diff --git a/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/.keep b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/.keep
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index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/Truemain.py b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/Truemain.py
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index 0000000000000000000000000000000000000000..875317a01c6fbdcd2e71312128ff1610899918ac
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+++ b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/Truemain.py
@@ -0,0 +1,518 @@
+#
+# 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.
+#
+# Imports and Global Variables
+# -*- coding: UTF-8 -*-
+from npu_bridge.npu_init import *
+import os
+import sys
+import numpy as np
+import tensorflow.compat.v1 as tf
+
+tf.disable_v2_behavior()
+import cv2
+import math
+import warnings
+import argparse
+# import datetime 此为格式化输出时间，但为方便之后计算，使用以秒为单位的包如下
+import time
+# 加两句
+# import sys
+# sys.path.remove('/usr/local/python3.7.5/lib/python3.7/site-packages')
+from cell import ConvLSTMCell  # added
+
+USE_CUDA = True
+
+LSTM_HIDDEN_SIZE = 550
+TIME_STEPS = 1
+K = 100
+
+
+# Build Model
+class DeepVONet(object):
+    def __init__(self, args, data):
+        rnn_layers = [tf.nn.rnn_cell.LSTMCell(size) for size in [LSTM_HIDDEN_SIZE, LSTM_HIDDEN_SIZE]]
+        # multi_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(rnn_layers)  # changed
+        multi_convlstm_cell = ConvLSTMCell(shape=[6, 20], filters=12, kernel=[3, 3])  # added
+        rnn_inputs = []
+        reuse = None
+        for stacked_img in data:
+            rnn_inputs.append(self.forward(stacked_img, reuse=reuse))
+            reuse = True
+
+        sess = tf.Session(config=npu_config_proto())
+        # print(sess.run(tf.shape(stacked_img)))
+        rnn_inputs = tf.transpose(rnn_inputs,perm = [1,0,2,3,4])
+        print(sess.run(tf.shape(rnn_inputs)))
+
+        # rnn_inputs = [tf.reshape(rnn_inputs[i], [-1, 20 * 6 * 1024]) for i in range(len(rnn_inputs))] #changed
+        # assert rnn_inputs[0].shape == (args.bsize, 20 * 6 * 1024) #changed
+        # rnn_inputs = (32,5,rnn_inputs)
+
+        # self.outputs, self.state = tf.nn.static_rnn(cell=multi_rnn_cell, inputs=rnn_inputs, dtype=tf.float32)  # changed
+        self.outputs, self.state = tf.nn.dynamic_rnn(cell=multi_convlstm_cell, inputs=rnn_inputs, dtype=tf.float32)  # added
+
+        # print("1")
+        # print(sess.run(tf.shape(self.outputs)))
+        # print("2")
+
+        self.outputs = [tf.reshape(self.outputs[i], [-1, 20 * 6 * 12]) for i in range(32)]  # added
+        self.outputs = tf.reshape(self.outputs, [32, 20 * 6 * 12])  # added  (32,1440)
+
+        # self.outputs = tf.transpose(self.outputs, perm=[1,0,2])#added
+        # print("3")
+        # print(sess.run(tf.shape(self.outputs)))
+        # print("4")
+        # assert self.outputs[0].shape == (args.bsize, LSTM_HIDDEN_SIZE)
+
+    def forward(self, x, reuse=None):
+        with tf.variable_scope("cnns", reuse=reuse):
+            x = tf.layers.conv2d(
+                inputs=x,
+                filters=64,
+                kernel_size=[7, 7],
+                padding="same",
+                strides=2,
+                reuse=reuse,
+                activation=tf.nn.relu, name='conv1')
+            x = tf.layers.conv2d(
+                inputs=x,
+                filters=128,
+                kernel_size=[5, 5],
+                padding="same",
+                strides=2,
+                reuse=reuse,
+                activation=tf.nn.relu, name='conv2')
+            x = tf.layers.conv2d(
+                inputs=x,
+                filters=256,
+                kernel_size=[5, 5],
+                padding="same",
+                strides=2,
+                reuse=reuse,
+                activation=tf.nn.relu, name='conv3')
+            x = tf.layers.conv2d(
+                inputs=x,
+                filters=256,
+                kernel_size=[3, 3],
+                padding="same",
+                strides=1,
+                reuse=reuse,
+                activation=tf.nn.relu, name='conv3_1')
+            x = tf.layers.conv2d(
+                inputs=x,
+                filters=512,
+                kernel_size=[3, 3],
+                padding="same",
+                strides=2,
+                reuse=reuse,
+                activation=tf.nn.relu, name='conv4')
+            x = tf.layers.conv2d(
+                inputs=x,
+                filters=512,
+                kernel_size=[3, 3],
+                padding="same",
+                strides=1,
+                reuse=reuse,
+                activation=tf.nn.relu, name='conv4_1')
+            x = tf.layers.conv2d(
+                inputs=x,
+                filters=512,
+                kernel_size=[3, 3],
+                padding="same",
+                strides=2,
+                reuse=reuse,
+                activation=tf.nn.relu, name='conv5')
+            x = tf.layers.conv2d(
+                inputs=x,
+                filters=512,
+                kernel_size=[3, 3],
+                padding="same",
+                strides=1,
+                reuse=reuse,
+                activation=tf.nn.relu, name='conv5_1')
+            x = tf.layers.conv2d(
+                inputs=x,
+                filters=1024,
+                kernel_size=[3, 3],
+                padding="same",
+                reuse=reuse,
+                strides=2, name='conv6')
+
+        return x
+
+
+# Train Model
+def initialize_uninitialized(sess):
+    global_vars = tf.global_variables()
+    is_not_initialized = sess.run([tf.is_variable_initialized(var) for var in global_vars])
+    not_initialized_vars = [v for (v, f) in zip(global_vars, is_not_initialized) if not f]
+
+    for i in not_initialized_vars:  # only for testing
+        print(i.name)
+
+    if len(not_initialized_vars):
+        sess.run(tf.variables_initializer(not_initialized_vars))
+
+
+def train_model(data_loader, sess, merged, loss_op, train_op, input_data, labels_, i, test_writer, train_writer):
+    print('Current epoch : %d----' % data_loader.current_epoch, end="")
+    print('step : %d----' % i, end="")
+    if i % 10 == 0:  # Record summaries and test-set accuracy
+        batch_x, batch_y = data_loader.get_next_batch()
+        summary, acc = sess.run(
+            [merged, loss_op], feed_dict={input_data: batch_x, labels_: batch_y})
+        test_writer.add_summary(summary, i)
+        print('Accuracy : %s----' % acc, end="")
+    else:  # Record train set summaries, and train
+        batch_x, batch_y = data_loader.get_next_batch()
+        summary, _ = sess.run(
+            [merged, train_op], feed_dict={input_data: batch_x, labels_: batch_y})
+        train_writer.add_summary(summary, i)
+        train_loss = sess.run(loss_op,
+                              feed_dict={input_data: batch_x, labels_: batch_y})
+        print('Train_error : %s----' % train_loss, end="")
+
+
+def train(args, datapath, outputpath, epoches, trajectory_length):
+    # configuration
+    data_loader = VisualOdometryDataLoader(args, datapath, trajectory_length)
+    if USE_CUDA:
+        sess = tf.Session(config=npu_config_proto())
+    else:
+        config_proto = tf.ConfigProto(device_count={'GPU': 0})
+        sess = tf.Session(config=npu_config_proto(config_proto=config_proto))
+    pose_size = 6
+
+    # only for gray scale dataset, for colored channels will be 6
+    height, width, channels = 384, 1280, 6
+
+    with tf.name_scope('input'):#定义命名空间
+        # placeholder for input
+        input_data = tf.placeholder(tf.float32, [args.bsize, args.time_steps, height, width, channels])
+        # placeholder for labels
+        labels_ = tf.placeholder(tf.float32, [args.bsize, args.time_steps, pose_size])
+
+    with tf.name_scope('unstacked_input'):
+        # Unstacking the input into list of time series
+        data = tf.unstack(input_data, args.time_steps, 1)
+        # Unstacking the labels into the time series
+        pose_labels = tf.unstack(labels_, args.time_steps, 1)
+
+    # Building the RCNN Network which
+    # which returns the time series of output layers
+    with tf.name_scope('RCNN'):
+        model = DeepVONet(args, data)
+        (outputs, _) = (model.outputs, model.state)
+    ## Output layer to compute the output
+    with tf.name_scope('weights'):
+        regression_w = tf.get_variable('regression_w', shape=[1440, pose_size], dtype=tf.float32)
+    with tf.name_scope('biases'):
+        regression_b = tf.get_variable("regression_b", shape=[pose_size], dtype=tf.float32)
+
+    # Pose estimate by multiplication with RCNN_output and Output layer
+    with tf.name_scope('Wx_plus_b'):
+        sess = tf.Session(config=npu_config_proto())
+        print(sess.run(tf.shape(outputs)))
+        print("--------------------------")
+        # with tf.Session():
+        #       print(outputs[3].dtype)#added
+        # print("2")
+
+        if isinstance([outputs],list):
+            print("outputs is list")
+        # pose_estimated = [tf.nn.xw_plus_b([tf.reshape(output_state, [1, 1440])], [regression_w],
+        #                                   [regression_b]) for output_state in outputs]
+        pose_estimated = [tf.nn.xw_plus_b(outputs, regression_w, regression_b)]
+        max_time = len(pose_estimated)
+
+    # Converting the list of tensor into a tensor
+    # Probably this is the part that is unnecessary and causing problems (slowing down the computations)
+
+    # Loss function for all the frames in a batch
+    with tf.name_scope('loss_l2_norm'):
+        position = [pose_es[:, :3] - pose_lab[:, :3] for pose_es, pose_lab in zip(pose_estimated, pose_labels)]
+        angles = [pose_es[:, 3:6] - pose_lab[:, 3:6] for pose_es, pose_lab in zip(pose_estimated, pose_labels)]
+        pose_error = (tf.square(position))
+        angle_error = (tf.square(angles))
+        loss_op = tf.reduce_sum(pose_error + K * angle_error, name='loss')
+        tf.summary.scalar('loss_l2_norm', loss_op)
+
+    # optimizer
+    with tf.name_scope('train'):
+        optimizer = tf.train.AdamOptimizer(learning_rate=args.lr,
+                                           beta1=0.9,
+                                           beta2=0.999,
+                                           epsilon=1e-08,
+                                           use_locking=False,
+                                           name='Adam')
+
+        train_op = optimizer.minimize(loss_op)
+    # Merge all the summeries and write them out to args.datapath
+    # by default ./args.datapath
+    merged = tf.summary.merge_all()
+
+    global_vars = tf.global_variables()
+    is_not_initialized = sess.run([tf.is_variable_initialized(var) for var in global_vars])
+    not_initialized_vars = [v for (v, f) in zip(global_vars, is_not_initialized) if not f]
+
+    if len(not_initialized_vars):
+        sess.run(tf.variables_initializer(not_initialized_vars))
+
+    saver = tf.train.Saver()
+    initialize_uninitialized(sess)
+    train_writer = tf.summary.FileWriter(outputpath + 'train', sess.graph)
+    test_writer = tf.summary.FileWriter(outputpath + 'test')
+
+    i = 0
+    while data_loader.current_epoch < epoches:
+        start = time.time()
+        train_model(data_loader, sess, merged, loss_op, train_op, input_data, labels_, i, test_writer, train_writer)
+        i += 1
+        end = time.time()
+        print('sec/step : %s' % (end - start))#单步时间计算
+        if data_loader.current_epoch % 40 == 0:#added，每过40个epoch存储一下
+            save_path = saver.save(sess, args.outputpath + 'model' + str(data_loader.current_epoch))#added
+    save_path = saver.save(sess, args.outputpath + 'model')
+    print("Model saved in file: %s" % save_path)
+    print("epochs trained: " + str(data_loader.current_epoch))
+    train_writer.close()
+    test_writer.close()
+
+
+# Dataset
+def default_image_loader(path):
+    img = cv2.imread(path)
+    if img is not None:
+        # Normalizing and Subtracting mean intensity value of the corresponding image
+        img = img / np.max(img)
+        img = img - np.mean(img)
+        img = cv2.resize(img, (1280, 384), fx=0, fy=0)
+    return img
+
+
+class VisualOdometryDataLoader(object):
+    def __init__(self, args, datapath, trajectory_length, loader=default_image_loader):
+        self.args = args
+        self._current_initial_frame = 0
+        self._current_trajectory_index = 0
+        self.current_epoch = 0
+
+        self.sequences = [0,2,8,9]
+
+        self.base_path = datapath
+        self.poses = self.load_poses()
+        self.trajectory_length = len(self.sequences)
+        self.loader = loader
+
+    def get_image(self, sequence, index):
+        image_path = os.path.join(self.base_path, 'sequences', '%02d' % sequence, 'image_0', '%06d' % index + '.png')
+        image = self.loader(image_path)
+        return image
+
+    def load_poses(self):
+        all_poses = []
+        for sequence in self.sequences:
+            with open(os.path.join(self.base_path, 'poses/', ('%02d' % sequence) + '.txt')) as f:
+                poses = np.array([[float(x) for x in line.split()] for line in f], dtype=np.float32)
+                all_poses.append(poses)
+        return all_poses
+
+    def _set_next_trajectory(self):
+        if (self._current_trajectory_index < self.trajectory_length-1):
+            self._current_trajectory_index += 1
+        else:
+            self.current_epoch += 1
+            self._current_trajectory_index = 0
+
+        self._current_initial_frame = 0
+
+    def get_next_batch(self):
+        img_batch = []
+        label_batch = []
+
+        poses = self.poses[self._current_trajectory_index]
+
+        for j in range(self.args.bsize):
+            img_stacked_series = []
+            labels_series = []
+
+            read_img = self.get_image(self.sequences[self._current_trajectory_index],
+                                      self._current_initial_frame + self.args.time_steps)
+            if (read_img is None): self._set_next_trajectory()
+
+            for i in range(self._current_initial_frame, self._current_initial_frame + self.args.time_steps):
+                img1 = self.get_image(self.sequences[self._current_trajectory_index], i)
+                img2 = self.get_image(self.sequences[self._current_trajectory_index], i + 1)
+                # print(self.sequences[self._current_trajectory_index])
+                # print(img1.size())
+                # print(img2.size())
+                img_aug = np.concatenate([img1, img2], -1)
+                img_stacked_series.append(img_aug)
+                # pose = self.get6DoFPose(poses[i, :]) - self.get6DoFPose(poses[self._current_initial_frame, :])#changed
+                pose = self.get6DoFPose(poses[i + 1, :]) - self.get6DoFPose(poses[i, :])#位姿差
+                labels_series.append(pose)
+            img_batch.append(img_stacked_series)
+            label_batch.append(labels_series)
+            self._current_initial_frame += self.args.time_steps
+        label_batch = np.array(label_batch)#创建数组
+        img_batch = np.array(img_batch)
+        return img_batch, label_batch#返回每个batch的拼接图片，位姿差真值
+
+    def isRotationMatrix(self, R):
+        Rt = np.transpose(R)
+        shouldBeIdentity = np.dot(Rt, R)
+        I = np.identity(3, dtype=R.dtype)
+        n = np.linalg.norm(I - shouldBeIdentity)
+        return n < 1e-6
+
+    def rotationMatrixToEulerAngles(self, R):
+        assert (self.isRotationMatrix(R))
+        sy = math.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0])
+        singular = sy < 1e-6
+
+        if not singular:
+            x = math.atan2(R[2, 1], R[2, 2])
+            y = math.atan2(-R[2, 0], sy)
+            z = math.atan2(R[1, 0], R[0, 0])
+        else:
+            x = math.atan2(-R[1, 2], R[1, 1])
+            y = math.atan2(-R[2, 0], sy)
+            z = 0
+
+        return np.array([x, y, z])
+
+    def get6DoFPose(self, p):
+        pos = np.array([p[3], p[7], p[11]])
+        R = np.array([[p[0], p[1], p[2]], [p[4], p[5], p[6]], [p[8], p[9], p[10]]])
+        angles = self.rotationMatrixToEulerAngles(R)
+        return np.concatenate((pos, angles))
+
+
+# Main_Args Class
+class MyArgs():
+    def __init__(self, datapath, bsize, trajectory_length, lr=0.001, time_steps=100, train_iter=5):
+        self.datapath = datapath
+        self.bsize = bsize
+        self.trajectory_length = trajectory_length
+        self.lr = lr
+        self.time_steps = time_steps
+        self.train_iter = train_iter
+
+
+# Main_code
+# args = MyArgs(datapath='/root/convlstm/DeepVO/data/color/dataset/',
+#               # args = MyArgs(datapath='/content/drive/My Drive/dataset/',
+#               bsize=32,
+#               trajectory_length=2,
+#               lr=0.0001,
+#               train_iter=10000,
+#               time_steps=TIME_STEPS)
+#                      outputpath = '/root/convlstm/DeepVO/data/color/dataset/output/'
+# train(args, args.datapath, args.train_iter, args.trajectory_length)
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser(description='''
+    This script computes the relative pose error from the ground truth trajectory and the estimated trajectory. 
+    ''')
+    parser.add_argument('--datapath')
+    parser.add_argument('--outputpath')
+    parser.add_argument('--bsize', default=32)
+    parser.add_argument('--trajectory_length', default=4)#此处在326已改好，不影响后面
+    parser.add_argument('--lr', default=0.0001)
+    parser.add_argument('--train_iter', default=1)
+    parser.add_argument('--time_steps', default=1)
+    args = parser.parse_args()
+    train(args, args.datapath, args.outputpath, args.train_iter, args.trajectory_length)
+"""以下为测试模块，可以注释掉上面if __name__ == '__main__'块代码，换好路径后进行测试"""
+# args = MyArgs(datapath='/root/convlstm/DeepVO/data/color/dataset/',
+#               outputpath='/root/convlstm/DeepVO/data/color/dataset/output4/',
+#               bsize=32,
+#               trajectory_length=4,
+#               train_iter=1,
+#               time_steps=TIME_STEPS)
+#
+# # configuration
+# config_proto = tf.ConfigProto(device_count = {'GPU': 0})
+# sess = tf.Session(config=config_proto)
+# data_loader = VisualOdometryDataLoader(args,args.datapath,args.trajectory_length)
+# """ input_batch must be in shape of [?, TIME_STEPS, 384, 1280, 6] """
+# #tf.reset_default_graph()
+# print('Restoring Entire Session from checkpoint : %s'%args.outputpath+"model80.meta")
+# imported_meta = tf.train.import_meta_graph(args.outputpath + "model80.meta")
+# print('Success')
+# imported_meta.restore(sess, tf.train.latest_checkpoint(args.outputpath))#查找最新保存的ckpt文件名，并加载,latest_filename=model240
+# input_data = tf.get_default_graph().get_tensor_by_name("input/Placeholder:0")#以下5句是加载变量的过程
+# # placeholder for labels 下两句的作用？？
+# labels_ = tf.get_default_graph().get_tensor_by_name("input/Placeholder_1:0")
+# loss_op = tf.get_default_graph().get_tensor_by_name("loss_l2_norm/loss:0")
+# poses = []
+# poses.append(tf.get_default_graph().get_tensor_by_name("Wx_plus_b/xw_plus_b:0"))
+# # poses.append(tf.get_default_graph().get_tensor_by_name("Wx_plus_b/xw_plus_b_1:0"))
+# # poses.append(tf.get_default_graph().get_tensor_by_name("Wx_plus_b/xw_plus_b_2:0"))
+# # poses.append(tf.get_default_graph().get_tensor_by_name("Wx_plus_b/xw_plus_b_3:0"))
+# # poses.append(tf.get_default_graph().get_tensor_by_name("Wx_plus_b/xw_plus_b_4:0"))
+# while data_loader.current_epoch < args.train_iter:
+#     input_, ground_truth_batch = data_loader.get_next_batch()#图片，位姿差真值
+#     #print("shape(ground_truth_batch)",ground_truth_batch.shape)
+#     output = sess.run(poses, feed_dict={input_data:input_})#位姿差预估值
+#     #print("shape(output)",np.array(output).shape)
+#     print('Current epoch : %d' % data_loader.current_epoch)
+#     print('output length : %d' % len(output))
+#     for i in range(len(output)):
+#        for j in range(32):
+#             fh = open("//root//convlstm//DeepVO//txtcsv//output_file.txt", "a")#位姿真值存放文件
+#             fh.write("%f %f %f %f %f %f\n"%(ground_truth_batch[j,i,0],
+#                                             ground_truth_batch[j,i,1],
+#                                             ground_truth_batch[j,i,2],
+#                                             ground_truth_batch[j,i,3],
+#                                             ground_truth_batch[j,i,4],
+#                                             ground_truth_batch[j,i,5]))
+#             fh.close()
+#             fh = open("//root//convlstm//DeepVO//txtcsv//estimated.txt","a")#位姿预估值存放文件
+#             fh.write("%f %f %f %f %f %f\n"%(output[i][j,0],
+#                                             output[i][j,1],
+#                                             output[i][j,2],
+#                                             output[i][j,3],
+#                                             output[i][j,4],
+#                                             output[i][j,5]))
+#             fh.close()
+# file_old = open("//root//convlstm//DeepVO//txtcsv//output_file.txt", 'rb+')
+# lines = file_old.readlines()
+# # 定位到最后一行的行首，若要删除后N行，将lines[-1]改为lines[-N:]即可
+# file_old.seek(-len(lines[-1]), os.SEEK_END)
+# file_old.truncate()  # 截断之后的数据
+# file_old.close()
+#
+# file_oldd = open("//root//convlstm//DeepVO//txtcsv//estimated.txt", 'rb+')
+# liness = file_oldd.readlines()
+# # 定位到最后一行的行首，若要删除后N行，将lines[-1]改为lines[-N:]即可
+# file_oldd.seek(-len(liness[-1]), os.SEEK_END)
+# file_oldd.truncate()  # 截断之后的数据
+# file_oldd.close()
diff --git a/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/cell.py b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/cell.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5f2fd94a29b772f831753480c1653aea2d4edb2
--- /dev/null
+++ b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/cell.py
@@ -0,0 +1,166 @@
+#
+# 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.
+#
+from npu_bridge.npu_init import *
+import tensorflow as tf
+
+class ConvLSTMCell(tf.nn.rnn_cell.RNNCell):
+  """A LSTM cell with convolutions instead of multiplications.
+
+  Reference:
+    Xingjian, S. H. I., et al. "Convolutional LSTM network: A machine learning approach for precipitation nowcasting." Advances in Neural Information Processing Systems. 2015.
+  """
+
+  def __init__(self, shape, filters, kernel, forget_bias=1.0, activation=tf.tanh, normalize=True, peephole=True, data_format='channels_last', reuse=None):
+    super(ConvLSTMCell, self).__init__(_reuse=reuse)
+    self._kernel = kernel
+    self._filters = filters
+    self._forget_bias = forget_bias
+    self._activation = activation
+    self._normalize = normalize
+    self._peephole = peephole
+    if data_format == 'channels_last':
+        self._size = tf.TensorShape(shape + [self._filters])
+        self._feature_axis = self._size.ndims
+        self._data_format = None
+    elif data_format == 'channels_first':
+        self._size = tf.TensorShape([self._filters] + shape)
+        self._feature_axis = 0
+        self._data_format = 'NC'
+    else:
+        raise ValueError('Unknown data_format')
+
+  @property
+  def state_size(self):
+    return tf.nn.rnn_cell.LSTMStateTuple(self._size, self._size)
+
+  @property
+  def output_size(self):
+    return self._size
+
+  def call(self, x, state):
+    c, h = state
+
+    x = tf.concat([x, h], axis=self._feature_axis)
+    n = x.shape[-1].value
+    m = 4 * self._filters if self._filters > 1 else 4
+    W = tf.get_variable('kernel', self._kernel + [n, m])
+    y = tf.nn.convolution(x, W, 'SAME', data_format=self._data_format)
+    if not self._normalize:
+      y += tf.get_variable('bias', [m], initializer=tf.zeros_initializer())
+    j, i, f, o = tf.split(y, 4, axis=self._feature_axis)
+
+    if self._peephole:
+      i += tf.get_variable('W_ci', c.shape[1:]) * c
+      f += tf.get_variable('W_cf', c.shape[1:]) * c
+
+    if self._normalize:
+      j = tf.contrib.layers.layer_norm(j)
+      i = tf.contrib.layers.layer_norm(i)
+      f = tf.contrib.layers.layer_norm(f)
+
+    f = tf.sigmoid(f + self._forget_bias)
+    i = tf.sigmoid(i)
+    c = c * f + i * self._activation(j)
+
+    if self._peephole:
+      o += tf.get_variable('W_co', c.shape[1:]) * c
+
+    if self._normalize:
+      o = tf.contrib.layers.layer_norm(o)
+      c = tf.contrib.layers.layer_norm(c)
+
+    o = tf.sigmoid(o)
+    h = o * self._activation(c)
+
+    state = tf.nn.rnn_cell.LSTMStateTuple(c, h)
+
+    return h, state
+
+
+class ConvGRUCell(tf.nn.rnn_cell.RNNCell):
+  """A GRU cell with convolutions instead of multiplications."""
+
+  def __init__(self, shape, filters, kernel, activation=tf.tanh, normalize=True, data_format='channels_last', reuse=None):
+    super(ConvGRUCell, self).__init__(_reuse=reuse)
+    self._filters = filters
+    self._kernel = kernel
+    self._activation = activation
+    self._normalize = normalize
+    if data_format == 'channels_last':
+        self._size = tf.TensorShape(shape + [self._filters])
+        self._feature_axis = self._size.ndims
+        self._data_format = None
+    elif data_format == 'channels_first':
+        self._size = tf.TensorShape([self._filters] + shape)
+        self._feature_axis = 0
+        self._data_format = 'NC'
+    else:
+        raise ValueError('Unknown data_format')
+
+  @property
+  def state_size(self):
+    return self._size
+
+  @property
+  def output_size(self):
+    return self._size
+
+  def call(self, x, h):
+    channels = x.shape[self._feature_axis].value
+
+    with tf.variable_scope('gates'):
+      inputs = tf.concat([x, h], axis=self._feature_axis)
+      n = channels + self._filters
+      m = 2 * self._filters if self._filters > 1 else 2
+      W = tf.get_variable('kernel', self._kernel + [n, m])
+      y = tf.nn.convolution(inputs, W, 'SAME', data_format=self._data_format)
+      if self._normalize:
+        r, u = tf.split(y, 2, axis=self._feature_axis)
+        r = tf.contrib.layers.layer_norm(r)
+        u = tf.contrib.layers.layer_norm(u)
+      else:
+        y += tf.get_variable('bias', [m], initializer=tf.ones_initializer())
+        r, u = tf.split(y, 2, axis=self._feature_axis)
+      r, u = tf.sigmoid(r), tf.sigmoid(u)
+
+    with tf.variable_scope('candidate'):
+      inputs = tf.concat([x, r * h], axis=self._feature_axis)
+      n = channels + self._filters
+      m = self._filters
+      W = tf.get_variable('kernel', self._kernel + [n, m])
+      y = tf.nn.convolution(inputs, W, 'SAME', data_format=self._data_format)
+      if self._normalize:
+        y = tf.contrib.layers.layer_norm(y)
+      else:
+        y += tf.get_variable('bias', [m], initializer=tf.zeros_initializer())
+      h = u * h + (1 - u) * self._activation(y)
+
+    return h, h
+
diff --git a/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/modelarts_entry_acc.py b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/modelarts_entry_acc.py
new file mode 100644
index 0000000000000000000000000000000000000000..59673529a665de35a67b4d2f9f852e7b5d5b26df
--- /dev/null
+++ b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/modelarts_entry_acc.py
@@ -0,0 +1,63 @@
+# 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 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")#真正的npu环境上的数据集路径
+parser.add_argument("--train_url", type=str, default="/home/ma-user/modelarts/outputs/train_url_0/")#真正的npu环境上的输出路径
+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 ./test/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)
diff --git a/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/modelarts_entry_perf.py b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/modelarts_entry_perf.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9e99ba4cce86b3dfdd77d914c65326fd32a7687
--- /dev/null
+++ b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/modelarts_entry_perf.py
@@ -0,0 +1,64 @@
+# 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 os
+import argparse
+import sys
+#环境变量日志设置error级别
+os.environ['ASCEND_GLOBAL_LOG_LEVEL'] = '3'
+# 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 ./test/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)
diff --git a/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/modelzoo_level.txt b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/modelzoo_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..463cef7e72801222e8435d5d1764ae72f4ca038a
--- /dev/null
+++ b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/modelzoo_level.txt
@@ -0,0 +1,3 @@
+FuncStatus:OK 
+PerfStatus:NOK 
+PrecisionStatus:NOK
\ No newline at end of file
diff --git a/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/requirements.txt b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..67609a0dcac25240871cd1121c2dea4b7820c832
--- /dev/null
+++ b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/requirements.txt
@@ -0,0 +1,2 @@
+运行环境和依赖：ubuntu16.04+python3.7.5+tensorflow-gpu1.15.0+opencv+opencv-contrib3.4.2.17+evo1.12.0
+
diff --git a/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/test/train_full_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ee1d54371f7d9d60737c26d249ae236d5da39105
--- /dev/null
+++ b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/test/train_full_1p.sh
@@ -0,0 +1,183 @@
+#!/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_${ASCEND_DEVICE_ID}.log"
+modelarts_flag=${MODELARTS_MODEL_PATH}
+if [ x"${modelarts_flag}" != x ];
+then
+    echo "running without etp..."
+    print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank`
+    print_log="/home/ma-user/modelarts/log/${print_log_name}"
+fi
+echo "### get your log here : ${print_log}"
+
+CaseName=""
+function get_casename()
+{
+    if [ x"${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==============
+#=========================================================
+#=========================================================
+# 基础参数，需要模型审视修改
+# 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=32
+
+if [ x"${modelarts_flag}" != x ];
+then
+    python3.7 ./Truemain.py --datapath=${data_path} --outputpath=${output_path}
+else
+    python3.7 ./Truemain.py --datapath=${data_path} --outputpath=${output_path}  > ${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+=$1} END {print sum/NR}'`
+#FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'`
+
+# 精度相关数据计算，此处可以计算最后10行的精度平均值
+train_accuracy=`grep "Train_error :" ${print_log} | tail -n 10 |awk -F ":" '{print $4}' | awk -F "-" '{print $1}' | awk '{sum+=$1} END {print sum/NR}'`
+# 提取所有loss打印信息
+grep "Train_error :" ${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/ConvLSTM_ID2358_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/test/train_performance_1p.sh
new file mode 100644
index 0000000000000000000000000000000000000000..311e56ea047fa9394770f493e7231dd66e1c6a06
--- /dev/null
+++ b/TensorFlow/contrib/cv/ConvLSTM_ID2358_for_TensorFlow/test/train_performance_1p.sh
@@ -0,0 +1,185 @@
+#!/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_${ASCEND_DEVICE_ID}.log"
+modelarts_flag=${MODELARTS_MODEL_PATH}
+if [ x"${modelarts_flag}" != x ];
+then
+    echo "running with modelarts..."
+    print_log_name=`ls /home/ma-user/modelarts/log/ | grep proc-rank`
+    print_log="/home/ma-user/modelarts/log/${print_log_name}"
+fi
+echo "### get your log here : ${print_log}"
+
+CaseName=""
+function get_casename()
+{
+    if [ x"${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==============
+#=========================================================
+#=========================================================
+# 基础参数，需要模型审视修改
+# 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
+train_epochs=1
+train_steps=500
+batch_size=32
+
+if [ x"${modelarts_flag}" != x ];
+then
+    python3.7 ./Truemain.py --datapath=${data_path} --outputpath=${output_path}
+else
+    python3.7 ./Truemain.py --datapath=${data_path} --outputpath=${output_path} > ${print_log}#--train_iter=${train_epochs}
+fi
+
+# 性能相关数据计算，按照关键字，取最后n行中每行的最后一列（默认空格分隔），求平均
+StepTime=`grep "sec/step :" ${print_log} | tail -n 10 | awk '{print $NF}' | awk '{sum+=$1} END {print sum/NR}'`
+FPS=`awk 'BEGIN{printf "%.2f\n", '${batch_size}'/'${StepTime}'}'`
+
+# 精度相关数据计算,这个sh文件是性能数据计算，所以可以不用管
+train_accuracy=`grep "Final Accuracy accuracy" ${print_log}  | awk '{print $NF}'`
+# 提取所有loss打印信息
+grep "Train_error :" ${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