position offset
+ """
+ return pos_from_grid_raw(cfg, gridpos) + pred_offset
+
+
+def make_nms_grid(nms_radius):
+ """
+ make nms grid
+ """
+ nms_radius = math.ceil(nms_radius)
+ dist_grid = np.zeros([2 * nms_radius + 1, 2 * nms_radius + 1], dtype=np.uint8)
+ for yidx in range(dist_grid.shape[0]):
+ for xidx in range(dist_grid.shape[1]):
+ if (yidx - nms_radius) ** 2 + (xidx - nms_radius) ** 2 <= nms_radius ** 2:
+ dist_grid[yidx][xidx] = 1
+ return dist_grid
+
+
+def extract_detections(cfg, scmap, locref, pairwise_diff):
+ """
+ extract detections
+ """
+ num_joints = cfg.num_joints
+ num_pairwise_relations = pairwise_diff.shape[2]
+
+ # get dist_grid
+ dist_grid = make_nms_grid(cfg.nms_radius)
+
+ unProb = [None] * num_joints
+ unPos = [None] * num_joints
+ unPos_grid = [None] * num_joints
+ pairwiseDiff = [None] * num_joints
+
+ # apply nms
+ for p_idx in range(num_joints):
+ # IMPORTANT, as C++ function expects row-major
+ prob_map = np.ascontiguousarray(scmap[:, :, p_idx])
+ # print(prob_map.flags) has to be C_CONTIGUOUS
+
+ dets = nms_grid(prob_map, dist_grid, cfg.det_min_score)
+
+ cur_prob = np.zeros([len(dets), 1], dtype=np.float64)
+ cur_pos = np.zeros([len(dets), 2], dtype=np.float64)
+ cur_pos_grid = np.zeros([len(dets), 2], dtype=np.float64)
+ cur_pairwise = np.zeros([len(dets), num_pairwise_relations, 2], dtype=np.float64)
+
+ for idx, didx in enumerate(dets):
+ ix = didx % scmap.shape[1]
+ iy = didx // scmap.shape[1]
+
+ cur_prob[idx, 0] = scmap[iy, ix, p_idx]
+ cur_pos_grid[idx, :] = pos_from_grid_raw(cfg, np.array([ix, iy]))
+ cur_pos[idx, :] = cur_pos_grid[idx, :] + locref[iy, ix, p_idx, :]
+ cur_pairwise[idx, :, :] = pairwise_diff[iy, ix, :, :]
+
+ unProb[p_idx] = cur_prob
+ unPos[p_idx] = cur_pos
+ unPos_grid[p_idx] = cur_pos_grid
+ pairwiseDiff[p_idx] = cur_pairwise
+
+ return Detections(coord=unPos, coord_grid=unPos_grid, conf=unProb, pairwise=pairwiseDiff)
diff --git a/research/cv/ArtTrack/src/multiperson/predict.py b/research/cv/ArtTrack/src/multiperson/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..75e6593cf476312a20823b2fc2b9d5cbf406b026
--- /dev/null
+++ b/research/cv/ArtTrack/src/multiperson/predict.py
@@ -0,0 +1,394 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import math
+import os
+from collections import namedtuple
+
+import numpy as np
+import scipy.io as sio
+from multicut import solve_nl_lmp
+
+from src.dataset.pose import get_pairwise_index
+
+
+def logit_transform(p):
+ """
+ calculate logit
+ """
+ p = np.minimum(np.maximum(p, 1e-7), 1.0 - 1e-7)
+ return np.log((1 - p) / p)
+
+
+def eval_graph(sm, detections):
+ """
+ eval graph
+ """
+ # time_start = time.time()
+
+ unary_prob = detections.conf
+ coordinates = detections.coord
+ # pairwise_regr = detections.pairwise
+
+ cidx_list = range(0, sm.num_keypoints)
+
+ unary_counts = []
+
+ for _, cidx in enumerate(cidx_list):
+ unary_counts.append(unary_prob[cidx].shape[0])
+
+ num_unary = sum(unary_counts)
+ num_pairwise = 0
+
+ for idx1, cidx1 in enumerate(cidx_list):
+ for cidx2 in cidx_list[idx1:]:
+ if cidx1 == cidx2:
+ num_pairwise += unary_prob[cidx1].shape[0] * (unary_prob[cidx1].shape[0] - 1) // 2
+ else:
+ num_pairwise += unary_prob[cidx1].shape[0] * unary_prob[cidx2].shape[0]
+
+ pos_array = np.zeros([num_unary, 2], dtype=np.float64)
+ unary_array = np.zeros([num_unary, 1], dtype=np.float64)
+ pw_array = np.zeros([num_pairwise, 1], dtype=np.float64)
+ pwidx_array = np.zeros([num_pairwise, 2], dtype=np.uint16)
+
+ firstidx = 0
+ firstidx_list = []
+ for _, cidx in enumerate(cidx_list):
+ lastidx = firstidx + unary_prob[cidx].shape[0]
+ unary_array[firstidx:lastidx] = unary_prob[cidx]
+ pos_array[firstidx:lastidx] = coordinates[cidx]
+
+ firstidx_list.append(firstidx)
+ firstidx = lastidx
+
+ firstidx = 0
+ for idx1, cidx1 in enumerate(cidx_list):
+ for idx2 in range(idx1, len(cidx_list)):
+
+ if coordinates[cidx1].shape[0] > 0:
+ cidx2 = cidx_list[idx2]
+
+ if coordinates[cidx2].shape[0] > 0:
+
+ if not sm.need_this_pairwise(cidx1, cidx2):
+ continue
+
+ cur_prob, ptidx = sm.eval(cidx1, cidx2, detections)
+ lastidx = firstidx + cur_prob.shape[0]
+
+ ptidx[:, 0] += firstidx_list[idx1]
+ ptidx[:, 1] += firstidx_list[idx2]
+
+ pw_array[firstidx:lastidx, 0] = cur_prob
+ pwidx_array[firstidx:lastidx, :] = ptidx
+
+ firstidx = lastidx
+
+ is_sparse_graph = True
+ solver_type = False
+ do_suppression = True
+ logit_in_solver = False
+
+ if unary_array.shape[0] > 0:
+
+ unary_array_solver = unary_array if logit_in_solver else logit_transform(unary_array)
+ pw_array_solver = pw_array if logit_in_solver else logit_transform(pw_array)
+
+ # time_start = time.time()
+
+ res = solve_nl_lmp(unary_array_solver, pwidx_array, pw_array_solver,
+ is_sparse_graph, solver_type, do_suppression, logit_in_solver)
+
+ unLab = np.array(res, dtype=np.uint64)
+
+ firstidx = 0
+ for cidx in cidx_list:
+ lastidx = firstidx + unary_prob[cidx].shape[0]
+ unLab[firstidx:lastidx, 0] = cidx
+ firstidx = lastidx
+
+ else:
+ unLab = np.array([])
+
+ return unLab, pos_array, unary_array, pwidx_array, pw_array
+
+
+def get_person_conf_single(sm, unProb, pos_array, pwidx_array, pw_array):
+ """
+ get single person config
+ """
+ assert len(unProb) == sm.num_keypoints
+ assert pwidx_array.shape[0] == pw_array.shape[0]
+ assert pw_array.shape[1] == 1
+
+ det_type_idx = []
+
+ firstidx = 0
+ for pidx in range(len(unProb)):
+ lastidx = firstidx + unProb[pidx].shape[0]
+
+ curidx = np.array([False] * pos_array.shape[0])
+ curidx[firstidx:lastidx] = True
+
+ firstidx = lastidx
+ det_type_idx.append(curidx)
+
+ # num people == number of heads
+ head_idx = 13
+ num_people = unProb[head_idx].shape[0]
+
+ connect_graph = dict()
+ connect_graph[13] = (12,)
+ connect_graph[12] = (8, 9, 2, 3)
+ connect_graph[8] = (7,)
+ connect_graph[7] = (6,)
+ connect_graph[9] = (10,)
+ connect_graph[10] = (11,)
+ connect_graph[2] = (1,)
+ connect_graph[1] = (0,)
+ connect_graph[3] = (4,)
+ connect_graph[4] = (5,)
+
+ person_conf = np.zeros([num_people, sm.num_keypoints, 2])
+ SearchNode = namedtuple('SearchNode', ['pidx', 'kidx', 'hidx'])
+
+ search_queue = []
+
+ for pidx, hidx in enumerate(np.flatnonzero(det_type_idx[head_idx])):
+ search_queue.append(SearchNode(pidx=pidx, kidx=head_idx, hidx=hidx))
+ person_conf[pidx, head_idx, :] = pos_array[hidx, :]
+
+ assert len(search_queue) == num_people
+
+ while search_queue:
+ node = search_queue.pop()
+
+ pidx = node.pidx
+ kidx = node.kidx
+ hidx = node.hidx
+ loop_graph(kidx, connect_graph, pwidx_array, hidx, det_type_idx, pw_array, person_conf, pos_array, pidx,
+ search_queue, SearchNode)
+
+ return person_conf
+
+
+def loop_graph(kidx, connect_graph, pwidx_array, hidx, det_type_idx, pw_array, person_conf, pos_array, pidx,
+ search_queue, SearchNode):
+ # find the closes match for current part
+ if kidx in connect_graph:
+
+ for kidx2 in connect_graph[kidx]:
+
+ # search all pairwise with compatible type
+ best_hidx, best_pw = search_pairwise_with_compatible_type(pwidx_array, hidx, det_type_idx, pw_array,
+ kidx2)
+
+ if best_pw > 0.5:
+ person_conf[pidx, kidx2, :] = pos_array[best_hidx, :]
+ search_queue.append(SearchNode(pidx=pidx, kidx=kidx2, hidx=best_hidx))
+
+
+def search_pairwise_with_compatible_type(pwidx_array, hidx, det_type_idx, pw_array, kidx2):
+ best_hidx = None
+ best_pw = None
+ for idx in range(pwidx_array.shape[0]):
+ if hidx in (pwidx_array[idx, 0], pwidx_array[idx, 1] == hidx):
+ idx2 = np.flatnonzero(pwidx_array[idx, :] != hidx)[0]
+ other_hidx = pwidx_array[idx, idx2]
+
+ if det_type_idx[kidx2][other_hidx]:
+ if pw_array[idx] > best_pw:
+ best_hidx = other_hidx
+ best_pw = pw_array[idx]
+ return best_hidx, best_pw
+
+
+def get_person_conf_multicut(sm, unLab, unary_array, pos_array):
+ """
+ get multicut person config
+ """
+ if unLab.shape[0] > 0:
+ num_people = int(np.max(unLab[:, 1])) + 1
+ else:
+ num_people = 0
+
+ person_conf = np.zeros([num_people, sm.num_keypoints, 2])
+ sum_prob = np.zeros([num_people, sm.num_keypoints, 1])
+
+ # compute weighted average of keypoints of the same time
+ for didx in range(unLab.shape[0]):
+ kidx = unLab[didx, 0]
+ pidx = unLab[didx, 1]
+
+ person_conf[pidx, kidx, :] += pos_array[didx, :] * unary_array[didx]
+ sum_prob[pidx, kidx] += unary_array[didx]
+
+ print("num_people: ", num_people)
+
+ for pidx in range(num_people):
+ for kidx in range(sm.num_keypoints):
+ if sum_prob[pidx, kidx] > 0:
+ person_conf[pidx, kidx, :] /= sum_prob[pidx, kidx]
+
+ return person_conf
+
+
+def compute_angle(deltaX, deltaY):
+ """
+ compute angle
+ """
+ angle = np.arctan2(deltaY, deltaX)
+
+ assert (angle > math.pi).sum() == 0
+ assert (angle < -math.pi).sum() == 0
+
+ return angle
+
+
+def wrap_angle(a):
+ """
+ wrap angle
+ """
+ larger = a > math.pi
+ smaller = a < -math.pi
+ a[larger] = a[larger] - 2 * math.pi
+ a[smaller] = a[smaller] + 2 * math.pi
+
+ return a
+
+
+def compute_features(delta_real, delta_predicted):
+ """
+ compute features
+ """
+ a = compute_angle(delta_real[:, 0], delta_real[:, 1])
+ a_forward = compute_angle(delta_predicted[:, 0], delta_predicted[:, 1])
+ delta1 = delta_real - delta_predicted
+
+ dist = np.linalg.norm(delta1, axis=1, ord=2)
+ abs_a = np.abs(wrap_angle(a - a_forward))
+ return dist, abs_a
+
+
+class SpatialModel:
+
+ def __init__(self, cfg):
+ self.num_keypoints = cfg.num_joints
+ num_keypoints = cfg.num_joints
+ self.cfg = cfg
+
+ self.graph_dict = dict()
+
+ self.same_part_pw_coef = 0.2
+
+ self.X_min = [[None] * num_keypoints for idx in range(num_keypoints)]
+ self.X_max = [[None] * num_keypoints for idx in range(num_keypoints)]
+ self.w = [[None] * num_keypoints for idx in range(num_keypoints)]
+
+ def load(self):
+ for cidx1 in range(self.num_keypoints):
+ for cidx2 in range(cidx1 + 1, self.num_keypoints):
+ model_name = "{}/spatial_model_cidx_{}_{}.mat".format(self.cfg.pairwise_model_dir, cidx1 + 1, cidx2 + 1)
+ # print "loading:", model_name
+ if not os.path.isfile(model_name):
+ continue
+
+ spatial_model = sio.loadmat(model_name)
+
+ self.X_max[cidx1][cidx2] = spatial_model['spatial_model']['training_opts'][0][0][0]['X_max'][0][0]
+ self.X_min[cidx1][cidx2] = spatial_model['spatial_model']['training_opts'][0][0][0]['X_min'][0][0]
+ self.w[cidx1][cidx2] = spatial_model['spatial_model']['log_reg'][0][0][0]['w'][0][0][:]
+
+ if not self.cfg.tensorflow_pairwise_order:
+ tmpval = sio.loadmat(self.cfg.pairwise_stats_fn)
+ self.graph = tmpval['graph']
+ self.pairwise_means = tmpval['means']
+ self.pairwise_std_devs = tmpval['std_devs']
+
+ for gidx in range(self.graph.shape[0]):
+ cidx1 = self.graph[gidx, 0]
+ cidx2 = self.graph[gidx, 1]
+ self.graph_dict[(cidx1, cidx2)] = gidx
+
+ def get_fwd_bwd_index(self, cidx1, cidx2):
+ if self.cfg.tensorflow_pairwise_order:
+ fwd_idx = get_pairwise_index(cidx1, cidx2, self.cfg.num_joints)
+ bwd_idx = get_pairwise_index(cidx2, cidx1, self.cfg.num_joints)
+ else:
+ fwd_idx = self.graph_dict[(cidx1 + 1, cidx2 + 1)]
+ bwd_idx = self.graph_dict[(cidx2 + 1, cidx1 + 1)]
+ return fwd_idx, bwd_idx
+
+ def need_this_pairwise(self, cidx1, cidx2):
+ if cidx1 == cidx2:
+ return True
+ sparse_graph = self.cfg.sparse_graph
+ return not sparse_graph or [cidx1, cidx2] in sparse_graph
+
+ def eval(self, cidx1, cidx2, detections):
+ unPos = detections.coord
+
+ idx_type1 = np.array(range(unPos[cidx1].shape[0]))
+ idx_type2 = np.array(range(unPos[cidx2].shape[0]))
+
+ assert idx_type1.shape[0] > 0
+ assert idx_type2.shape[0] > 0
+
+ num_edges = len(idx_type1) * len(idx_type2)
+
+ tmpidx1, tmpidx2 = np.meshgrid(idx_type1, idx_type2)
+ ptidx = np.hstack((tmpidx1.T.reshape((num_edges, 1)), tmpidx2.T.reshape((num_edges, 1))))
+
+ if cidx1 != cidx2:
+ cur_prob = self.compute_different_part_pairwise(cidx1, cidx2, detections, ptidx, num_edges)
+ else:
+ cur_prob = None
+ ptidx = ptidx[ptidx[:, 0] < ptidx[:, 1]]
+
+ delta = unPos[cidx2][ptidx[:, 1], :] - unPos[cidx1][ptidx[:, 0], :]
+ dists = np.linalg.norm(delta, axis=1, ord=2)
+
+ cur_prob = 1. / (1 + np.exp(self.same_part_pw_coef * dists - 7.5))
+
+ return cur_prob, ptidx
+
+ def compute_different_part_pairwise(self, cidx1, cidx2, detections, ptidx, num_edges):
+ unPos = detections.coord
+ unPos_grid = detections.coord_grid
+ nextReg = detections.pairwise
+
+ fwd_idx, bwd_idx = self.get_fwd_bwd_index(cidx1, cidx2)
+
+ assert ptidx.shape[0] > 0
+
+ delta_real_forward = unPos[cidx2][ptidx[:, 1], :] - unPos_grid[cidx1][ptidx[:, 0], :]
+ delta_real_backward = unPos[cidx1][ptidx[:, 0], :] - unPos_grid[cidx2][ptidx[:, 1], :]
+
+ delta_forward = nextReg[cidx1][ptidx[:, 0], fwd_idx, :]
+ delta_backward = nextReg[cidx2][ptidx[:, 1], bwd_idx, :]
+
+ dist1, abs_a1 = compute_features(delta_real_forward, delta_forward)
+ dist2, abs_a2 = compute_features(delta_real_backward, delta_backward)
+
+ featAugm = np.hstack((dist1.reshape(num_edges, 1), abs_a1.reshape(num_edges, 1), dist2.reshape(num_edges, 1),
+ abs_a2.reshape(num_edges, 1)))
+
+ featAugm = np.hstack((featAugm, np.exp(-featAugm), np.ones((num_edges, 1))))
+ temp = self.X_max[cidx1][cidx2] - self.X_min[cidx1][cidx2]
+ featAugm[:, :-1] = (featAugm[:, :-1] - self.X_min[cidx1][cidx2]) / temp
+ cur_prob = 1 / (1 + np.exp(-featAugm.dot(self.w[cidx1][cidx2])))
+
+ return cur_prob
diff --git a/research/cv/ArtTrack/src/multiperson/visualize.py b/research/cv/ArtTrack/src/multiperson/visualize.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd062a5cb5134132a238ab3efa24cbff3be95b59
--- /dev/null
+++ b/research/cv/ArtTrack/src/multiperson/visualize.py
@@ -0,0 +1,288 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import math
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import munkres
+import numpy as np
+import scipy.spatial
+from PIL import Image
+
+
+def _npcircle(image, cx, cy, radius, color, transparency=0.0):
+ """Draw a circle on an image using only numpy methods."""
+ radius = int(radius)
+ cx = int(cx)
+ cy = int(cy)
+ y, x = np.ogrid[-radius: radius, -radius: radius]
+ index = x ** 2 + y ** 2 <= radius ** 2
+ temp = image[cy - radius:cy + radius, cx - radius:cx + radius][index].astype('float32') * transparency + np.array(
+ color).astype('float32') * (1.0 - transparency)
+ image[cy - radius:cy + radius, cx - radius:cx + radius][index] = temp.astype('uint8')
+
+
+def check_point(cur_x, cur_y, minx, miny, maxx, maxy):
+ """
+ check whether the point is in the area
+ """
+ return minx < cur_x < maxx and miny < cur_y < maxy
+
+
+def visualize_joints(image, pose):
+ """
+ visualize joints
+ """
+ _marker_size = 8
+ minx = 2 * _marker_size
+ miny = 2 * _marker_size
+ maxx = image.shape[1] - 2 * _marker_size
+ maxy = image.shape[0] - 2 * _marker_size
+ num_joints = pose.shape[0]
+
+ visim = image.copy()
+ colors = [[255, 0, 0], [0, 255, 0], [0, 0, 255], [0, 245, 255], [255, 131, 250], [255, 255, 0],
+ [255, 0, 0], [0, 255, 0], [0, 0, 255], [0, 245, 255], [255, 131, 250], [255, 255, 0],
+ [0, 0, 0], [255, 255, 255], [255, 0, 0], [0, 255, 0], [0, 0, 255]]
+ for p_idx in range(num_joints):
+ cur_x = pose[p_idx, 0]
+ cur_y = pose[p_idx, 1]
+ if check_point(cur_x, cur_y, minx, miny, maxx, maxy):
+ _npcircle(visim,
+ cur_x, cur_y,
+ _marker_size,
+ colors[p_idx],
+ 0.0)
+ return visim
+
+
+def show_heatmaps(cfg, img, scmap, pose, cmap="jet"):
+ interp = "bilinear"
+ all_joints = cfg.all_joints
+ all_joints_names = cfg.all_joints_names
+ subplot_width = 3
+ subplot_height = math.ceil((len(all_joints) + 1) / subplot_width)
+ _, axarr = plt.subplots(subplot_height, subplot_width)
+ for pidx, part in enumerate(all_joints):
+ plot_j = (pidx + 1) // subplot_width
+ plot_i = (pidx + 1) % subplot_width
+ scmap_part = np.sum(scmap[:, :, part], axis=2)
+ scmap_part = Image.fromarray(scmap_part).resize((scmap_part.shape[0] * 8, scmap_part.shape[0] * 8),
+ Image.BICUBIC)
+ scmap_part = np.array(scmap_part)
+ # scmap_part = imresize(scmap_part, 8.0, interp='bicubic')
+ scmap_part = np.lib.pad(scmap_part, ((4, 0), (4, 0)), 'minimum')
+ curr_plot = axarr[plot_j, plot_i]
+ curr_plot.set_title(all_joints_names[pidx])
+ curr_plot.axis('off')
+ curr_plot.imshow(img, interpolation=interp)
+ curr_plot.imshow(scmap_part, alpha=.5, cmap=cmap, interpolation=interp)
+
+ curr_plot = axarr[0, 0]
+ curr_plot.set_title('Pose')
+ curr_plot.axis('off')
+ curr_plot.imshow(visualize_joints(img, pose))
+
+ plt.show()
+
+
+def show_arrows(cfg, img, pose, arrows):
+ fig = plt.figure()
+ a = fig.add_subplot(2, 2, 1)
+ plt.imshow(img)
+ a.set_title('Initial Image')
+
+ b = fig.add_subplot(2, 2, 2)
+ plt.imshow(img)
+ b.set_title('Predicted Pairwise Differences')
+
+ color_opt = ['r', 'g', 'b', 'c', 'm', 'y', 'k']
+ joint_pairs = [(6, 5), (6, 11), (6, 8), (6, 15), (6, 0)]
+ color_legends = []
+ for _id, joint_pair in enumerate(joint_pairs):
+ end_joint_side = ("r " if joint_pair[1] % 2 == 0 else "l ") if joint_pair[1] != 0 else ""
+ end_joint_name = end_joint_side + cfg.all_joints_names[int(math.ceil(joint_pair[1] / 2))]
+ start = arrows[joint_pair][0]
+ end = arrows[joint_pair][1]
+ b.arrow(start[0], start[1], end[0] - start[0], end[1] - start[1], head_width=3, head_length=6,
+ fc=color_opt[_id],
+ ec=color_opt[_id], label=end_joint_name)
+ color_legend = mpatches.Patch(color=color_opt[_id], label=end_joint_name)
+ color_legends.append(color_legend)
+
+ plt.legend(handles=color_legends, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+ plt.show()
+
+
+def waitforbuttonpress():
+ plt.waitforbuttonpress(timeout=1)
+
+
+min_match_dist = 200
+marker_size = 5
+
+draw_conf_min_count = 3
+
+
+def get_ref_points(person_conf):
+ avg_conf = np.sum(person_conf, axis=1) / person_conf.shape[1]
+
+ # last points is tip of the head -> use it as reference
+ ref_points = person_conf[:, -1, :]
+
+ # use average of other points if head tip is missing
+ emptyidx = (np.sum(ref_points, axis=1) == 0)
+ ref_points[emptyidx, :] = avg_conf[emptyidx, :]
+
+ return ref_points
+
+
+class PersonDraw:
+ def __init__(self):
+ self.mk = munkres.Munkres()
+
+ self.prev_person_conf = np.zeros([0, 1])
+ self.prev_color_assignment = None
+
+ track_colors_str = ["#F5591E",
+ "#3870FB",
+ "#FE5DB0",
+ "#B4A691",
+ "#43053F",
+ "#3475B1",
+ "#642612",
+ "#B3B43D",
+ "#DD9BFE",
+ "#28948D",
+ "#E99D53",
+ "#012B46",
+ "#9D2DA3",
+ "#04220A",
+ "#62CB22",
+ "#EE8F91",
+ "#D71638",
+ "#00613A",
+ "#318918",
+ "#B770FF",
+ "#82C091",
+ "#6C1333",
+ "#973405",
+ "#B19CB2",
+ "#F6267B",
+ "#284489",
+ "#97BF17",
+ "#3B899C",
+ "#931813",
+ "#FA76B6"]
+
+ self.track_colors = [(int(s[1:3], 16), int(s[3:5], 16), int(s[5:7], 16)) for s in track_colors_str]
+
+ def draw(self, visim, dataset, person_conf):
+ minx = 2 * marker_size
+ miny = 2 * marker_size
+ maxx = visim.shape[1] - 2 * marker_size
+ maxy = visim.shape[0] - 2 * marker_size
+
+ num_people = person_conf.shape[0]
+ color_assignment = dict()
+
+ # MA: assign same color to matching body configurations
+ if self.prev_person_conf.shape[0] > 0 and person_conf.shape[0] > 0:
+ ref_points = get_ref_points(person_conf)
+ prev_ref_points = get_ref_points(self.prev_person_conf)
+
+ # MA: this munkres implementation assumes that num(rows) >= num(columns)
+ if person_conf.shape[0] <= self.prev_person_conf.shape[0]:
+ cost_matrix = scipy.spatial.distance.cdist(ref_points, prev_ref_points)
+ else:
+ cost_matrix = scipy.spatial.distance.cdist(prev_ref_points, ref_points)
+
+ assert cost_matrix.shape[0] <= cost_matrix.shape[1]
+ conf_assign = self.mk.compute(cost_matrix)
+
+ if person_conf.shape[0] > self.prev_person_conf.shape[0]:
+ conf_assign = [(idx2, idx1) for idx1, idx2 in conf_assign]
+ cost_matrix = cost_matrix.T
+
+ for pidx1, pidx2 in conf_assign:
+ if cost_matrix[pidx1][pidx2] < min_match_dist:
+ color_assignment[pidx1] = self.prev_color_assignment[pidx2]
+
+ print("#tracked objects:", len(color_assignment))
+
+ free_coloridx = sorted(list(set(range(len(self.track_colors))).difference(set(color_assignment.values()))),
+ reverse=True)
+
+ for pidx in range(num_people):
+ # color_idx = pidx % len(self.track_colors)
+ if pidx in color_assignment:
+ color_idx = color_assignment[pidx]
+ else:
+ if free_coloridx:
+ color_idx = free_coloridx[-1]
+ free_coloridx = free_coloridx[:-1]
+ else:
+ color_idx = np.random.randint(len(self.track_colors))
+
+ color_assignment[pidx] = color_idx
+
+ assert color_idx < len(self.track_colors)
+ if np.sum(person_conf[pidx, :, 0] > 0) < draw_conf_min_count:
+ continue
+
+ for kidx1, kidx2 in dataset.get_pose_segments():
+ p1 = (int(math.floor(person_conf[pidx, kidx1, 0])), int(math.floor(person_conf[pidx, kidx1, 1])))
+ p2 = (int(math.floor(person_conf[pidx, kidx2, 0])), int(math.floor(person_conf[pidx, kidx2, 1])))
+
+ if check_point(p1[0], p1[1], minx, miny, maxx, maxy) and check_point(p2[0], p2[1], minx, miny, maxx,
+ maxy):
+ color = np.array(self.track_colors[color_idx][::-1], dtype=np.float64) / 255.0
+ plt.plot([p1[0], p2[0]], [p1[1], p2[1]], marker='o', linestyle='solid', linewidth=2.0, color=color)
+
+ self.prev_person_conf = person_conf
+ self.prev_color_assignment = color_assignment
+
+
+keypoint_colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (0, 255, 255), (255, 0, 255), (255, 255, 0),
+ (255, 0, 0), (0, 255, 0), (0, 0, 255), (0, 255, 255), (255, 0, 255), (255, 255, 0),
+ (255, 0, 0), (0, 255, 0), (255, 0, 0), (0, 255, 0), (0, 0, 255)]
+
+
+def visualize_detections(cfg, img, detections):
+ vis_scale = 1.0
+ _marker_size = 4
+
+ minx = 2 * _marker_size
+ miny = 2 * _marker_size
+ maxx = img.shape[1] - 2 * _marker_size
+ maxy = img.shape[0] - 2 * _marker_size
+
+ unPos = detections.coord
+ joints_to_visualise = range(cfg.num_joints)
+ visim_dets = img.copy()
+ for pidx in joints_to_visualise:
+ for didx in range(unPos[pidx].shape[0]):
+ cur_x = unPos[pidx][didx, 0] * vis_scale
+ cur_y = unPos[pidx][didx, 1] * vis_scale
+
+ # / cfg.global_scale
+
+ if check_point(cur_x, cur_y, minx, miny, maxx, maxy):
+ _npcircle(visim_dets,
+ cur_x, cur_y,
+ _marker_size,
+ keypoint_colors[pidx])
+ return visim_dets
diff --git a/research/cv/ArtTrack/src/tool/__init__.py b/research/cv/ArtTrack/src/tool/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/research/cv/ArtTrack/src/tool/decorator.py b/research/cv/ArtTrack/src/tool/decorator.py
new file mode 100644
index 0000000000000000000000000000000000000000..415be68d35ff83b27f11745dd8bbef6ee5b816f2
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/decorator.py
@@ -0,0 +1,17 @@
+import pprint
+
+from src.config import check_config
+from src.log import log
+
+
+def process_cfg(func):
+ """
+ process config decorator
+ """
+
+ def wrapper(cfg, *args, **kwargs):
+ cfg = check_config(cfg)
+ log.info("config: %s", pprint.pformat(cfg))
+ return func(cfg, *args, **kwargs)
+
+ return wrapper
diff --git a/research/cv/ArtTrack/src/tool/eval/__init__.py b/research/cv/ArtTrack/src/tool/eval/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/research/cv/ArtTrack/src/tool/eval/coco.py b/research/cv/ArtTrack/src/tool/eval/coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b933f506b4b2d33b9deaaf26edc4eb05f037070
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/eval/coco.py
@@ -0,0 +1,85 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import json
+
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+from ..decorator import process_cfg
+
+
+def apply_threshold(in_file, threshold):
+ """
+ apply threshold
+ """
+ out_file = in_file[:-5] + '-' + str(threshold) + '.json'
+
+ with open(in_file) as data_file:
+ data = json.load(data_file)
+
+ for person_id in range(len(data)):
+ keypoints = data[person_id]["keypoints"]
+ keypoints = [int(keypoints[i] > threshold) if i % 3 == 2 else int(keypoints[i]) for i in range(len(keypoints))]
+ data[person_id]["keypoints"] = keypoints
+
+ with open(out_file, 'w') as outfile:
+ json.dump(data, outfile)
+
+ return out_file
+
+
+def eval_init(cfg, prediction=None):
+ """
+ init
+ """
+ dataset_path = cfg.dataset.path
+ dataset_phase = cfg.dataset.phase
+ dataset_ann = cfg.dataset.ann
+ threshold = 0
+
+ # initialize coco_gt api
+ ann_file = '%s/annotations/%s_%s.json' % (dataset_path, dataset_ann, dataset_phase)
+ coco_gt = COCO(ann_file)
+
+ # initialize coco_pred api
+ pred_file = apply_threshold(prediction or cfg.gt_segm_output, threshold)
+ coco_pred = coco_gt.loadRes(pred_file)
+
+ return coco_gt, coco_pred
+
+
+@process_cfg
+def eval_coco(cfg=None, prediction=None):
+ """
+ eval coco entry
+ """
+ coco_gt, coco_pred = eval_init(cfg, prediction)
+ eval_mscoco_with_segm(coco_gt, coco_pred)
+
+
+def eval_mscoco_with_segm(coco_gt, coco_pred):
+ """
+ eval mscoco
+
+ Args:
+ coco_gt: ground truth
+ coco_pred: prediction
+ """
+ # running evaluation
+ coco_eval = COCOeval(coco_gt, coco_pred, "keypoints")
+ coco_eval.evaluate()
+ coco_eval.accumulate()
+ coco_eval.summarize()
diff --git a/research/cv/ArtTrack/src/tool/eval/multiple.py b/research/cv/ArtTrack/src/tool/eval/multiple.py
new file mode 100644
index 0000000000000000000000000000000000000000..47e1fed285d30fa5642eb8744902bcd535bac360
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/eval/multiple.py
@@ -0,0 +1,191 @@
+# Copyright 2022 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.
+# ============================================================================
+import os
+import json
+from json import encoder
+
+import matplotlib.pyplot as plt
+import mindspore as ms
+import numpy as np
+from scipy import io as sio
+
+from src.dataset.coco import MSCOCO
+from src.dataset.pose import Batch
+from src.log import log
+from src.model.pose import PoseNet, PoseNetTest
+from src.model.predict import argmax_arrows_predict, argmax_pose_predict, extract_cnn_output, pose_predict_with_gt_segm
+from src.multiperson import visualize
+from src.multiperson.detections import extract_detections
+from src.multiperson.predict import eval_graph, get_person_conf_multicut, SpatialModel
+from src.multiperson.visualize import PersonDraw
+from src.tool.decorator import process_cfg
+
+encoder.FLOAT_REPR = lambda o: format(o, '.2f')
+
+
+def test_one(cfg, test_net, sample, score_maps_cached, cache_score_maps, visual, dataset, sm, graph=False):
+ """
+ predict one sample
+ Args:
+ cfg: config
+ test_net: eval net
+ sample: sample
+ cache_score_maps: if True, cache score maps to scoremap_dir in cfg
+ visual: if True, visualize prediction
+ score_maps_cached: if True, load score from cache
+ graph: if True, calculate graph
+ dataset: dataset object
+ sm: spatial model
+ """
+ coco_results = []
+ draw_multi = PersonDraw()
+ cache_name = "{}.mat".format(sample[Batch.data_item].coco_id)
+ if not score_maps_cached:
+ outputs_np, pairwise_pred, locref = test_net(
+ ms.Tensor(np.expand_dims(sample[Batch.inputs], axis=0),
+ dtype=ms.dtype.float32))
+ scmap, locref, pairwise_diff = extract_cnn_output(outputs_np.transpose([0, 2, 3, 1]).asnumpy(),
+ locref.transpose([0, 2, 3, 1]).asnumpy(),
+ pairwise_pred.transpose([0, 2, 3, 1]).asnumpy(),
+ cfg, dataset.pairwise_stats)
+
+ if cache_score_maps:
+ out_fn = os.path.join(cfg.scoremap_dir, cache_name)
+ d = {'scoremaps': scmap.astype('float32'),
+ 'locreg_pred': locref.astype('float32'),
+ 'pairwise_diff': pairwise_diff.astype('float32')}
+ sio.savemat(out_fn, mdict=d)
+ else:
+ # cache_name = '1.mat'
+ full_fn = os.path.join(cfg.cached_scoremaps, cache_name)
+ mlab = sio.loadmat(full_fn)
+ scmap = mlab["scoremaps"]
+ locref = mlab["locreg_pred"]
+ pairwise_diff = mlab["pairwise_diff"]
+
+ person_conf_multi = None
+ if graph:
+ detections = extract_detections(cfg, scmap, locref, pairwise_diff)
+ unLab, pos_array, unary_array, _, _ = eval_graph(sm, detections)
+ person_conf_multi = get_person_conf_multicut(sm, unLab, unary_array, pos_array)
+
+ coco_img_results = None
+ if cfg.use_gt_segm:
+ coco_img_results = pose_predict_with_gt_segm(scmap, locref, cfg.stride, sample[Batch.data_item].gt_segm,
+ sample[Batch.data_item].coco_id)
+ coco_results += coco_img_results
+
+ if visual:
+ img = np.transpose(np.squeeze(sample[Batch.inputs]).astype('uint8'), [1, 2, 0])
+ pose = argmax_pose_predict(scmap, locref, cfg.stride)
+ arrows = argmax_arrows_predict(scmap, locref, pairwise_diff, cfg.stride)
+ visualize.show_arrows(cfg, img, pose, arrows)
+ visualize.waitforbuttonpress()
+ # visualize.show_heatmaps(cfg, img, scmap, pose)
+
+ # visualize part detections after NMS
+ # visim_dets = visualize_detections(cfg, img, detections)
+ # plt.imshow(visim_dets)
+ # plt.show()
+ # visualize.waitforbuttonpress()
+
+ if person_conf_multi is not None and graph:
+ visim_multi = img.copy()
+ draw_multi.draw(visim_multi, dataset, person_conf_multi)
+ plt.imshow(visim_multi)
+ plt.show()
+
+ if coco_img_results is not None and coco_img_results:
+ dataset.visualize_coco(coco_img_results, sample[Batch.data_item].visibilities)
+ visualize.waitforbuttonpress()
+ return coco_results
+
+
+def test_list(cfg, test_net, idx_list, dataset, score_maps_cached, cache_score_maps, visual, sm, graph):
+ """
+ predict multiple sample
+ Args:
+ cfg: config
+ test_net: eval net
+ idx_list: sample indices
+ cache_score_maps: if True, cache score maps to scoremap_dir in cfg
+ visual: if True, visualize prediction
+ score_maps_cached: if True, load score from cache
+ graph: if True, calculate graph
+ dataset: dataset object
+ sm: spatial model
+ """
+ coco_results = []
+ count = 0
+ total = len(idx_list)
+ for k in idx_list:
+ count += 1
+ log.info('processing image id: %s %s/%s', k, count, total)
+
+ batch = dataset.get_item(k)
+ result = test_one(cfg, test_net, batch, score_maps_cached, cache_score_maps, visual, dataset, sm, graph)
+ if result is not None:
+ coco_results.extend(result)
+ return coco_results
+
+
+@process_cfg
+def test(cfg, cache_score_maps=False, visual=False, development=False, score_maps_cached=False, graph=False,
+ output=None, range_num=None, range_index=None):
+ """
+ entry for predicting multiple coco
+ Args:
+ cfg: config
+ cache_score_maps: if True, cache score maps to scoremap_dir in cfg
+ visual: if True, visualize prediction
+ development: development mode. only predict head 10 samples
+ score_maps_cached: if True, load score from cache
+ graph: if True, calculate graph
+ output: path to output
+ range_num: split eval dataset to multiple range. must work with range_index
+ range_index: only predict specified range. start is 0
+ """
+ cfg.train = False
+ # noinspection PyUnresolvedReferences
+ ms.context.set_context(**cfg.context)
+ dataset = MSCOCO(cfg)
+
+ sm = SpatialModel(cfg)
+ sm.load()
+
+ net = PoseNet(cfg=cfg)
+ test_net = PoseNetTest(net, cfg)
+ if hasattr(cfg, 'load_ckpt') and os.path.exists(cfg.load_ckpt):
+ ms.load_checkpoint(cfg.load_ckpt, net=test_net)
+
+ if cache_score_maps:
+ out_dir = cfg.scoremap_dir
+ if not os.path.exists(out_dir):
+ os.makedirs(out_dir)
+
+ num_images = len(dataset) if not development else min(10, dataset.num_images)
+ coco_results = []
+ if range_num is None or range_num == 1 or range_index is None:
+ coco_results.extend(
+ test_list(cfg, test_net, range(num_images), dataset, score_maps_cached, cache_score_maps, visual,
+ sm, graph))
+ else:
+ lists = np.array_split(range(num_images), range_num)
+ coco_results.extend(
+ test_list(cfg, test_net, lists[range_index], dataset, score_maps_cached, cache_score_maps, visual,
+ sm, graph))
+ if cfg.use_gt_segm:
+ with open(output or cfg.gt_segm_output, 'w') as outfile:
+ json.dump(coco_results, outfile)
diff --git a/research/cv/ArtTrack/src/tool/eval/pck.py b/research/cv/ArtTrack/src/tool/eval/pck.py
new file mode 100644
index 0000000000000000000000000000000000000000..b196662bff1988bdac2f6cc6ee591891aab5ef5d
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/eval/pck.py
@@ -0,0 +1,51 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import numpy as np
+from numpy import array as arr
+
+
+def enclosing_rect(points):
+ """
+ enclose rectangle
+ """
+ xs = points[:, 0]
+ ys = points[:, 1]
+ return np.array([np.amin(xs), np.amin(ys), np.amax(xs), np.amax(ys)])
+
+
+def rect_size(rect):
+ """
+ get rectangle size
+ """
+ return np.array([rect[2] - rect[0], rect[3] - rect[1]])
+
+
+def print_results(pck, cfg):
+ """
+ print result
+ """
+ _str = ""
+ for heading in cfg.all_joints_names + ["total"]:
+ _str += " & " + heading
+ print(_str)
+
+ _str = ""
+ all_joint_ids = cfg.all_joints + [np.arange(cfg.num_joints)]
+ for j_ids in all_joint_ids:
+ j_ids_np = arr(j_ids)
+ pck_av = np.mean(pck[j_ids_np])
+ _str += " & {0:.1f}".format(pck_av)
+ print(_str)
diff --git a/research/cv/ArtTrack/src/tool/preprocess/__init__.py b/research/cv/ArtTrack/src/tool/preprocess/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/research/cv/ArtTrack/src/tool/preprocess/crop.py b/research/cv/ArtTrack/src/tool/preprocess/crop.py
new file mode 100644
index 0000000000000000000000000000000000000000..8eec6bb274b96b913c0f8be975d2f798a6ff1367
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/preprocess/crop.py
@@ -0,0 +1,336 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import json
+import os
+from copy import deepcopy
+
+import numpy as np
+from easydict import EasyDict
+from imageio import imread, imsave
+from PIL import Image
+
+from src.log import log
+from src.tool.preprocess.utils import json_default, pad_zeros, util_set_scale
+
+
+def crop_data(option):
+ """
+ crop image
+
+ Args:
+ option: crop parameters
+
+ """
+ log.debug("crop_data()")
+
+ b_train = option["bTrain"]
+ ref_height = option["refHeight"]
+ delta_crop = option["deltaCrop"]
+ b_single = option["bSingle"]
+ b_crop_isolated = option["bCropIsolated"]
+ b_multi = option["bMulti"]
+ b_objpos_offset = option["bObjposOffset"]
+ save_dir = option["saveDir"]
+ dataset = option["dataset"]
+ # annolist = dataset.annolist
+ img_list = np.array(dataset['img_list'])
+
+ log.debug('bTrain: %s', b_train)
+ log.debug('refHeight: %s', ref_height)
+ log.debug('deltaCrop: %s', delta_crop)
+ log.debug('bSingle: %s', b_single)
+ log.debug('bCropIsolated: %s', b_crop_isolated)
+ log.debug('bMulti: %s', b_multi)
+ log.debug('bObjposOffset: %s', b_objpos_offset)
+
+ rect_ids = list()
+ if b_single:
+ mode = 'singlePerson'
+ for _, v in enumerate(dataset['img_list']):
+ single_person = v['single_person']
+ rect_ids.append(single_person)
+ else:
+ mode = 'multPerson'
+ for _, v in enumerate(dataset['img_list']):
+ single_person = v['single_person']
+ if v['rect']:
+ rect_ids.append([])
+ else:
+ r_len = len(v['rect'])
+ rect_ids.append(np.setdiff1d(
+ np.arange(0, r_len, dtype=np.uint8), single_person))
+ rect_ids = np.asarray(rect_ids, dtype=object)
+ annalists_full_name = os.path.join(
+ save_dir, "annolist-{}-h{}.json".format(mode, ref_height))
+ if os.path.exists(annalists_full_name):
+ with open(annalists_full_name, 'r') as f:
+ try:
+ img_list = json.load(f)
+ return img_list
+ except json.decoder.JSONDecodeError:
+ pass
+
+ img_ids1 = filter(lambda item: len(item[1]) != 0, enumerate(rect_ids))
+ img_ids1 = map(lambda item: item[0], img_ids1)
+ img_ids1 = [i for i in img_ids1]
+
+ img_ids2 = filter(lambda item: item[1]['img_train'] == b_train,
+ enumerate(dataset['img_list']))
+ img_ids2 = map(lambda item: item[0], img_ids2)
+ img_ids2 = [i for i in img_ids2]
+
+ log.debug("imgidxs1 len: %s imgidxs2 len: %s", len(img_ids1), len(img_ids2))
+ imgidxs = np.intersect1d(img_ids1, img_ids2)
+ imgidxs_sub = imgidxs
+
+ img_list_subset = util_set_scale(img_list[imgidxs_sub], 200)
+ log.debug("img_list_subset shape: %s", img_list_subset.shape)
+
+ if b_train == 0:
+ assert False
+ result_list = func_crop_data_test()
+ else:
+ if b_multi == 1:
+ func_crop_data_test()
+ else:
+ result_list = func_crop_data_train(
+ option, img_list_subset, imgidxs_sub, rect_ids[imgidxs_sub])
+
+ with open(annalists_full_name, 'w') as f:
+ f.write(json.dumps(result_list, default=json_default))
+ return result_list
+
+
+def func_crop_data_test():
+ return []
+
+
+def get_points_all(points):
+ points_all = np.array([])
+ for f_p in points:
+ pp = [f_p['x'], f_p['y']]
+ points_all = np.r_[points_all, pp]
+ return points_all
+
+
+def get_rect_position(points):
+ points_all = get_points_all(points)
+ points_all = points_all.reshape((-1, 2))
+ log.debug("points_all: %s", points_all)
+ min_x = np.min(points_all[:, 0])
+ max_x = np.max(points_all[:, 0])
+ min_y = np.min(points_all[:, 1])
+ max_y = np.max(points_all[:, 1])
+ rp = EasyDict(x1=min_x, x2=max_x, y1=min_y, y2=max_y)
+ log.debug("RectPosition:%s", rp)
+ return rp
+
+
+def get_scale_and_delta(ref_height, rect_value, img, delta_crop):
+ if ref_height > 0:
+ sc = rect_value['scale'] * 200 / ref_height
+ img_sc = np.array(
+ Image.fromarray(img).resize((int(img.shape[1] / sc), int(img.shape[0] / sc)), Image.BICUBIC))
+ delta_x = delta_crop
+ delta_y = delta_crop
+ else:
+ sc = 1.0
+ img_sc = img
+ delta_x = np.round(delta_crop * rect_value['scale'])
+ delta_y = np.round(delta_crop * rect_value['scale'])
+ log.debug('sc: %s', sc)
+ log.debug('img_sc shape: %s', img_sc.shape)
+ log.debug('delta_x: %s', delta_x)
+ log.debug('delta_y: %s', delta_y)
+ return sc, img_sc, delta_x, delta_y
+
+
+def get_position(rp, sc):
+ pos = EasyDict(
+ x1=np.round(rp.x1 / sc),
+ x2=np.round(rp.x2 / sc),
+ y1=np.round(rp.y1 / sc),
+ y2=np.round(rp.y2 / sc),
+ )
+ log.debug("pos %s", pos)
+ return pos
+
+
+def get_position_new(pos, delta_x, delta_y, img_sc):
+ new = EasyDict(
+ x1=np.round(max(1, pos.x1 - delta_x)),
+ x2=np.round(min(img_sc.shape[1], pos.x2 + delta_x)),
+ y1=max(1, pos.y1 - delta_y),
+ y2=min(img_sc.shape[0], pos.y2 + delta_y),
+ )
+ log.debug("1st new %s", new)
+ return new
+
+
+def update_position_new(b_crop_isolated, rect, rect2, r_id, img_list, img_id, sc, pos, ref_height, rect_value, pos_new):
+ if b_crop_isolated and len(rect) > 1:
+
+ points2_all = []
+
+ for r_id2, rect_value2 in enumerate(rect2):
+ if r_id2 == r_id:
+ continue
+ points2 = rect_value2['points']
+ if points2 is None or not points2:
+ continue
+ for f_p in points2:
+ pp = [f_p['x'], f_p['y']]
+ points2_all.append(pp)
+ points2_all = np.array(points2_all)
+ log.debug("points2_all: %s", points2_all)
+ if points2_all:
+ def max_index(d, idx):
+ return np.argmax(d[idx])
+
+ log.debug("img_list len :%s img_id:%d r_id:%s", len(img_list), img_id, r_id)
+ points2_all = np.true_divide(points2_all, sc)
+ d = points2_all[:, 0] - pos.x1
+ idx = np.where(d < 0)[0]
+ # log.debug("idx:%s max_index:%s", idx, max_index(d, idx))
+ pos_x1other = None if not idx.any() else points2_all[idx[max_index(d, idx)], 0]
+
+ d = points2_all[:, 1] - pos.y1
+ idx = np.where(d < 0)[0]
+ # pos_y1other = None if not idx.any() else points2_all[idx[max_index(d, idx)], 1]
+
+ d = pos.x2 - points2_all[:, 0]
+ idx = np.where(d < 0)[0]
+ pos_x2other = None if not idx.any() else points2_all[idx[max_index(d, idx)], 0]
+
+ d = pos.y2 - points2_all[:, 1]
+ idx = np.where(d < 0)[0]
+ # pos_y2other = None if not idx.any() else points2_all[idx[max_index(d, idx)], 1]
+
+ if ref_height > 0:
+ delta2 = ref_height / 200 * 10
+ else:
+ delta2 = rect_value['scale'] * 10
+
+ if pos_x1other is not None:
+ pos_new.x1 = np.round(max(pos_new.x1, pos_x1other + delta2))
+
+ if pos_x2other is not None:
+ pos_new.x2 = np.round(min(pos_new.x2, pos_x2other - delta2))
+ pos_new.y1 = int(pos_new.y1)
+ pos_new.y2 = int(pos_new.y2)
+ pos_new.x1 = int(pos_new.x1)
+ pos_new.x2 = int(pos_new.x2)
+
+ log.debug("2nd new: %s", pos_new)
+ return pos_new
+
+
+def transform_annotation(points, pos, sc, rect_value):
+ # transfer annotations
+ log.debug("before transfer: %s pos.x1: %s pos.y1: %s sc: %s", points, pos.x1, pos.y1, sc)
+ for pid in points:
+ pid['x'] = pid['x'] / sc - pos.x1 + 1
+ pid['y'] = pid['y'] / sc - pos.y1 + 1
+ log.debug("after transfer: %s pos_new.x1: %s pos_new.y1: %s sc: %s", points, pos.x1, pos.y1, sc)
+ rect_value['x1'] = rect_value['x1'] / sc - pos.x1 + 1
+ rect_value['y1'] = rect_value['y1'] / sc - pos.y1 + 1
+ rect_value['x2'] = rect_value['x2'] / sc - pos.x1 + 1
+ rect_value['y2'] = rect_value['y2'] / sc - pos.y1 + 1
+
+ rect_value['objpos_x'] = rect_value['objpos_x'] / sc - pos.x1 + 1
+ rect_value['objpos_y'] = rect_value['objpos_y'] / sc - pos.y1 + 1
+
+
+def get_annotation(annolists2, f_name, rect_value, image_size, num_crops):
+ if not annolists2:
+ obj = dict()
+ obj['name'] = f_name
+ obj['imgnum'] = 1
+ obj['rect'] = rect_value
+ obj['image_size'] = image_size
+ else:
+ num_crops = num_crops + 1
+ obj = dict()
+ obj['name'] = f_name
+ obj['imgnum'] = num_crops
+ obj['rect'] = rect_value
+ obj['image_size'] = image_size
+ return obj
+
+
+def func_crop_data_train(option, img_list, img_ids, rectidxs):
+ """
+ crop train dataset
+ """
+ save_dir = option['saveDir']
+ ref_height = option['refHeight']
+ delta_crop = option['deltaCrop']
+ b_crop_isolated = option['bCropIsolated']
+ if not os.path.isdir(save_dir):
+ os.mkdir(save_dir)
+
+ annolists2 = []
+ num_crops = 1
+ num_images = 0
+
+ for img_id, alv in enumerate(img_list):
+ log.info("==> start img_id/all: %s/%s", img_id + 1, len(img_list))
+ rect = alv['rect']
+ rect2 = deepcopy(rect)
+ name = alv['name']
+ img = imread(os.path.join(option['imageDir'], name))
+ log.debug("img :%s", alv)
+ for r_id, rect_value in enumerate(rect):
+ if r_id not in rectidxs[img_id]:
+ continue
+ log.info("==> start img_id: %s/%s r_id: %s/%s", img_id + 1, len(img_list), r_id + 1, len(rect))
+ num_images = num_images + 1
+ points = rect_value['points']
+ if points is None or not points:
+ continue
+ rp = get_rect_position(points)
+ sc, img_sc, delta_x, delta_y = get_scale_and_delta(ref_height, rect_value, img, delta_crop)
+ pos = get_position(rp, sc)
+ pos_new = get_position_new(pos, delta_x, delta_y, img_sc)
+ pos_new = update_position_new(b_crop_isolated, rect, rect2, r_id, img_list, img_id, sc, pos, ref_height,
+ rect_value, pos_new)
+ img_crop = np.array([img_sc[pos_new.y1:pos_new.y2, pos_new.x1:pos_new.x2, 0],
+ img_sc[pos_new.y1:pos_new.y2, pos_new.x1:pos_new.x2, 1],
+ img_sc[pos_new.y1:pos_new.y2, pos_new.x1:pos_new.x2, 2]])
+
+ img_crop = img_crop.transpose((1, 2, 0))
+ # save image
+ f_name = os.path.join(
+ save_dir, 'im' + pad_zeros(img_ids[img_id], 5) + '_' + str(r_id) + '.png')
+ f_name_t = os.path.join(
+ save_dir, 'T_' + pad_zeros(img_ids[img_id], 5) + '_' + str(r_id) + '.json')
+ log.debug('file name: %s', f_name)
+ log.debug("image shape: %s", img_crop.shape)
+ # noinspection PyTypeChecker
+ imsave(f_name, img_crop)
+ image_size = [img_crop.shape[0], img_crop.shape[1]]
+
+ T = sc * np.array([[1, 0, pos_new.x1], [0, 1, pos_new.y1], [0, 0, 1]])
+ with open(f_name_t, 'w') as f:
+ f.write(json.dumps(T, default=json_default))
+
+ transform_annotation(points, pos_new, sc, rect_value)
+ anno = get_annotation(annolists2, f_name, rect_value, image_size, num_crops)
+ num_crops = num_crops + 1
+ annolists2.append(anno)
+ log.info("==> finish img_id: %s/%s r_id: %s/%s ", img_id + 1, len(img_list), r_id + 1, len(rect))
+ log.info("==> finish img_id: %s/%s", img_id + 1, len(img_list))
+ return annolists2
diff --git a/research/cv/ArtTrack/src/tool/preprocess/mat2json.py b/research/cv/ArtTrack/src/tool/preprocess/mat2json.py
new file mode 100644
index 0000000000000000000000000000000000000000..6284b0fead431f27ec21ea1f88224da694248a2d
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/preprocess/mat2json.py
@@ -0,0 +1,56 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import json
+import os
+
+import numpy as np
+import scipy.io as scio
+
+
+def mat2json(index_mat, name, dataset_json=None, output_dir=None, index_offset=-1, stdout=False):
+ """
+ select sample from matlab index mat and and save as json format
+
+ Args:
+ index_mat: path to index mat
+ name: field in index mat
+ dataset_json: path to json format dataset which contains full sample
+ output_dir: output dir
+ index_offset: offset for index mat. first index in matlab is usually 1, but in python it's 0.
+ stdout: if True, print index
+ """
+ mat = scio.loadmat(index_mat)
+ ids = mat.get(name).flatten().tolist()
+ ids = [i + index_offset for i in ids]
+ if stdout:
+ print(ids)
+ out_dataset = None
+ if dataset_json is not None:
+ with open(dataset_json, 'r') as f:
+ dataset = f.read()
+ dataset = np.array(json.loads(dataset))
+ out_dataset = dataset[ids].tolist()
+
+ if output_dir is not None:
+ output_dir = os.path.abspath(output_dir)
+ os.makedirs(os.path.abspath(output_dir), exist_ok=True)
+ s = json.dumps(ids)
+ with open(os.path.join(output_dir, '%s.json' % name), 'w') as f:
+ f.write(s)
+ if out_dataset is not None:
+ s = json.dumps(out_dataset)
+ with open(os.path.join(output_dir, '%s_dataset.json' % name), 'w') as f:
+ f.write(s)
diff --git a/research/cv/ArtTrack/src/tool/preprocess/pairwise_stats.py b/research/cv/ArtTrack/src/tool/preprocess/pairwise_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..164d117a812947dcd1bdbdafa1461b5629552dcd
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/preprocess/pairwise_stats.py
@@ -0,0 +1,72 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import os
+
+import numpy as np
+import scipy.io
+
+from src.dataset.coco import MSCOCO
+from src.dataset.pose import Batch
+from src.tool.decorator import process_cfg
+
+
+def remap_keys(mapping):
+ return [{'key': k, 'value': v} for k, v in mapping.items()]
+
+
+def save_stats(stats, cfg):
+ mat_stats = {"graph": [], "means": [], "std_devs": []}
+ for start in range(cfg.num_joints):
+ for end in range(cfg.num_joints):
+ if start != end:
+ joint_pair = (start, end)
+ mat_stats["graph"].append([start, end])
+ mat_stats["means"].append(stats[joint_pair]["mean"])
+ mat_stats["std_devs"].append(stats[joint_pair]["std"])
+ print(mat_stats)
+ os.makedirs(os.path.dirname(cfg.pairwise_stats_fn), exist_ok=True)
+ scipy.io.savemat(cfg.pairwise_stats_fn, mat_stats)
+
+
+# Compute pairwise statistics at reference scale
+@process_cfg
+def pairwise_stats(cfg):
+ dataset = MSCOCO(cfg)
+ dataset.set_pairwise_stats_collect(True)
+
+ num_images = dataset.num_images
+ all_pairwise_differences = {}
+
+ if cfg.dataset.mirror:
+ num_images *= 2
+
+ for k in range(num_images):
+ print('processing image {}/{}'.format(k, num_images - 1))
+
+ batch = dataset.get_item(k)
+ batch_stats = batch[Batch.data_item].pairwise_stats
+ for joint_pair in batch_stats:
+ if joint_pair not in all_pairwise_differences:
+ all_pairwise_differences[joint_pair] = []
+ all_pairwise_differences[joint_pair] += batch_stats[joint_pair]
+
+ stats = {}
+ for joint_pair in all_pairwise_differences:
+ stats[joint_pair] = {}
+ stats[joint_pair]["mean"] = np.mean(all_pairwise_differences[joint_pair], axis=0)
+ stats[joint_pair]["std"] = np.std(all_pairwise_differences[joint_pair], axis=0)
+
+ save_stats(stats, cfg)
diff --git a/research/cv/ArtTrack/src/tool/preprocess/parts.json b/research/cv/ArtTrack/src/tool/preprocess/parts.json
new file mode 100644
index 0000000000000000000000000000000000000000..1f2440689be9f31d8367d3cbd5bdc042376bc9bf
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/preprocess/parts.json
@@ -0,0 +1,30 @@
+[
+ {"id":0,"pos":[0,0]},
+ {"id":1,"pos":[0,1]},
+ {"id":2,"pos":[1,1]},
+ {"id":3,"pos":[1,2]},
+ {"id":4,"pos":[2,2]},
+ {"id":5,"pos":[3,3]},
+ {"id":6,"pos":[3,4]},
+ {"id":7,"pos":[4,4]},
+ {"id":8,"pos":[4,5]},
+ {"id":9,"pos":[5,5]},
+ {"id":10,"pos":[6,7]},
+ {"id":11,"pos":[8,9]},
+ {"id":12,"pos":[10,10]},
+ {"id":13,"pos":[11,10]},
+ {"id":14,"pos":[11,11]},
+ {"id":15,"pos":[12,11]},
+ {"id":16,"pos":[12,12]},
+ {"id":17,"pos":[13,13]},
+ {"id":18,"pos":[13,14]},
+ {"id":19,"pos":[14,14]},
+ {"id":20,"pos":[14,15]},
+ {"id":21,"pos":[15,15]},
+ {"id":22,"pos":[8,8]},
+ {"id":23,"pos":[9,9]},
+ {"id":24,"pos":[6,6]},
+ {"id":25,"pos":[7,7]},
+ {"id":26,"pos":[16,16]},
+ {"id":27,"pos":[17,17]}
+]
diff --git a/research/cv/ArtTrack/src/tool/preprocess/preprocess_single.py b/research/cv/ArtTrack/src/tool/preprocess/preprocess_single.py
new file mode 100644
index 0000000000000000000000000000000000000000..c74d2569daf6b77fde9bdc1914159092868d7244
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/preprocess/preprocess_single.py
@@ -0,0 +1,163 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import json
+import os
+import sys
+
+import numpy as np
+import scipy.io as scio
+
+from src.log import log
+from src.tool.preprocess.crop import crop_data
+from src.tool.preprocess.utils import json_default, mpii_mat2dict
+
+np.set_printoptions(threshold=sys.maxsize)
+basedir = os.path.abspath(os.path.dirname(__file__))
+
+
+def preprocess_single(dataset_dir, dataset_name, save_dir=None, image_dir=None):
+ """
+ preprocess single person dataset
+
+ Args:
+ dataset_dir: path to dataset
+ dataset_name: annotation file relative path
+ save_dir: path to cropped images
+ image_dir: path to original images
+ """
+ if save_dir is None:
+ save_dir = os.path.join(dataset_dir, 'cropped')
+ if image_dir is None:
+ image_dir = os.path.join(dataset_dir, 'images')
+
+ p = dict()
+
+ p["bTrain"] = 1
+ p["refHeight"] = 400
+ p["deltaCrop"] = 130
+ p["bSingle"] = 1
+ p["bCropIsolated"] = 1
+ p["bMulti"] = 0
+ p["bObjposOffset"] = 1
+
+ p["datasetDir"] = dataset_dir
+ p["datasetName"] = os.path.join(p["datasetDir"], dataset_name)
+
+ p["saveDir"] = save_dir
+ p["imageDir"] = image_dir
+
+ mat = scio.loadmat(p["datasetName"], struct_as_record=False)
+ p["dataset"] = mpii_mat2dict(mat)
+
+ img_list = crop_data(p)
+ p['deltaCrop'] = 65
+ p['bSingle'] = 0
+ p['bCropIsolated'] = 0
+ img_list2 = crop_data(p)
+
+ img_list = img_list + img_list2
+
+ img_list_full_name = os.path.join(p['saveDir'], 'annolist-full-h' + str(p['refHeight']) + '.json')
+ with open(img_list_full_name, 'w') as f:
+ f.write(json.dumps(img_list, default=json_default))
+
+ prepare_training_data(img_list, p['saveDir'])
+
+
+def prepare_training_data(img_list, saveDir):
+ """
+ generate final dataset file
+ """
+ zero_indexed_joints_ids = True
+ pidxs = [0, 2, 4, 5, 7, 9, 12, 14, 16, 17, 19, 21, 22, 23]
+
+ num_joints = len(pidxs)
+ with open(os.path.join(basedir, 'parts.json'), 'r') as f:
+ parts = json.load(f)
+
+ if not os.path.exists(saveDir):
+ os.makedirs(saveDir)
+
+ num_images = len(img_list)
+ channels = 3
+ dataset = []
+
+ for imgidx, imgv in enumerate(img_list):
+ if imgidx + 1 % 100 == 0:
+ log.info('processing image %s/%s', imgidx, num_images)
+
+ filename = imgv['name']
+
+ joints = np.zeros((num_joints, 3))
+ all_joints = []
+ rectv = imgv['rect']
+
+ joint_list = get_anno_joints(rectv, pidxs, parts)
+
+ n = 0
+ for j in range(0, num_joints):
+ jnt = joint_list[j, :]
+ if not np.isnan(jnt[0]):
+ joints[n, :] = np.concatenate([[j], jnt])
+ n = n + 1
+
+ joints = joints[:n, :]
+ if zero_indexed_joints_ids:
+ joints[:, 0] = joints[:, 0]
+ else:
+ joints[:, 0] = joints[:, 0] + 1
+ all_joints.append(joints)
+
+ entry = dict()
+ entry['image'] = filename
+ entry['size'] = np.concatenate([[channels], imgv['image_size']])
+ entry['joints'] = all_joints
+ dataset.append(entry)
+ os.makedirs(saveDir, exist_ok=True)
+ out_filename = os.path.join(saveDir, 'dataset.json')
+ log.debug("Generated dataset definition file:%s", out_filename)
+ with open(out_filename, 'w') as f:
+ f.write(json.dumps(dataset, default=json_default))
+
+
+def get_anno_joints(rect, pidxs, parts):
+ """
+ get annotation joints
+ """
+ num_joints = len(pidxs)
+ joints = np.full((num_joints, 2), np.nan)
+ points = rect['points']
+ for j, pidx in enumerate(pidxs):
+ annopoint_idxs = parts[pidx]['pos']
+ assert annopoint_idxs[0] == annopoint_idxs[1]
+ pt, _ = get_annopoint_by_id(points, annopoint_idxs[0])
+ if pt is not None:
+ joints[j, :] = np.array([pt['x'], pt['y']])
+ return joints
+
+
+def get_annopoint_by_id(points, idx):
+ """
+ get annotation point by id
+ """
+ point = None
+ ind = None
+ for i, v in enumerate(points):
+ if v['id'] == idx:
+ point = v
+ ind = i
+ return (point, ind)
+ return (point, ind)
diff --git a/research/cv/ArtTrack/src/tool/preprocess/split.py b/research/cv/ArtTrack/src/tool/preprocess/split.py
new file mode 100644
index 0000000000000000000000000000000000000000..39db8f9419d052e3449e3e9e286bbfac9688d424
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/preprocess/split.py
@@ -0,0 +1,42 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import json
+import os
+
+import numpy as np
+
+
+def split(path, out, test_ratio=0.2):
+ """
+ split dataset
+ Args:
+ path: path to json format dataset which contains full sample
+ out: output dir
+ test_ratio: eval ratio
+ """
+ with open(path, 'r') as f:
+ dataset = json.load(f)
+ dataset = np.array(dataset)
+ np.random.seed(1256)
+ dataset_len = len(dataset)
+ shuffled_indices = np.random.permutation(dataset_len)
+ test_size = int(dataset_len * test_ratio)
+ test_indices = shuffled_indices[:test_size]
+ train_indices = shuffled_indices[test_size:]
+ with open(os.path.join(out, "train-dataset.json"), 'w') as f:
+ f.write(json.dumps(dataset[train_indices].tolist()))
+ with open(os.path.join(out, "eval-dataset.json"), 'w') as f:
+ f.write(json.dumps(dataset[test_indices].tolist()))
diff --git a/research/cv/ArtTrack/src/tool/preprocess/tf2ms.py b/research/cv/ArtTrack/src/tool/preprocess/tf2ms.py
new file mode 100644
index 0000000000000000000000000000000000000000..8565910e16fced100dfcab43c300ddedecf69cab
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/preprocess/tf2ms.py
@@ -0,0 +1,59 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import json
+import os
+
+import numpy as np
+import tensorflow.compat.v1 as tf
+from tensorflow.python.framework.errors_impl import NotFoundError
+from mindspore import Parameter
+from mindspore.train.serialization import save_checkpoint
+
+from src.log import log
+
+
+def tf2ms(tf_checkpoint, out_path, map_path):
+ """
+ convert tensorflow's checkpoint to mindspore model
+ Args:
+ tf_checkpoint: path to tensorflow's checkpoint
+ out_path: path to output
+ map_path: map config
+ """
+ reader = tf.train.NewCheckpointReader(tf_checkpoint)
+ new_params_list = []
+ with open(map_path) as f:
+ s = f.read()
+ param = json.loads(s)
+ for k, v in param.items():
+ param_dict = {}
+ try:
+ parameter = reader.get_tensor(k)
+ except NotFoundError:
+ log.warning("not found %s skip", k)
+ continue
+ if len(parameter.shape) == 4:
+ parameter = np.transpose(parameter, axes=[3, 2, 0, 1])
+ elif len(parameter.shape) != 1:
+ log.error('unknown shape %s for %s', parameter.shape, k)
+ exit(1)
+ log.info('convert %s -> %s', k, v)
+
+ param_dict['name'] = param[k]
+ param_dict['data'] = Parameter(parameter, requires_grad=False)
+ new_params_list.append(param_dict)
+ os.makedirs(os.path.dirname(os.path.abspath(out_path)), exist_ok=True)
+ save_checkpoint(new_params_list, out_path)
diff --git a/research/cv/ArtTrack/src/tool/preprocess/utils.py b/research/cv/ArtTrack/src/tool/preprocess/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8dc5eaa48fe310f7c3f6b8758a1e6563f267930c
--- /dev/null
+++ b/research/cv/ArtTrack/src/tool/preprocess/utils.py
@@ -0,0 +1,190 @@
+# Copyright 2022 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.
+# ============================================================================
+
+import json
+
+import numpy as np
+
+from src.log import log
+
+
+def util_set_scale(img_list, ref_height=200):
+ """
+ set images scale
+ """
+ head_height_ratio = 1.0 / 8
+
+ for _, v in enumerate(img_list):
+ rect = getattr(v, 'rect', None)
+ if rect is not None and rect:
+ for _, rv in enumerate(rect):
+ points = getattr(rv, "points", None)
+ if points is not None and points:
+ head_size = util_get_head_size(rv)
+ sc = ref_height * head_height_ratio / head_size
+ assert 100 > sc > 0.01
+ points.scale = 1 / sc
+ v['rect'] = rect
+
+ return img_list
+
+
+def util_get_head_size(rect):
+ sc_bias = 0.6
+ return sc_bias * np.linalg.norm(np.array([rect['x2'], rect['y2']]) - np.array([rect['x1'], rect['y1']]))
+
+
+def pad_zeros(s, npad):
+ n = len(str(s))
+
+ assert n <= npad
+ return '0' * (npad - n) + str(s)
+
+
+class NumpyEncoder(json.JSONDecoder):
+ def default(self, obj):
+ if isinstance(obj, np.integer):
+ return int(obj)
+ if isinstance(obj, np.uint8):
+ return int(obj)
+ if isinstance(obj, np.floating):
+ return float(obj)
+ return json.JSONDecoder.default(self, obj)
+
+
+def set_rect_field(rect_dict, rv, field):
+ if hasattr(rv, field):
+ rect_dict[field] = getattr(rv, field)[0, 0]
+
+
+def set_rect_scale(rect_dict, rv):
+ if hasattr(rv, 'scale'):
+ scale = rv.scale.flatten()
+ if scale:
+ rect_dict['scale'] = scale[0]
+
+
+def set_rect_vidx(rect_dict, rv):
+ if hasattr(rv, 'vidx'):
+ rect_dict['vidx'] = rv.vidx - 1
+
+
+def set_rect_frame_sec(rect_dict, rv):
+ if hasattr(rv, 'frame_sec'):
+ rect_dict['frame_sec'] = rv.frame_sec
+
+
+def set_rect_objpos(rect_dict, rv):
+ if hasattr(rv, 'objpos'):
+ objpos = rv.objpos.flatten()
+ if rect_dict.get('scale') is not None:
+ rect_dict['objpos_x'] = objpos[0].x[0, 0]
+ rect_dict['objpos_y'] = objpos[0].y[0, 0]
+
+
+def set_rect_points_list(rect_dict, rv):
+ rect_points = getattr(rv, 'annopoints', None)
+ if rect_points is None or not rect_points:
+ points_list = []
+ points = []
+ else:
+ rect_points = rect_points[0, 0]
+ points_list = []
+ points = rect_points.point[0]
+ for f_p in points:
+ pp = {'x': f_p.x[0, 0],
+ 'y': f_p.y[0, 0],
+ 'id': f_p.id[0, 0],
+ }
+ if hasattr(f_p, 'is_visible'):
+ visible = f_p.is_visible.flatten()
+ if visible:
+ pp['is_visible'] = visible[0]
+ points_list.append(pp)
+ rect_dict['points'] = points_list
+
+
+def set_act_name(act_dict, field, name):
+ if name:
+ act_dict[field] = name[0]
+
+
+def mpii_mat2dict(mpii):
+ """
+ raw mat to dict
+ """
+ mpii = mpii['RELEASE'][0, 0]
+ mpii.annolist = mpii.annolist.flatten()
+ mpii.img_train = mpii.img_train.flatten()
+ mpii.act = mpii.act.flatten()
+ mpii.single_person = mpii.single_person.flatten()
+ mpii.video_list = mpii.video_list.flatten()
+ img_list = []
+ for imgidx, alv in enumerate(mpii.annolist):
+ img_train = mpii.img_train[imgidx]
+ act = mpii.act[imgidx]
+ single_person = mpii.single_person[imgidx].flatten()
+ name = alv.image[0, 0].name[0]
+
+ # annorect
+ rect = alv.annorect.flatten()
+ rect_list = list()
+ for _, rv in enumerate(rect):
+ rect_dict = dict()
+ set_rect_field(rect_dict, rv, 'x1')
+ set_rect_field(rect_dict, rv, 'y1')
+ set_rect_field(rect_dict, rv, 'x2')
+ set_rect_field(rect_dict, rv, 'y2')
+ set_rect_scale(rect_dict, rv)
+ set_rect_vidx(rect_dict, rv)
+ set_rect_frame_sec(rect_dict, rv)
+ set_rect_objpos(rect_dict, rv)
+ set_rect_points_list(rect_dict, rv)
+ rect_list.append(rect_dict)
+
+ single_person = [i - 1 for i in single_person]
+ act_dict = dict()
+ act_name = act.act_name.flatten()
+ cat_name = act.cat_name.flatten()
+ set_act_name(act_dict, 'act_name', act_name)
+ set_act_name(act_dict, 'cat_name', cat_name)
+ act_dict['act_id'] = act.act_id[0, 0]
+ if len(act_name) > 1 or len(cat_name) > 1:
+ log.debug("%s %s", act_name, cat_name)
+
+ value = dict()
+ value['name'] = name
+ value['rect'] = rect_list
+ value['img_train'] = img_train
+ value['single_person'] = single_person
+ value['act'] = act_dict
+ img_list.append(value)
+
+ video_list = [i[0] for i in mpii.video_list]
+ result = {"img_list": img_list,
+ "video_list": video_list
+ }
+ return result
+
+
+def json_default(obj):
+ """
+ numpy to json
+ """
+ if type(obj).__module__ == np.__name__:
+ if isinstance(obj, np.ndarray):
+ return obj.tolist()
+ return obj.item()
+ return obj
diff --git a/research/cv/ArtTrack/train.py b/research/cv/ArtTrack/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..538dc3385d028df14e640b5d46448007d0c3d235
--- /dev/null
+++ b/research/cv/ArtTrack/train.py
@@ -0,0 +1,85 @@
+# Copyright 2022 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.
+# ============================================================================
+import os
+import sys
+
+import mindspore
+import mindspore.context as ctx
+from mindspore import Model, nn
+from mindspore.communication import get_group_size, get_rank, init
+from mindspore.train.callback import CheckpointConfig, LossMonitor, ModelCheckpoint, TimeMonitor
+
+from src.args_util import command, create_arg_parser, TARGET_COCO_MULTI, TARGET_MPII_SINGLE
+from src.dataset.util import create_dataset
+from src.model.pose import PoseNet, PoseNetTotalLoss
+from src.tool.decorator import process_cfg
+
+
+@command
+def train(parser, args, cfg):
+ if args.target == TARGET_MPII_SINGLE:
+ from src.dataset import MPII
+ start_train(cfg, MPII)
+ elif args.target == TARGET_COCO_MULTI:
+ from src.dataset import MSCOCO
+ start_train(cfg, MSCOCO)
+ else:
+ parser.print_help()
+
+
+@process_cfg
+def start_train(cfg, dataset_class):
+ """
+ start train
+ """
+ ctx.set_context(**cfg.context)
+ group_size = None
+ rank_id = None
+ if hasattr(cfg, 'parallel_context') and cfg.parallel_context is not None:
+ init()
+ rank_id = get_rank()
+ group_size = get_group_size()
+ ctx.set_auto_parallel_context(device_num=group_size, **cfg.parallel_context)
+ ctx.set_auto_parallel_context(parameter_broadcast=True)
+ dataset = dataset_class(cfg)
+ dataset = create_dataset(cfg.dataset.type, dataset, cfg.dataset.shuffle, cfg.dataset.batch_size,
+ parallel=cfg.dataset.parallel, train=True, num_shards=group_size, rank_id=rank_id)
+ net = PoseNet(cfg=cfg)
+ loss = PoseNetTotalLoss(net, cfg)
+ optimizer = nn.SGD(loss.trainable_params(),
+ learning_rate=nn.dynamic_lr.piecewise_constant_lr(cfg.multi_step[1], cfg.multi_step[0]))
+ train_net = nn.TrainOneStepCell(loss, optimizer)
+ train_net.set_train()
+ if hasattr(cfg, 'load_ckpt') and os.path.exists(cfg.load_ckpt):
+ mindspore.load_checkpoint(cfg.load_ckpt, net=train_net)
+ model = Model(train_net)
+ steps_per_epoch = dataset.get_dataset_size()
+ ckpt_config = CheckpointConfig(save_checkpoint_steps=steps_per_epoch, keep_checkpoint_max=10)
+ ckpt_dir = cfg.get('ckpt_dir', 'ckpt')
+ ckpt_dir = ckpt_dir if rank_id is None else os.path.join(ckpt_dir, 'rank_%s' % str(rank_id))
+ ckpt_cb = ModelCheckpoint(prefix=cfg.get('ckpt_prefix', 'arttrack'), directory=ckpt_dir,
+ config=ckpt_config)
+ callbacks = [TimeMonitor(data_size=steps_per_epoch), LossMonitor(), ckpt_cb]
+ model.train(cfg.epoch, dataset, callbacks=callbacks, dataset_sink_mode=False)
+
+
+def main():
+ parser = create_arg_parser()['train']
+ args = parser.parse_args(sys.argv[1:])
+ train(parser, args)
+
+
+if __name__ == '__main__':
+ main()