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diff --git a/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/cfg.py b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/cfg.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bb891047241bdbde9f4f073de9c66a82d266662
--- /dev/null
+++ b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/cfg.py
@@ -0,0 +1,111 @@
+"""
+SRNet - Editing Text in the Wild
+Some configurations.
+Copyright (c) 2019 Netease Youdao Information Technology Co.,Ltd.
+Licensed under the GPL License (see LICENSE for details)
+Written by Yu Qian
+"""
+
+# 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 moxing as mox
+
+# pretrained vgg
+vgg19_weights = r'/home/ma-user/modelarts/user-job-dir/code/model_logs/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.pb'
+
+# model parameters
+lt = 1.
+lt_alpha = 1.
+lb = 1.
+lb_beta = 10.
+lf = 1.
+lf_theta_1 = 10.
+lf_theta_2 = 1.
+lf_theta_3 = 500.
+epsilon = 1e-8
+
+# train
+learning_rate = 1e-4 # default 1e-3
+decay_rate = 0.9
+decay_steps = 10000
+staircase = False
+beta1 = 0.9 # default 0.9
+beta2 = 0.999 # default 0.999
+max_iter = 500000
+show_loss_interval = 50
+write_log_interval = 50
+save_ckpt_interval = 10000
+gen_example_interval = 1000
+checkpoint_savedir = '/cache/out/model_logs/checkpoints'
+tensorboard_dir = '/cache/out/model_logs/train_logs'
+pretrained_ckpt_path = None
+train_name = None # used for name examples and tensorboard logdirs, set None to use time
+
+# data
+batch_size = 8
+data_shape = [64, None]
+data_dir = '/cache/data'
+i_t_dir = 'i_t'
+i_s_dir = 'i_s'
+t_sk_dir = 't_sk'
+t_t_dir = 't_t'
+t_b_dir = 't_b'
+t_f_dir = 't_f'
+mask_t_dir = 'mask_t'
+example_data_dir = r'/home/ma-user/modelarts/user-job-dir/code/examples/labels'
+example_result_dir = '/cache/out/genLogs'
+
+# predict
+predict_ckpt_path = None
+predict_data_dir = None
+predict_result_dir = 'examples/result'
+
+#out_dir = ''
+
+def main():
+    code_dir = os.path.dirname(__file__)
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--train_url", type=str)
+    parser.add_argument("--data_url", type=str)
+    parser.add_argument("--modelarts_data_dir", type=str, default="/cache/data")
+    parser.add_argument("--modelarts_output_dir", type=str, default="/cache/out")
+    paraConfig = parser.parse_args()
+
+    #out_dir = paraConfig.modelarts_output_dir
+    os.makedirs(paraConfig.modelarts_data_dir)
+    os.makedirs(paraConfig.modelarts_output_dir)
+    os.makedirs(r'/cache/out/genLogs')
+    mox.file.copy_parallel(src_url=paraConfig.data_url, dst_url=paraConfig.modelarts_data_dir)
+    os.system("python3" + ' ' + code_dir + '/train.py')
+
+if __name__ == "__main__":
+    main()
diff --git a/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/datagen.py b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/datagen.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d71eb94b5bb99dbbe2eb29c0d27e929b5013c61
--- /dev/null
+++ b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/datagen.py
@@ -0,0 +1,139 @@
+"""
+SRNet - Editing Text in the Wild
+Data generator.
+Copyright (c) 2019 Netease Youdao Information Technology Co.,Ltd.
+Licensed under the GPL License (see LICENSE for details)
+Written by Yu Qian
+"""
+
+# 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 os
+import cv2
+import numpy as np
+import random
+import cfg 
+
+def srnet_datagen():
+   
+    # generator SRNet data for training
+    name_list = os.listdir(os.path.join(cfg.data_dir, cfg.t_b_dir))
+    random.shuffle(name_list)
+    name_num = len(name_list)
+    idx = 0
+    
+    while True:
+        i_t_batch, i_s_batch = [], []
+        t_sk_batch, t_t_batch, t_b_batch, t_f_batch = [], [], [], []
+        mask_t_batch = []
+        
+        for _ in range(cfg.batch_size):
+            name = name_list[idx]
+
+            i_t = cv2.imread(os.path.join(cfg.data_dir, cfg.i_t_dir, name))
+            i_s = cv2.imread(os.path.join(cfg.data_dir, cfg.i_s_dir, name))
+            t_sk = cv2.imread(os.path.join(cfg.data_dir, cfg.t_sk_dir, name), cv2.IMREAD_GRAYSCALE)
+            t_t = cv2.imread(os.path.join(cfg.data_dir, cfg.t_t_dir, name))
+            t_b = cv2.imread(os.path.join(cfg.data_dir, cfg.t_b_dir, name))
+            t_f = cv2.imread(os.path.join(cfg.data_dir, cfg.t_f_dir, name))
+            mask_t = cv2.imread(os.path.join(cfg.data_dir, cfg.mask_t_dir, name), cv2.IMREAD_GRAYSCALE)
+
+            i_t_batch.append(i_t)
+            i_s_batch.append(i_s)
+            t_sk_batch.append(t_sk)
+            t_t_batch.append(t_t)
+            t_b_batch.append(t_b)
+            t_f_batch.append(t_f)
+            mask_t_batch.append(mask_t)
+            idx = (idx + 1) % name_num
+        
+        w_sum = 0
+        for t_b in t_b_batch:
+            h, w = t_b.shape[:2]
+            scale_ratio = cfg.data_shape[0] / h
+            w_sum += int(w * scale_ratio)
+        
+        to_h = cfg.data_shape[0]
+        to_w = 128
+        #to_w = w_sum // cfg.batch_size
+        #to_w = int(round(to_w / 8)) * 8
+        to_scale = (to_w, to_h) # w first for cv2
+        for i in range(cfg.batch_size): 
+            i_t_batch[i] = cv2.resize(i_t_batch[i], to_scale)
+            i_s_batch[i] = cv2.resize(i_s_batch[i], to_scale)
+            t_sk_batch[i] = cv2.resize(t_sk_batch[i], to_scale, interpolation=cv2.INTER_NEAREST)
+            t_t_batch[i] = cv2.resize(t_t_batch[i], to_scale)
+            t_b_batch[i] = cv2.resize(t_b_batch[i], to_scale)
+            t_f_batch[i] = cv2.resize(t_f_batch[i], to_scale)
+            mask_t_batch[i] = cv2.resize(mask_t_batch[i], to_scale, interpolation=cv2.INTER_NEAREST)
+
+        i_t_batch = np.stack(i_t_batch)
+        i_s_batch = np.stack(i_s_batch)
+        t_sk_batch = np.expand_dims(np.stack(t_sk_batch), axis = -1)
+        t_t_batch = np.stack(t_t_batch)
+        t_b_batch = np.stack(t_b_batch)
+        t_f_batch = np.stack(t_f_batch)
+        mask_t_batch = np.expand_dims(np.stack(mask_t_batch), axis = -1)
+        
+        i_t_batch = i_t_batch.astype(np.float32) / 127.5 - 1. 
+        i_s_batch = i_s_batch.astype(np.float32) / 127.5 - 1. 
+        t_sk_batch = t_sk_batch.astype(np.float32) / 255. 
+        t_t_batch = t_t_batch.astype(np.float32) / 127.5 - 1. 
+        t_b_batch = t_b_batch.astype(np.float32) / 127.5 - 1. 
+        t_f_batch = t_f_batch.astype(np.float32) / 127.5 - 1. 
+        mask_t_batch = mask_t_batch.astype(np.float32) / 255.
+        
+        yield [i_t_batch, i_s_batch, t_sk_batch, t_t_batch, t_b_batch, t_f_batch, mask_t_batch]
+
+#每隔一段时间我们要利用当前训练的结果进行预测
+#这里就是获取用来预测的数据的
+def get_input_data(data_dir = cfg.example_data_dir):
+    
+    # get input data from dir
+    data_list = os.listdir(data_dir)
+    data_list = [data_name.split('_')[0] + '_' for data_name in data_list]
+    data_list = list(set(data_list))
+    res_list = []
+    for data_name in data_list:
+        i_t = cv2.imread(os.path.join(cfg.example_data_dir, data_name + 'i_t.png'))
+        i_s = cv2.imread(os.path.join(cfg.example_data_dir, data_name + 'i_s.png'))
+        h, w = i_t.shape[:2]
+        scale_ratio = cfg.data_shape[0] / h
+        to_h = cfg.data_shape[0]
+        to_w = 128
+        #to_w = int(round(int(w * scale_ratio) / 8)) * 8
+        to_scale = (to_w, to_h) # w first for cv2
+        i_t = cv2.resize(i_t, to_scale).astype(np.float32) / 127.5 - 1.
+        i_s = cv2.resize(i_s, to_scale).astype(np.float32) / 127.5 - 1.
+        i_t = np.expand_dims(i_t, axis = 0)
+        i_s = np.expand_dims(i_s, axis = 0)
+        res_list.append([i_t, i_s, (w, h), data_name]) # w first for cv2
+    return res_list
+
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diff --git a/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/loss.py b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc9fd1fa32c78f7a1cac3a3ce27bfac647985a83
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+++ b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/loss.py
@@ -0,0 +1,133 @@
+"""
+SRNet - Editing Text in the Wild
+Definition of loss functions.
+Copyright (c) 2019 Netease Youdao Information Technology Co.,Ltd.
+Licensed under the GPL License (see LICENSE for details)
+Written by Yu Qian
+"""
+
+# 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
+import cfg
+
+def build_discriminator_loss(x, name = 'd_loss'):
+
+    x_true, x_pred = tf.split(x, 2, name = name + '_split')
+    d_loss = -tf.reduce_mean(tf.log(tf.clip_by_value(x_true, cfg.epsilon, 1.0)) \
+                + tf.log(tf.clip_by_value(1.0 - x_pred, cfg.epsilon, 1.0)))
+    return d_loss
+
+def build_dice_loss(x_t, x_o, name = 'dice_loss'):
+       
+    intersection = tf.reduce_sum(x_t * x_o, axis = [1,2,3])
+    union = tf.reduce_sum(x_t, axis = [1,2,3]) + tf.reduce_sum(x_o, axis = [1,2,3])
+    return 1. - tf.reduce_mean((2. * intersection + cfg.epsilon)/(union + cfg.epsilon), axis = 0)
+
+def build_l1_loss(x_t, x_o, name = 'l1_loss'):
+        
+    return tf.reduce_mean(tf.abs(x_t - x_o))
+
+def build_l1_loss_with_mask(x_t, x_o, mask, name = 'l1_loss'):
+    
+    mask_ratio = 1. - tf.reduce_sum(mask) / tf.cast(tf.size(mask), tf.float32)
+    l1 = tf.abs(x_t - x_o)
+    return mask_ratio * tf.reduce_mean(l1 * mask) + (1. - mask_ratio) * tf.reduce_mean(l1 * (1. - mask))
+
+def build_perceptual_loss(x, name = 'per_loss'):
+        
+    l = []
+    for i, f in enumerate(x):
+        l.append(build_l1_loss(f[0], f[1], name = name + '_l1_' + str(i + 1)))
+    l = tf.stack(l, axis = 0, name = name + '_stack')
+    l = tf.reduce_sum(l, name = name + '_sum')
+    return l
+
+def build_gram_matrix(x, name = 'gram_matrix'):
+        
+    x_shape = tf.shape(x)
+    h, w, c = x_shape[1], x_shape[2], x_shape[3]
+    matrix = tf.reshape(x, shape = [-1, h * w, c])
+    gram = tf.matmul(matrix, matrix, transpose_a = True) / tf.cast(h * w * c, tf.float32)
+    return gram
+
+def build_style_loss(x, name = 'style_loss'):
+        
+    l = []
+    for i, f in enumerate(x):
+        f_shape = tf.size(f[0])
+        f_norm = 1. / tf.cast(f_shape, tf.float32)
+        gram_true = build_gram_matrix(f[0], name = name + '_gram_true_' + str(i + 1))
+        gram_pred = build_gram_matrix(f[1], name = name + '_gram_pred_' + str(i + 1))
+        l.append(f_norm * (build_l1_loss(gram_true, gram_pred, name = name + '_l1_' + str(i + 1))))
+    l = tf.stack(l, axis = 0, name = name + '_stack')
+    l = tf.reduce_sum(l, name = name + '_sum')
+    return l
+
+def build_vgg_loss(x, name = 'vgg_loss'):
+        
+    splited = []
+    for i, f in enumerate(x):
+        splited.append(tf.split(f, 2, name = name + '_split_' + str(i + 1)))
+    l_per = build_perceptual_loss(splited, name = name + '_per')
+    l_style = build_style_loss(splited, name = name + '_style')
+    return l_per, l_style
+
+def build_gan_loss(x, name = 'gan_loss'):
+    
+    x_true, x_pred = tf.split(x, 2, name = name + '_split')
+    gan_loss = -tf.reduce_mean(tf.log(tf.clip_by_value(x_pred, cfg.epsilon, 1.0)))
+    return gan_loss
+
+def build_generator_loss(out_g, out_d, out_vgg, labels, name = 'g_loss'):
+        
+    o_sk, o_t, o_b, o_f, mask_t = out_g
+    o_db, o_df = out_d
+    o_vgg = out_vgg
+    t_sk, t_t, t_b, t_f = labels
+
+    l_t_sk = cfg.lt_alpha * build_dice_loss(t_sk, o_sk, name = name + '_dice_loss')
+    l_t_l1 = build_l1_loss_with_mask(t_t, o_t, mask_t, name = name + '_lt_l1_loss')
+    l_t =  l_t_l1 + l_t_sk
+
+    l_b_gan = build_gan_loss(o_db, name = name + '_lb_gan_loss')
+    l_b_l1 = cfg.lb_beta * build_l1_loss(t_b, o_b, name = name + '_lb_l1_loss')
+    l_b = l_b_gan + l_b_l1
+    
+    l_f_gan = build_gan_loss(o_df, name = name + '_lf_gan_loss')
+    l_f_l1 = cfg.lf_theta_1 * build_l1_loss(t_f, o_f, name = name + '_lf_l1_loss')
+    l_f_vgg_per, l_f_vgg_style = build_vgg_loss(o_vgg, name = name + '_lf_vgg_loss')
+    l_f_vgg_per = cfg.lf_theta_2 * l_f_vgg_per
+    l_f_vgg_style = cfg.lf_theta_3 * l_f_vgg_style
+    l_f = l_f_gan + l_f_l1 + l_f_vgg_per + l_f_vgg_style
+    
+    l = cfg.lt * l_t + cfg.lb * l_b + cfg.lf * l_f
+    return l, [l_t_sk, l_t_l1, l_b_gan, l_b_l1, l_f_gan, l_f_l1, l_f_vgg_per, l_f_vgg_style]
+
diff --git a/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/model.py b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..80694c25d2bd402639aa843612729f983ebc5ba0
--- /dev/null
+++ b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/model.py
@@ -0,0 +1,346 @@
+"""
+SRNet - Editing Text in the Wild
+The main SRNet model implementation.
+Copyright (c) 2019 Netease Youdao Information Technology Co.,Ltd.
+Licensed under the GPL License (see LICENSE for details)
+Written by Yu Qian
+"""
+
+# 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 os
+import cv2
+import numpy as np
+import tensorflow as tf
+from loss import build_discriminator_loss, build_generator_loss
+import cfg
+
+class SRNet():
+
+    def __init__(self, shape = [224, 224], name = ''):
+
+        self.name = name
+        self.cnum = 32
+        self.graph = tf.Graph()
+        with self.graph.as_default():
+            self.i_t = tf.placeholder(dtype = tf.float32, shape = [None] + shape + [3])
+            self.i_s = tf.placeholder(dtype = tf.float32, shape = [None] + shape + [3])
+            self.t_sk = tf.placeholder(dtype = tf.float32, shape = [None] + shape + [1])
+            self.t_t = tf.placeholder(dtype = tf.float32, shape = [None] + shape + [3])
+            self.t_b = tf.placeholder(dtype = tf.float32, shape = [None] + shape + [3])
+            self.t_f = tf.placeholder(dtype = tf.float32, shape = [None] + shape + [3])
+            self.mask_t = tf.placeholder(dtype = tf.float32, shape = [None] + shape + [1])
+            self.global_step = tf.Variable(tf.constant(0))
+            self.build_whole_net_with_loss()
+            self.build_optimizer()
+            self.build_summary_op()
+
+    def _res_block(self, x, activation = tf.nn.leaky_relu, padding = 'SAME', name = 'res_block'):
+        
+        cnum = x.get_shape().as_list()[-1]
+        xin = x
+        x = tf.layers.conv2d(x, cnum // 4, kernel_size = 1, strides = 1, activation = activation, padding = padding, name = name + '_conv1')
+        x = tf.layers.conv2d(x, cnum // 4, kernel_size = 3, strides = 1, activation = activation, padding = padding, name = name + '_conv2')
+        x = tf.layers.conv2d(x, cnum, kernel_size = 1, strides = 1, activation = None, padding = padding, name = name + '_conv3')
+        x = tf.add(xin, x, name = name + '_add')
+        x = tf.layers.batch_normalization(x, name = name + '_bn')
+        x = activation(x, name = name + '_out')
+        return x
+
+    def _conv_bn_relu(self, x, cnum = None, activation = tf.nn.leaky_relu, padding = 'SAME', name = 'conv_bn_relu'):
+        
+        cnum = x.get_shape().as_list()[-1] if cnum is None else cnum
+        x = tf.layers.conv2d(x, cnum, kernel_size = 3, strides = 1, activation = None, padding = padding, name = name + '_conv')
+        x = tf.layers.batch_normalization(x, name = name + '_bn')
+        x = activation(x, name = name + '_out')
+        return x
+
+    def build_res_net(self, x, activation = tf.nn.leaky_relu, padding = 'SAME', name = 'res_net'):
+        
+        x = self._res_block(x, activation = activation, padding = padding, name = name + '_block1')
+        x = self._res_block(x, activation = activation, padding = padding, name = name + '_block2')
+        x = self._res_block(x, activation = activation, padding = padding, name = name + '_block3')
+        x = self._res_block(x, activation = activation, padding = padding, name = name + '_block4')
+        return x
+
+    def build_encoder_net(self, x, activation = tf.nn.leaky_relu, padding = 'SAME', name = 'encoder_net', get_feature_map = False):
+        
+        x = self._conv_bn_relu(x, self.cnum, name = name + '_conv1_1')
+        x = self._conv_bn_relu(x, self.cnum, name = name + '_conv1_2')
+        
+        x = tf.layers.conv2d(x, 2 * self.cnum, kernel_size = 3, strides = 2, activation = activation, padding = padding, name = name + '_pool1')
+        x = self._conv_bn_relu(x, 2 * self.cnum, name = name + '_conv2_1')
+        x = self._conv_bn_relu(x, 2 * self.cnum, name = name + '_conv2_2')
+        f1 = x
+        
+        x = tf.layers.conv2d(x, 4 * self.cnum, kernel_size = 3, strides = 2, activation = activation, padding = padding, name = name + '_pool2')
+        x = self._conv_bn_relu(x, 4 * self.cnum, name = name + '_conv3_1')
+        x = self._conv_bn_relu(x, 4 * self.cnum, name = name + '_conv3_2')
+        f2 = x
+        
+        x = tf.layers.conv2d(x, 8 * self.cnum, kernel_size = 3, strides = 2, activation = activation, padding = padding, name = name + '_pool3')
+        x = self._conv_bn_relu(x, 8 * self.cnum, name = name + '_conv4_1')
+        x = self._conv_bn_relu(x, 8 * self.cnum, name = name + '_conv4_2')
+        if get_feature_map:
+            return x, [f2, f1]
+        else:
+            return x
+
+    def build_decoder_net(self, x, fuse = None, activation = tf.nn.leaky_relu, padding = 'SAME', name = 'decoder_net', get_feature_map = False):
+
+        if fuse and fuse[0] is not None:
+            x = tf.concat([x, fuse[0]], axis = -1, name = name + '_fuse1')
+        x = self._conv_bn_relu(x, 8 * self.cnum, name = name + '_conv1_1')
+        x = self._conv_bn_relu(x, 8 * self.cnum, name = name + '_conv1_2')
+        f1 = x
+        
+        x = tf.layers.Conv2DTranspose(4 * self.cnum, kernel_size = 3, strides = 2, activation = activation, padding = padding, name = name + '_deconv1')(x)
+        if fuse and fuse[1] is not None:
+            x = tf.concat([x, fuse[1]], axis = -1, name = name + '_fuse2')
+        x = self._conv_bn_relu(x, 4 * self.cnum, name = name + '_conv2_1')
+        x = self._conv_bn_relu(x, 4 * self.cnum, name = name + '_conv2_2')
+        f2 = x
+        
+        x = x = tf.layers.Conv2DTranspose(2 * self.cnum, kernel_size = 3, strides = 2, activation = activation, padding = padding, name = name + '_deconv2')(x)
+        if fuse and fuse[2] is not None:
+            x = tf.concat([x, fuse[2]], axis = -1, name = name + '_fuse3')
+        x = self._conv_bn_relu(x, 2 * self.cnum, name = name + '_conv3_1')
+        x = self._conv_bn_relu(x, 2 * self.cnum, name = name + '_conv3_2')
+        f3 = x
+        
+        x = x = tf.layers.Conv2DTranspose(self.cnum, kernel_size = 3, strides = 2, activation = activation, padding = padding, name = name + '_deconv3')(x)
+        x = self._conv_bn_relu(x, self.cnum, name = name + '_conv4_1')
+        x = self._conv_bn_relu(x, self.cnum, name = name + '_conv4_2')
+        if get_feature_map:
+            return x, [f1, f2, f3]
+        else:
+            return x
+        
+    def build_text_conversion_net(self, x_t, x_s, padding = 'SAME', name = 'tcn'):
+        
+        x_t = self.build_encoder_net(x_t, name = name + '_t_encoder')
+        x_t = self.build_res_net(x_t, name = name + '_t_res')
+
+        x_s = self.build_encoder_net(x_s, name = name + '_s_encoder')
+        x_s = self.build_res_net(x_s, name = name + '_s_res')
+
+        x = tf.concat([x_t, x_s], axis = -1, name = name + '_concat1')
+        
+        y_sk = self.build_decoder_net(x, name = name + '_sk_decoder')
+        y_sk_out = tf.layers.conv2d(y_sk, 1, kernel_size = 3, strides = 1, activation = 'sigmoid', padding = padding, name = name + '_sk_out')
+
+        y_t = self.build_decoder_net(x, name = name + '_t_decoder')
+        y_t = tf.concat([y_sk, y_t], axis = -1, name = name + '_concat2')
+        y_t = self._conv_bn_relu(y_t, name = name + '_t_cbr')
+        y_t_out = tf.layers.conv2d(y_t, 3, kernel_size = 3, strides = 1, activation = 'tanh', padding = padding, name = name + '_t_out')
+        return y_sk_out, y_t_out
+        
+    def build_background_inpainting_net(self, x, padding = 'SAME', name = 'bin'):
+        
+        x, f_encoder = self.build_encoder_net(x, name = name + '_encoder', get_feature_map = True)
+        x = self.build_res_net(x, name = name + '_res')
+        x, fuse = self.build_decoder_net(x, fuse = [None] + f_encoder, name = name + '_decoder', get_feature_map = True)
+        x = tf.layers.conv2d(x, 3, kernel_size = 3, strides = 1, activation = 'tanh', padding = padding, name = name + '_out')
+        return x, fuse
+
+    def build_fusion_net(self, x, fuse, padding = 'SAME', name = 'fn'):
+
+        x = self.build_encoder_net(x, name = name + '_encoder')
+        x = self.build_res_net(x, name = name + '_res')
+        x = self.build_decoder_net(x, fuse, name = name + '_decoder')
+        x = tf.layers.conv2d(x, 3, kernel_size = 3, strides = 1, activation = 'tanh', padding = padding, name = name + '_out')
+        return x
+
+    def build_discriminator(self, x, activation = tf.nn.leaky_relu, padding = 'SAME', name = 'discriminator'):
+        
+        with tf.variable_scope('D'):
+            x = tf.layers.conv2d(x, 64, kernel_size = 3, strides = 2, activation = activation, padding = padding, name = name + '_conv1')
+            x = tf.layers.conv2d(x, 128, kernel_size = 3, strides = 2, activation = None, padding = padding, name = name + '_conv2')
+            x = tf.layers.batch_normalization(x, name = name + '_conv2_bn')
+            x = activation(x, name = name + '_conv2_activation')
+            x = tf.layers.conv2d(x, 256, kernel_size = 3, strides = 2, activation = None, padding = padding, name = name + '_conv3')
+            x = tf.layers.batch_normalization(x, name = name + '_conv3_bn')
+            x = activation(x, name = name + '_conv3_activation')
+            x = tf.layers.conv2d(x, 512, kernel_size = 3, strides = 2, activation = None, padding = padding, name = name + '_conv4')
+            x = tf.layers.batch_normalization(x, name = name + '_conv4_bn')
+            x = activation(x, name = name + '_conv4_activation')
+            x = tf.layers.conv2d(x, 1, kernel_size = 3, strides = 1, activation = None, padding = padding, name = name + '_conv5')
+            x = tf.layers.batch_normalization(x, name = name + '_conv5_bn')
+            x = tf.nn.sigmoid(x, name = '_out')
+        return x
+
+    def build_generator(self, inputs, name = 'generator'):
+        
+        i_t, i_s = inputs
+        with tf.variable_scope('G'):
+            o_sk, o_t = self.build_text_conversion_net(i_t, i_s, name = name + '_tcn')
+            o_b, fuse = self.build_background_inpainting_net(i_s, name = name + '_bin')
+            o_f = self.build_fusion_net(o_t, fuse, name = name + '_fn')
+        return o_sk, o_t, o_b, o_f
+    
+    def build_whole_net_with_loss(self):
+
+        i_t, i_s = self.i_t, self.i_s
+        t_sk, t_t, t_b, t_f, mask_t = self.t_sk, self.t_t, self.t_b, self.t_f, self.mask_t
+        inputs = [i_t, i_s]
+        labels = [t_sk, t_t, t_b, t_f]
+        
+        o_sk, o_t, o_b, o_f = self.build_generator(inputs)
+        self.o_sk = tf.identity(o_sk, name = 'o_sk')
+        self.o_t = tf.identity(o_t, name = 'o_t')
+        self.o_b = tf.identity(o_b, name = 'o_b')
+        self.o_f = tf.identity(o_f, name = 'o_f')
+
+        i_db_true = tf.concat([t_b, i_s], axis = -1, name = 'db_true_concat')
+        i_db_pred = tf.concat([o_b, i_s], axis = -1, name = 'db_pred_concat')
+        i_db = tf.concat([i_db_true, i_db_pred], axis = 0, name = 'db_concat')
+
+        i_df_true = tf.concat([t_f, i_t], axis = -1, name = 'df_true_concat')
+        i_df_pred = tf.concat([o_f, i_t], axis = -1, name = 'df_pred_concat')
+        i_df = tf.concat([i_df_true, i_df_pred], axis = 0, name = 'df_concat')
+
+        o_db = self.build_discriminator(i_db, name = 'db')
+        o_df = self.build_discriminator(i_df, name = 'df')
+            
+        i_vgg = tf.concat([t_f, o_f], axis = 0, name = 'vgg_concat')
+
+        vgg_graph_def = tf.GraphDef()
+        vgg_graph_path = cfg.vgg19_weights
+        with open(vgg_graph_path, 'rb') as f:
+            vgg_graph_def.ParseFromString(f.read())
+            _ = tf.import_graph_def(vgg_graph_def, input_map = {"inputs:0": i_vgg})
+        with tf.Session(config=npu_config_proto()) as sess:
+            o_vgg_1 = sess.graph.get_tensor_by_name("import/block1_conv1/Relu:0")
+            o_vgg_2 = sess.graph.get_tensor_by_name("import/block2_conv1/Relu:0")
+            o_vgg_3 = sess.graph.get_tensor_by_name("import/block3_conv1/Relu:0")
+            o_vgg_4 = sess.graph.get_tensor_by_name("import/block4_conv1/Relu:0")
+            o_vgg_5 = sess.graph.get_tensor_by_name("import/block5_conv1/Relu:0")
+        
+        out_g = [o_sk, o_t, o_b, o_f, mask_t]
+        out_d = [o_db, o_df]
+        out_vgg = [o_vgg_1, o_vgg_2, o_vgg_3, o_vgg_4, o_vgg_5]
+
+        db_loss = build_discriminator_loss(o_db, name = 'db_loss')
+        df_loss = build_discriminator_loss(o_df, name = 'df_loss')
+        self.d_loss_detail = [db_loss, df_loss]
+        self.d_loss = tf.add(db_loss, df_loss, name = 'd_loss')
+        self.g_loss, self.g_loss_detail = build_generator_loss(out_g, out_d, out_vgg, labels, name = 'g_loss')
+     
+    def build_optimizer(self):
+
+        self.learning_rate = tf.train.exponential_decay(learning_rate = cfg.learning_rate, global_step = self.global_step,
+                                decay_steps = cfg.decay_steps, decay_rate = cfg.decay_rate, staircase = cfg.staircase)
+        d_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='D')
+        with tf.control_dependencies(d_update_ops):
+            self.d_train_step = tf.train.AdamOptimizer(self.learning_rate, cfg.beta1, cfg.beta2).minimize(self.d_loss,
+                var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope = 'D'))
+        g_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='G')
+        with tf.control_dependencies(g_update_ops):
+            self.g_train_step = tf.train.AdamOptimizer(self.learning_rate, cfg.beta1, cfg.beta2).minimize(self.g_loss,
+                var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope = 'G'))
+
+    def build_summary_op(self):
+        
+        d_summary_loss = tf.summary.scalar("loss", self.d_loss)
+        d_summary_loss_db = tf.summary.scalar("l_db", self.d_loss_detail[0])
+        d_summary_loss_df = tf.summary.scalar("l_df", self.d_loss_detail[1])
+        
+        g_summary_loss = tf.summary.scalar("loss", self.g_loss)
+        g_summary_loss_t_sk = tf.summary.scalar("l_t_sk", self.g_loss_detail[0])
+        g_summary_loss_t_l1 = tf.summary.scalar("l_t_l1", self.g_loss_detail[1])
+        g_summary_loss_b_gan = tf.summary.scalar("l_b_gan", self.g_loss_detail[2])
+        g_summary_loss_b_l1 = tf.summary.scalar("l_b_l1", self.g_loss_detail[3])
+        g_summary_loss_f_gan = tf.summary.scalar("l_f_gan", self.g_loss_detail[4])
+        g_summary_loss_f_l1 = tf.summary.scalar("l_f_l1", self.g_loss_detail[5])
+        g_summary_loss_f_vgg_per = tf.summary.scalar("l_f_vgg_per", self.g_loss_detail[6])
+        g_summary_loss_f_vgg_style = tf.summary.scalar("l_f_vgg_style", self.g_loss_detail[7])
+        
+        self.d_summary_op = tf.summary.merge([d_summary_loss, d_summary_loss_db, d_summary_loss_df])
+        self.g_summary_op = tf.summary.merge([g_summary_loss, g_summary_loss_t_sk, g_summary_loss_t_l1,
+                                              g_summary_loss_b_gan, g_summary_loss_b_l1, g_summary_loss_f_gan,
+                                              g_summary_loss_f_l1, g_summary_loss_f_vgg_per, g_summary_loss_f_vgg_style])
+        
+        self.d_writer = tf.summary.FileWriter(os.path.join(cfg.tensorboard_dir, self.name, 'descriminator'), self.graph)
+        self.g_writer = tf.summary.FileWriter(os.path.join(cfg.tensorboard_dir, self.name, 'generator'), self.graph)
+     
+    def train_step(self, sess, global_step, i_t, i_s, t_sk, t_t, t_b, t_f, mask_t):
+
+        feed_dict = {
+            self.i_t: i_t,
+            self.i_s: i_s,
+            self.t_sk: t_sk,
+            self.t_t: t_t,
+            self.t_b: t_b,
+            self.t_f: t_f,
+            self.mask_t: mask_t,
+            self.global_step: global_step
+        }
+        
+        with self.graph.as_default():
+            _, d_loss, d_log = sess.run([self.d_train_step, self.d_loss, self.d_summary_op], feed_dict = feed_dict)
+            _, g_loss, g_log = sess.run([self.g_train_step, self.g_loss, self.g_summary_op], feed_dict = feed_dict)
+        return d_loss, g_loss, d_log, g_log
+    
+    def predict(self, sess, i_t, i_s, to_shape = None):
+        
+        assert i_t.shape == i_s.shape and i_t.dtype == i_s.dtype
+        assert len(i_t.shape) == 3 or (len(i_t.shape) == 4 and to_shape is not None \
+                                        and i_t.shape[1] == cfg.data_shape[0] \
+                                        and i_t.shape[2] % 8 == 0 \
+                                        and i_t.dtype == np.float32)
+        assert i_t.dtype == np.uint8 \
+               or (i_t.dtype == np.float32 and np.min(i_t) >= -1 and np.max(i_t) <= 1)
+
+        # process raw image, len(i_t.shape) == 3
+        if len(i_t.shape) == 3:
+            if not to_shape:
+                h, w = i_t.shape[:2]
+                to_shape = (w, h) # w first for cv2
+            if i_t.shape[0] != cfg.data_shape[0]:
+                ratio = cfg.data_shape[0] / h
+                predict_h = cfg.data_shape[0]
+                predict_w = round(int(w * ratio) / 8) * 8
+                predict_scale = (predict_w, predict_h) # w first for cv2
+                i_t = cv2.resize(i_t, predict_scale)
+                i_s = cv2.resize(i_s, predict_scale)
+            if i_t.dtype == np.uint8:
+                i_t = i_t.astype(np.float32) / 127.5 - 1.
+                i_s = i_s.astype(np.float32) / 127.5 - 1.
+            i_t = np.expand_dims(i_t, axis = 0)
+            i_s = np.expand_dims(i_s, axis = 0)
+
+        result = sess.run([self.o_sk, self.o_t, self.o_b, self.o_f], feed_dict = {self.i_t: i_t, self.i_s: i_s})
+        o_sk, o_t, o_b, o_f = result
+        o_sk = cv2.resize((o_sk[0] * 255.).astype(np.uint8), to_shape, interpolation=cv2.INTER_NEAREST)
+        o_t = cv2.resize(((o_t[0] + 1.) * 127.5).astype(np.uint8), to_shape)
+        o_b = cv2.resize(((o_b[0] + 1.) * 127.5).astype(np.uint8), to_shape)
+        o_f = cv2.resize(((o_f[0] + 1.) * 127.5).astype(np.uint8), to_shape)
+        return [o_sk, o_t, o_b, o_f]
+
diff --git a/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/model_logs/vgg19/.keep b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/model_logs/vgg19/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/modelzoo_level.txt b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/modelzoo_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2a8d1227b23f1e1a9664ab221f934ff27258b908
--- /dev/null
+++ b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/modelzoo_level.txt
@@ -0,0 +1,6 @@
+FuncStatus:OK
+GPUStatus:OK
+NPUMigrationStatus:OK
+PrecisionStatus:OK 
+AutoTune:OK
+PerfStatus:POK
diff --git a/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/predict.py b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..995119cf791f68057432df8d8fa7c9253e594665
--- /dev/null
+++ b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/predict.py
@@ -0,0 +1,101 @@
+"""
+SRNet - Editing Text in the Wild
+Data prediction.
+Copyright (c) 2019 Netease Youdao Information Technology Co.,Ltd.
+Licensed under the GPL License (see LICENSE for details)
+Written by Yu Qian
+"""
+
+# 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
+from model import SRNet
+import numpy as np
+import os
+import cfg
+from utils import *
+from datagen import srnet_datagen, get_input_data
+import argparse
+
+def main():
+    
+    parser = argparse.ArgumentParser() 
+    parser.add_argument('--gpu', help = 'gpu id', default = 0)
+    parser.add_argument('--i_s', help = 'input original text patch')
+    parser.add_argument('--i_t', help = 'input standard text patch')
+    parser.add_argument('--input_dir', help = 'Directory containing xxx_i_s and xxx_i_t with same prefix',
+                        default = cfg.predict_data_dir)
+    parser.add_argument('--save_dir', help = 'Directory to save result', default = cfg.predict_result_dir)
+    parser.add_argument('--save_mode', help = '1 to save all and 0 to save onle o_f', type = int, default = 0)
+    parser.add_argument('--checkpoint', help = 'tensorflow ckpt', default = cfg.predict_ckpt_path)
+    args = parser.parse_args()
+
+    assert (args.input_dir is not None and args.i_s is None and args.i_t is None) \
+            or (args.input_dir is None and args.i_s is not None and args.i_t is not None)
+    assert args.save_dir is not None
+    assert args.save_mode == 0 or args.save_mode == 1
+    assert args.checkpoint is not None
+
+    # gpu
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
+    
+    # define model
+    print_log('model compiling start.', content_color = PrintColor['yellow'])
+    model = SRNet(shape = cfg.data_shape, name = 'predict')
+    print_log('model compiled.', content_color = PrintColor['yellow'])
+
+    with model.graph.as_default():
+        with tf.Session(config=npu_config_proto()) as sess:
+            saver = tf.train.Saver(tf.global_variables())
+            
+            # load pretrained weights
+            print_log('weight loading start.', content_color = PrintColor['yellow'])
+            saver.restore(sess, args.checkpoint)
+            print_log('weight loaded.', content_color = PrintColor['yellow'])
+            
+            # predict
+            print_log('predicting start.', content_color = PrintColor['yellow'])
+            if args.input_dir is None:
+                i_s = cv2.imread(args.i_s)
+                i_t = cv2.imread(args.i_t)
+                o_sk, o_t, o_b, o_f = model.predict(sess, i_t, i_s)
+                
+                cv2.imwrite(os.path.join(args.save_dir, 'result.png'), o_f)
+                if args.save_mode == 1:
+                    cv2.imwrite(os.path.join(args.save_dir, 'result_sk.png'), o_sk)
+                    cv2.imwrite(os.path.join(args.save_dir, 'result_t.png'), o_t)
+                    cv2.imwrite(os.path.join(args.save_dir, 'result_b.png'), o_b)
+            else:
+                predict_data_list(model, sess, args.save_dir, get_input_data(args.input_dir), mode = args.save_mode)
+            print_log('predicting finished.', content_color = PrintColor['yellow'])
+            
+if __name__ == '__main__':
+    main()
+
diff --git a/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/readme.md b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..b0ef0b2870a8f3af8e0acef9fede8f238dbc517f
--- /dev/null
+++ b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/readme.md
@@ -0,0 +1,94 @@
+# SRNet - the implementation of paper "Editing Text in the Wild"
+*** 
+
+## 基本信息
+#### 发布者(Publisher):Huawei
+#### 应用领域（Application Domain）： Image Generator
+#### 版本（Version）：1.0
+#### 修改时间(Modified) ：2022.3.4 
+#### 大小(size): 78.5M左右
+#### 框架(Framework)：TensorFlow 1.15.0
+#### 模型格式(Model Format): pb
+#### 处理器(Processor): 昇腾910
+#### 描述(Description): 基于TensorFlow框架的文本图片风格迁移网络训练代码
+***
+
+## 概述
+此模型的来源是ACM Multimedia 2019上的一篇文章“Editing Text in the Wild”.  
+这篇文章主要设计了一个模块化的DNN，完成了对替换给定图片之中文本内容的任务。在替换过程之中，源文本的文字大小、字体、颜色、朝向等细节将会被保留。DNN的各个模块分别完成整个任务的一部分，最后由一个合成模块将其余模块的输出融合在一起，生成目标图片。
+
+本实现是基于[有道ai团队](https://github.com/youdao-ai)对原论文的一个[开源实现](https://github.com/youdao-ai/SRNet)之上进行NPU适配得到的结果。
+
+参考论文:
+* [Editing Text in the Wild](https://dl.acm.org/doi/pdf/10.1145/3343031.3350929)
+
+论文的主要亮点是：
+* 以往的图片文本迁移工作都是以单字母或者单字为单位的，此文之中实现的方法是以单词为单位的  
+* 网络是采用模块化设计的，但是训练并不是一个模块一个模块单独进行训练，而是将整个网络进行端到端的训练
+***
+
+## 训练
+### 数据集获取
+按照原文，训练此模型，需要提供的数据集包含两部分，即输入图片i\_s, i\_t以及标记图片t\_sk, t\_t, t\_b, t\_f。  
+在此版本的实现之中，新增了标记项mask\_t,代表原图片之中文本部分的二进制掩码。加入此项的原因是，此项会使得模型的训练收敛的更快。  
+由于对于现实之中的图片，无法模型训练所需要提供的标记部分。因此，训练模型的数据集是人工合成的数据集。  
+合成数据集需要提供纯净无文字的背景图片以及一些字体文件(.tff文件)，在完成准备工作之中，可以使用[SRNet_datagenerator](https://github.com/youdao-ai/SRNet-Datagen)来合成数据集。
+
+### 模型训练
+由于本实现针对华为的Modelarts平台进行了适配，因此训练部分将分成NPU训练(基于modelarts平台)以及GPU训练两部分。
+测试所使用的环境为：
+* numpy 1.21.2
+* opencv-python 4.5.3
+* opencv-contrib-python 4.5.4.58
+* python 3.7
+* tensorflow 1.15
+
+由于Gitee平台对单文件大小的限制，训练过程之中采用的预训练模型无法直接上传，请通过OBS服务器[下载](https://cann-nju-srnet.obs.cn-north-4.myhuaweicloud.com:443/vgg19_weights_tf_dim_ordering_tf_kernels_notop.pb?AccessKeyId=DNOFMBDXF3DTPNGYLYN7&Expires=1679819348&Signature=xZTguiVqpKyGuGzko/AI8fu0ilM%3D)  
+进行训练时，请将预训练模型保存在model_logs/vgg19目录下  
+
+训练依托于华为的Modelarts平台。  
+首先，下载并安装pycharm以及[华为modelart插件](https://modelarts-pycharm-plugin.obs.cn-north-1.myhuaweicloud.com/Pycharm-ToolKit-3.0.zip)，配置密钥  
+然后，将准备好的数据集上传到华为OBS服务器上，并在modelarts插件之中进行如下配置  
+* Fruquently-used  
+  * AI Engine: Ascend-Powered-Engine, tensorflow_1.15-xxx  
+  * Boot File Path: 仓库中npu_version/cfg.py对应的本地文件路径  
+  * Code Directory: 仓库中npu-version对应的本地文件夹路径  
+  * OBS Path: 训练时会自动将代码上传至OBS服务器，OBS Path对应OBS服务器中代码自动上传的位置  
+  * Data Path in OBS: 数据集在OBS服务器之中存放的位置    
+然后即可进行训练，最终的训练结果需要拷贝回OBS服务器，通过更改train.py第101行之中的参数dst_url来指定拷贝的目标路径。
+
+## 测试
+### 测试流程
+在完成对模型的训练之后，可以采用训练得到的结果进行预测。  
+预测的方式如下：
+```console
+$ python3 predict.py --i_s xxx --i_t xxx --save_dir xxx --checkpoint xxx
+```
+或是预测一整个文件夹之中的数据：
+```console
+$ python3 predict.py --input_dir xxx --save_dir xxx --checkpoint xxx
+```
+此时，需要保证input\_dir之中的i\_s和i\_t有相同的前缀，且后缀分别为"\_i\_s"以及"\_i\_t"。  
+或者可以直接修改cfg.py之中的路径信息，然后直接调用predict.py
+```console
+$ python3 predict.py 
+```
+
+### 训练结果对比
+模型以同样的数据集、参数在GPU和NPU平台上进行了训练。  
+GPU平台使用的GPU为：NVIDIA T4， 1 * 16G
+NPU平台使用的NPU为：Ascend 910
+#### 时间（性能）对比
+GPU平台上每50轮训练平均耗时为30s
+NPU平台上每50轮训练平均耗时约59s
+#### 效果对比
+GPU和NPU上训练效果对比如下图：
+![训练效果](Figure_1.png) 
+不难看出，二者的训练效果相当，且均达到了[复现团队训练时的效果](https://github.com/youdao-ai/SRNet/issues/11)。
+***
+
+## 数据集与OBS桶
+训练过程之中所采用的数据集为：[数据集](https://e-share.obs-website.cn-north-1.myhuaweicloud.com?token=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)  
+数据集的Access Code为：xx11yy  
+训练采用的OBS桶的地址为：[OBS桶](obs://cann-nju-srnet/)
+
diff --git a/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/train.py b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3ff3d842a2eb79ef8be576d1b905c00d8ff573c
--- /dev/null
+++ b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/train.py
@@ -0,0 +1,133 @@
+"""
+SRNet - Editing Text in the Wild
+Model training.
+Copyright (c) 2019 Netease Youdao Information Technology Co.,Ltd.
+Licensed under the GPL License (see LICENSE for details)
+Written by Yu Qian
+"""
+
+# 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 argparse
+import moxing as mox
+
+from npu_bridge.npu_init import *
+
+import tensorflow as tf
+from model import SRNet
+import numpy as np
+import os
+import cfg
+from utils import *
+from datagen import srnet_datagen, get_input_data
+
+from tensorflow.core.protobuf.rewriter_config_pb2 import  RewriterConfig
+
+def main():
+    # define train_name
+    if not cfg.train_name:
+        train_name = get_train_name()
+    else: 
+        train_name = cfg.train_name
+    
+    # define model
+    print_log('model compiling start.', content_color = PrintColor['yellow'])
+    model = SRNet(shape = cfg.data_shape, name = train_name)
+    print_log('model compiled.', content_color = PrintColor['yellow'])
+
+    # define data generator
+    #datagen()之中包含yield，所以不会正真执行，而是返回一个生成器(迭代器)
+    gen = srnet_datagen()
+    
+    with model.graph.as_default():
+        init = tf.global_variables_initializer()
+        trainCfg = tf.ConfigProto()
+        custom_op = trainCfg.graph_options.rewrite_options.custom_optimizers.add()
+        custom_op.name = "NpuOptimizer"
+        trainCfg.graph_options.rewrite_options.remapping = RewriterConfig.OFF
+
+        #trainCfg.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF
+        #custom_op.parameter_map["dynamic_input"].b = True
+
+        #custom_op.parameter_map["dynamic_graph_execute_mode"].s = tf.compat.as_bytes("dynamic_execute")
+        #custom_op.parameter_map["dynamic_inputs_shape_range"].s = tf.compat.as_bytes("data:[-1, 224, 224, 3], [-1, 224, 224, 3], [-1, 224, 224, 1], [-1, 224, 224, 3], [-1, 224, 224, 3], [-1, 224, 224, 3], [-1, 224, 224, 1]")
+
+
+        with tf.Session(config=npu_config_proto()) as sess:
+            saver = tf.train.Saver(tf.global_variables(), max_to_keep = 100)
+            
+            # load pretrained weights or initialize variables
+            if cfg.pretrained_ckpt_path:
+                print_log('weight loading start.', content_color = PrintColor['yellow'])
+                saver.restore(sess, cfg.pretrained_ckpt_path)
+                print_log('weight loaded.', content_color = PrintColor['yellow'])
+            else:
+                print_log('weight initialize start.', content_color = PrintColor['yellow'])
+                sess.run(init)
+                print_log('weight initialized.', content_color = PrintColor['yellow'])
+            
+            # train
+            print_log('training start.', content_color = PrintColor['yellow'])
+            for step in range(cfg.max_iter):
+                global_step = step + 1
+                
+                # train and get loss
+                d_loss, g_loss, d_log, g_log = model.train_step(sess, global_step, *next(gen))
+
+                # show loss
+                if global_step % cfg.show_loss_interval == 0 or step == 0:
+                    print_log ("step: {:>6d}   d_loss: {:>3.5f}   g_loss: {:>3.5f}".format(global_step, d_loss, g_loss))
+                
+                # write tensorboard
+                if global_step % cfg.write_log_interval == 0:
+                    write_summary(model.d_writer, model.g_writer, d_log, g_log, global_step)
+
+                # gen example
+                if global_step % cfg.gen_example_interval == 0:
+                    savedir = os.path.join(cfg.example_result_dir, train_name, 'iter-' + str(global_step).zfill(len(str(cfg.max_iter))))
+                    predict_data_list(model, sess, savedir, get_input_data())
+                    print_log ("example generated in dir {}".format(savedir), content_color = PrintColor['green'])
+
+                # save checkpoint
+                if global_step % cfg.save_ckpt_interval == 0:
+                    savedir = os.path.join(cfg.checkpoint_savedir, train_name, 'iter')
+                    if not os.path.exists(savedir):
+                        os.makedirs(savedir)
+                    save_checkpoint(sess, saver, savedir, global_step)
+                    print_log ("checkpoint saved in dir {}".format(savedir), content_color = PrintColor['green'])
+
+            print_log('training finished.', content_color = PrintColor['yellow'])
+            pb_savepath = os.path.join(cfg.checkpoint_savedir, train_name, 'final.pb')
+            save_pb(sess, pb_savepath, ['o_sk', 'o_t', 'o_b', 'o_f'])
+            print_log('pb model saved in dir {}'.format(pb_savepath), content_color = PrintColor['green'])
+
+    mox.file.copy_parallel(src_url = r'\cache\out', dst_url = r'obs:\\cann-nju-srnet\trainout')
+
+if __name__ == '__main__':
+    main()
+
diff --git a/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/utils.py b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..54e6fa7c4e44cf807d2169f76c75d46ffabfaad0
--- /dev/null
+++ b/TensorFlow/contrib/graph/SRNET_ID1089_for_TensorFlow/utils.py
@@ -0,0 +1,127 @@
+"""
+SRNet - Editing Text in the Wild
+Common utility functions and classes.
+Copyright (c) 2019 Netease Youdao Information Technology Co.,Ltd.
+Licensed under the GPL License (see LICENSE for details)
+Written by Yu Qian
+"""
+
+# 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
+from tensorflow.python.framework import graph_util
+import os
+import cv2
+from datetime import datetime
+
+PrintColor = {
+    'black': 30,
+    'red': 31,
+    'green': 32,
+    'yellow': 33,
+    'blue': 34,
+    'amaranth': 35,
+    'ultramarine': 36,
+    'white': 37
+}
+
+PrintStyle = {
+    'default': 0,
+    'highlight': 1,
+    'underline': 4,
+    'flicker': 5,
+    'inverse': 7,
+    'invisible': 8
+}
+
+def get_train_name():
+    
+    # get current time for train name
+    return datetime.now().strftime('%Y%m%d%H%M%S')
+
+def print_log(s, time_style = PrintStyle['default'], time_color = PrintColor['blue'],
+                content_style = PrintStyle['default'], content_color = PrintColor['white']):
+    
+    # colorful print s with time log
+    cur_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]
+    log = '\033[{};{}m[{}]\033[0m \033[{};{}m{}\033[0m'.format \
+        (time_style, time_color, cur_time, content_style, content_color, s)
+    print (log)
+    
+def print_nodes(graph):
+    
+    # print all nodes of the graph
+    nodes = [n.name for n in graph.as_graph_def().node]
+    for node in nodes:
+        print (node)
+
+def write_summary(d_writer, g_writer, d_log, g_log, global_step):
+
+    # write summaries for tensorboard
+    d_writer.add_summary(d_log, global_step)
+    g_writer.add_summary(g_log, global_step)
+
+def save_result(save_dir, result, name, mode):
+
+    # save output images
+    o_sk, o_t, o_b, o_f = result
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+    cv2.imwrite(os.path.join(save_dir, name + 'o_f.png'), o_f, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
+    if mode == 1:
+        cv2.imwrite(os.path.join(save_dir, name + 'o_sk.png'), o_sk, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
+        cv2.imwrite(os.path.join(save_dir, name + 'o_t.png'), o_t, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
+        cv2.imwrite(os.path.join(save_dir, name + 'o_b.png'), o_b, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
+
+def predict_data_list(model, sess, save_dir, input_data_list, mode = 1):
+
+    # predict output images and save them
+    for data in input_data_list:
+        i_t, i_s, ori_shape, data_name = data
+        result = model.predict(sess, i_t, i_s, ori_shape)
+        save_result(save_dir, result, data_name, mode = mode)
+
+def save_checkpoint(sess, saver, save_dir, global_step):
+    
+    # save tensorflow ckpt files
+    saver.save(sess, save_dir, global_step = global_step)
+
+def save_pb(sess, save_path, outputs = ['o_sk', 'o_t', 'o_b', 'o_f']):
+
+    # save tensorflow pb model
+    save_dir = os.path.split(save_path)[0]
+    if not os.path.exists(save_dir):
+        os.makedirs(save_dir)
+    constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, outputs)
+    with tf.gfile.FastGFile(save_path, mode='wb') as f:
+        f.write(constant_graph.SerializeToString())
+
+