From 10dc46754f5be0d3600edac1a411b8a36a947635 Mon Sep 17 00:00:00 2001 From: JamingTest <997116975@qq.com> Date: Tue, 12 Apr 2022 12:05:32 +0000 Subject: [PATCH 1/4] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20TNT=5FID1233=5Ffor=5FA?= =?UTF-8?q?CL?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/.keep diff --git a/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/.keep b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 00afb845144de4f9987461e70aa0361f49b8f639 Mon Sep 17 00:00:00 2001 From: JamingTest <997116975@qq.com> Date: Tue, 12 Apr 2022 12:06:11 +0000 Subject: [PATCH 2/4] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=8E=A8=E7=90=86?= =?UTF-8?q?=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/TNT_ID1233_for_ACL/README.md | 171 ++++ .../contrib/cv/TNT_ID1233_for_ACL/ckpt2pb.py | 70 ++ .../contrib/cv/TNT_ID1233_for_ACL/getAcc.py | 74 ++ .../cv/TNT_ID1233_for_ACL/modelzoo_level.txt | 2 + .../contrib/cv/TNT_ID1233_for_ACL/tobin.py | 806 ++++++++++++++++++ 5 files changed, 1123 insertions(+) create mode 100644 ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/README.md create mode 100644 ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/ckpt2pb.py create mode 100644 ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/getAcc.py create mode 100644 ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/modelzoo_level.txt create mode 100644 ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/tobin.py diff --git a/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/README.md new file mode 100644 index 000000000..27a1cdf7a --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/README.md @@ -0,0 +1,171 @@ +# 基本信息 + +**发布者（Publisher）：Huawei** + +**应用领域（Application Domain）： Image Classification** + +**版本（Version）：1.0** + +**框架（Framework）：TensorFlow 1.15.0** + +**模型格式（Model Format）：ckpt/pb/om** + +**精度（Precision）：FP32** + +**处理器（Processor）：昇腾910** + +**应用级别（Categories）：Research** + +# 概述 + +TNT（TrackletNet Tracker）是一种性能优秀的跟踪器。 + +关键技术： + +* Tracklet-based Graph Model： 将tracklet作为顶点、将两个tracklets间相似度（的减函数）作为边权的无向图，可以通过顶点聚类算法完成 “tracklet-to-trajectory” 过程； +* Multi-scale TrackletNet：输入两 tracklets，输出其相似度，最大特点是用时域1D滤波器充分利用了 tracklets 的时态信息（temporal info）； +* EG-IOU： 在做帧间detections关联时，使用 Epipolar Geometry（对极几何）对下一帧检测框做最佳预测，从而优化 IOU 算法； + +关键能力： + +* Graph Model 的设计可以充分使用时域信息、降低计算复杂度等； +* TrackletNet 作为一个统一(unified)的系统，将外观信息(appearance)和时态信息(temporal)合理地结合了起来；注意，传统的时态信息一般是 bbox 的位置、大小、运动等信息，而 TrackletNet 通过时域卷积池化等，挖掘了外观信息中蕴含的时态信息（即外观信息的时域连续性） +* EG 技术可以有效对抗相机运动带来的错误关联问题 + +参考论文： + +[https://arxiv.org/abs/1811.07258](https://gitee.com/link?target=https%3A%2F%2Farxiv.org%2Fabs%2F1811.07258) + +第三方博客地址 + +[https://blog.csdn.net/qq_42191914/article/details/103619045](https://gitee.com/link?target=https%3A%2F%2Fblog.csdn.net%2Fqq_42191914%2Farticle%2Fdetails%2F103619045) + +# 数据集准备 + +1. 模型训练使用MOT17Det数据集，数据集请用户自行获取。 + +2. TNT/General/MOT_to_UA_Detrac.m MOT_to_UA_Detrac(gt_file, seq_name, save_folder, img_size) + + **作者提供的典型参数**：MOT_to_UA_Detrac('.txt', '1', 'save_dir', [800,600]); gt_file: MOT gt txt seq_name: save name save_folder: save dir + +3. TNT/General/crop_UA_Detrac.m crop_UA_Detrac(gt_path, seq_name, img_folder, img_format, save_folder) + + **作者提供的典型参数**：crop_UA_Detrac('gt_path.mat', '1', 'the folder contains the sequence images', 'jpg', 'save_folder'); gt_path: gtInfo.mat, X,Y,W,H seq_name: name of the sequence img_folder: input image dir img_format: input image format, etc, png, jpg save_folder: cropped image dir + +4. TNT/General/create_pair create_pair(dataset_dir, save_dir, num_pair, n_fold) + + **作者提供的典型参**：create_pair('dataset_dir', 'save_dir', 300, 10) dataset_dir: cropped image dir save_dir: output dir num_pair: number of pairs for each fold n_fold: the number of folds 此步骤完成后，需打开所有pairs.txt，在文件头添加一个空行 + +**注意事项**： + +- matlab版本需为2014b及以后的版本，否则会找不到bboxOverlapRatio这个函数（该函数于2014b版本被引进，之前有源码现已无法找到） +- create_pair.m中可能会出现K、pairNum报错的现象，解决方法是将其替换为纯数字就好 + +**文件安排**： 将所有mat文件，放在一个文件夹下，放在原始数据MOT17Det的下级目录中，（为定位该位置，此处应有两个原始文件夹分别为train和test）而裁剪所得到的图片和pairs.txt仿照lfw数据集格式存放，即存放在类似MOT17Det/train/MOT17-02/img1和MOT17Det/train/MOT17-02/pairs.txt + +# 推理过程 + +* 首先根据模型ckpt文件的存储目录，运行ckpt2pb.py文件，将ckpt文件转为pb文件，冻结模型参数 + +``` +python ckpt2pb.py --ckpt_path ./path/to/ckpt --output_path ./path/to/output_path +``` + +参数解释： + +``` +--ckpt_path ckpt保存目录 +--output_path pb输出文件夹，pb名称默认frozen_model.pb +``` + + +* 数据预处理，运行pic2bin.py文件将.jpg文件转为.bin文件，开始之前请对文件开始部分的参数进行修改： + +```python + python tobin.py +``` + +参数含义 + +``` +--MAT_folder 数据预处理步骤生成的mat文件地址 +--img_folder 原始数据集地址 +--triplet_model faceNet模型文件，用于对于数据进行预处理 +--res_dir bin文件存储地址 +``` + +* 在华为云镜像服务器上将pb文件转为om文件 + +``` +atc --model=model/frozen_model.pb --framework=3 --output=res --soc_version=Ascend310 --input_shape="Placeholder:1,1,64,1;Placeholder_1:1,1,64,1;Placeholder_2:1,1,64,1;Placeholder_3:1,1,64,1;Placeholder_4:1,512,64,1;Placeholder_5:1,1,64,2;Placeholder_6:1,512,64,2;Placeholder_8:1" --log=info --out_nodes="add_62:0" --debug_dir=$HOME/module/out/debug_info +``` + + +* 应用msame工具运行模型推理 + +``` + /root/AscendProjects/tools/msame/out/msame --model /home/HwHiAiUser/zjm/res.om --input /home/HwHiAiUser/zjm/bin/0/batch_X_x.bin,/home/HwHiAiUser/zjm/bin/0/batch_X_y.bin,/home/HwHiAiUser/zjm/bin/0/batch_X_w.bin,/home/HwHiAiUser/zjm/bin/0/batch_X_h.bin,/home/HwHiAiUser/zjm/bin/0/batch_X_a.bin,/home/HwHiAiUser/zjm/bin/0/batch_mask_1.bin,/home/HwHiAiUser/zjm/bin/0/batch_mask_2.bin,/home/HwHiAiUser/zjm/bin/0/keep_prob.bin --output /home/HwHiAiUser/zjm/out/output2/0 --outfmt TXT +``` + +注意根据上下文，此处的bin数据应有多组，最后根据总体平均acc判定模型效果，此处文件组织为bin/group_num/xxx.bin,请根据自身情况自行组织生成的bin文件及更换参数。 + +* 得到最终结果，如果想比对准确率，其中与预测结果做对比的是之前生成的batch_y.bin文件，可以运行getAcc.py + +```python + python getAcc.py --predict_path ./predict --bin_path ./bin_path +``` + +参数解释： + +``` +--predict_path msame输出结果总目录，默认为'./predict' +--bin_path bin文件地址,默认为'./bin' +``` + +典型目录如下 + +``` +project_path +|-predict +| |-0 +| | |-res_output_0.txt +| |-1 +| | |-res_output_0.txt +... +|-bin +| |-0 +| | |-batch_y.bin +... +``` + + + +# 推理模型下载 + +ckpt模型： +[百度网盘获取链接](https://pan.baidu.com/s/1lzMpTGf910bhWkqUpbAoJw) +提取码：zeu4 + +obs地址：obs://cann-id1233/inference/ckpt/ + +pb模型： +[百度网盘获取链接](https://pan.baidu.com/s/1SXy_iyVQYNAtPZp82yqA0A) +提取码：ub2g + +obs地址：obs://cann-id1233/inference/pb/ + +om模型： +[百度网盘获取链接](https://pan.baidu.com/s/1eUKpTNeHXcpfRYgAX-liZw) +提取码：2iwo + +obs地址：obs://cann-id1233/inference/om/ + +# 推理精度 + +精度结果： + +![](.\img\acc.png) + +| 迁移模型 | Acc on val | Seconds per Image | +| :------- | ---------- | ----------------- | +| TNT | 0.95625 | 1.03s | diff --git a/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/ckpt2pb.py b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/ckpt2pb.py new file mode 100644 index 000000000..d9b38c7db --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/ckpt2pb.py @@ -0,0 +1,70 @@ +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================ +# Copyright 2021 Huawei Technologies Co., Ltd +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import tensorflow as tf +from tensorflow.python.framework import graph_util +import argparse +import sys + +def main(args): + # 调用freeze_graph将ckpt转为pb + freeze_graph(args.ckpt_path, args.output_path) + +def parse_arguments(argv): + parser = argparse.ArgumentParser() + # 输入ckpt模型路径 + parser.add_argument('--ckpt_path', type=str, + help='file where to load ckpt.', default="/home/ma-user/modelarts/inputs/data_url_0/model.ckpt") + # 输出pb模型的路径 + parser.add_argument('--output_path', type=str, + help='file where to output pb.', default="/home/ma-user/modelarts/outputs/train_url_0/frozen_model.pb") + return parser.parse_args(argv) + +def freeze_graph(input_checkpoint, output_graph): + ''' + :param input_checkpoint: + :param output_graph: the path to store pb file + :return: + ''' + + # 指定输出的节点名称,该节点名称必须是原模型中存在的节点 + output_node_names = "add_62" + saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True) + + with tf.Session() as sess: + saver.restore(sess, input_checkpoint) # 恢复图并得到数据 + output_graph_def = graph_util.convert_variables_to_constants( # 模型持久化，将变量值固定 + sess=sess, + input_graph_def=sess.graph_def, # 等于:sess.graph_def + output_node_names=output_node_names.split(",")) # 如果有多个输出节点，以逗号隔开 + + with tf.gfile.GFile(output_graph, "wb") as f: # 保存模型 + f.write(output_graph_def.SerializeToString()) # 序列化输出 + print("%d ops in the final graph." % len(output_graph_def.node)) # 得到当前图有几个操作节点 + +if __name__ == "__main__" : + main(parse_arguments(sys.argv[1:])) diff --git a/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/getAcc.py b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/getAcc.py new file mode 100644 index 000000000..c002c535d --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/getAcc.py @@ -0,0 +1,74 @@ +# 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.path +import argparse +import numpy as np +max_length = 64 + +def main(args): + acc = [] + for i in range(10): + file_path = args.bin_path+"/"+str(i)+"/batch_y.bin" + predict_filepath = args.predict_path+"/"+str(i)+"/res_output_0.txt" + batch_y = np.fromfile(file_path,dtype=np.float32) + y_pred = read_predict(predict_filepath) + batch_y = batch_y.reshape((-1, 2)) + wrong_idx = [] + for mm in range(len(y_pred)): + if (y_pred[mm, 0] > y_pred[mm, 1] and batch_y[mm, 0] == 0) or ( + y_pred[mm, 0] <= y_pred[mm, 1] and batch_y[mm, 0] == 1): + wrong_idx.append(mm) + + train_accuracy = (len(y_pred) - len(wrong_idx)) / len(y_pred) + acc.append(train_accuracy) + print(train_accuracy) + acc = np.array(acc) + print('accuracy : {}'.format(np.mean(acc))) + + +def read_predict(predict_filename): + predicts = [] + with open(predict_filename, 'r') as f: + for line in f.readlines(): + predict = line.strip().split() + predicts.append(predict) + return np.array(predicts) + + +def parse_arguments(argv): + parser = argparse.ArgumentParser() + # 输入ckpt模型路径 + parser.add_argument('--predict_path', type=str, + help='the path of inference result', default="./predict") + # 输出pb模型的路径 + parser.add_argument('--bin_path', type=str, + help='the path of batch_y.bin.', default="./bin") + return parser.parse_args(argv) + +if __name__ == '__main__': + main(parse_arguments(sys.argv[1:])) \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/modelzoo_level.txt b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/modelzoo_level.txt new file mode 100644 index 000000000..39175166a --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/modelzoo_level.txt @@ -0,0 +1,2 @@ +ModelConvert:OK +QuantStatus:OK \ No newline at end of file diff --git a/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/tobin.py b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/tobin.py new file mode 100644 index 000000000..0007111d3 --- /dev/null +++ b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/tobin.py @@ -0,0 +1,806 @@ +# 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 __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import re +import tensorflow as tf +import numpy as np +import argparse +import facenet +from npu_bridge.npu_init import * +from skimage.transform import resize +import os +import sys +from tensorflow.python.ops import data_flow_ops +from sklearn import metrics +from scipy.optimize import brentq +from scipy import interpolate +from scipy.interpolate import interp1d +from scipy.io import loadmat +from scipy import spatial +import matplotlib.pyplot as plt +import random +import math +import scipy +import shutil + +MAT_folder = '/home/ma-user/modelarts/inputs/data_url_0/gt_mat' +img_folder = '/home/ma-user/modelarts/inputs/data_url_0/MOT17Det/train' +temp_folder = '/home/ma-user/modelarts/outputs/train_url_0/temp' +triplet_model = '/home/ma-user/modelarts/inputs/data_url_0/model/20211206-213038/' +res_dir = '/home/ma-user/modelarts/outputs/train_url_0/bin/' + +bbox_size = 182 +max_length = 64 +feature_size = 4 + 512 +batch_size = 32 +num_classes = 2 +margin = 0.15 +sample_prob = [600, 105, 837, 525, 654, 900, 750] +lr = 1e-3 + + +def main(): + total_batch_x, total_batch_y = generate_data(feature_size, max_length, batch_size * 10, MAT_folder, + img_folder) + total_batch_x = interp_batch(total_batch_x) + + # delete temp folder + shutil.rmtree(temp_folder) + + remove_idx = [] + for k in range(len(total_batch_x)): + if np.sum(total_batch_x[k, 0, :, 1]) == 0: + remove_idx.append(k) + total_batch_x = np.delete(total_batch_x, np.array(remove_idx), axis=0) + total_batch_y = np.delete(total_batch_y, np.array(remove_idx), axis=0) + print(len(total_batch_y)) + + total_batch_x[:, 4:, :, 0] = 10 * total_batch_x[:, 4:, :, 0] + temp_X = np.copy(total_batch_x) + temp_Y = np.copy(total_batch_y) + idx = np.arange(total_batch_x.shape[0]) + np.random.shuffle(idx) + for k in range(len(idx)): + total_batch_x[idx[k], :, :, :] = temp_X[k, :, :, :] + total_batch_y[idx[k], :] = temp_Y[k, :] + num_batch = int(np.ceil(len(total_batch_y) / batch_size)) + + # shuffle 4 times + acc = [] + for kk in range(num_batch): + temp_batch_size = batch_size + if kk == num_batch - 1: + temp_batch_size = len(total_batch_y) - batch_size * (num_batch - 1) + batch_x = total_batch_x[kk * batch_size:kk * batch_size + temp_batch_size, :, :, :] + batch_y = total_batch_y[kk * batch_size:kk * batch_size + temp_batch_size, :] + + x = np.zeros((temp_batch_size, 1, max_length, 1)) + y = np.zeros((temp_batch_size, 1, max_length, 1)) + w = np.zeros((temp_batch_size, 1, max_length, 1)) + h = np.zeros((temp_batch_size, 1, max_length, 1)) + ap = np.zeros((temp_batch_size, feature_size - 4, max_length, 1)) + mask_1 = np.zeros((temp_batch_size, 1, max_length, 2)) + mask_2 = np.zeros((temp_batch_size, feature_size - 4, max_length, 2)) + + x[:, 0, :, 0] = batch_x[:, 0, :, 0] + y[:, 0, :, 0] = batch_x[:, 1, :, 0] + w[:, 0, :, 0] = batch_x[:, 2, :, 0] + h[:, 0, :, 0] = batch_x[:, 3, :, 0] + + ap[:, :, :, 0] = batch_x[:, 4:, :, 0] + + mask_1[:, 0, :, :] = batch_x[:, 0, :, 1:] + mask_2[:, :, :, :] = batch_x[:, 4:, :, 1:] + + temp_path = res_dir + '{}/'.format(str(kk)) + if not os.path.exists(temp_path): + os.makedirs(temp_path) + x.tofile(temp_path + 'x.bin') + y.tofile(temp_path + 'y.bin') + w.tofile(temp_path + 'w.bin') + h.tofile(temp_path + 'h.bin') + ap.tofile(temp_path + 'ap.bin') + mask_1.tofile(temp_path + 'mask_1.bin') + mask_2.tofile(temp_path + 'mask_2.bin') + batch_y.tofile(temp_path + 'batch_y.bin') + print("+++++++++++++++++++++++++++++finished++++++++++++++++++++++++++++++") + + + + +# In[3]: +def draw_traj(x, mask_1): + fig, ax = plt.subplots() + ax.plot(x, color=[0.5, 0.5, 0.5], marker='o', linestyle='None') + t1 = np.where(mask_1[:, 0] == 1)[0] + t2 = np.where(mask_1[:, 1] == 1)[0] + ax.plot(t1, x[mask_1[:, 0] == 1], color=[0.2, 0.6, 0.86], marker='o', linestyle='None') + ax.plot(t2, x[mask_1[:, 1] == 1], color=[0.18, 0.8, 0.44], marker='o', linestyle='None') + ax.axhline(y=0, color='k') + ax.axvline(x=0, color='k') + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + ax.spines['bottom'].set_visible(False) + ax.spines['left'].set_visible(False) + plt.xlim(0, 64) + y_range = np.max(x) - np.min(x) + plt.ylim(np.min(x) - y_range / 50, np.max(x) + y_range / 20) + plt.show() + + +def draw_fea_map(x): + fig, ax = plt.subplots() + ax.imshow(np.power(np.transpose(x), 0.2), cmap='gray') + ax.set_aspect(0.1) + plt.axis('off') + # plt.xticks(range(8)) + plt.show() + + +def interp_batch(total_batch_x): + interp_batch_x = total_batch_x.copy() + N_batch = total_batch_x.shape[0] + for n in range(N_batch): + temp_idx = np.where(total_batch_x[n, 0, :, 1] == 1)[0] + t1 = int(temp_idx[-1]) + temp_idx = np.where(total_batch_x[n, 0, :, 2] == 1)[0] + t2 = int(temp_idx[0]) + if t2 - t1 <= 1: + continue + interp_t = np.array(range(t1 + 1, t2)) + for k in range(total_batch_x.shape[1]): + # temp_std = np.std(total_batch_x[n,k,total_batch_x[n,k,:,0]!=0,0]) + temp_std1 = np.std(total_batch_x[n, k, total_batch_x[n, 0, :, 1] != 0, 0]) + temp_std2 = np.std(total_batch_x[n, k, total_batch_x[n, 0, :, 2] != 0, 0]) + x_p = [t1, t2] + f_p = [total_batch_x[n, k, t1, 0], total_batch_x[n, k, t2, 0]] + # ************************************* + # interp_batch_x[n,k,t1+1:t2,0] = np.interp(interp_t,x_p,f_p)+np.random.normal(0, temp_std, t2-t1-1) + # ************************************* + interp_batch_x[n, k, t1 + 1:t2, 0] = np.interp(interp_t, x_p, f_p) + np.random.normal(0, ( + temp_std1 + temp_std2) * 0.5, t2 - t1 - 1) + return interp_batch_x + + +def num_str(num, length): + cnt = 1 + temp = num + while 1: + temp = int(temp / 10) + if temp > 0: + cnt = cnt + 1 + else: + break + num_len = cnt + for n in range(length - num_len): + if n == 0: + out_str = '0' + else: + out_str = out_str + '0' + if length - num_len > 0: + return out_str + str(num) + else: + return str(num) + + +def file_name(num, length): + cnt = 1 + temp = num + while 1: + temp = int(temp / 10) + if temp > 0: + cnt = cnt + 1 + else: + break + num_len = cnt + for n in range(length - num_len): + if n == 0: + out_str = '0' + else: + out_str = out_str + '0' + if length - num_len > 0: + return out_str + str(num) + else: + return str(num) + + +def evaluate(sess, iterator, image_paths_placeholder, labels_placeholder, phase_train_placeholder, + batch_size_placeholder, control_placeholder, + embeddings, labels, image_paths, batch_size, distance_metric): + # Run forward pass to calculate embeddings + # print('Runnning forward pass on LFW images') + + use_flipped_images = False + use_fixed_image_standardization = False + use_random_rotate = False + use_radnom_crop = False + # Enqueue one epoch of image paths and labels + nrof_embeddings = len(image_paths) # nrof_pairs * nrof_images_per_pair + nrof_flips = 2 if use_flipped_images else 1 + nrof_images = nrof_embeddings * nrof_flips + labels_array = np.expand_dims(np.arange(0, nrof_images), 1) + image_paths_array = np.expand_dims(np.repeat(np.array(image_paths), nrof_flips), 1) + control_array = np.zeros_like(labels_array, np.int32) + + if use_fixed_image_standardization: + control_array += np.ones_like(labels_array) * facenet.FIXED_STANDARDIZATION + if use_flipped_images: + # Flip every second image + control_array += (labels_array % 2) * facenet.FLIP + if use_random_rotate: + control_array += facenet.RANDOM_ROTATE + if use_radnom_crop: + control_array += facenet.RANDOM_CROP + + sess.run(iterator.initializer, {batch_size_placeholder: batch_size, image_paths_placeholder: image_paths_array, + labels_placeholder: labels_array, control_placeholder: control_array}) + + embedding_size = int(embeddings.get_shape()[1]) + assert nrof_images % batch_size == 0, 'The number of LFW images must be an integer multiple of the LFW batch size' + nrof_batches = nrof_images // batch_size + emb_array = np.zeros((nrof_images, embedding_size)) + lab_array = np.zeros((nrof_images,)) + for i in range(nrof_batches): + emb, lab = sess.run([embeddings, labels], feed_dict={phase_train_placeholder: False}) + lab_array[lab] = lab + emb_array[lab, :] = emb + if i % 10 == 9: + print('.', end='') + sys.stdout.flush() + # import pdb; pdb.set_trace() + # np.savetxt("emb_array.csv", emb_array, delimiter=",") + return emb_array + + +def split_track(X_2d, Y_2d, W_2d, H_2d, V_2d, img_size, obj_id, noise_scale, connect_thresh): + err_flag = 0 + # 取出指定轨迹的所有w的值 + part_W_mat = W_2d[:, obj_id] + # 找到所有w不为0的轨迹，即该物体出现的帧号 + non_zero_idx = np.where(part_W_mat > 0)[0] + # 确保语义正确 + if len(non_zero_idx) <= 1 or np.max(non_zero_idx) - np.min(non_zero_idx) + 1 != len(non_zero_idx): + err_flag = 1 + return [], [], err_flag + + # 该轨迹出现的frame_id和结束的frame_id + st_fr = np.min(non_zero_idx) + end_fr = np.max(non_zero_idx) + + bbox_tracklet = [] + + # + v_flag = 1 + rand_num = random.uniform(0.0, 1.0) + if rand_num < 0.5 or len(V_2d) == 0: # 0.5 + v_flag = 0 + + # v_flag = 0 + for k in range(st_fr, end_fr + 1): + # 生成四个小于noise_scale的随机数， + rand_num = np.zeros((1, 4)) + for kk in range(4): + while 1: + rand_num[0, kk] = np.random.normal(0, 0.05, size=1)[0] + if abs(rand_num[0, kk]) < noise_scale: + break + + # k frame的id + # 不要超过图片边界 + x0 = max(0, X_2d[k, obj_id]) + x1 = min(img_size[0] - 1, X_2d[k, obj_id] + W_2d[k, obj_id]) + + y0 = max(0, Y_2d[k, obj_id]) + y1 = min(img_size[1] - 1, Y_2d[k, obj_id] + H_2d[k, obj_id]) + + w0 = max(1, x1 - x0) + h0 = max(1, y1 - y0) + + xmin = max(0, x0 + rand_num[0, 0] * w0) + xmin = min(img_size[0] - 2, xmin) + + ymin = max(0, y0 + rand_num[0, 1] * h0) + ymin = min(img_size[1] - 2, ymin) + + ww = max(1, w0 + rand_num[0, 2] * w0) + hh = max(1, h0 + rand_num[0, 3] * h0) + + xmax = min(xmin + ww, img_size[0] - 1) + ymax = min(ymin + hh, img_size[1] - 1) + + ww = max(1, xmax - xmin + 1) + hh = max(1, ymax - ymin + 1) + # print(rand_num) + # 随机从该帧的该轨迹的图片中裁剪一部分 + temp_bbox = [int(k), int(xmin), int(ymin), int(xmax), int(ymax), float(V_2d[k, obj_id])] + + if k == st_fr: + bbox = [] + + # 如果一个bbox，除去重合的剩余部分的比值，小于0.5 + # 说明此刻该物体的轨迹不值得被研究（样本像素点太少）， + # 即认为它的轨迹到此中断了，只将之前的一段添加到它的轨迹框图列表中。 + if temp_bbox[-1] < 0.5: + if len(bbox) == 0: + continue + else: + bbox_tracklet.append(np.array(bbox)) + bbox = [] + continue + + rand_num = random.uniform(0.0, 1.0) + if v_flag == 0: + temp_connect = connect_thresh + else: + temp_connect = connect_thresh * math.sqrt(V_2d[k, obj_id]) + + # 如果达到链接片段的标准，则将当前帧中的bbox添加到当前的轨迹段中 + if rand_num < temp_connect: + bbox.append(temp_bbox) + # 如果已经分析完整个视频帧，则将当前轨迹段添加到轨迹段集合中 + if k == end_fr and len(bbox) != 0: + bbox_tracklet.append(np.array(bbox)) + # bbox_num.append(len(bbox)) + else: + # 当前轨迹段，到此结束，将当前轨迹段入档，并重新维护一个新的轨迹段，并将当前bbox添加到新的轨迹段上 + if len(bbox) != 0: + bbox_tracklet.append(np.array(bbox)) + # bbox_num.append(len(bbox)) + bbox = [] + bbox.append(temp_bbox) + + ''' + if k==st_fr: + bbox = [] + bbox.append(temp_bbox) + else: + rand_num = random.uniform(0.0,1.0) + if v_flag==0: + temp_connect = connect_thresh + else: + temp_connect = connect_thresh*math.sqrt(V_2d[k,obj_id]) + + if rand_num t_interval[k2, 0]: + t_dist = t_interval[k2, 0] - t_interval[k1, 1] + cand_pairs.append([k1, k2, t_dist]) + if len(cand_pairs) == 0: + continue + + cand_pairs = np.array(cand_pairs) + rand_num = np.random.rand(1)[0] + # print(rand_num) + if rand_num < 0.7: + select_p = np.exp(-np.power(cand_pairs[:, 2], 2) / 100) + select_p = select_p / sum(select_p) + # print(select_p) + pair_idx = np.random.choice(len(cand_pairs), size=1, p=select_p)[0] + else: + pair_idx = np.random.randint(len(cand_pairs), size=1)[0] + select_pair = cand_pairs[pair_idx] + select_pair = select_pair.astype(int) + + abs_fr_t1 = int(t_interval[select_pair[0], 0]) + abs_fr_t2 = int(t_interval[select_pair[0], 1]) + abs_fr_t3 = int(t_interval[select_pair[1], 0]) + abs_fr_t4 = int(min(abs_fr_t1 + max_length - 1, t_interval[select_pair[1], 1])) + + t1 = 0 + t2 = abs_fr_t2 - abs_fr_t1 + t3 = abs_fr_t3 - abs_fr_t1 + t4 = abs_fr_t4 - abs_fr_t1 + + # mask + X[n, :, t1:t2 + 1, 1] = 1 + X[n, :, t3:t4 + 1, 2] = 1 + + # X + X[n, 0, t1:t2 + 1, 0] = 0.5 * (bbox_tracklet[select_pair[0]][:, 1] + bbox_tracklet[select_pair[0]][:, 3]) / \ + img_size[0] + X[n, 0, t3:t4 + 1, 0] = 0.5 * ( + bbox_tracklet[select_pair[1]][0:t4 - t3 + 1, 1] + bbox_tracklet[select_pair[1]][0:t4 - t3 + 1, + 3]) / img_size[0] + + # Y + X[n, 1, t1:t2 + 1, 0] = 0.5 * (bbox_tracklet[select_pair[0]][:, 2] + bbox_tracklet[select_pair[0]][:, 4]) / \ + img_size[1] + X[n, 1, t3:t4 + 1, 0] = 0.5 * ( + bbox_tracklet[select_pair[1]][0:t4 - t3 + 1, 2] + bbox_tracklet[select_pair[1]][0:t4 - t3 + 1, + 4]) / img_size[1] + + # W + X[n, 2, t1:t2 + 1, 0] = (bbox_tracklet[select_pair[0]][:, 3] - bbox_tracklet[select_pair[0]][:, 1]) / \ + img_size[0] + X[n, 2, t3:t4 + 1, 0] = (bbox_tracklet[select_pair[1]][0:t4 - t3 + 1, 3] - bbox_tracklet[select_pair[1]][ + 0:t4 - t3 + 1, 1]) / img_size[0] + + # H + X[n, 3, t1:t2 + 1, 0] = (bbox_tracklet[select_pair[0]][:, 4] - bbox_tracklet[select_pair[0]][:, 2]) / \ + img_size[1] + X[n, 3, t3:t4 + 1, 0] = (bbox_tracklet[select_pair[1]][0:t4 - t3 + 1, 4] - bbox_tracklet[select_pair[1]][ + 0:t4 - t3 + 1, 2]) / img_size[1] + ''' + plt.plot(X[n,0,:,0], 'ro') + plt.show() + plt.plot(X[n,1,:,0], 'ro') + plt.show() + plt.plot(X[n,2,:,0], 'ro') + plt.show() + plt.plot(X[n,3,:,0], 'ro') + plt.show() + plt.plot(X[n,0,:,1], 'ro') + plt.show() + plt.plot(X[n,0,:,2], 'ro') + plt.show() + import pdb; pdb.set_trace() + ''' + + # save all bbox + temp_crop_bbox = np.concatenate( + (bbox_tracklet[select_pair[0]], bbox_tracklet[select_pair[1]][0:t4 - t3 + 1, :]), axis=0) + temp_crop_bbox = temp_crop_bbox.astype(int) + crop_bbox.append(temp_crop_bbox) + break + + # import pdb; pdb.set_trace() + + # negative + for n in range(int(batch_size / 2), batch_size): + fr_num = Mat_files[n]['gtInfo'][0][0][0].shape[0] + id_num = Mat_files[n]['gtInfo'][0][0][0].shape[1] + Y[n, 1] = 1 + + X_2d = Mat_files[n]['gtInfo'][0][0][0] + Y_2d = Mat_files[n]['gtInfo'][0][0][1] + W_2d = Mat_files[n]['gtInfo'][0][0][2] + H_2d = Mat_files[n]['gtInfo'][0][0][3] + + ######################################### + X_2d = X_2d - margin * W_2d + Y_2d = Y_2d - margin * H_2d + W_2d = (1 + 2 * margin) * W_2d + H_2d = (1 + 2 * margin) * H_2d + ########################################## + + if len(Mat_files[n]['gtInfo'][0][0]) <= 4: + V_2d = [] + else: + V_2d = Mat_files[n]['gtInfo'][0][0][4] + if len(Mat_files[n]['gtInfo'][0][0]) == 6: + img_size = Mat_files[n]['gtInfo'][0][0][5][0] + temp_size = re.findall(r"\d+\.?\d*", img_size) + img_size = [int(temp_size[0]), int(temp_size[1])] + else: + img_size = [1920, 1080] + # V_2d = [] + # img_size = [1920,1080] + + # check candidate obj pairs + # pair_mat = np.zeros((id_num,id_num)) + cand_idx_pairs = [] + for n1 in range(id_num - 1): + for n2 in range(n1 + 1, id_num): + cand_fr1 = np.where(W_2d[:, n1] > 0)[0] + cand_fr2 = np.where(W_2d[:, n2] > 0)[0] + if max(cand_fr1[0], cand_fr2[0]) < min(cand_fr1[-1], cand_fr2[-1]): + cand_idx_pairs.append([n1, n2]) + # pair_mat[n1,n2] = 1 + + # cand_pairs = np.nonzero(pair_mat) + while 1: + # + if len(cand_idx_pairs) == 0: + import pdb; + pdb.set_trace() + pair_idx = np.random.randint(len(cand_idx_pairs), size=1)[0] + obj_id1 = cand_idx_pairs[pair_idx][0] + obj_id2 = cand_idx_pairs[pair_idx][1] + # import pdb; pdb.set_trace() + + part_W_mat1 = W_2d[:, obj_id1] + non_zero_idx1 = np.where(part_W_mat1 > 0)[0] + part_W_mat2 = W_2d[:, obj_id2] + non_zero_idx2 = np.where(part_W_mat2 > 0)[0] + if len(non_zero_idx1) == 0 or len(non_zero_idx2) == 0 or \ + max(non_zero_idx1) + max_length < min(non_zero_idx2) or min(non_zero_idx1) > max(non_zero_idx2): + continue + + bbox_tracklet1, t_interval1, err_flag = split_track(X_2d, Y_2d, W_2d, H_2d, V_2d, img_size, obj_id1, + noise_scale, connect_thresh) + if err_flag == 1: + continue + bbox_tracklet2, t_interval2, err_flag = split_track(X_2d, Y_2d, W_2d, H_2d, V_2d, img_size, obj_id2, + noise_scale, connect_thresh) + if err_flag == 1: + continue + + cand_pairs = [] + if len(bbox_tracklet1) <= 1 or len(bbox_tracklet2) <= 1: + continue + for k1 in range(len(bbox_tracklet1)): + for k2 in range(len(bbox_tracklet2)): + if t_interval1[k1, 0] + max_length > t_interval2[k2, 0] and t_interval1[k1, 1] < t_interval2[k2, 0]: + t_dist = t_interval2[k2, 0] - t_interval1[k1, 1] + cand_pairs.append([k1, k2, t_dist]) + if len(cand_pairs) == 0: + continue + + cand_pairs = np.array(cand_pairs) + rand_num = np.random.rand(1)[0] + # print(rand_num) + if rand_num < 0.7: + select_p = np.exp(-np.power(cand_pairs[:, 2], 2) / 100) + select_p = select_p / sum(select_p) + # print(select_p) + pair_idx = np.random.choice(len(cand_pairs), size=1, p=select_p)[0] + else: + pair_idx = np.random.randint(len(cand_pairs), size=1)[0] + select_pair = cand_pairs[pair_idx] + select_pair = select_pair.astype(int) + + abs_fr_t1 = int(t_interval1[select_pair[0], 0]) + abs_fr_t2 = int(t_interval1[select_pair[0], 1]) + abs_fr_t3 = int(t_interval2[select_pair[1], 0]) + abs_fr_t4 = int(min(abs_fr_t1 + max_length - 1, t_interval2[select_pair[1], 1])) + + t1 = 0 + t2 = abs_fr_t2 - abs_fr_t1 + t3 = abs_fr_t3 - abs_fr_t1 + t4 = abs_fr_t4 - abs_fr_t1 + + # mask + X[n, :, t1:t2 + 1, 1] = 1 + X[n, :, t3:t4 + 1, 2] = 1 + # X[n,4:,t1:t2+1,1] = bbox_tracklet1[select_pair[0]][:,5] + # X[n,4:,t3:t4+1,2] = bbox_tracklet2[select_pair[1]][0:t4-t3+1,5] + + # X + X[n, 0, t1:t2 + 1, 0] = 0.5 * ( + bbox_tracklet1[select_pair[0]][:, 1] + bbox_tracklet1[select_pair[0]][:, 3]) / img_size[0] + X[n, 0, t3:t4 + 1, 0] = 0.5 * ( + bbox_tracklet2[select_pair[1]][0:t4 - t3 + 1, 1] + bbox_tracklet2[select_pair[1]][0:t4 - t3 + 1, + 3]) / img_size[0] + + # Y + X[n, 1, t1:t2 + 1, 0] = 0.5 * ( + bbox_tracklet1[select_pair[0]][:, 2] + bbox_tracklet1[select_pair[0]][:, 4]) / img_size[1] + X[n, 1, t3:t4 + 1, 0] = 0.5 * ( + bbox_tracklet2[select_pair[1]][0:t4 - t3 + 1, 2] + bbox_tracklet2[select_pair[1]][0:t4 - t3 + 1, + 4]) / img_size[1] + + # W + X[n, 2, t1:t2 + 1, 0] = (bbox_tracklet1[select_pair[0]][:, 3] - bbox_tracklet1[select_pair[0]][:, 1]) / \ + img_size[0] + X[n, 2, t3:t4 + 1, 0] = (bbox_tracklet2[select_pair[1]][0:t4 - t3 + 1, 3] - bbox_tracklet2[select_pair[1]][ + 0:t4 - t3 + 1, 1]) / img_size[0] + + # H + X[n, 3, t1:t2 + 1, 0] = (bbox_tracklet1[select_pair[0]][:, 4] - bbox_tracklet1[select_pair[0]][:, 2]) / \ + img_size[1] + X[n, 3, t3:t4 + 1, 0] = (bbox_tracklet2[select_pair[1]][0:t4 - t3 + 1, 4] - bbox_tracklet2[select_pair[1]][ + 0:t4 - t3 + 1, 2]) / img_size[1] + + # save all bbox + temp_crop_bbox = np.concatenate( + (bbox_tracklet1[select_pair[0]], bbox_tracklet2[select_pair[1]][0:t4 - t3 + 1, :]), axis=0) + temp_crop_bbox = temp_crop_bbox.astype(int) + crop_bbox.append(temp_crop_bbox) + break + + # crop data to a temp folder + if not os.path.exists(temp_folder): + os.makedirs(temp_folder) + all_paths = [] + for n in range(batch_size): + temp_all_path = [] + seq_name = Mat_paths[choose_idx[n]][:-4] + img_path = img_folder + '/' + seq_name + '/img1/' + track_name = file_name(n + 1, 4) + save_path = temp_folder + '/' + track_name + if not os.path.exists(save_path): + os.makedirs(save_path) + + for k in range(len(crop_bbox[n])): + fr_id = crop_bbox[n][k, 0] + 1 + temp_img_path = img_path + file_name(fr_id, 6) + '.jpg' + img = plt.imread(temp_img_path) + bbox_img = img[crop_bbox[n][k, 2]:crop_bbox[n][k, 4], crop_bbox[n][k, 1]:crop_bbox[n][k, 3], :] + # import pdb; pdb.set_trace() + # bbox_img = scipy.misc.imresize(bbox_img, size=(bbox_size,bbox_size)) + bbox_img = resize(bbox_img, output_shape=(bbox_size, bbox_size)) + # bbox_img = np.array(Image.fromarray(bbox_img).astype(int)).resize((bbox_size,bbox_size)) + bbox_img_path = save_path + '/' + file_name(k, 4) + '.png' + temp_all_path.append(bbox_img_path) + plt.imsave(bbox_img_path, bbox_img) + all_paths.append(temp_all_path) + # import pdb; pdb.set_trace() + + f_image_size = 160 + distance_metric = 0 + with tf.Graph().as_default(): + # config = tf.ConfigProto() + # custom_op = config.graph_options.rewrite_options.custom_optimizers.add() + # custom_op.name = "NpuOptimizer" + # config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF + with tf.Session() as sess: + # import pdb; pdb.set_trace() + image_paths_placeholder = tf.placeholder(tf.string, shape=(None, 1), name='image_paths') + labels_placeholder = tf.placeholder(tf.int32, shape=(None, 1), name='labels') + batch_size_placeholder = tf.placeholder(tf.int64, name='batch_size') + control_placeholder = tf.placeholder(tf.int32, shape=(None, 1), name='control') + phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train') + + image_size = (f_image_size, f_image_size) + dataset = tf.data.Dataset.from_tensor_slices( + (image_paths_placeholder, labels_placeholder, control_placeholder)) + dataset = dataset.map( + lambda filenames, label, control: facenet.parser_data(filenames, label, control, image_size)) + dataset = dataset.unbatch() + dataset = dataset.batch(batch_size=batch_size_placeholder, drop_remainder=True) + iterator = tf.data.make_initializable_iterator(dataset) + image_batch, label_batch = iterator.get_next() + + # Load the model + input_map = {'image_batch': image_batch, 'label_batch': label_batch, 'phase_train': phase_train_placeholder} + facenet.load_model(triplet_model, input_map=input_map) + + # Get output tensor + embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0") + # + # coord = tf.train.Coordinator() + # tf.train.start_queue_runners(coord=coord, sess=sess) + + for n in range(len(all_paths)): + # print(n) + lfw_batch_size = len(all_paths[n]) + emb_array = evaluate(sess, iterator, image_paths_placeholder, labels_placeholder, + phase_train_placeholder, + batch_size_placeholder, control_placeholder, embeddings, label_batch, all_paths[n], + lfw_batch_size, distance_metric) + + if X[n, 4:, X[n, 0, :, 1] + X[n, 0, :, 2] > 0.5, 0].shape[0] != emb_array.shape[0]: + aa = 0 + import pdb; + pdb.set_trace() + + X[n, 4:, X[n, 0, :, 1] + X[n, 0, :, 2] > 0.5, 0] = emb_array + + # import pdb; pdb.set_trace() + return X, Y + + +# In[4]: + + +if __name__ == '__main__': + main() \ No newline at end of file -- Gitee From dddc9aa7fb81baa1575321f2f78311d7a732cb80 Mon Sep 17 00:00:00 2001 From: JamingTest <997116975@qq.com> Date: Tue, 12 Apr 2022 12:06:17 +0000 Subject: [PATCH 3/4] =?UTF-8?q?=E6=96=B0=E5=BB=BA=20img?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/img/.keep | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/img/.keep diff --git a/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/img/.keep b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/img/.keep new file mode 100644 index 000000000..e69de29bb -- Gitee From 36c7d2d3a5d25cfbb99e0569cdab44f0780c8bd2 Mon Sep 17 00:00:00 2001 From: JamingTest <997116975@qq.com> Date: Tue, 12 Apr 2022 12:06:43 +0000 Subject: [PATCH 4/4] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E6=8E=A8=E7=90=86?= =?UTF-8?q?=E7=B2=BE=E5=BA=A6=E5=9B=BE=E7=89=87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../contrib/cv/TNT_ID1233_for_ACL/img/acc.png | Bin 0 -> 21580 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/img/acc.png diff --git a/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/img/acc.png b/ACL_TensorFlow/contrib/cv/TNT_ID1233_for_ACL/img/acc.png new file mode 100644 index 0000000000000000000000000000000000000000..4972a252693bfcb72f03ea494a87a1a751c153aa GIT binary patch literal 21580 zcmeFZbyQp3o;O~3T1qKUptzObP@uTOQwUJJxI=N*;FcQ2p_EcQ6ew=RB}j3KrC2B~ z0fGk)n!Mqed*7LR=b5=P_qTp)y?@-SMNY`c%HC(6eZKo+Cy_5SlnC$B+`n<-2BGqE zp!SU$w{PFLaZ~c%E!;aqC;geY4Zg>7BM|PtuIs;>N!&!VxHn0>6%4&~-0i&mEWK=R z=(st0Sc1H5%_xWGZrpf$LmBu?*FR%>F2GdB03(Y*D;tsmzC6CwAJ>^#d#A>wPmkd-aNTg)dzX? z`#pRc@iy5GHjihIob3++F$!+OAq${kY32DJ;>iQIZr|Pz@sMG%8EN@+rS2;Y%@k`B zc9;bBc(&iW0xKC^$au7cDD-O@)NU_S1!2uLu_$dAm~tJ)kTVnalglTIRxs#*V|=y# z&dxbZjF&1Ki+VKdDo4qik!#-ApNS9DclXk`W)cw<|x}ejcA=(FSRgLoO)P zFJxhrudLxHek6jj^5IycN^UKrT5Hn$$sR*LBs0nA^ZTjJG=VO z_cFT`;Lvap54zFw`cO(PCctoXYv4YDph@x|Rg}qP2(fP&ms!NP05o`D1`+HH!=lbU zeI#}PEmJP0-LaHBXIm)vh{k?!PpYw2tO)kV zp9nb1GKa8Vtuj`G>UE?JR1LwVLkJA}8I)IbvqCs506}t^}EK zKO)?B2Q2F#)dn(0{u!6p*1jz|z&i={t^WSi_IW&2RX0ig8hn$7wmPRUKTQKaulW%# z=#txjL)}*Wid*g0LXj+9TxTb$+4p0@Rfwk4hBrG=Ut?F$NPcfl>48A`FAdtT1Cb@} zwdu9(tMWH~ZIoEaW2~8W&^cq)RdCDJvx4HCwUeqg_P$yX$?V-Y7P%P5D(tb72+3R} zY+TY9tK}^(9Zl4Bb*`+KvnUKs(}G&}8i{M!n@r-5Ipyh~_QhX#<-x=li<`fY_+i{P z4xJ6_!Smp-_OlXdDY8u$9gY==fX;R@8FXp7OzOcw>`bH0W+z`6v4h0f(#aU1bgTi= z#v;&!Ht-BrJbUl6Cxfa);)>9S2x8VN~qgF<)j(7xX%r*xIExKJOd zHgt4y5>Q2*Jh^1G2Q|C>eipky3@E&SrcHcI#+sZQbK?~wrJ%>H{b&nGZP0=a78&RZ zvXVjQ&mS7}DxOQI`0$V${jdnhoamz_IqPzt;tC<`ajSCHUQr~@=XH;nRwp%n5@1P;$Yiga?%ZyegR61X4^Us$r;Gp~Q0s{&A<*`Lr27V^`SvDz=TVVnB_0b2#K+;@6lKSPT3EDSgvwZsPn zI{rkrloQoJ*?*NuTv&J^Dkg7TDa*HDA56Xzaq-k+#0xGi1K0w40p?@it>7vtIRiEAu0rh?A;8>-PD1z+$R;N z=IWj9v$z#9Y)_rvd;l_Yl96aJLnW>i4L5OTTlKE7Wp6!OOLuQQgCpB&NQ60PY!Tbt zLY7j_Y(aSZ;?q;Y{acZsk(zLZpx=!>{CiJ!u$S4=FI_Ly(&sld_p6n$Ru{b&2TuTS zX)3@{1}Fhm)k_)I;dO#a+AyUI`@3O$JD^}lZJlQNVVd3kq(}mg?<02 z0<13}a&$^v**=eMifLo_*Y?q{>wJ3U_&~7lBK`$dC9Apa)n|2FPk$j#DB3@V9JV`Gw4AK%e|)V;cp&p+ua*BujBW%u zve-+0Nk{E=NxZGeEje+<7pfgLh;BGItJf>9wr?Oj6Bo$AP*30MnV}avb(fP%Ip~PP zLap-J%A5D2wd~z|+cz5?T6k(%y4&{l*);X^b5Cv_moD|4%9ED~$Q&Mb3(#v# z{1{BtJPmwd%%BH3Kbj)|TujAd-L$7qt{O9eU)|WO(%?-^`fdeF8KM(#c;)Uw6wOK* zVtc#$z2}a?>c{;PKv|}m);utV`k?jn(4r~MXD7J5Lut-z16jUM)0I@DG+Mq%RU%F= zB?+HNttubwx?$nio?zWb9#eR-(rpCOJjp*alG~w?XqCD+(qx^CEY?_^s~`3u)ynn= zz_Vb4a@)_VL5rD-CA?fNt32ys1{z;r?{%@KEpFKPtd37o+e!?VfbpY_8X^OUd|Tgd zhPO}aB^vznY6E}iZX}>zp?hkpHiEtr*z*ns{T+Ni!(y4a1QyYo{?q>(V>bjZYKTYs8tCVm)%ZQ zVT;nA-Mi@cjuCQW+`h8k!&naf3^g0k00Q-$q>n>s$(<$_QS2<r@UC3}AZnXj@?ZUoN*AGcdZ^w1^LaYlt#YSPd?dMACAe)qZ+2`O*@}%zW zY3ChY;XAghWN8#J^)v?{8=EV^uj!2LqCp3WZZ@e8NjCJ<`(K;XHcp+DAa?i3$W3>P z+?4I|H!EwwA&$NZ6SW7kHCbNHlGMyqAB`L*xXSh)6zVz+>|1wbZ29!RmtaM@N{n(j zJ8gMiSyWz4hziH~)Mx#kliv|o;7Ek1IpHBBybDkixjDcXhwoEgO)6*w5Dd|9r>)&_ zmFS6hZV`z2j;}>Vk`R^`rm&YglTRCi_b9r;FiKoBw2*kZoN2ja270bIHsZ)zO;y!g z>Mko}OOKiTWiI27UH=WC%JSK4l^U_*L})bn?U=6A>@E83o@ddeSN57G7+g&-WnFL{ z*A|HJDySU3g*5ZXxJTs~q9`&?Me8l~Eg??+^{K?tet?pv8#x8pj;{AnHTU$hVxFJa zG~cBC_!HmS#awHfTGk^wo(4G-7~OvY-1F%- zG}+8f6f_TgYJ4_!^2s^4Z@-k$YkUHGG&OJ01jj!f0ON%$AZC~5zeKK5`iag_`ToRw zZC?Ptr^ymsAeP8RCo;trV*EU%?oH_2vpyKyUXy*Nb{6mfR*Y$w8O5Te@U~-!A2j0g z?F>=xa@^kHJZ)_crwJFu2C&S^%4IRj)1&jx6Mdvb$B}*NXxL9h=jOS%C6jK))8nL= z4N&!gK}F*vY}+dot#V(z{k5yRoqFaPVpk$y_XsQ56!W>Q7)p&M2Bf`3%3;i2Kw= zA#V1~$8q5!fTuDjDL$|)q)|{$*w91~j*X>GF6T*S*naWku>OgZ{+G5bZl^mI1y);? z)(!gHmGs<9+JqM75q0Y329wnq>uknbk0j=b06e*#U(G;>3)u76Z6E$mIAp*Z7oLCy z$=l)3!#jWThRD#OGtWsV^>R5Ly*%bdu=9Jy@J=v8EL@DgX#>-2l4SvGJMpJ2Hp&pB zbv6oDobzKAOf-zZ6X$6fFghE=nq%k3qawTL)w?NS8Dgj1Cz@afZ*(SZ87(xMaN|u% zr5)(A871MMwwW=<@)^$mA~rj#x{A?jOP5WZQ^9)sdiy3JGmMm> z1_BQZU^kJ@DWO#FTKBvv`z9CoUPcAkhPuI&D!vC$O7M~p28Jq`W{uFf%iX0umD|75 zgUsx8P+JjXY{p_PP;FRDQ_)qtm;+X|)TT$awDfBxO>8BY%K672XV#;qe6xOPOL+sL z-qSA~={~r}q;T#f zfPO=a&M_eSP)0X{3Tk|L|35)5sAd$_U}PN6CKXzheIa=Gef zFV=l)i|>gib;EmIiw{0ii(t(!5g@?wEMU8`tOCP34Rh`IjdI~-Mdi*=EIklNXrPi7 zvR2wWIo8ed5|a~D8Wr6iUXfo_tG-4rA$sE(Wi_~#aVNF6 z;N?{}oJa%r7(O8-N&F=)DJeds{NA*P=SAzv=>^Jd>$B9}o3r+Fu$;$H=?~s=%t-WZ zWl?eI_SAfe=SG_9dW&y&K(f0j;nTh|QZMc+Sed=de!qo9h-ag-JL{d^x1AZM93_Jl zI`>1x4?26uarY5IJNzu85Ceso-u~pN`hF+fTTR(a3A`EZ0y!4k$o#MF^ z5FEuVXa{mgv!}y-=Q@j^75KURw@Kj9j ziPQ_~bmsW?K9ctw-7!ig#QMN=4>MFHtV#v1_GDl_1DLI;ywC4X@|`y#*?B=dRMb(4 z*t4PH2HSK0~H|Z4SJei#>c5R>%xO4`f*Y0F605v*Q{8WR9!q zcD`5(zw2FZ;NAk-1`=?ToF{(%v+2--i=Camm6&if$Kklgp%w- zdEpI}*>T*#t?xI&pA~@!`t^G+gdiGD>s;L4^I>r_w%^a_9YYiYn(;-%uDV8wwmx6& zYwFv@Z0gJhB7m7MFQ4@mcsPWM$D_@KTYN!Jk!O*7!D(!#nfisz`9*cb_O>U$q3>}t zU$NW-PR;rBc= z%m_r6;Ndlzp83woU7`B@scIk|1ED%sXEW69%&opI9OvAr$-?n>@KWvbu|4jVf&zc2 zYhZ#HgPHknMb!Zj-LIH=z=G#TZTkbMi^_ZVAR;H5=;UeCsc02gGy@v;+f-6UKdE7A z=*+$XsR@*yVsLQU7T)2Qc?KPD7qx0Tt=Gp|gh)Lw)1)d|umfW#dS)E#{+~%oS-#gsqyEhZrwKh5y_NO}u4`lNj2c1JrUA|W2>Q0(MQk$_38i)^~ z;#Tn1X#KK?l;y`GA_g{jEc6=AKhdA-i589u8V@p2bX;RTt}CSVjm~hXMbyhYXI|L3 zFLrXn>o!6}3+5W>i3-BYh#Q2NPp9`9_VxIQ{uUinheQQg_J)l#77*ill+u7neTbwO zV+aq_9RE&5 z;<>-leYnHNvb~}Hw&97@mCne=!wcDAmrwpmy%hm2mP01&P7x97)AgtjNI3vdm0G!! z>UbZa)eT^itw)Si6ZYJrcYRZjyo>7xRl@6PYu-?qCtIi4F;&I=mpXW`$-vVyV~-^$ z*^~ysfs!yx7bS_9W z4IHSnd>pBH?h)0rdNt}TGtj)W2xtVtsB~=ELq5&<6bK{tULd0^eETD*kbs^DI)I2P z(eWooCy*u^gPOs*r~LEl!@ncPo$qSrK7DYY4O_a+(choMkWgtEM_QR$2CG`%Sawd* z0|HlQYQICp57RT3UuV&ZSZ$C`A7iA}HYgcSHBe)7i5hgd z$(XAeunfprbuLKW3AtL-lSay}D#%{Q%?>ul!%CVeXsS~v7+CsM`}Aq;`>7E9O(p%i z)gnSa8tJ8;LWMAVU3vyR1Llu=t^2?KR zr>fjOdXQVnsn~jw#8$vDTro`Ox*8~Ue^)Ck{+1H{B%m#0MbV#m3X*}|^V1s{5*I`SiZsVFA7z&y0P|BGb7#rK7&RwfI z*kT|qO{HDjToDLRyj*^%Z)844*2nf_&>$%(FbusWL8Dqx=(8VP&?Eu9Gic!*ucxea zw(f_sX>O#j?7zb&JD0Ijv#>C3mtv-Pl0qpSB4=`v&8PXO{&2S$=z^qV5G&7HM2-vS zWii?ThYv+%Rz4(rNST}_Q!>+fVY^Gu@Aqt64Lp-ya~q%2mcEOs&D?ZcfN34Ges*XV z-Vn(RyL86Jd$}5LgkK=6de%aHN(;**#jH)ujWu+3Et+lS)!Hg!H}F~|bS9pis{b)Y zHwhj*XrA%G_3%c6q{jk3Zu>tgpZ{%b{mV{U5f8HDfT8&4*dw1$n`r}riDnWSCE3f%IH$f-4OSHtvz*c8u!B4aJGOMnsA5J4K!IA@O`0O1y`Pr zcQ6%Wt9N@`b09OMh#Tgb;Tn+<7Ew_~;t1Z}tqVGIcg zrKY*m0!FR_GA|S{0D#Mq^N6=$5rc}6e%_a9y%g|fpPcs*5#=El^T-V@03e;37LuC( zltJ%_)d5ISMtbID%+Ha0#yQ^WQB4%~@Ez=ih1=50e$vv{O9gK(o*5I%S`ZB1x&7-O zlvu}$?Zxe-e3-iUXB{maJ#Sv|TKJD4`9blG3JdPCLbngxkM1{S1f6M?4nHrkj)?#Q z@?G7vy>*=4abM55r=+*-drQBP`Xu)M-W5?;W;Vp4jjvVk;znd1t79#_S^SZ4*)tbA zwTcEI8^?FLC86G1Gj@{%nmejeX4#g&i9Fky$6`Ov`&acNgB)IktgezuTdO5y9% ze_5ep*fiZA&=4mPWl@(0SEFXh;!Ee6eaebk&9}Y`%_o*y#(=W=QggCo zH8>q@$J2Zlz5ARG3!v=e$z-vf#P{oTC`=o4t>qIimZ6A4oz?d;!fB)ofC1TzqFrNY z*FNd078MhnsX`_$O$Z$(h?rO+OOnPF57z@7_D}c;=4HAhiL=0awwP?!im$&+qI-tU zIuoY_IRlKXVz%el;~aUD{gNCFObt|R^?{}<)xkSQRV@{a&Be&eg64I5i-`18^nQRI zTF;tdtm7>(0DsB}p+;?OYUHOk74k$cy}x5vrK|C^<(un+cANY@Rq0!+$d%~h%4p4+ zHyqa*{jN(u(m5JqbZ@MPhSUGYlf$DGK`-mLoH6RY_8+y}ab<_Um2Abv0)^nNAJuh2 z%HkrZ5C=5kevp+}`f$)ji{LFu-aIq-u}eZgn3B5BwnUB^+s@Cz4|i|k09GMY3cemW z#o-5A;`jbd4R;1jsKqO*3ZIW<26$|xYBnEC8d#X_1|lYGGSW58NO!bP<{~?G2fB>C zK>ap?2!pVo#zsw()8{oo0fB+i*5ju7p>v4W!l2uxnWk*82n)JFu7Q%ETT}1C&WfwF zEB5A1m8>_&Yk5QWbq(z6U!CFDBo`+a?Zfw%_I$rNQ&t4@Es!Q^7k}t@54ws~%v}!C$i#apbi=c=j;^ zssoPwSJBCX=^LVT{8!%%?29Gtwc%fn5y#8duV4E?|G=q!c)OC;-gR3Rs5tYiOlXbMHUkCN9bj_BW8Y`*`uCTHP?i~!@OMAU zaV_stQI^Pnf3eRzNSB86=`w(ym5C@mTY;}>iliIVmwUZ8>r266Y76_9&X|!bNP&^ zMizOZMa!5z1Xep|DflrOQWl(eawm9Q6>z)GTQiY$#gF4y4Oo!a<2oNDb| z8Sw+Qb9&_Rw(^BidZ>5$b>878?E4fyr7YuTpUW4DMtZiGqPIwA>C0dqG0F!oC3*Tv zmeiK_w{ZZrqZeqQ3} zgDk_$1pQtrl1QN1%>38&{PBB?kFL*$(1Q&6>W?bp>Rh);m~-lUg%h8%0yE`qvV8vh zb;8$Mdepi0Zl#fX;HsdzLSdqjYFCH2utkVLv0Y42RiaG4$X)jT9({(VC0&>22K9do zKc!)xGk;-D@SvLd;=HpD5$^MTvi|^KPIKVC`&GI?Uz8&N;MDTr8;{A1%2MYx?14e+ zl;3Wo65MURNng*R!sn+QN_oqC%DP*Pofm6p6NL`wV;&A{L-gRY~!h7XUQbC3aAjEewljgb%gFXd;?y2Eha{6+jfbhm>NzF^QcF?h|GAW1l@_bpTH3QN&whXU?D`7v>;ErH z*%bcYl(JnbiYm+m)<`TeA_A09R;xQ#zR3w|JesF|O`kly=y0@7uGFhGxe4U!s~YPP+w7_^adrD;Fw1|nqN257_PD3yhnQ$q z6U%hoHk2%Ex?DZ;PO_>?hc(5WrY8UJUY>g|kZZa}yD!No)=K(SS~`M2^s#vc<4vdC zI%b)3x7PTYdW*jn29KK%;M)9YfSuy+k>I1BMo=+At#sbS7;_7dwuzZ7_!Lo+Z0M@^ z6ev!?QQ86thqHN1X&^>S>9!G5mobcen;yz*w^Kc`wo@C>>&wUu*FsklEm=YiVs6{s z-Q=l&$u=V|Cb$t02=j(RQ`-3%#@iOzw|aIXT&7RWYN-*8ulx{;a=S-?c7ZLRJ8-d=5eH^mpT94j+skE`sDbxJ z*7uF@e0P&oLntfEu^y^J;j$d>PLQ)QP6+Gjvv}pK6(OTQSgc8l^1^9Pwzl+!AgMWD z6$>D8D`TQ^eqy6rTGH&|qy;vHV|F5fB2t3tmNC2({AZ2ySrFH!6q^oX@oqZTLw8Os zCb_nZ?5v7o?B^&c9+dUj3oZ-KsY%eF5O}^dslr`MyK+BFju&Z;AuACa-K1g$Bt$sQq(7SbM6>kv zTBC@0k7MHc+L2AlwvVlVWkizta)D%fF$oAU&o;|ap-eLYcAfqbSy3|ht11{74O4^Ug5Q{)-5 zB_7WWSQ-0&;NW92an0bgj^W-a=YnSG)fPS3E(K=LQnB^nzIRarcgEr6@aMKw?99{2jjF43&tH8TBQ33LA~ zvpjCQ%}wH&I(gaf>$!zeQF)`3U;uU`L$ULPpPcQV=yrmD<^QbI9msf!flN=a5Ms{r z^&JFx2LJF7AS;l4zP|o zT6$X&J&@NLZBPMj$Xn}Vd?a@Pa$~IBubjH<{i^Ae_M`>bQ#SwAJs9z#2+{*A1#=mD zg<^YCV@Xl#r^4|(r2$(&by(bgh`-s2pU1qLjD8aNd|ATYr_|3U&{K2Dd7%{@&=eKX zXV>%IKA$tDmLxjmayCvACPGEvQ?sFzE062g_jfo91KK~dH0mX`{iLV2mL=ie3h#tX zz5%u0s2h9JOf$^F0OKtwej=6rA;X@QZuuE-}gybL7QT*${N_n070jKxr7d_~~bKP-bOHg@#?{Q@fOIcm+R)Tqn zaKsQ0c+EOj$mtx0#FCrB?*20P{CS<488ED{duaV8sUY6mVS{nVem?3&fhPkA0n%Sp zoE?zguW`P<$STYX@FF7iDQ`$&6TNF0)5l~rwl-OZ+2Mng->2tO`~zDvGuK#6+nwa| z@bMkf$-K%bHQgYgrd{J8NuN4@5)I*P)i+Y;N|BKEG)TKDPKJQb8;3OS>Vq3EbVDY2 z5vceU%{1Q?U3z=LQ1twoEpN+6zlIC=;_V!J2_2yEDT{z-wAunf)C5v~B;a$m=#z-;#esLqSOv zn!^eQGl^`ulvTKvm56^>uJ;LeMN4kJqcg_2#8h?JnYvlt@}NB) zu=@L&d)LMQ*Zd7R91h!Yiq&N!Bk=^#{J~j=Wi+F0ef6ie`iE&LR-a}TC+r6Ke-1de zw_oWowJDupNzWfQrYEtE8E{?+9%ix6u>vSs1G3H@R|PFJNmwi3N;8ewF?ur2fCLFJ zaV|10+Kl`w9`v<VKoN;mOked4gqH zn@|oftJOw;y5IZuYiBZ9FVO_-kF8~%JjcW{y2%6N+tp~aYnI9+~;_^Uffu{!e7tA=Z6Pu{AB1o{Y9P*uo*W_ zNI31@I4@tYjCfw4T`VcwUpGvPCKZR7(d3Aumtg(7YGKsn3{#%JU;b?Nfjy}e;&3Kt zo$-9CH$=s(uH&kwgN`E;mhN?x&idxJ-IP)bQ%tX)T%(@}DK(s|{?PZ=ivP1Zj6Vdk zB)w2!PK`dIQ&t+A7>A(?xP!K?FKiDo!Ru@YwU>_ql)1lWZ`CI!ZpkmfgnJ&i^yI;gIT6N*0NQv@VBW(Fu- z&)U<13PDK<$ObvOGgvdxF3dfAvO5K7Lxs^(de@Ip;*Rg7(?5$uVcZ>}s^9B~8oz?lh%}+gXL!>PwRyA*shVsJK^=ON zlOznq5W>`SsGOPq7O*OY|1mrM_kgu;s#85H-Rns~PJZ#ash3k(VtROkBvJBd?~{|( zzyrf)_LK3_{~VJxAiW-}r?&-}xQl>gIEROLdU`?)ko17$Z|xl%Yb#bA=;IdqndZss zLqPbg;t9`)pd#?!^OrwIWz%5IH2>b)}dh)BgJ?Qv1 z5wgPdYQ_R$MlN9<^M~uFsQ5onAo+a)m&=7N^!TK)N1*hq2c;M1^GH1@-}DTf)i#z)PIVftwgWq zi85*byXO`9#Sw(Ud~o$Y1MdXN&tGUt)H_Db3Iuj z<_p?Wu{X{g_tRSG>IdO0q6dFiM0_P|^_V@3vr_r-6{GB5T%u243SU@&aB`A`#pU<3 zM)d{C8sjE~Cq#BhcqNe$DsgSt-Hl!F?G?t0%a)!t2?E~aQqzakh1NGoG3u5Ht_ySH5T#tBJ zoW`%1mOovbze~^V6c{%XC}WBG<-_ls6)WOiF(g@cVOUvi&y*nVpHd)&_&qh-p1}J2 zvNNdFqD5eLL;Pc0eo0XlO4Hy3PKN8(BHOp`-@il6u7&Pb(A#MG>(c*U`0W2Jod5Hk zw^0h3rfb5oydtBMTE#ejU2PsLR50>%JX1Zj7L}vN4agUJUNb~)#Hk!UMspMcs_rY8 zk3{dx@v|?*2k{EHksZ%|rb4%(EB~cr{!7XHmy-D}CG#IxF8@+8|D|O9;U51>$^4g+ z`TwYr`F9D#g`ejCndZ8^{EKkeQlHO0pKvy%8gxiSFm<~7{-r1E zE1!24ci(d~J0HXy%w(zQPSa&evXvF}Ib#9qX_wn2DG@qkP2fB)p(PIl{yl4r=lZ{_ zRdM~=X1S)*uYS<{FKw3G3+3KI)^hR4-hrABUyl=yF=Rg7Kv7{4mqc=g@z%;3(^|&S zWJQv`u4o_m{V>zvmP>Yh`>zxHia-F#Se+aVG zO~C3yne0Nw+mR^Py>`AK5) z2@pod{HU6*YiRJ|w7K}Am_OQa=~+(mJC|C|{22c7QfTqE%;&%Jm+PGuw+*4j%t=3* z>f$RuYTGL*X*Rt;vU+jjSH?DJ;125+K$7axv&u1-k5{ztY@=wBoN&6KCvDC?f1<2>>~ zL96yR0SRO5HMWfB2S86r)UaxoZ|Kr%gz^1Q4IZ82GfTx4DKJ8wh#EgKpiJ6xocxa7 z+B^w}5;$~J@2VpC;e$5(S(BHG47JlJwVKfRGbug)$k=R!f5y2qbye6?0>8M{(orR@OfiJUspi zd+y)T_c4N{9HieHXiz|j-uB*^LCa_wkdRCb{@NQ93gd4w>{>zk1_#!)8y4oO%QmTB z8?Wp3xFB_O7V&^YC`WE-u>f|w{jioHw3Q-*;O@6fij@fuwtatJTyAD*>fMUe%;H00 zz&~f>5MPIJp3>lQyhh5bn`}w1BS^GNplQlqNpm!^Z;-00jyg#;F>KTtzx`Dz+H#Ou z4;cSZ%4)Tgkyg>lttZ%{JEhEMOZzqvhRV4#f1 z!65y@kRRPO6o|Y3S&lPty^i>NSEsh=79t_3HGlr=bD3-2p5=-Cd+vp8Npu&pv#7gS zNpZ!bghMs@t8)`2uzDDNJ`Hb5yUiJDl8o1&wKO541o>+Skjv%IGJrudgx8kuLze%L z0bIo{%+D$5nXLxVVRzp?4!^C=mVEqn{hMXSB9)n1W(h-MDB& zt#~s3T8Mw_Up1$9pBf5Ndg5)(HgG)B%-@ z%wjb(!DVHl3s&yD!?M3N9V=Pw?ecnk^y{WdhtxH!aG|({oF+D=-UNY6e*0%V5vNOz zg1ec%oj*pkVQ$HBX$M_VNQ=`|Y5e3?^?VK?vmID9 z)3@RxYl|V{;``WCcC73tmrxMMsKm5^CSBYSc-$IGzT;I1p2^mnpAKDUiN-hv`h#50 zCQ&XNg>7Ouv-~^xmu=sR2?%Zx{65i+x|aM70oPf;fAaa@>W;p@gNfI0&e@nYqO=Uj zg?69%R#ccjw@zuPr@L9oddLbzHp?VW!UeIPcqHpoO8 zt7yx6ahy3(fmJeBaJKiP7xr4~2&j)^pxZ6y!P*^Oq!Yo5c5#uSSLToL&kB5gHQb4} zCFYtvJ%8`yyt#eBU>=CMFldt^YruXlRbp(@!(Me_xr%qPypTB;pKB*BnNa>vZ6*r6 z7U!KTtp3DEbz}CLZ^YbWlQSLN2!@y8!}D`zVIm-6`tS)J!UKaPebQW;`*x2ohI< zXD54TYPXT4AG-DeKpJ>x&5eZ4n^v~!((=r#?;Yh~4?K)_EX>WSb5*F1A4M_>s*txP z*H_3zi^)t&V??dBJAHNQe{13n+&*0?&g7Pre31*J+p3E(6y{^*MEN&Hbmz>**4huB zDrEWGOrDoM@JAAZb42Dv+zbjqz%_;xWGKpo%iClDk?(QL6kxwaPZX|zscs4 zP~~4Mc$7Bpoae?oR|hdUsJqj>gk&UT@@_TpzKp!Q&M4o-J_nN&VF|A03r)5Tnq4mm zNp2CLCf$aK_3}N;RrikD?q$W$jvMiYJ;}&)&Zp`X=CO2Qqx?9Wy ze!2XF_yw5@PbVcM0|Hq@d~^p#GHDM7=;LysvUAPI3?O&e?Rr{#@w4NCv(-4V?sQ+e z2eb(SHAn=Po{6D1m)rKKSXsA#)3!rMLqknVx-Em&anNMz=_mnsHn<83p?uKbGlhb* z1Od_XFkM|ZyL!4TC$H33i=#sEDTJIxrgua{;lt<$%8}BTAGGel4VpcC8_)d#A3q2S zuFhi0DzG!E6)xwNzcQ3|8CDM)jjY_EAeRA*dwwG6_pQMNGWnYangqJf15W^3Ew zhA!d(F1|*5KAQRs2sh<0V?OW?~ZT~Hs!5K6msc@$3Tko zd{rpsbOto&84Ny2w6zs_8BWIxj`uNl2n`#Fi2{gk?tm?9;B21W7he)_i-1{i zm(f9^DIFcxg^)9hQc%gPN@U;fL2&geB3Hih&4Gr@df^GhY5T7k^ZUg_$mz7lB)oU0O^zev&q&W2NsblhUfhZY*# zOcbg)5>iao*5at8SVA^;=-pIxSi!g-276I(p@gHL>xr!a~_v9YP`A z+_u;vNk#C8{#F=q8@IMub@Xq^st?vcMxSjihufjuhc9Hn{$mm@vu}E<@W|bQL*#3P zeNIA%an}D;i3Mci;^?~8yizWFi#|}?D*?7F`aNo6rgEhoxjLZve@L&0PcL5J`4T$;)BJC41vPk%(teE8yr+>nr&K~DIADH zho1UVv$Xr;y{M{GzxrJWH*S?J_Nj~JAaQi?F7j}0cpkkZjXUx2mn_+j>sy-K&b?b` zq_O}q;3F%7Cn;J@4C@<#Ys$BfDRI6iY8n=XEVr9c#JGh`aI6)4kpyZ1E8<9pRPJ1* z8z>U_D8XL6@3D}#WYX`?x*Cib{c5Ew3n`W2-fW}JeqQD`sUj`v{pJSnW_>9cDm!FMRTDo5g~uZp_6xa+i)Kt+8T&TSfH}G6q6oOGh^)iJ-uX}lS}G!6W3MZ zCUJh(@AmG6@fSvSD=f}pN#^nM{oFr98su-NlDN+?JSEQ9`=i7T0df>r1Uk5QhUiFd z-B?uFV#B9ixt(?Lc!5u_oY5xgpmD%GZbV`l5@OU}6Tahrd&`Sn_Mph|!|Pk| z>tfpZ$-_n-HLPttX%f!UgzSDvchf|0*OKWaJ5!r8j{!i`Kc2&Em=kYoNKkY^m1M(j zR@(YImf>`JREorb`p4d>226wbY>r59p83zwes7Qs&fMbxfqs1aC}i3?0|EC-YW{ZS(4I9ZlA<>0ctL0aQCxkpfcZcBHh)|E9zPzS z|GI+9cK^*f|FFwr#_v1!>VNNdudj(bE(e@F=XkLEXY`jS_dcIJ?{1j09#H@eeLPU# r4O*ahVeR~9vw*!I2Lozl`hW6qiOU<3V@*