diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..25ea1bb7511d72cb317ebb08f7a17ff3e5cfe15d
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/README.md
@@ -0,0 +1,89 @@
+# SVD_ID2019_for ACL
+
+#### 概述
+给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。
+
+- 开源代码：训练获取
+
+    https://github.com/google-research/google-research/tree/master/special_orthogonalization。
+
+- 参考论文：
+
+    [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616)
+
+#### 数据集
+
+训练数据集 points 
+	
+测试数据集 points_test
+        
+旋转后数据集 points_test_modified
+
+#### 模型固化
+-直接获取
+
+直接下载获取，百度网盘
+链接：https://pan.baidu.com/s/17zKWq2aY06cF9IQW6htn_A 
+提取码：2019
+-训练获取
+
+训练获取
+训练完成saved_model模型网盘链接：https://pan.baidu.com/s/1Y4ato6Ob-6-rcXr31AvgoA 
+提取码：2019
+
+1.按照SVD_ID2019_for_Tensorflow中的流程训练，模型保存为saved_model格式
+
+2.将saved_model格式文件冻结为pb文件（需要在freeze.py文件中修改路径）
+
+python freeze.py
+
+得到svd.pd
+
+#### 使用ATC工具将pb文件转换为om模型
+命令行代码示例
+
+atc --model=/home/test_user04/svd.pb --framework=3 --output=/home/test_user04/svd --soc_version=Ascend310 --input_shape="data_1:1,1410,3;rot_1:1,3,3"
+
+注意所使用机器的Ascend的型号
+
+模型直接下载百度网盘链接：https://pan.baidu.com/s/14-m0ZhPQyIr8enpUgVytpg 
+提取码：2019
+
+得到svd.om
+#### 制作数据集
+-直接下载,数据集在svd_inference/data_1
+
+
+
+-自己制作
+原数据链接：链接：https://pan.baidu.com/s/1aGAO3os8ifDnYm1yXrxndQ 
+提取码：2019
+
+使用pts2txt制作数据集(注意修改数据路径，数据路径为/xxx/points_test_modified/*.pts,注意修改产生数据集后的路径)
+
+python pts2txt.py
+
+#### 获取离线推理输出bin文件
+
+推理文件在压缩包svd_inference中，下载百度网盘链接：https://pan.baidu.com/s/1OfCxHMUJcnyqp2IcvV3eWg 
+提取码：2019
+
+脚本在src文件夹中，直接运行
+
+python svdom_inference.py
+
+推理结果直接下载网盘链接：https://pan.baidu.com/s/1NFNfJkTUW4u7YJcaHK9mLw 
+提取码：2019
+
+#### 使用输出的bin文件验证推理精度
+
+运行脚本
+
+python calc_acc.py
+
+得到推理精度:3.150504164928697
+
+与在线推理精度近似
+
+关于以上所有文件的百度网盘链接：https://pan.baidu.com/s/1sR8gYK8jM6xCZwbq7eK50A 
+提取码：2019
\ No newline at end of file
diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/calc_acc.py b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/calc_acc.py
new file mode 100644
index 0000000000000000000000000000000000000000..b71c645e3be27303ba1d56628328983a05611541
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/calc_acc.py
@@ -0,0 +1,33 @@
+import numpy as np
+import glob
+import pathlib
+import os
+import utils_gpu
+import tensorflow as tf
+
+INFERENCE_DIR = "C:/Users/1young/Desktop/svd_output/svd_output/*.bin"
+TEST_DIR = "D:/svd_code2/data_1"
+tf.enable_eager_execution()
+
+
+def main():
+    input_test_files = glob.glob(INFERENCE_DIR)
+    mean_err = []
+    for in_file in input_test_files:
+        out_file_prefix = pathlib.Path(in_file).stem
+        rot_path = os.path.join(TEST_DIR,'%s.txt' % out_file_prefix)
+        rot = np.loadtxt(rot_path)[:3,:].reshape((-1,3,3))
+        r = np.loadtxt(in_file).reshape((-1,3,3))
+        theta = utils_gpu.relative_angle(rot, r)
+        mean_theta = tf.reduce_mean(theta)
+        mean_theta_deg = mean_theta * 180.0 / np.pi
+        mean_theta_deg = mean_theta_deg.numpy()
+        mean_err.append(mean_theta_deg)
+    print("the mean of error")
+    print(np.mean(np.array(mean_err)))
+
+if __name__=="__main__":
+    main()
+
+
+
diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/frezze.py b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/frezze.py
new file mode 100644
index 0000000000000000000000000000000000000000..70014c274a48fe831a544872df55ec61accd3998
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/frezze.py
@@ -0,0 +1,50 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ============================================================================
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tensorflow as tf
+from tensorflow.python.tools import freeze_graph
+#from npu_bridge.npu_init import *
+
+saved_model_path = 'C:/Users/1young/Desktop/1666004668'
+def main():
+	freeze_graph.freeze_graph(
+					input_saved_model_dir=saved_model_path,
+					output_node_names='rotation_matrix',
+					output_graph='svd.pb',
+							initializer_nodes='',
+					input_graph= None,
+					input_saver= False,
+					input_binary=False,
+					input_checkpoint=None,
+					restore_op_name=None,
+					filename_tensor_name=None,
+					clear_devices=False,
+					input_meta_graph=False)
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/pts2txt.py b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/pts2txt.py
new file mode 100644
index 0000000000000000000000000000000000000000..01f84fb3af7f203150f1a86204c81f76f61228cb
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/pts2txt.py
@@ -0,0 +1,44 @@
+import os
+import numpy as np
+import tensorflow as tf
+import glob
+import pathlib
+
+inputpath = "D:\special_orthogonalization\points_test_modified\*.pts"
+outputpath = "D:\svd_code2\data_1"
+
+
+tf.enable_eager_execution()
+
+def data_processing(pts_path):
+    file_buffer = tf.read_file(pts_path)
+    lines = tf.string_split([file_buffer], delimiter='\n')
+    lines1 = tf.string_split(lines.values, delimiter='\r')
+    values = tf.stack(tf.decode_csv(lines1.values,
+                                    record_defaults=[[0.0], [0.0], [0.0]], field_delim=' '))
+    values = tf.transpose(values)  # 3xN --> Nx3.
+    diff_num = 1414-tf.shape(values)[0]
+    repeat_pts = tf.tile(tf.reshape(values[4,:],(1,-1)),[diff_num,1])
+
+    values = tf.concat([values,repeat_pts],axis=0)
+    # First three rows are the rotation matrix, remaining rows the point cloud.
+    values = tf.concat([values, repeat_pts], axis=0)
+    # First three rows are the rotation matrix, remaining rows the point cloud.
+    return values.numpy()
+
+def file_save(path,datapath):
+    input_test_files = glob.glob(path)
+    for in_file in input_test_files:
+        out_file_prefix = pathlib.Path(in_file).stem
+        values = data_processing(in_file)
+        out_file1 = os.path.join(
+            datapath, '%s.txt' % out_file_prefix)
+        np.savetxt(out_file1,values)
+
+def main():
+    os.makedirs(outputpath, exist_ok=True)
+    file_save(inputpath,outputpath)
+
+if __name__ == '__main__':
+    main()
+
diff --git a/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/utils_gpu.py b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/utils_gpu.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff47d69c208dfa44d00c739d81dec4532675d64e
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/SVD-ID2019_for_ACL/utils_gpu.py
@@ -0,0 +1,115 @@
+# coding=utf-8
+# Copyright 2021 The Google Research Authors.
+#
+# 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.
+
+"""Utility functions."""
+import numpy as np
+from scipy.stats import special_ortho_group
+import tensorflow as tf
+
+
+def relative_angle(r1, r2):
+  """Relative angle (radians) between 3D rotation matrices."""
+  rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2)
+  trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2]
+  cos_theta = (trace - 1.0) / 2.0
+  cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta))
+  cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta))
+  theta = tf.acos(cos_theta)
+  return theta
+
+
+def random_rotation_benchmark_np(n):
+  """Sample a random 3D rotation by method used in Zhou et al, CVPR19.
+
+  This numpy function is a copy of the PyTorch function
+  get_sampled_rotation_matrices_by_axisAngle() in the code made available
+  for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/.
+
+  Args:
+    n: the number of rotation matrices to return.
+
+  Returns:
+    [n, 3, 3] np array.
+  """
+  theta = np.random.uniform(-1, 1, n) * np.pi
+  sin = np.sin(theta)
+  axis = np.random.randn(n, 3)
+  axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7)
+  qw = np.cos(theta)
+  qx = axis[:, 0] * sin
+  qy = axis[:, 1] * sin
+  qz = axis[:, 2] * sin
+
+  xx = qx*qx
+  yy = qy*qy
+  zz = qz*qz
+  xy = qx*qy
+  xz = qx*qz
+  yz = qy*qz
+  xw = qx*qw
+  yw = qy*qw
+  zw = qz*qw
+
+  row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1)
+  row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1)
+  row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1)
+  matrix = np.stack((row0, row1, row2), axis=1)
+
+  return matrix
+
+
+def random_rotation_benchmark(n):
+  """A TF wrapper for random_rotation_benchmark_np()."""
+  mat = tf.py_func(
+      func=lambda t: np.float32(random_rotation_benchmark_np(t)),
+      inp=[n],
+      Tout=tf.float32,
+      stateful=True)
+  return tf.reshape(mat, (n, 3, 3))
+
+
+def random_rotation(n):
+  """Sample rotations from a uniform distribution on SO(3)."""
+  mat = tf.py_func(
+      func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)),
+      inp=[n],
+      Tout=tf.float32,
+      stateful=True)
+  return tf.reshape(mat, (n, 3, 3))
+
+
+def symmetric_orthogonalization(x):
+  """Maps 9D input vectors onto SO(3) via symmetric orthogonalization."""
+  # Innner dimensions of the input should be 3x3 matrices.
+  m = tf.reshape(x, (-1, 3, 3))
+  _, u, v = tf.svd(m)
+  det = tf.linalg.det(tf.matmul(u, v, transpose_b=True))
+  r = tf.matmul(
+      tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2),
+      v, transpose_b=True)
+  return r
+
+
+def gs_orthogonalization(p6):
+  """Gram-Schmidt orthogonalization from 6D input."""
+  # Input should be [batch_size, 6]
+  x = p6[:, 0:3]
+  y = p6[:, 3:6]
+  xn = tf.math.l2_normalize(x, axis=-1)
+  z = tf.linalg.cross(xn, y)
+  zn = tf.math.l2_normalize(z, axis=-1)
+  y = tf.linalg.cross(zn, xn)
+  r = tf.stack([xn, y, zn], -1)
+  return r
diff --git "a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU\347\262\276\345\272\246.txt" "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU\347\262\276\345\272\246.txt"
new file mode 100644
index 0000000000000000000000000000000000000000..954bca4325a70676256edbe452b9f8bc2fb1f948
--- /dev/null
+++ "b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/NPU\347\262\276\345\272\246.txt"
@@ -0,0 +1,106 @@
+Evaluated 39900 examples
+Mean error: %f degrees 2.9278567
+Median error: %f degrees 1.7031833
+Std: %f degrees 8.454575
+
+Percentiles:
+  1%: 0.322641
+  2%: 0.414959
+  3%: 0.483760
+  4%: 0.539592
+  5%: 0.581482
+  6%: 0.623068
+  7%: 0.659679
+  8%: 0.691538
+  9%: 0.723617
+ 10%: 0.751734
+ 11%: 0.778837
+ 12%: 0.803569
+ 13%: 0.829923
+ 14%: 0.853175
+ 15%: 0.875810
+ 16%: 0.898746
+ 17%: 0.920686
+ 18%: 0.941284
+ 19%: 0.963068
+ 20%: 0.985562
+ 21%: 1.006582
+ 22%: 1.029266
+ 23%: 1.050157
+ 24%: 1.071371
+ 25%: 1.092174
+ 26%: 1.113466
+ 27%: 1.135911
+ 28%: 1.157751
+ 29%: 1.178524
+ 30%: 1.201056
+ 31%: 1.223973
+ 32%: 1.247724
+ 33%: 1.270570
+ 34%: 1.294524
+ 35%: 1.317301
+ 36%: 1.340275
+ 37%: 1.363005
+ 38%: 1.388467
+ 39%: 1.411946
+ 40%: 1.436132
+ 41%: 1.460185
+ 42%: 1.484376
+ 43%: 1.511031
+ 44%: 1.535951
+ 45%: 1.562352
+ 46%: 1.589054
+ 47%: 1.618583
+ 48%: 1.644610
+ 49%: 1.671404
+ 50%: 1.703183
+ 51%: 1.733704
+ 52%: 1.763252
+ 53%: 1.791003
+ 54%: 1.822308
+ 55%: 1.854563
+ 56%: 1.886785
+ 57%: 1.922339
+ 58%: 1.957048
+ 59%: 1.992330
+ 60%: 2.030376
+ 61%: 2.073393
+ 62%: 2.113592
+ 63%: 2.152860
+ 64%: 2.195707
+ 65%: 2.241841
+ 66%: 2.286874
+ 67%: 2.330366
+ 68%: 2.379650
+ 69%: 2.427613
+ 70%: 2.480492
+ 71%: 2.534432
+ 72%: 2.593293
+ 73%: 2.657559
+ 74%: 2.726347
+ 75%: 2.795964
+ 76%: 2.867306
+ 77%: 2.933115
+ 78%: 3.011294
+ 79%: 3.097182
+ 80%: 3.192182
+ 81%: 3.284318
+ 82%: 3.385412
+ 83%: 3.501069
+ 84%: 3.619581
+ 85%: 3.753794
+ 86%: 3.906153
+ 87%: 4.080823
+ 88%: 4.264343
+ 89%: 4.474133
+ 90%: 4.726048
+ 91%: 5.013212
+ 92%: 5.333844
+ 93%: 5.743374
+ 94%: 6.223213
+ 95%: 6.846590
+ 96%: 7.720470
+ 97%: 9.113199
+ 98%: 11.549816
+ 99%: 18.274883
+100%: 179.585983
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..cbb95c3f1388153ad36c9e415694ca4391ce4a8a
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/README.md
@@ -0,0 +1,252 @@
+<h2 id="基本信息.md">基本信息</h2>
+
+**发布者（Publisher）：Huawei**
+
+**应用领域（Application Domain）：CV**
+
+**版本（Version）：**
+
+**修改时间（Modified） ：2022.05.05**
+
+**框架（Framework）：TensorFlow 1.15.0**
+
+**处理器（Processor）：昇腾910**
+
+**描述（Description）：使用训练好的SVD模型，评估对称正交化在点云对准中的应用效果。** 
+
+<h2 id="概述.md">概述</h2>
+
+给定两个三维点云图，利用SVD正交化过程SVDO+(M)将其投射到SO(3)上，要求网络预测最佳对齐它们的3D旋转。代码的训练逻辑是每训练10w步保存一个模型，并且在测试集上检验该模型的精度，最后比较的都是260w步的模型精度
+
+- 开源代码：
+
+    https://github.com/google-research/google-research/tree/master/special_orthogonalization。
+
+- 参考论文：
+
+    [An Analysis of SVD for Deep Rotation Estimation](https://arxiv.org/abs/2006.14616)
+
+- 参考实现：
+	
+	数据下载百度网盘链接：https://pan.baidu.com/s/1up1HW6McgSor3JF0yqQZSA 
+提取码：2019
+	
+	共有3个数据集
+	
+	训练数据集points 
+	
+	测试数据集 points_test
+	
+	第一步旋转后的数据集 test_points_modified
+	
+	npu训练出来的模型下载百度网盘链接：https://pan.baidu.com/s/1JU1koZR7uGlkKfRYIk8tsw 
+提取码：2019
+	
+	
+
+	
+- 相关迁移的工作：
+  在进行代码迁移到NPU上时，输入的训练数据为点云数据，点云数据的shape为(N,3)，其中N并不是固定的，因此在NPU上存在动态shape的问题，导致模型训练无法正常进行。我们为此想了三个解决方法：1、找出所有点云数据中最小的N，对于大于N的点云数据，仅取前N行的数据输入训练。2、找到所有点云数据中最大的N，对于小于N的点云进行补0操作，将所有数据固定为最大的N后，输入网络进行训练。3、找到所有点云数据中最大的N，对小于N的点云数据，从原数据中选择一个点云进行填补至行数为N，再将数据输入网络进行训练。该三种方法均成功解决了NPU上的动态shape问题，但是第一种方法删除了样本点，因此导致最后训练出的模型精度很差；第二种方法虽然并没有丢失样本信息，但是向数据中填入大量的0，改变了本来的代码逻辑，导致最后训练出的模型精度也并不高。对于第三种方法，即没有丢失样本信息，对每个点云数据中的某一个点云样本点进行重复操作，没有改变原始的代码逻辑，最后也获得了不错的精度表现。
+
+- 通过Git获取对应commit\_id的代码方法如下：
+  
+    ```
+    git clone {repository_url}    # 克隆仓库的代码
+    cd {repository_name}    # 切换到模型的代码仓目录
+    git checkout  {branch}    # 切换到对应分支
+    git reset --hard ｛commit_id｝     # 代码设置到对应的commit_id
+    cd ｛code_path｝    # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换
+    ```
+
+## 默认配置<a name="section91661242121611"></a>
+
+- 数据集获取百度网盘链接：
+
+- 训练超参
+
+    - log_step_count=200 
+    - save_summaries_steps=25000 
+    - train_steps=2600000 
+    - save_checkpoints_steps=100000
+    - eval_examples=39900
+
+    
+
+<h2 id="训练环境准备.md">训练环境准备</h2>
+
+1.  硬件环境准备请参见各硬件产品文档"[驱动和固件安装升级指南]( https://support.huawei.com/enterprise/zh/category/ai-computing-platform-pid-1557196528909)"。需要在硬件设备上安装与CANN版本配套的固件与驱动。
+2.  宿主机上需要安装Docker并登录[Ascend Hub中心](https://ascendhub.huawei.com/#/detail?name=ascend-tensorflow-arm)获取镜像。
+
+    当前模型支持的镜像列表如[表1](#zh-cn_topic_0000001074498056_table1519011227314)所示。
+
+    **表 1** 镜像列表
+
+    <a name="zh-cn_topic_0000001074498056_table1519011227314"></a>
+    <table><thead align="left"><tr id="zh-cn_topic_0000001074498056_row0190152218319"><th class="cellrowborder" valign="top" width="47.32%" id="mcps1.2.4.1.1"><p id="zh-cn_topic_0000001074498056_p1419132211315"><a name="zh-cn_topic_0000001074498056_p1419132211315"></a><a name="zh-cn_topic_0000001074498056_p1419132211315"></a><em id="i1522884921219"><a name="i1522884921219"></a><a name="i1522884921219"></a>镜像名称</em></p>
+    </th>
+    <th class="cellrowborder" valign="top" width="25.52%" id="mcps1.2.4.1.2"><p id="zh-cn_topic_0000001074498056_p75071327115313"><a name="zh-cn_topic_0000001074498056_p75071327115313"></a><a name="zh-cn_topic_0000001074498056_p75071327115313"></a><em id="i1522994919122"><a name="i1522994919122"></a><a name="i1522994919122"></a>镜像版本</em></p>
+    </th>
+    <th class="cellrowborder" valign="top" width="27.16%" id="mcps1.2.4.1.3"><p id="zh-cn_topic_0000001074498056_p1024411406234"><a name="zh-cn_topic_0000001074498056_p1024411406234"></a><a name="zh-cn_topic_0000001074498056_p1024411406234"></a><em id="i723012493123"><a name="i723012493123"></a><a name="i723012493123"></a>配套CANN版本</em></p>
+    </th>
+    </tr>
+    </thead>
+    <tbody><tr id="zh-cn_topic_0000001074498056_row71915221134"><td class="cellrowborder" valign="top" width="47.32%" headers="mcps1.2.4.1.1 "><a name="zh-cn_topic_0000001074498056_ul81691515131910"></a><a name="zh-cn_topic_0000001074498056_ul81691515131910"></a><ul id="zh-cn_topic_0000001074498056_ul81691515131910"><li><em id="i82326495129"><a name="i82326495129"></a><a name="i82326495129"></a>ARM架构：<a href="https://ascend.huawei.com/ascendhub/#/detail?name=ascend-tensorflow-arm" target="_blank" rel="noopener noreferrer">ascend-tensorflow-arm</a></em></li><li><em id="i18233184918125"><a name="i18233184918125"></a><a name="i18233184918125"></a>x86架构：<a href="https://ascend.huawei.com/ascendhub/#/detail?name=ascend-tensorflow-x86" target="_blank" rel="noopener noreferrer">ascend-tensorflow-x86</a></em></li></ul>
+    </td>
+    <td class="cellrowborder" valign="top" width="25.52%" headers="mcps1.2.4.1.2 "><p id="zh-cn_topic_0000001074498056_p1450714271532"><a name="zh-cn_topic_0000001074498056_p1450714271532"></a><a name="zh-cn_topic_0000001074498056_p1450714271532"></a><em id="i72359495125"><a name="i72359495125"></a><a name="i72359495125"></a>20.2.0</em></p>
+    </td>
+    <td class="cellrowborder" valign="top" width="27.16%" headers="mcps1.2.4.1.3 "><p id="zh-cn_topic_0000001074498056_p18244640152312"><a name="zh-cn_topic_0000001074498056_p18244640152312"></a><a name="zh-cn_topic_0000001074498056_p18244640152312"></a><em id="i162363492129"><a name="i162363492129"></a><a name="i162363492129"></a><a href="https://support.huawei.com/enterprise/zh/ascend-computing/cann-pid-251168373/software" target="_blank" rel="noopener noreferrer">20.2</a></em></p>
+    </td>
+    </tr>
+    </tbody>
+    </table>
+
+
+
+## 脚本参数<a name="section6669162441511"></a>
+
+```
+gen_pt_test_data_gpu.py 中的参数
+
+--input_test_files 传入需要进行旋转的点云数据集
+--output_directory 存储旋转后点云集的路径
+--random_rotation_axang boole型，若为真将会对传入的数据集采用指定方法旋转，默认为真
+--num_rotations_per_file  每个测试点云的随机旋转增加数。默认为100
+
+
+main_point_cloud_gpu.py 中的参数
+
+--pt_cloud_test_files  测试数据集路径 
+--pt_cloud_train_files 熟练数据集路径 
+--method   指定用于预测旋转的方式。选项为"svd", "svd-inf", or "gs"。默认为“svd”
+--checkpoint_dir   训练模型的存放位置
+--train_steps   训练迭代的次数。默认为2600000
+--save_checkpoints_steps   保存检查点的频率。默认为10000
+--log_step_count   日志记录一次的步数。默认为200
+--save_summaries_steps   保存一次summary的步数。默认为5000
+--learning_rate   默认为1e-5
+--lr_decay   如果为真，则衰减learning rate。默认为假
+--lr_decay_steps   learning rate衰减步数。默认为35000
+--lr_decay_rate   learning rate衰减速率。默认为0.95
+--predict_all_test   如果为真，则在最新的检查点上运行eval作业，并打印每个输入的误差信息。默认为假
+--eval_examples   测试样本的数量。默认为0
+--print_variable_names   打印模型变量名。默认为假
+--num_train_augmentations   增加每个输入点云的随机旋转数。默认为10
+
+```
+
+
+
+## 运行
+
+GPU运行命令如下：
+
+**生成测试数据**
+
+注：生成的文件test_points_modified、points已包含在dataset文件夹中。
+```bash
+# 将路径设置到训练点云图文件
+IN_FILES=/points_test/*.pts
+
+NEW_TEST_FILES_DIR=/test_points_modified
+
+AXANG_SAMPLING=True
+
+# 决定旋转轴角的分布
+AXANG_SAMPLING=True
+
+python -m special_orthogonalization.gen_pt_test_data_gpu --input_test_files=$IN_FILES --output_directory=$NEW_TEST_FILES_DIR --random_rotation_axang=$AXANG_SAMPLING
+```
+
+**训练与评价**
+```bash
+# 将路径设置到原始训练数据
+TRAIN_FILES=/points/*.pts
+
+#将路径设置到旋转后的训练数据
+TEST_FILES=$NEW_TEST_FILES_DIR/*.pts
+
+# 指定旋转预测方式
+METHOD=svd
+
+# 指定ckpt、summaries、评价结果等的存储路径
+OUT_DIR=/path/to/model
+
+python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --log_step_count=200 --save_summaries_steps=25000 --pt_cloud_train_files=$TRAIN_FILES --pt_cloud_test_files=$TEST_FILES --train_steps=2600000 --save_checkpoints_steps=100000 --eval_examples=39900
+```
+
+**从所有训练样本中生成统计数据**
+```bash
+# 打印均值、中位数、标准差和分位数
+python -m special_orthogonalization.main_point_cloud_gpu --method=$METHOD --checkpoint_dir=$OUT_DIR --pt_cloud_test_files=$TEST_FILES --predict_all_test=True
+```
+## 运行
+
+NPU运行命令方式如下:
+
+对于所有的三个步骤程序来说，modelarts插件obs桶中的数据路径均要写到真正包含数据的那一个路径
+如在dataset文件夹中含有points、points_test等包含数据的文件夹
+modelarts插件中的数据路径写为 obs://cann-id2019/dataset/
+
+**生成测试数据**
+
+运行这一步我们需要的程序文件为gen_pt_test_data.py、modelarts_entry_Axang.py、genTestData.sh
+这三个文件中的代码均不需要修改
+最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified，
+为进行第二步模型训练，需要将生成旋转后的文件转移至obs桶中存放data的路径
+
+注：需要确保的是存在obs桶里的data文件名为points_test
+
+**训练与评价**
+
+运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_acc_train.py、train_full_1p.sh
+这三个文件中的代码均不需要修改
+
+由于采用混合精度提高训练性能，一些算子计算溢出，为此增添switch_config.txt文件，该文件应该和代码所在目录一致。
+
+最后生成的旋转后的数据文件存放在obs桶当次程序文件的output路径中，文件名为test_points_modified，
+为进行第三步，需要将生成的output文件转移至obs桶中存放data的路径
+
+注意：该次训练的模型保存在该次的obs文件夹中，进行第三步时又需要重启一次新的modelarts，因此我们需要将output文件中的
+checkpoint文件中最新模型的路径修改
+"/home/ma-user/modelarts/inputs/data_url_0/output"
+这样第三步才能跑出正确的精度指标
+
+
+**从所有训练样本中生成统计数据**
+
+运行这一步我们需要的程序文件为main_point_cloud_boostPerf.py、modelarts_entry_stat.py、genStatistical.sh
+这三个文件的代码均不需要修改
+
+运行成功后将会在屏幕上打印出关于精度相应的统计量值
+
+
+
+## 训练结果
+**精度对比：**
+
+
+
+|      测地线误差（°）        | 论文发布 | GPU(初始代码未改动版本) | GPU实测|NPU实测 |
+| ------------------------ | ------- | ----- | --------- |----------|
+|     平均值                |   1.63  |   2.58   |     3.98    |  2.92  |
+|     中值                 |   0.89  |    1.68   |     2.6    |  1.7  |
+|     标准差             |   6.70  |  6.93    |     9.36    |  8.45  |
+
+相比于论文中的精度，我们NPU迁移后实测差距依然较大，但是与我们未对代码任何改动初始的版本在GPU上跑出来的精度相差较小，
+且对于相同的代码的代码改动，NPU上的精度优于GPU上的精度。需要注意的是，在NPU上运行程序时，我们采用混合精度来提升训练
+的性能，但是其中产生了未知的错误，导致代表的精度指标mean_degree_err在整个训练过程中始终为0，因此我们无法得知在NPU训练的
+260w步的过程中，精度指标是下降的过程是怎样的。值得庆幸的是通过NPU训练出的模型，能够在GPU上计算出精度，并且精度还不错。
+
+**性能对比：**
+
+取华为v100上GPU运行的前2w步的global_step/sec平均值和NPU运行的前1w步的global_step/sec平均值进行对比，以达到性能对比的目的。
+对于NPU上的性能计算 需要的程序为main_point_cloud_boostPerf.py、modelarts_entry_perf.py、train_performance_1p.sh，其中参数已经
+设置完毕，无需更改。同时我们上传了我们计算性能的代码calc_perf.py，运行该代码需要将产生的日志文件从obs上下载下来，传入obs文件在本地的路径即可
+|       性能指标项       | 论文发布 | GPU实测 | NPU实测 |
+| ------------------- | ------- | ------ | ------  |
+|     global_step/sec|    无    | 87.64  |   116.77  |
+
+## 离线推理
+参考 SVD_ID2019_for_ACL
+
+
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/calc_perf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/calc_perf.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce34e8801d5baedea64957f4c992358fbf421c47
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/calc_perf.py
@@ -0,0 +1,15 @@
+import re
+import numpy as np
+
+#NPU上运行的日志文件下载到本地的路径
+path = ""
+
+with open(path, mode='r') as f:
+    NpulogInfo = f.readlines()
+
+NpulogInfo = str(NpulogInfo)
+valuesNPU = re.findall(r"global_step/sec:(.*?)\\n", NpulogInfo)
+valuesNPU = [float(x) for x in valuesNPU]
+
+perf_npu = np.mean(valuesNPU)
+print(perf_npu)
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f9a760ef8cc851ef42e7d8fa1aacd6588a1a36ee
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genStatistical.sh
@@ -0,0 +1,203 @@
+#!/bin/bash
+
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+# shell脚本所在路径
+cur_path=`echo $(cd $(dirname $0);pwd)`
+
+# 判断当前shell是否是performance
+perf_flag=`echo $0 | grep performance | wc -l`
+
+# 当前执行网络的名称
+Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'`
+
+export RANK_SIZE=1
+export RANK_ID=0
+export JOB_ID=10087
+
+# 路径参数初始化
+data_path=""
+output_path=""
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --data_path              # dataset of training
+    --output_path            # output of training
+    --train_steps            # max_step for training
+	  --train_epochs           # max_epoch for training
+    --batch_size             # batch size
+    -h/--help                show help message
+    "
+    exit 1
+fi
+
+# 参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --output_path* ]];then
+        output_path=`echo ${para#*=}`
+    elif [[ $para == --train_steps* ]];then
+        train_steps=`echo ${para#*=}`
+	elif [[ $para == --train_epochs* ]];then
+        train_epochs=`echo ${para#*=}`
+    elif [[ $para == --batch_size* ]];then
+        batch_size=`echo ${para#*=}`
+    fi
+done
+
+# 校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be config"
+    exit 1
+fi
+
+# 校验是否传入output_path,不需要修改
+if [[ $output_path == "" ]];then
+    output_path="./test/output/${ASCEND_DEVICE_ID}"
+fi
+
+# 设置打屏日志文件名，请保留，文件名为${print_log}
+print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log"
+etp_flag=${etp_running_flag}
+if [ x"${etp_flag}" != xtrue ];
+then
+    echo "running without etp..."
+    print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank`
+    print_log="/home/ma-user/modelarts/log/${print_log_name}"
+fi
+echo ${print_log}
+
+CaseName=""
+function get_casename()
+{
+    if [ x"${perf_flag}" = x1 ];
+    then
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf'
+    else
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc'
+    fi
+}
+
+# 跳转到code目录
+cd ${cur_path}/../
+rm -rf ./test/output/${ASCEND_DEVICE_ID}
+mkdir -p ./test/output/${ASCEND_DEVICE_ID}
+
+# 训练开始时间记录，不需要修改
+start_time=$(date +%s)
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+
+#=========================================================
+#=========================================================
+#========训练执行命令，需要根据您的网络进行修改==============
+#=========================================================
+#=========================================================
+# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取
+# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取
+# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值
+batch_size=1
+
+echo "------------------***PRINT DATA PATH***-----------------"
+
+docuClass="test_points_modified/*.pts"
+modelPath="output"
+
+testData_Path="$data_path$docuClass"
+outputPath="$data_path$modelPath"
+
+echo "$outputPath"
+echo "$testData_Path"
+
+
+if [ x"${modelarts_flag}" != x ];
+then
+    python  /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \
+                              --method=svd \
+                              --checkpoint_dir=${outputPath} \
+                              --pt_cloud_test_files=${testData_Path} \
+                              --predict_all_test=True
+else
+    python  /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \
+                              --method=svd \
+                              --checkpoint_dir=${outputPath} \
+                              --pt_cloud_test_files=${testData_Path} \
+                              --predict_all_test=True
+fi
+
+
+# 性能相关数据计算
+StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'`
+FPS=`awk 'BEGIN{  "%.2f\n", '${batch_size}'/'${StepTime}'}'`
+
+# 精度相关数据计算
+train_accuracy=`grep "Final Accuracy accuracy" ${print_log}  | awk '{print $NF}'`
+# 提取所有loss打印信息
+grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt
+
+
+###########################################################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+###########################################################
+
+# 判断本次执行是否正确使用Ascend NPU
+use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l`
+if [ x"${use_npu_flag}" == x0 ];
+then
+    echo "------------------ ERROR NOTICE START ------------------"
+    echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration."
+    echo "------------------ ERROR NOTICE END------------------"
+else
+    echo "------------------ INFO NOTICE START------------------"
+    echo "INFO, your task have used Ascend NPU, please check your result."
+    echo "------------------ INFO NOTICE END------------------"
+fi
+
+# 获取最终的casename，请保留，case文件名为${CaseName}
+get_casename
+
+# 重命名loss文件
+if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ];
+then
+    mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt
+fi
+
+# 训练端到端耗时
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+echo "------------------ Final result ------------------"
+# 输出性能FPS/单step耗时/端到端耗时
+echo "Final Performance images/sec : $FPS"
+echo "Final Performance sec/step : $StepTime"
+echo "E2E Training Duration sec : $e2e_time"
+
+# 输出训练精度
+echo "Final Train Accuracy : ${train_accuracy}"
+
+# 最后一个迭代loss值，不需要修改
+ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`)
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genTestData.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genTestData.sh
new file mode 100644
index 0000000000000000000000000000000000000000..416b71b047aa9d6757b497dcd71c1199c0546b8a
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/genTestData.sh
@@ -0,0 +1,203 @@
+#!/bin/bash
+
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+# shell脚本所在路径
+cur_path=`echo $(cd $(dirname $0);pwd)`
+
+# 判断当前shell是否是performance
+perf_flag=`echo $0 | grep performance | wc -l`
+
+# 当前执行网络的名称
+Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'`
+
+export RANK_SIZE=1
+export RANK_ID=0
+export JOB_ID=10087
+
+# 路径参数初始化
+data_path=""
+output_path=""
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --data_path              # dataset of training
+    --output_path            # output of training
+    --train_steps            # max_step for training
+	  --train_epochs           # max_epoch for training
+    --batch_size             # batch size
+    -h/--help                show help message
+    "
+    exit 1
+fi
+
+# 参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --output_path* ]];then
+        output_path=`echo ${para#*=}`
+    elif [[ $para == --train_steps* ]];then
+        train_steps=`echo ${para#*=}`
+	elif [[ $para == --train_epochs* ]];then
+        train_epochs=`echo ${para#*=}`
+    elif [[ $para == --batch_size* ]];then
+        batch_size=`echo ${para#*=}`
+    fi
+done
+
+# 校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be config"
+    exit 1
+fi
+
+# 校验是否传入output_path,不需要修改
+if [[ $output_path == "" ]];then
+    output_path="./test/output/${ASCEND_DEVICE_ID}"
+fi
+
+# 设置打屏日志文件名，请保留，文件名为${print_log}
+print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log"
+etp_flag=${etp_running_flag}
+if [ x"${etp_flag}" != xtrue ];
+then
+    echo "running without etp..."
+    print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank`
+    print_log="/home/ma-user/modelarts/log/${print_log_name}"
+fi
+echo ${print_log}
+
+CaseName=""
+function get_casename()
+{
+    if [ x"${perf_flag}" = x1 ];
+    then
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf'
+    else
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc'
+    fi
+}
+
+# 跳转到code目录
+cd ${cur_path}/../
+rm -rf ./test/output/${ASCEND_DEVICE_ID}
+mkdir -p ./test/output/${ASCEND_DEVICE_ID}
+
+# 训练开始时间记录，不需要修改
+start_time=$(date +%s)
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+
+#=========================================================
+#=========================================================
+#========训练执行命令，需要根据您的网络进行修改==============
+#=========================================================
+#=========================================================
+# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取
+# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取
+# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值
+batch_size=1
+
+echo "------------------***PRINT DATA PATH***-----------------"
+
+inputDataPath="points_test/*.pts"
+TestdataPath="test_points_modified"
+
+Data_Path="$data_path$inputDataPath"
+outputPath="$output_path$TestdataPath"
+AXANG_SAMPLIE="True"
+
+
+echo "$outputPath"
+echo "$testData_Path"
+
+
+if [ x"${modelarts_flag}" != x ];
+then
+    python  /home/ma-user/modelarts/user-job-dir/code/gen_pt_test_data.py\
+                            --input_test_files=${Data_Path} \
+                            --output_directory=${outputPath} \
+                            --random_rotation_axang=${AXANG_SAMPLIE}
+else
+    python  /home/ma-user/modelarts/user-job-dir/code/gen_pt_test_data.py\
+                            --input_test_files=${Data_Path} \
+                            --output_directory=${outputPath} \
+                            --random_rotation_axang=${AXANG_SAMPLIE}
+fi
+
+
+# 性能相关数据计算
+StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'`
+FPS=`awk 'BEGIN{  "%.2f\n", '${batch_size}'/'${StepTime}'}'`
+
+# 精度相关数据计算
+train_accuracy=`grep "Final Accuracy accuracy" ${print_log}  | awk '{print $NF}'`
+# 提取所有loss打印信息
+grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt
+
+
+###########################################################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+###########################################################
+
+# 判断本次执行是否正确使用Ascend NPU
+use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l`
+if [ x"${use_npu_flag}" == x0 ];
+then
+    echo "------------------ ERROR NOTICE START ------------------"
+    echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration."
+    echo "------------------ ERROR NOTICE END------------------"
+else
+    echo "------------------ INFO NOTICE START------------------"
+    echo "INFO, your task have used Ascend NPU, please check your result."
+    echo "------------------ INFO NOTICE END------------------"
+fi
+
+# 获取最终的casename，请保留，case文件名为${CaseName}
+get_casename
+
+# 重命名loss文件
+if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ];
+then
+    mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt
+fi
+
+# 训练端到端耗时
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+echo "------------------ Final result ------------------"
+# 输出性能FPS/单step耗时/端到端耗时
+echo "Final Performance images/sec : $FPS"
+echo "Final Performance sec/step : $StepTime"
+echo "E2E Training Duration sec : $e2e_time"
+
+# 输出训练精度
+echo "Final Train Accuracy : ${train_accuracy}"
+
+# 最后一个迭代loss值，不需要修改
+ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`)
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1c299409adab81101b3e547896afad923d8ae2f
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/gen_pt_test_data.py
@@ -0,0 +1,72 @@
+# coding=utf-8
+# Copyright 2021 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Generate modified point cloud test data with fixed random rotations."""
+import glob
+import os
+import pathlib
+
+import utils
+from absl import app
+from absl import flags
+import numpy as np
+from scipy.stats import special_ortho_group
+from npu_bridge.npu_init import *
+
+FLAGS = flags.FLAGS
+flags.DEFINE_string('input_test_files', '',
+                    'Regular expression for the original input test files.')
+flags.DEFINE_string('output_directory', '',
+                    'Output directory where new test files will be stored.')
+flags.DEFINE_integer('num_rotations_per_file', 100,
+                     'Number of random rotation augmentations per test point '
+                     'cloud.')
+flags.DEFINE_boolean('random_rotation_axang', True,
+                     'If true, samples random rotations using the method '
+                     'from the original benchmark code. Otherwise samples '
+                     'by Haar measure.')
+
+
+def gen_test_data():
+  """Generate the new (modified) test data."""
+  # Create output directory.
+  os.makedirs(FLAGS.output_directory, exist_ok=True)
+
+  # Get all test point cloud files in the original dataset.
+  input_test_files = glob.glob(FLAGS.input_test_files)
+
+  for in_file in input_test_files:
+    out_file_prefix = pathlib.Path(in_file).stem
+    pts = np.loadtxt(in_file)  # N x 3
+    num_pts_to_keep = pts.shape[0] // 2
+    pts = pts[:num_pts_to_keep, :]  # N//2 x 3.
+
+    for k in range(FLAGS.num_rotations_per_file):
+      if FLAGS.random_rotation_axang:
+        r = utils.random_rotation_benchmark_np(1)
+        r = r[0]
+      else:
+        r = special_ortho_group.rvs(3)
+      joined = np.float32(np.concatenate((r, pts), axis=0))  # (N//2+3) x 3.
+      out_file = os.path.join(
+          FLAGS.output_directory, '%s_r%03d.pts'%(out_file_prefix, k))
+      np.savetxt(out_file, joined)
+
+
+def main(unused_argv):
+  gen_test_data()
+
+if __name__ == '__main__':
+  app.run(main)
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_perf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_perf.py
new file mode 100644
index 0000000000000000000000000000000000000000..316d7da2065cbbbc3959ba40397e6342d0c218cc
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/main_point_cloud_perf.py
@@ -0,0 +1,441 @@
+# coding=utf-8
+# Copyright 2021 The Google Research Authors.
+#
+# 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.
+
+"""Training and evaluation for the point cloud alignment experiment."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import utils
+from absl import flags
+import numpy as np
+import tensorflow as tf
+import glob
+import pathlib
+import datetime
+from npu_bridge.npu_init import *
+from npu_bridge.estimator.npu.npu_estimator import NPUEstimatorSpec
+from npu_bridge.estimator.npu.npu_estimator import NPUEstimator
+from npu_bridge.estimator.npu.npu_config import NPURunConfig
+import os
+FLAGS = tf.app.flags.FLAGS
+
+# General flags.
+flags.DEFINE_string('method', 'svd',
+                    'Specifies the method to use for predicting rotations. '
+                    'Choices are "svd", "svd-inf", or "gs".')
+flags.DEFINE_string('checkpoint_dir', '',
+                    'Locations for checkpoints, summaries, etc.')
+flags.DEFINE_integer('train_steps', 2600000, 'Number of training iterations.')
+flags.DEFINE_integer('save_checkpoints_steps', 10000,
+                     'How often to save checkpoints')
+flags.DEFINE_integer('log_step_count', 500, 'How often to log the step count')
+flags.DEFINE_integer('save_summaries_steps', 5000,
+                     'How often to save summaries.')
+flags.DEFINE_float('learning_rate', 1e-5, 'Learning rate')
+flags.DEFINE_boolean('lr_decay', False, 'Decay the learning rate if True.')
+flags.DEFINE_integer('lr_decay_steps', 35000,
+                     'Learning rate decays steps.')
+flags.DEFINE_float('lr_decay_rate', 0.95,
+                   'Learning rate decays rate.')
+flags.DEFINE_boolean('predict_all_test', False,
+                     'If true, runs an eval job on latest checkpoint and '
+                     'prints the error for each input.')
+flags.DEFINE_integer('eval_examples', 39900, 'Number of test examples.')
+flags.DEFINE_boolean('print_variable_names', False,
+                     'Print model variable names.')
+
+# Flags only used in the point cloud alignment experiment.
+flags.DEFINE_integer('num_train_augmentations', 10,
+                     'Number of random rotations for augmenting each input '
+                     'point cloud.')
+flags.DEFINE_string('pt_cloud_train_files', '',
+                    'Expression matching all training point files, e.g. '
+                    '/path/to/files/pc_plane/points/*.pts')
+flags.DEFINE_string('pt_cloud_test_files','',
+                    'Expression matching all modified test point files, e.g. '
+                    '/path/to/files/pc_plane/points_test/*.pts')
+flags.DEFINE_boolean('random_rotation_axang', True,
+                     'If true, samples random rotations using the method '
+                     'from the original benchmark code. Otherwise samples '
+                     'by Haar measure.')
+
+
+def pt_features(batch_pts):
+  """Input shape: [B, N, 3], output shape: [B, 1024]."""
+  with tf.variable_scope('ptenc', reuse=tf.AUTO_REUSE):
+    f1 = tf.layers.conv1d(inputs=batch_pts, filters=64, kernel_size=1)
+    f1 = tf.nn.leaky_relu(f1)
+    f2 = tf.layers.conv1d(inputs=f1, filters=128, kernel_size=1)
+    f2 = tf.nn.leaky_relu(f2)
+    f3 = tf.layers.conv1d(inputs=f2, filters=1024, kernel_size=1)
+  f = tf.reduce_max(f3, axis=1, keep_dims=False)
+  return f
+
+
+def regress_from_features(batch_features, out_dim):
+  """Regress to a rotation representation from point cloud encodings.
+
+  In Zhou et al, CVPR19, the paper describes this regression network as an MLP
+  mapping 2048->512->512->out_dim, but the associated code implements it with
+  one less layer: 2048->512->out_dim. We mimic the code.
+
+  Args:
+    batch_features: [batch_size, in_dim].
+    out_dim: desired output dimensionality.
+
+  Returns:
+    A [batch_size, out_dim] tensor.
+  """
+  f1 = tf.layers.dense(batch_features, 512)
+  f1 = tf.nn.leaky_relu(f1)
+  f2 = tf.layers.dense(f1, out_dim)
+  return f2
+
+
+def net_point_cloud(points1, points2, mode):
+  """Predict a relative rotation given two point clouds.
+
+  Args:
+    points1: [batch_size, N, 3] float tensor.
+    points2: [batch_size, N, 3] float tensor.
+    mode: tf.estimator.ModeKeys.
+
+  Returns:
+    [batch_size, 3, 3] matrices.
+  """
+  f1 = pt_features(points1)
+  f2 = pt_features(points2)
+  f = tf.concat([f1, f2], axis=-1)
+
+  if FLAGS.method == 'svd':
+    p = regress_from_features(f, 9)
+    return utils.symmetric_orthogonalization(p)
+
+  if FLAGS.method == 'svd-inf':
+    p = regress_from_features(f, 9)
+    if mode == tf.estimator.ModeKeys.TRAIN:
+      return tf.reshape(p, (-1, 3, 3))
+    else:
+      return utils.symmetric_orthogonalization(p)
+
+  if FLAGS.method == 'gs':
+    p = regress_from_features(f, 6)
+    return utils.gs_orthogonalization(p)
+
+
+def model_fn(features, labels, mode, params):
+  """The model_fn used to construct the tf.Estimator."""
+  del params  # Unused.
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    # Training data has point cloud of size [1, N, 3] and random rotations
+    # of size [1, FLAGS.num_train_augmentations, 3, 3]
+    rot = labels[0]
+    data = features
+    num_rot = FLAGS.num_train_augmentations
+    batch_pts1 = tf.tile(data, [num_rot, 1, 1])
+    # In this experiment it does not matter if we pre or post-multiply the
+    # rotation as long as we are consistent between training and eval.
+    batch_pts2 = tf.matmul(batch_pts1, rot)  # post-multiplying!
+  else:
+    # Test data has point cloud of size [1, N, 3] and a single random
+    # rotation of size [1, 3, 3]
+    batch_pts1 = features['data']
+    rot = features['rot']
+    batch_pts2 = tf.matmul(batch_pts1, rot)
+  rot = tf.reshape(rot, (-1, 3, 3))
+
+  # Predict the rotation.
+
+
+  r = net_point_cloud(batch_pts1, batch_pts2, mode)
+
+  unit_one = tf.constant(1.0,dtype=tf.float32)
+
+  rotation_matrix = tf.multiply(r,unit_one,name='rotation_matrix')
+  # Compute the loss.
+  loss = tf.nn.l2_loss(rot - r)
+
+  # Compute the relative angle in radians.
+  theta = utils.relative_angle(rot, r)
+
+  # Mean angle error over the batch.
+  mean_theta = tf.reduce_mean(theta)
+
+  mean_theta_deg = mean_theta * 180.0 / np.pi
+
+  # Train, eval, or predict depending on mode.
+  if mode == tf.estimator.ModeKeys.TRAIN:
+    tf.summary.scalar('train/loss', loss)
+    tf.summary.scalar('train/theta', mean_theta_deg)
+    global_step = tf.train.get_or_create_global_step()
+
+    if FLAGS.lr_decay:
+      learning_rate = tf.train.exponential_decay(
+          FLAGS.learning_rate,
+          global_step,
+          FLAGS.lr_decay_steps,
+          FLAGS.lr_decay_rate)
+    else:
+      learning_rate = FLAGS.learning_rate
+
+    tf.summary.scalar('train/learning_rate', learning_rate)
+    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
+    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+    with tf.control_dependencies(update_ops):
+      train_op = optimizer.minimize(loss, global_step=global_step)
+      #train_op = util.set_graph_exec_config(train_op, dynamic_input=True,dynamic_graph_execute_mode="lazy_recompile")
+
+    return NPUEstimatorSpec(
+        mode=mode,
+        loss=loss,
+        train_op=train_op,
+        predictions=rotation_matrix)
+
+  if mode == tf.estimator.ModeKeys.EVAL:
+    if FLAGS.predict_all_test:
+      print_error_op = tf.print('error:', mean_theta_deg)
+      with tf.control_dependencies([print_error_op]):
+        eval_metric_ops = {
+            'mean_degree_err': tf.metrics.mean(mean_theta_deg),
+        }
+    else:
+      eval_metric_ops = {
+          'mean_degree_err': tf.metrics.mean(mean_theta_deg),
+      }
+
+    return NPUEstimatorSpec(
+        mode=mode,
+        loss=loss,
+        eval_metric_ops=eval_metric_ops)
+
+  if mode == tf.estimator.ModeKeys.PREDICT:
+    pred = {'error': mean_theta_deg}
+    return NPUEstimatorSpec(
+        mode=mode,
+        predictions=pred)
+
+
+def train_input_fn():
+  """Generate training data iterator from the .pts files."""
+  def _file_to_matrix(pts_path):
+    """Read Nx3 point cloud from a .pts file."""
+    file_buffer = tf.read_file(pts_path)
+    lines = tf.string_split([file_buffer], delimiter='\n')
+    lines1 = tf.string_split(lines.values, delimiter='\r')
+    values = tf.stack(tf.decode_csv(lines1.values,
+                                    record_defaults=[[0.0], [0.0], [0.0]],
+                                    field_delim=' '))
+    values = tf.transpose(values) # 3xN --> Nx3.
+    values = values[:tf.shape(values)[0] // 2, :]
+
+    # modified by Lyoung
+    diff_num = 1500-tf.shape(values)[0]
+    repeat_pts = tf.tile(tf.reshape(values[1, :], (1, -1)), [diff_num, 1])
+    values = tf.concat([values,repeat_pts], axis=0)
+    # The experiment code in
+    # github.com/papagina/RotationContinuity/.../shapenet/code/train_pointnet.py
+    # only used the first half of the points in each file.
+    return values
+
+  def _random_rotation(pts):
+    """Attach N random rotations to a point cloud."""
+    if FLAGS.random_rotation_axang:
+      rotations = utils.random_rotation_benchmark(FLAGS.num_train_augmentations)
+    else:
+      rotations = utils.random_rotation(FLAGS.num_train_augmentations)
+    return pts,rotations
+
+  pts_paths = tf.gfile.Glob(FLAGS.pt_cloud_train_files)
+  dataset = tf.data.Dataset.from_tensor_slices(pts_paths)
+  dataset = dataset.map(_file_to_matrix)
+  dataset = dataset.cache()  # Comment out if memory cannot hold all the data.
+  dataset = dataset.shuffle(buffer_size=50, reshuffle_each_iteration=True)
+  dataset = dataset.repeat()
+  dataset = dataset.map(_random_rotation)
+  dataset = dataset.batch(1)
+  iterator = tf.data.make_one_shot_iterator(dataset)
+  batch_data, batch_rot = iterator.get_next()
+  #features_dict = {'data': batch_data, 'rot': batch_rot}
+  #batch_size = tf.shape(batch_data)[0]
+  #batch_labels_dummy = tf.zeros(shape=(batch_size, 1))
+  return batch_data,batch_rot
+
+
+def eval_input_fn():
+  """Generate test data from *modified* .pts files.
+
+  See README and comments below for details on how the data is modified.
+
+  Returns:
+    A tuple of features and associated labels.
+  """
+  def _file_to_matrix(pts_path):
+    """Read Nx3 point cloud and 3x3 rotation matrix from a .pts file.
+
+    The test data is a modified version of the original files. For each .pts
+    file we have (1) added a 3x3 rotation matrix for testing, and (2) removed
+    the second half of the point cloud since it is not used at all.
+
+    Args:
+      pts_path: path to a .pts file.
+
+    Returns:
+      A Nx3 point cloud.
+      A 3x3 rotation matrix.
+    """
+    file_buffer = tf.read_file(pts_path)
+    lines = tf.string_split([file_buffer], delimiter='\n')
+    lines1 = tf.string_split(lines.values,delimiter='\r')
+    values = tf.stack(tf.decode_csv(lines1.values,
+              record_defaults=[[0.0], [0.0], [0.0]],field_delim=' '))
+    values = tf.transpose(values)# 3xN --> Nx3.
+
+    #modified by Lyoung
+    diff_num = 1414-tf.shape(values)[0]
+    repeat_pts = tf.tile(tf.reshape(values[4, :], (1, -1)), [diff_num, 1])
+    values = tf.concat([values, repeat_pts], axis=0)
+    # First three rows are the rotation matrix, remaining rows the point cloud.
+    rot = values[:3, :]
+
+    return values[4:, :], rot
+
+  pts_paths = tf.io.gfile.glob(FLAGS.pt_cloud_test_files)
+  dataset = tf.data.Dataset.from_tensor_slices(pts_paths)
+  dataset = dataset.map(_file_to_matrix)
+  dataset = dataset.batch(1)
+  iterator = tf.data.make_one_shot_iterator(dataset)
+  batch_data, batch_rot  = iterator.get_next()
+  features_dict = {'data': batch_data, 'rot': batch_rot}
+  #batch_size = tf.shape(batch_data)[0]
+  #batch_labels_dummy = tf.zeros(shape=(batch_size, 1))
+  return features_dict
+
+
+def print_variable_names():
+  """Print variable names in a model."""
+  params = {'dummy': 0}
+  estimator = NPUEstimator(
+          model_fn=model_fn,
+          model_dir=FLAGS.checkpoint_dir,
+          params=params)
+
+  names = estimator.get_variable_names()
+  for name in names:
+    print(name)
+
+
+def predict_all_test():
+  """Print error statistics for the test dataset."""
+
+  config = NPURunConfig(
+      save_summary_steps=None,
+      save_checkpoints_steps=None,
+      log_step_count_steps=None,
+      keep_checkpoint_max=None)
+
+  params = {'dummy': 0}
+  estimator = NPUEstimator(
+          model_fn=model_fn,
+          model_dir=FLAGS.checkpoint_dir,
+          params=params,
+          config=config)
+  evals = estimator.predict(input_fn=eval_input_fn, yield_single_examples=True)
+
+  # Print error statistics.
+
+  all_errors = [x['error'] for x in evals]
+  errors = np.array(all_errors)
+  print('Evaluated %d examples'%np.size(errors))
+  print('Mean error: %f degrees',np.mean(errors))
+  print('Median error: %f degrees', np.median(errors))
+  print('Std: %f degrees', np.std(errors))
+  sorted_errors = np.sort(errors)
+  n = np.size(sorted_errors)
+  print('\nPercentiles:')
+  for perc in range(1, 101):
+    index = np.int32(np.float32(n * perc) / 100.0) - 1
+    print('%3d%%: %f'%(perc, sorted_errors[index]))
+
+def serving_input_fn():
+    input_data = tf.placeholder(tf.float32, [None, None, 3], name='data')
+    input_rot = tf.placeholder(tf.float32, [None, None, 3], name='rot')
+    input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
+        'data': input_data,
+        'rot': input_rot
+      })()
+    return input_fn
+
+
+
+def train_and_eval():
+  """Train and evaluate a model."""
+  save_summary_steps = FLAGS.save_summaries_steps
+  save_checkpoints_steps = FLAGS.save_checkpoints_steps
+  log_step_count = FLAGS.log_step_count
+
+  config = NPURunConfig(
+          save_summary_steps=save_summary_steps,
+          save_checkpoints_steps=save_checkpoints_steps,
+          log_step_count_steps=log_step_count,
+          keep_checkpoint_max=None,
+          precision_mode = "allow_mix_precision")
+
+
+
+  params = {'dummy': 0}
+  estimator = NPUEstimator(
+          model_fn=model_fn,
+          model_dir=FLAGS.checkpoint_dir,
+          config=config,
+          params=params)
+
+
+  # train_spec = tf.estimator.TrainSpec(
+  #     input_fn=train_input_fn,
+  #     max_steps=FLAGS.train_steps)
+  #
+  # eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn,
+  #                                   start_delay_secs=60,
+  #                                   steps=FLAGS.eval_examples,
+  #                                   throttle_secs=60)
+
+  #tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
+
+  estimator.train(input_fn=train_input_fn,
+      max_steps=FLAGS.train_steps)
+
+
+  estimator.export_savedmodel(FLAGS.checkpoint_dir,
+                              serving_input_fn)
+
+  estimator.evaluate(input_fn=eval_input_fn, steps=FLAGS.eval_examples)
+
+
+def main(argv=None):  # pylint: disable=unused-argument
+
+  if FLAGS.print_variable_names:
+    print_variable_names()
+    return
+
+  if FLAGS.predict_all_test:
+    predict_all_test()
+  else:
+    train_and_eval()
+
+
+if __name__ == '__main__':
+
+  tf.app.run()
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_Axang.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_Axang.py
new file mode 100644
index 0000000000000000000000000000000000000000..40f00a348a0b41247b5fed4c13780f87b47ddc93
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_Axang.py
@@ -0,0 +1,36 @@
+import os
+import argparse
+import sys
+
+# 解析输入参数data_url
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0")
+parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/")
+config = parser.parse_args()
+
+print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0]))
+code_dir = sys.path[0]
+os.chdir(code_dir)
+print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd()))
+
+print("[CANN-Modelzoo] before train - list my run files:")
+os.system("ls -al /usr/local/Ascend/ascend-toolkit/")
+
+print("[CANN-Modelzoo] before train - list my dataset files:")
+os.system("ls -al %s" % config.data_url)
+
+
+print("[CANN-Modelzoo] start run train shell")
+# 设置sh文件格式为linux可执行
+os.system("dos2unix ./test/*")
+
+# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定
+# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS
+os.system("bash /home/ma-user/modelarts/user-job-dir/code/genTestData.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url))
+
+print("[CANN-Modelzoo] finish run train shell")
+
+# 将当前执行目录所有文件拷贝到obs的output进行备份
+print("[CANN-Modelzoo] after train - list my output files:")
+os.system("cp -r %s %s " % (code_dir, config.train_url))
+os.system("ls -al %s" % config.train_url)
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_acc_train.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_acc_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..b86164d74beedbf2f86c760ab244aa91135da99d
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_acc_train.py
@@ -0,0 +1,36 @@
+import os
+import argparse
+import sys
+
+# 解析输入参数data_url
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0")
+parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/")
+config = parser.parse_args()
+
+print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0]))
+code_dir = sys.path[0]
+os.chdir(code_dir)
+print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd()))
+
+print("[CANN-Modelzoo] before train - list my run files:")
+os.system("ls -al /usr/local/Ascend/ascend-toolkit/")
+
+print("[CANN-Modelzoo] before train - list my dataset files:")
+os.system("ls -al %s" % config.data_url)
+
+
+print("[CANN-Modelzoo] start run train shell")
+# 设置sh文件格式为linux可执行
+os.system("dos2unix ./test/*")
+
+# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定
+# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS
+os.system("bash /home/ma-user/modelarts/user-job-dir/code/train_full_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url))
+
+print("[CANN-Modelzoo] finish run train shell")
+
+# 将当前执行目录所有文件拷贝到obs的output进行备份
+print("[CANN-Modelzoo] after train - list my output files:")
+os.system("cp -r %s %s " % (code_dir, config.train_url))
+os.system("ls -al %s" % config.train_url)
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_perf.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_perf.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb613e0f4ba7d41872c91e67a2ba5b2cc4793149
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_perf.py
@@ -0,0 +1,36 @@
+import os
+import argparse
+import sys
+
+# 解析输入参数data_url
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0")
+parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/")
+config = parser.parse_args()
+
+print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0]))
+code_dir = sys.path[0]
+os.chdir(code_dir)
+print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd()))
+
+print("[CANN-Modelzoo] before train - list my run files:")
+os.system("ls -al /usr/local/Ascend/ascend-toolkit/")
+
+print("[CANN-Modelzoo] before train - list my dataset files:")
+os.system("ls -al %s" % config.data_url)
+
+
+print("[CANN-Modelzoo] start run train shell")
+# 设置sh文件格式为linux可执行
+os.system("dos2unix ./test/*")
+
+# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定
+# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS
+os.system("bash /home/ma-user/modelarts/user-job-dir/code/train_performance_1p.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url))
+
+print("[CANN-Modelzoo] finish run train shell")
+
+# 将当前执行目录所有文件拷贝到obs的output进行备份
+print("[CANN-Modelzoo] after train - list my output files:")
+os.system("cp -r %s %s " % (code_dir, config.train_url))
+os.system("ls -al %s" % config.train_url)
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_stat.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_stat.py
new file mode 100644
index 0000000000000000000000000000000000000000..78debea2131c670cceaa58ddfa49704e72dde535
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/modelarts_entry_stat.py
@@ -0,0 +1,40 @@
+import os
+import argparse
+import sys
+
+# 解析输入参数data_url
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_url", type=str, default="/home/ma-user/modelarts/inputs/data_url_0")
+parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/")
+config = parser.parse_args()
+
+print("[CANN-Modelzoo] code_dir path is [%s]" % (sys.path[0]))
+code_dir = sys.path[0]
+os.chdir(code_dir)
+print("[CANN-Modelzoo] work_dir path is [%s]" % (os.getcwd()))
+
+print("[CANN-Modelzoo] before train - list my run files:")
+os.system("ls -al /usr/local/Ascend/ascend-toolkit/")
+
+print("[CANN-Modelzoo] before train - list my dataset files:")
+os.system("ls -al %s" % config.data_url)
+
+files = os.listdir(config.data_url)
+print("===>>>Files:", files)
+
+
+
+print("[CANN-Modelzoo] start run train shell")
+# 设置sh文件格式为linux可执行
+os.system("dos2unix ./test/*")
+
+# 执行train_full_1p.sh或者train_performance_1p.sh，需要用户自己指定
+# full和performance的差异，performance只需要执行很少的step，控制在15分钟以内，主要关注性能FPS
+os.system("bash /home/ma-user/modelarts/user-job-dir/code/genStatistical.sh --data_path=%s --output_path=%s " % (config.data_url, config.train_url))
+
+print("[CANN-Modelzoo] finish run train shell")
+
+# 将当前执行目录所有文件拷贝到obs的output进行备份
+print("[CANN-Modelzoo] after train - list my output files:")
+os.system("cp -r %s %s " % (code_dir, config.train_url))
+os.system("ls -al %s" % config.train_url)
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1f2a5e41d83caa361f20f792da4e4431a8f14bb1
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/requirements.txt
@@ -0,0 +1,35 @@
+absl-py==0.11.0
+astunparse==1.6.3
+cachetools==4.1.1
+certifi==2020.6.20
+chardet==3.0.4
+gast==0.3.3
+google-auth==1.23.0
+google-auth-oauthlib==0.4.2
+google-pasta==0.2.0
+grpcio==1.33.2
+h5py==2.10.0
+idna==2.10
+importlib-metadata==2.0.0
+Keras-Preprocessing==1.1.2
+Markdown==3.3.3
+numpy==1.18.5
+oauthlib==3.1.0
+opt-einsum==3.3.0
+protobuf==3.13.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+requests==2.24.0
+requests-oauthlib==1.3.0
+rsa==4.6
+scipy==1.5.3
+six==1.15.0
+tensorboard==2.3.0
+tensorboard-plugin-wit==1.7.0
+tensorflow==2.3.1
+tensorflow-estimator==2.3.0
+termcolor==1.1.0
+urllib3==1.25.11
+Werkzeug==1.0.1
+wrapt==1.12.1
+zipp==3.4.0
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/switch_config.txt b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/switch_config.txt
new file mode 100644
index 0000000000000000000000000000000000000000..995f4cea2905f7a19343d8bf259579c3d4b4bca5
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/switch_config.txt
@@ -0,0 +1,3 @@
+OpType::MatMulV2:InputDtype:float16,float16,float32,OutputDtype:float32
+OpType::BatchMatMul:InputDtype:float16,float16,OutputDtype:float32
+OpType::BatchMatMulV2:InputDtype:float16,float16,OutputDtype:float32
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..13753883246ddb6859ebaa098e03f88e528f1ea6
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_full_1p.sh
@@ -0,0 +1,209 @@
+#!/bin/bash
+
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+# shell脚本所在路径
+cur_path=`echo $(cd $(dirname $0);pwd)`
+
+# 判断当前shell是否是performance
+perf_flag=`echo $0 | grep performance | wc -l`
+
+# 当前执行网络的名称
+Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'`
+
+export RANK_SIZE=1
+export RANK_ID=0
+export JOB_ID=10087
+
+# 路径参数初始化
+data_path=""
+output_path=""
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --data_path              # dataset of training
+    --output_path            # output of training
+    --train_steps            # max_step for training
+	  --train_epochs           # max_epoch for training
+    --batch_size             # batch size
+    -h/--help                show help message
+    "
+    exit 1
+fi
+
+# 参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --output_path* ]];then
+        output_path=`echo ${para#*=}`
+    elif [[ $para == --train_steps* ]];then
+        train_steps=`echo ${para#*=}`
+	elif [[ $para == --train_epochs* ]];then
+        train_epochs=`echo ${para#*=}`
+    elif [[ $para == --batch_size* ]];then
+        batch_size=`echo ${para#*=}`
+    fi
+done
+
+# 校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be config"
+    exit 1
+fi
+
+# 校验是否传入output_path,不需要修改
+if [[ $output_path == "" ]];then
+    output_path="./test/output/${ASCEND_DEVICE_ID}"
+fi
+
+# 设置打屏日志文件名，请保留，文件名为${print_log}
+print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log"
+etp_flag=${etp_running_flag}
+if [ x"${etp_flag}" != xtrue ];
+then
+    echo "running without etp..."
+    print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank`
+    print_log="/home/ma-user/modelarts/log/${print_log_name}"
+fi
+echo ${print_log}
+
+CaseName=""
+function get_casename()
+{
+    if [ x"${perf_flag}" = x1 ];
+    then
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf'
+    else
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc'
+    fi
+}
+
+# 跳转到code目录
+cd ${cur_path}/../
+rm -rf ./test/output/${ASCEND_DEVICE_ID}
+mkdir -p ./test/output/${ASCEND_DEVICE_ID}
+
+# 训练开始时间记录，不需要修改
+start_time=$(date +%s)
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+
+#=========================================================
+#=========================================================
+#========训练执行命令，需要根据您的网络进行修改==============
+#=========================================================
+#=========================================================
+# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取
+# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取
+# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值
+batch_size=1
+
+echo "------------------***PRINT DATA PATH***-----------------"
+train_data="points/*.pts"
+test_data="test_points_modified/*.pts"
+
+trainData_Path="$data_path$train_data"
+testData_Path="$data_path$test_data"
+
+
+if [ x"${modelarts_flag}" != x ];
+then
+    python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \
+                              --method=svd \
+                               --checkpoint_dir=${output_path} \
+                               --log_step_count=200 \
+                               --save_summaries_steps=25000 \
+                               --pt_cloud_train_files=${trainData_Path} \
+                               --pt_cloud_test_files=${testData_Path} \
+                               --train_steps=2600000 \
+                               --save_checkpoints_steps=100000 \
+                               --eval_examples=39900
+else
+    python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \
+                               --method=svd \
+                               --checkpoint_dir=${output_path} \
+                               --log_step_count=200 \
+                               --save_summaries_steps=25000 \
+                               --pt_cloud_train_files=${trainData_Path}\
+                               --pt_cloud_test_files=${testData_Path} \
+                               --train_steps=2600000 \
+                               --save_checkpoints_steps=100000 \
+                               --eval_examples=39900 > ${print_log}
+fi
+
+
+# 性能相关数据计算
+StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'`
+FPS=`awk 'BEGIN{  "%.2f\n", '${batch_size}'/'${StepTime}'}'`
+
+# 精度相关数据计算
+train_accuracy=`grep "Final Accuracy accuracy" ${print_log}  | awk '{print $NF}'`
+# 提取所有loss打印信息
+grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt
+
+
+###########################################################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+###########################################################
+
+# 判断本次执行是否正确使用Ascend NPU
+use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l`
+if [ x"${use_npu_flag}" == x0 ];
+then
+    echo "------------------ ERROR NOTICE START ------------------"
+    echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration."
+    echo "------------------ ERROR NOTICE END------------------"
+else
+    echo "------------------ INFO NOTICE START------------------"
+    echo "INFO, your task have used Ascend NPU, please check your result."
+    echo "------------------ INFO NOTICE END------------------"
+fi
+
+# 获取最终的casename，请保留，case文件名为${CaseName}
+get_casename
+
+# 重命名loss文件
+if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ];
+then
+    mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt
+fi
+
+# 训练端到端耗时
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+echo "------------------ Final result ------------------"
+# 输出性能FPS/单step耗时/端到端耗时
+echo "Final Performance images/sec : $FPS"
+echo "Final Performance sec/step : $StepTime"
+echo "E2E Training Duration sec : $e2e_time"
+
+# 输出训练精度
+echo "Final Train Accuracy : ${train_accuracy}"
+
+# 最后一个迭代loss值，不需要修改
+ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`)
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..330245b56975c30b60663151a083f713b336b6a4
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/train_performance_1p.sh
@@ -0,0 +1,209 @@
+#!/bin/bash
+
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+# shell脚本所在路径
+cur_path=`echo $(cd $(dirname $0);pwd)`
+
+# 判断当前shell是否是performance
+perf_flag=`echo $0 | grep performance | wc -l`
+
+# 当前执行网络的名称
+Network=`echo $(cd $(dirname $0);pwd) | awk -F"/" '{print $(NF-1)}'`
+
+export RANK_SIZE=1
+export RANK_ID=0
+export JOB_ID=10087
+
+# 路径参数初始化
+data_path=""
+output_path=""
+
+# 帮助信息，不需要修改
+if [[ $1 == --help || $1 == -h ]];then
+    echo"usage:./train_performance_1P.sh <args>"
+    echo " "
+    echo "parameter explain:
+    --data_path              # dataset of training
+    --output_path            # output of training
+    --train_steps            # max_step for training
+	  --train_epochs           # max_epoch for training
+    --batch_size             # batch size
+    -h/--help                show help message
+    "
+    exit 1
+fi
+
+# 参数校验，不需要修改
+for para in $*
+do
+    if [[ $para == --data_path* ]];then
+        data_path=`echo ${para#*=}`
+    elif [[ $para == --output_path* ]];then
+        output_path=`echo ${para#*=}`
+    elif [[ $para == --train_steps* ]];then
+        train_steps=`echo ${para#*=}`
+	elif [[ $para == --train_epochs* ]];then
+        train_epochs=`echo ${para#*=}`
+    elif [[ $para == --batch_size* ]];then
+        batch_size=`echo ${para#*=}`
+    fi
+done
+
+# 校验是否传入data_path,不需要修改
+if [[ $data_path == "" ]];then
+    echo "[Error] para \"data_path\" must be config"
+    exit 1
+fi
+
+# 校验是否传入output_path,不需要修改
+if [[ $output_path == "" ]];then
+    output_path="./test/output/${ASCEND_DEVICE_ID}"
+fi
+
+# 设置打屏日志文件名，请保留，文件名为${print_log}
+print_log="./test/output/${ASCEND_DEVICE_ID}/train_${ASCEND_DEVICE_ID}.log"
+etp_flag=${etp_running_flag}
+if [ x"${etp_flag}" != xtrue ];
+then
+    echo "running without etp..."
+    print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank`
+    print_log="/home/ma-user/modelarts/log/${print_log_name}"
+fi
+echo ${print_log}
+
+CaseName=""
+function get_casename()
+{
+    if [ x"${perf_flag}" = x1 ];
+    then
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'perf'
+    else
+        CaseName=${Network}_bs${batch_size}_${RANK_SIZE}'p'_'acc'
+    fi
+}
+
+# 跳转到code目录
+cd ${cur_path}/../
+rm -rf ./test/output/${ASCEND_DEVICE_ID}
+mkdir -p ./test/output/${ASCEND_DEVICE_ID}
+
+# 训练开始时间记录，不需要修改
+start_time=$(date +%s)
+##########################################################
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+#########第3行 至 100行，请一定不要、不要、不要修改##########
+##########################################################
+
+#=========================================================
+#=========================================================
+#========训练执行命令，需要根据您的网络进行修改==============
+#=========================================================
+#=========================================================
+# 您的训练数据集在${data_path}路径下，请直接使用这个变量获取
+# 您的训练输出目录在${output_path}路径下，请直接使用这个变量获取
+# 您的其他基础参数，可以自定义增加，但是batch_size请保留，并且设置正确的值
+batch_size=1
+
+echo "------------------***PRINT DATA PATH***-----------------"
+train_data="points/*.pts"
+test_data="sub_test/*.pts"
+
+trainData_Path="$data_path$train_data"
+testData_Path="$data_path$test_data"
+
+
+if [ x"${modelarts_flag}" != x ];
+then
+    python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \
+                              --method=svd \
+                               --checkpoint_dir=${output_path} \
+                               --log_step_count=200 \
+                               --save_summaries_steps=250 \
+                               --pt_cloud_train_files=${trainData_Path} \
+                               --pt_cloud_test_files=${testData_Path} \
+                               --train_steps=10000 \
+                               --save_checkpoints_steps=10000 \
+                               --eval_examples=399
+else
+    python3.7 /home/ma-user/modelarts/user-job-dir/code/main_point_cloud_perf.py \
+                               --method=svd \
+                               --checkpoint_dir=${output_path} \
+                               --log_step_count=200 \
+                               --save_summaries_steps=250 \
+                               --pt_cloud_train_files=${trainData_Path}\
+                               --pt_cloud_test_files=${testData_Path} \
+                               --train_steps=10000 \
+                               --save_checkpoints_steps=10000 \
+                               --eval_examples=399> ${print_log}
+fi
+
+
+# 性能相关数据计算
+StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'`
+FPS=`awk 'BEGIN{  "%.2f\n", '${batch_size}'/'${StepTime}'}'`
+
+# 精度相关数据计算
+train_accuracy=`grep "Final Accuracy accuracy" ${print_log}  | awk '{print $NF}'`
+# 提取所有loss打印信息
+grep "loss :" ${print_log} | awk -F ":" '{print $4}' | awk -F "-" '{print $1}' > ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt
+
+
+###########################################################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+#########后面的所有内容请不要修改###########################
+###########################################################
+
+# 判断本次执行是否正确使用Ascend NPU
+use_npu_flag=`grep "The model has been compiled on the Ascend AI processor" ${print_log} | wc -l`
+if [ x"${use_npu_flag}" == x0 ];
+then
+    echo "------------------ ERROR NOTICE START ------------------"
+    echo "ERROR, your task haven't used Ascend NPU, please check your npu Migration."
+    echo "------------------ ERROR NOTICE END------------------"
+else
+    echo "------------------ INFO NOTICE START------------------"
+    echo "INFO, your task have used Ascend NPU, please check your result."
+    echo "------------------ INFO NOTICE END------------------"
+fi
+
+# 获取最终的casename，请保留，case文件名为${CaseName}
+get_casename
+
+# 重命名loss文件
+if [ -f ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ];
+then
+    mv ./test/output/${ASCEND_DEVICE_ID}/my_output_loss.txt ./test/output/${ASCEND_DEVICE_ID}/${CaseName}_loss.txt
+fi
+
+# 训练端到端耗时
+end_time=$(date +%s)
+e2e_time=$(( $end_time - $start_time ))
+
+echo "------------------ Final result ------------------"
+# 输出性能FPS/单step耗时/端到端耗时
+echo "Final Performance images/sec : $FPS"
+echo "Final Performance sec/step : $StepTime"
+echo "E2E Training Duration sec : $e2e_time"
+
+# 输出训练精度
+echo "Final Train Accuracy : ${train_accuracy}"
+
+# 最后一个迭代loss值，不需要修改
+ActualLoss=(`awk 'END {print $NF}' $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}_loss.txt`)
+
+#关键信息打印到${CaseName}.log中，不需要修改
+echo "Network = ${Network}" > $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "RankSize = ${RANK_SIZE}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "BatchSize = ${batch_size}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "DeviceType = `uname -m`" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "CaseName = ${CaseName}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualFPS = ${FPS}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "TrainingTime = ${StepTime}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "ActualLoss = ${ActualLoss}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
+echo "E2ETrainingTime = ${e2e_ti_time}" >> $cur_path/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils.py b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..38d66a8da4ae6de1241edc0818795ee92fbc3d9f
--- /dev/null
+++ b/TensorFlow/contrib/cv/SVD_ID2019_for_Tensorflow/utils.py
@@ -0,0 +1,116 @@
+# coding=utf-8
+# Copyright 2021 The Google Research Authors.
+#
+# 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.
+
+"""Utility functions."""
+import numpy as np
+from scipy.stats import special_ortho_group
+import tensorflow as tf
+from npu_bridge.npu_init import *
+
+
+def relative_angle(r1, r2):
+  """Relative angle (radians) between 3D rotation matrices."""
+  rel_rot = tf.matmul(tf.transpose(r1, perm=[0, 2, 1]), r2)
+  trace = rel_rot[:, 0, 0] + rel_rot[:, 1, 1] + rel_rot[:, 2, 2]
+  cos_theta = (trace - 1.0) / 2.0
+  cos_theta = tf.minimum(cos_theta, tf.ones_like(cos_theta))
+  cos_theta = tf.maximum(cos_theta, (-1.0) * tf.ones_like(cos_theta))
+  theta = tf.acos(cos_theta)
+  return theta
+
+
+def random_rotation_benchmark_np(n):
+  """Sample a random 3D rotation by method used in Zhou et al, CVPR19.
+
+  This numpy function is a copy of the PyTorch function
+  get_sampled_rotation_matrices_by_axisAngle() in the code made available
+  for Zhou et al, CVPR19, at https://github.com/papagina/RotationContinuity/.
+
+  Args:
+    n: the number of rotation matrices to return.
+
+  Returns:
+    [n, 3, 3] np array.
+  """
+  theta = np.random.uniform(-1, 1, n) * np.pi
+  sin = np.sin(theta)
+  axis = np.random.randn(n, 3)
+  axis = axis / np.maximum(np.linalg.norm(axis, axis=-1, keepdims=True), 1e-7)
+  qw = np.cos(theta)
+  qx = axis[:, 0] * sin
+  qy = axis[:, 1] * sin
+  qz = axis[:, 2] * sin
+
+  xx = qx*qx
+  yy = qy*qy
+  zz = qz*qz
+  xy = qx*qy
+  xz = qx*qz
+  yz = qy*qz
+  xw = qx*qw
+  yw = qy*qw
+  zw = qz*qw
+
+  row0 = np.stack((1-2*yy-2*zz, 2*xy-2*zw, 2*xz+2*yw), axis=-1)
+  row1 = np.stack((2*xy+2*zw, 1-2*xx-2*zz, 2*yz-2*xw), axis=-1)
+  row2 = np.stack((2*xz-2*yw, 2*yz+2*xw, 1-2*xx-2*yy), axis=-1)
+  matrix = np.stack((row0, row1, row2), axis=1)
+
+  return matrix
+
+
+def random_rotation_benchmark(n):
+  """A TF wrapper for random_rotation_benchmark_np()."""
+  mat = tf.py_func(
+      func=lambda t: np.float32(random_rotation_benchmark_np(t)),
+      inp=[n],
+      Tout=tf.float32,
+      stateful=True)
+  return tf.reshape(mat, (n, 3, 3))
+
+
+def random_rotation(n):
+  """Sample rotations from a uniform distribution on SO(3)."""
+  mat = tf.py_func(
+      func=lambda t: np.float32(special_ortho_group.rvs(3, size=t)),
+      inp=[n],
+      Tout=tf.float32,
+      stateful=True)
+  return tf.reshape(mat, (n, 3, 3))
+
+
+def symmetric_orthogonalization(x):
+  """Maps 9D input vectors onto SO(3) via symmetric orthogonalization."""
+  # Innner dimensions of the input should be 3x3 matrices.
+  m = tf.reshape(x, (-1, 3, 3))
+  _, u, v = tf.svd(m)
+  det = tf.linalg.det(tf.matmul(u, v, transpose_b=True))
+  r = tf.matmul(
+      tf.concat([u[:, :, :-1], u[:, :, -1:] * tf.reshape(det, [-1, 1, 1])], 2),
+      v, transpose_b=True)
+  return r
+
+
+def gs_orthogonalization(p6):
+  """Gram-Schmidt orthogonalization from 6D input."""
+  # Input should be [batch_size, 6]
+  x = p6[:, 0:3]
+  y = p6[:, 3:6]
+  xn = tf.math.l2_normalize(x, axis=-1)
+  z = tf.linalg.cross(xn, y)
+  zn = tf.math.l2_normalize(z, axis=-1)
+  y = tf.linalg.cross(zn, xn)
+  r = tf.stack([xn, y, zn], -1)
+  return r