diff --git a/TensorFlow/contrib/cv/CSGM_ID2109_for_TensorFlow/README.md b/TensorFlow/contrib/cv/CSGM_ID2109_for_TensorFlow/README.md index e8bd391485e72929096a71c755892b3b3009e442..12f47ff8f2b9aa280f3db2d0d02112066ee89307 100644 --- a/TensorFlow/contrib/cv/CSGM_ID2109_for_TensorFlow/README.md +++ b/TensorFlow/contrib/cv/CSGM_ID2109_for_TensorFlow/README.md @@ -112,6 +112,7 @@ To train: a.请用户自行准备好数据集，包含训练集和验证集两部分，数据集包括Mnist等，包含train和 val两部分。以Mnist数据集为例。 b.上传数据压缩包到训练环境上,无需解压 +``` ``` ├── /datasets/mnist @@ -166,43 +167,72 @@ Extracting cache/dataset/t10k-images-idx3-ubyte.gz Extracting cache/dataset/t10k-labels-idx1-ubyte.gz start training 2021-12-08 13:13:49.596612: I /home/jenkins/agent/workspace/Compile_GraphEngine_Centos_ARM/tensorflow/tf_adapter/kernels/geop_npu.cc:765] The model has been compiled on the Ascend AI processor, current graph id is:11 -step 0, loss = 621.13 (3.9 examples/sec; 25.653 sec/batch) -step 0, loss = 473.88 (15663.8 examples/sec; 0.006 sec/batch) -step 0, loss = 393.31 (22854.8 examples/sec; 0.004 sec/batch) -step 0, loss = 329.65 (23974.3 examples/sec; 0.004 sec/batch) -step 0, loss = 269.96 (24415.3 examples/sec; 0.004 sec/batch) -step 0, loss = 244.54 (24506.6 examples/sec; 0.004 sec/batch) -step 0, loss = 240.48 (23996.2 examples/sec; 0.004 sec/batch) -step 0, loss = 232.23 (24784.6 examples/sec; 0.004 sec/batch) -step 0, loss = 231.09 (25579.7 examples/sec; 0.004 sec/batch) -step 0, loss = 227.22 (26723.8 examples/sec; 0.004 sec/batch) -step 0, loss = 234.37 (24330.3 examples/sec; 0.004 sec/batch) -step 0, loss = 228.46 (23971.6 examples/sec; 0.004 sec/batch) -step 0, loss = 224.78 (25015.2 examples/sec; 0.004 sec/batch) -step 0, loss = 237.21 (24771.5 examples/sec; 0.004 sec/batch) -step 0, loss = 228.91 (24708.7 examples/sec; 0.004 sec/batch) -step 0, loss = 218.84 (24153.8 examples/sec; 0.004 sec/batch) -step 0, loss = 213.00 (24834.5 examples/sec; 0.004 sec/batch) -step 0, loss = 214.56 (25046.6 examples/sec; 0.004 sec/batch) -step 0, loss = 217.74 (25662.7 examples/sec; 0.004 sec/batch) -step 0, loss = 217.85 (24633.3 examples/sec; 0.004 sec/batch) -step 0, loss = 210.44 (24662.2 examples/sec; 0.004 sec/batch) -step 0, loss = 208.78 (24595.7 examples/sec; 0.004 sec/batch) -step 0, loss = 208.42 (24978.0 examples/sec; 0.004 sec/batch) -step 0, loss = 210.64 (24394.0 examples/sec; 0.004 sec/batch) -step 0, loss = 210.62 (24500.9 examples/sec; 0.004 sec/batch) -step 0, loss = 214.60 (25016.7 examples/sec; 0.004 sec/batch) -step 0, loss = 207.89 (24577.0 examples/sec; 0.004 sec/batch) -step 0, loss = 202.35 (24890.5 examples/sec; 0.004 sec/batch) -step 0, loss = 205.51 (24786.1 examples/sec; 0.004 sec/batch) -step 0, loss = 211.48 (23782.6 examples/sec; 0.004 sec/batch) -step 0, loss = 216.67 (24902.4 examples/sec; 0.004 sec/batch) -step 0, loss = 212.29 (24771.5 examples/sec; 0.004 sec/batch) -step 0, loss = 204.17 (24849.2 examples/sec; 0.004 sec/batch) -step 0, loss = 210.40 (24969.1 examples/sec; 0.004 sec/batch) -step 0, loss = 210.41 (29371.9 examples/sec; 0.003 sec/batch) -step 0, loss = 203.19 (28197.0 examples/sec; 0.004 sec/batch) -step 0, loss = 210.02 (28771.5 examples/sec; 0.003 sec/batch) +step 12, loss = 103.19 (51654.0 examples/sec; 0.002 sec/batch) +step 12, loss = 103.04 (53065.6 examples/sec; 0.002 sec/batch) +step 12, loss = 98.33 (51807.1 examples/sec; 0.002 sec/batch) +step 12, loss = 100.44 (52187.4 examples/sec; 0.002 sec/batch) +step 12, loss = 98.23 (52911.6 examples/sec; 0.002 sec/batch) +step 12, loss = 103.22 (52083.7 examples/sec; 0.002 sec/batch) +step 12, loss = 102.44 (51974.0 examples/sec; 0.002 sec/batch) +step 12, loss = 103.07 (52389.5 examples/sec; 0.002 sec/batch) +step 12, loss = 99.82 (52435.4 examples/sec; 0.002 sec/batch) +step 12, loss = 100.67 (52402.6 examples/sec; 0.002 sec/batch) +step 12, loss = 103.34 (52851.6 examples/sec; 0.002 sec/batch) +step 12, loss = 104.92 (52632.8 examples/sec; 0.002 sec/batch) +step 12, loss = 99.91 (51539.7 examples/sec; 0.002 sec/batch) +step 12, loss = 101.96 (53410.2 examples/sec; 0.002 sec/batch) +step 12, loss = 103.25 (53166.5 examples/sec; 0.002 sec/batch) +step 12, loss = 107.41 (53335.5 examples/sec; 0.002 sec/batch) +step 12, loss = 106.33 (53546.6 examples/sec; 0.002 sec/batch) +step 12, loss = 104.44 (52291.5 examples/sec; 0.002 sec/batch) +step 12, loss = 97.90 (51609.5 examples/sec; 0.002 sec/batch) +step 12, loss = 101.81 (52298.1 examples/sec; 0.002 sec/batch) +step 12, loss = 104.01 (51590.5 examples/sec; 0.002 sec/batch) +step 12, loss = 98.72 (52032.1 examples/sec; 0.002 sec/batch) +step 12, loss = 98.53 (53200.2 examples/sec; 0.002 sec/batch) +step 12, loss = 98.74 (52317.6 examples/sec; 0.002 sec/batch) +step 12, loss = 105.42 (52232.9 examples/sec; 0.002 sec/batch) +step 12, loss = 103.77 (52665.8 examples/sec; 0.002 sec/batch) +step 12, loss = 102.57 (52396.1 examples/sec; 0.002 sec/batch) +step 12, loss = 99.36 (53234.0 examples/sec; 0.002 sec/batch) +step 12, loss = 101.95 (53615.0 examples/sec; 0.002 sec/batch) +step 12, loss = 105.18 (52422.2 examples/sec; 0.002 sec/batch) +step 12, loss = 102.93 (51704.9 examples/sec; 0.002 sec/batch) +step 12, loss = 100.61 (52369.9 examples/sec; 0.002 sec/batch) +step 12, loss = 106.17 (51225.0 examples/sec; 0.002 sec/batch) +step 12, loss = 102.04 (51813.5 examples/sec; 0.002 sec/batch) +step 12, loss = 107.66 (52369.9 examples/sec; 0.002 sec/batch) +step 12, loss = 109.57 (51673.1 examples/sec; 0.002 sec/batch) +step 12, loss = 104.66 (51388.2 examples/sec; 0.002 sec/batch) +step 12, loss = 101.40 (52514.1 examples/sec; 0.002 sec/batch) +step 12, loss = 99.98 (51337.9 examples/sec; 0.002 sec/batch) +step 12, loss = 103.62 (51916.1 examples/sec; 0.002 sec/batch) +step 12, loss = 101.46 (53105.9 examples/sec; 0.002 sec/batch) +step 12, loss = 104.52 (52599.7 examples/sec; 0.002 sec/batch) +step 12, loss = 99.36 (52396.1 examples/sec; 0.002 sec/batch) +step 12, loss = 95.70 (52884.9 examples/sec; 0.002 sec/batch) +step 12, loss = 103.42 (47148.2 examples/sec; 0.002 sec/batch) +step 12, loss = 102.05 (50889.4 examples/sec; 0.002 sec/batch) +step 12, loss = 104.18 (52441.9 examples/sec; 0.002 sec/batch) +step 12, loss = 102.44 (52109.6 examples/sec; 0.002 sec/batch) +step 12, loss = 101.26 (51546.1 examples/sec; 0.002 sec/batch) +step 12, loss = 102.05 (52468.2 examples/sec; 0.002 sec/batch) +step 12, loss = 102.58 (52051.4 examples/sec; 0.002 sec/batch) +step 12, loss = 99.98 (52540.4 examples/sec; 0.002 sec/batch) +step 12, loss = 103.54 (53553.4 examples/sec; 0.002 sec/batch) +step 12, loss = 102.53 (53342.3 examples/sec; 0.002 sec/batch) +step 12, loss = 104.03 (52402.6 examples/sec; 0.002 sec/batch) +step 12, loss = 99.35 (52805.0 examples/sec; 0.002 sec/batch) +step 12, loss = 104.60 (51903.3 examples/sec; 0.002 sec/batch) +step 12, loss = 104.71 (51896.9 examples/sec; 0.002 sec/batch) +step 12, loss = 103.18 (52945.0 examples/sec; 0.002 sec/batch) +step 12, loss = 98.93 (52019.1 examples/sec; 0.002 sec/batch) +step 12, loss = 101.61 (51858.4 examples/sec; 0.002 sec/batch) +step 12, loss = 105.64 (52187.4 examples/sec; 0.002 sec/batch) +step 12, loss = 98.17 (52070.8 examples/sec; 0.002 sec/batch) +step 12, loss = 101.07 (51698.6 examples/sec; 0.002 sec/batch) +step 12, loss = 96.97 (52858.3 examples/sec; 0.002 sec/batch) +step 12, loss = 101.28 (52045.0 examples/sec; 0.002 sec/batch) ```

精度指标

diff --git a/TensorFlow/contrib/cv/CSGM_ID2109_for_TensorFlow/mnist_vae/src/main.py b/TensorFlow/contrib/cv/CSGM_ID2109_for_TensorFlow/mnist_vae/src/main.py index 57e1d382f566ced1778e45e2050a190b3cd59590..8fb84d4058509dd49c01325b11e3a4756c3f3961 100644 --- a/TensorFlow/contrib/cv/CSGM_ID2109_for_TensorFlow/mnist_vae/src/main.py +++ b/TensorFlow/contrib/cv/CSGM_ID2109_for_TensorFlow/mnist_vae/src/main.py @@ -43,7 +43,13 @@ import time from npu_bridge.npu_init import * flags = tf.flags FLAGS = flags.FLAGS - +config = tf.ConfigProto() +custom_op = config.graph_options.rewrite_options.custom_optimizers.add() +custom_op.name = "NpuOptimizer" +custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") +#关闭日志级别 +os.system('export ASCEND_SLOG_PRINT_TO_STDOUT=0') +os.system('export ASCEND_GLOBAL_LOG_LEVEL=3') #import argparse #import moxing as mox # 解析输入参数data_url diff --git a/TensorFlow/contrib/cv/DMSP_ID1290_for_Tensorflow/README.md b/TensorFlow/contrib/cv/DMSP_ID1290_for_Tensorflow/README.md deleted file mode 100644 index e729540505203c94f4fb1b63c3e78231ad10de62..0000000000000000000000000000000000000000 --- a/TensorFlow/contrib/cv/DMSP_ID1290_for_Tensorflow/README.md +++ /dev/null @@ -1,241 +0,0 @@ -DMSP - -- [基本信息](#基本信息.md) -- [概述](#概述.md) -- [默认配置](#默认配置.md) -- [训练过程](#训练过程.md) -- [精度指标](#精度指标.md) -

基本信息

- -**发布者（Publisher）：Huawei** - -**应用领域（Application Domain）：Image Restorationg** - -**版本（Version）：1.1** - -**修改时间（Modified） ：2022.0224.20** - -**大小（Size）：25M** - -**框架（Framework）：TensorFlow 1.15.0** - -**模型格式（Model Format）：ckpt** - -**精度（Precision）：Mixed** - -**处理器（Processor）：昇腾910** - -**应用级别（Categories）：Official** - -**描述（Description）：基于TensorFlow框架的用于图像恢复的深度均值偏移先验代码** - -

概述

- -在本文中，作者介绍了一个自然图像先验，它直接表示自然图像分布的高斯平滑版本。 作者将先验包含在图像恢复的公式中，作为贝叶斯估计器，这允许解决噪声盲图像恢复问题。 实验表明先验梯度对应于自然图像分布上的均值偏移向量。 此外，作者使用去噪自编码器学习均值偏移向量场，并将其用于梯度下降方法以执行贝叶斯风险最小化。 论文展示了噪声盲去模糊、超分辨率和去马赛克的竞争结果 -- 参考论文： - - [Deep Mean-Shift Priors for Image Restoration](https://arxiv.org/abs/1709.03749) - -- 参考实现： - - - -- Tensorflow的实现： - - [https://github.com/siavashBigdeli/DMSP-tensorflow](https://github.com/siavashBigdeli/DMSP-tensorflow) - - -- 通过Git获取对应commit\_id的代码方法如下： - - ``` - git clone {repository_url} # 克隆仓库的代码 - cd {repository_name} # 切换到模型的代码仓目录 - git checkout {branch} # 切换到对应分支 - git reset --hard ｛commit_id｝ # 代码设置到对应的commit_id - cd ｛code_path｝ # 切换到模型代码所在路径，若仓库下只有该模型，则无需切换 -## 默认配置 - -- 训练数据集预处理（以ImageNet训练集为例，仅作为用户参考示例）： - - - 图像的输入尺寸统一为(180，180，3) - -- 测试数据集预处理（以Berkeley验证集为例，仅作为用户参考示例） - - - 图像的输入尺寸为统一为(180，180，3) - -- 训练超参 - - - Batch size: 1 - - Gaussian noise levels = 11 - - Learning rate(LR): 0.01 - - momentum : 0.9 - - Optimizer: SGD with Momentum - - Train epoch: 300 iterations - - **npu实现：** - -##### 支持特性 - -支持混合精度训练，脚本中默认开启了混合精度，参考示例，见“开启混合精度”。 - -##### 混合精度训练 - -昇腾910 AI处理器提供自动混合精度功能，可以针对全网中float32数据类型的算子，按照内置的优化策略，自动将部分float32的算子降低精度到float16，从而在精度损失很小的情况下提升系统性能并减少内存使用。 - --开启混合精度 - -``` -custom_op.parameter_map["precision_mode"].s = tf.compat.as_bytes("allow_mix_precision") -``` - - -### 准备工作 - -##### 训练环境的准备 - -硬件环境：Ascend: 1*Ascend 910(32GB) | ARM: 24 核 96GB - -运行环境：ascend-share/5.0.3.alpha005_tensorflow-ascend910-cp37-euleros2.8-aarch64-training:1.15.0-21.0.2_1101 - -``` -## 训练 - -To train: - -#### 环境依赖 - -制作数据集的环境上已安装Python3.7和TensorFlow 1.15.0。 - -#### 操作步骤 - -1. 数据集准备。 - - a.请用户自行准备好数据集，包含训练集和验证集两部分，数据集包括Mnist等，包含train和 val两部分。以Mnist数据集为例。 - - b.上传数据压缩包到训练环境上,无需解压 - - - ├── /datasets/imagenet - │ ├──imagenet - │ ├──Berkeley - ``` - - ``` -## 脚本和示例代码 - -``` -``` -├── src -│ ├──config.py //训练定义 -│ ├──DAE.py //模型定义 -│ ├──DAE_model.py //重载模型 -│ ├──demo_DMSP.py //主程序 -│ ├──DMSPDeblur.py //先验去噪 -│ ├──network.py //其他功能函数 -│ ├──ops.py //算子定义 -``` -2. 模型训练。 - - 运行脚本如下： - -```shell -$ python ./src/demo_DMSP.py -``` - -3. 使用pycharm在ModelArts训练启动文件为： - - ``` - /src/demo_DMSP.py - ``` -## 训练过程 - -通过“模型训练”中的训练指令启动单卡训练 -``` -2022-02-23 22:32:03.855277: W tensorflow/core/platform/profile_utils/cpu_utils.cc:98] Failed to find bogomips in /proc/cpuinfo; cannot determine CPU frequency -2022-02-23 22:32:03.864021: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xaaaade38ad00 initialized for platform Host (this does not guarantee that XLA will be used). Devices: -2022-02-23 22:32:03.864068: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version -============start non-blind deblurring on Berkeley segmentation dataset============== -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:37: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead. - -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:37: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead. - -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:38: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead. - -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:38: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead. - -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:42: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead. - -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:42: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead. - -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:7: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. -Instructions for updating: -Call initializer instance with the dtype argument instead of passing it to the constructor -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:7: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. -Instructions for updating: -Call initializer instance with the dtype argument instead of passing it to the constructor -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:8: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead. - -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:8: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead. - -====================dae================ -{'layer0': , 'layer1': , 'layer2': , 'layer3': , 'layer4': , 'layer5': , 'layer6': , 'layer7': , 'layer8': , 'layer9': , 'layer10': , 'layer11': , 'layer12': , 'layer13': , 'layer14': , 'layer15': , 'layer16': , 'layer17': , 'layer18': , 'layer19': , 'layer20': , 'layer21': , 'layer22': , 'layer23': , 'layer24': , 'layer25': , 'layer26': , 'layer27': , 'layer28': , 'layer29': , 'layer30': , 'layer31': , 'layer32': , 'layer33': , 'layer34': , 'layer35': , 'layer36': , 'layer37': , 'layer38': } -====================dae output========= -Tensor("strided_slice_1:0", shape=(?, ?, ?, 3), dtype=float32) -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:51: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead. - -WARNING:tensorflow:From /home/ma-user/modelarts/user-job-dir/code/dmsp-tensorflow/DAE_model.py:51: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead. - -2022-02-23 22:32:14.897055: I /home/jenkins/agent/workspace/Compile_GraphEngine_Centos_ARM/tensorflow/tf_adapter/kernels/geop_npu.cc:694] The model has been compiled on the Ascend AI processor, current graph id is:1 -Initialized with PSNR: 17.78958876047073 -2022-02-23 22:32:40.320450: I /home/jenkins/agent/workspace/Compile_GraphEngine_Centos_ARM/tensorflow/tf_adapter/kernels/geop_npu.cc:694] The model has been compiled on the Ascend AI processor, current graph id is:11 -Finished psnr = 27.65 (1.5 examples/sec; 0.646 sec/batch) -Initialized with PSNR: 21.71935774044799 -Finished psnr = 29.31 (1.4 examples/sec; 0.697 sec/batch) -Initialized with PSNR: 12.418238314349477 -Finished psnr = 21.70 (1.4 examples/sec; 0.704 sec/batch) -Initialized with PSNR: 17.761670521195924 -Finished psnr = 27.69 (1.5 examples/sec; 0.672 sec/batch) -Initialized with PSNR: 23.028104067351563 -Finished psnr = 32.53 (1.4 examples/sec; 0.704 sec/batch) -Initialized with PSNR: 15.075084013742561 -Finished psnr = 27.08 (1.4 examples/sec; 0.703 sec/batch) -Initialized with PSNR: 17.302924438930848 -Finished psnr = 24.16 (1.2 examples/sec; 0.824 sec/batch) -Initialized with PSNR: 17.10059787725738 -Finished psnr = 25.20 (1.3 examples/sec; 0.751 sec/batch) -Initialized with PSNR: 16.07467978560146 -Finished psnr = 25.66 (1.4 examples/sec; 0.712 sec/batch) -Initialized with PSNR: 15.523285818788821 -Finished psnr = 25.79 (1.4 examples/sec; 0.718 sec/batch) -Initialized with PSNR: 20.173765682212093 -Finished psnr = 33.91 (1.5 examples/sec; 0.688 sec/batch) -Initialized with PSNR: 17.809478987327715 -Finished psnr = 29.48 (1.6 examples/sec; 0.640 sec/batch) -Initialized with PSNR: 18.0941733503732 -Finished psnr = 33.18 (1.4 examples/sec; 0.702 sec/batch) -Initialized with PSNR: 17.11170706335929 -Finished psnr = 24.92 (1.4 examples/sec; 0.705 sec/batch) -Initialized with PSNR: 16.409065638468267 -Finished psnr = 29.45 (1.4 examples/sec; 0.727 sec/batch) -Initialized with PSNR: 16.58872443970573 -Finished psnr = 26.77 (1.4 examples/sec; 0.702 sec/batch) -Initialized with PSNR: 16.632015946049982 -Finished psnr = 28.54 (1.2 examples/sec; 0.805 sec/batch) -Initialized with PSNR: 14.895557404412923 -Finished psnr = 25.84 (1.3 examples/sec; 0.741 sec/batch) -Initialized with PSNR: 17.557421710572992 -Finished psnr = 25.67 (1.4 examples/sec; 0.702 sec/batch) -Initialized with PSNR: 23.73822886222646 -Finished psnr = 31.20 (1.1 examples/sec; 0.895 sec/batch) -Initialized with PSNR: 14.288116614544533 -Finished psnr = 21.96 (1.4 examples/sec; 0.735 sec/batch) -Initialized with PSNR: 19.533104118880125 -Finished psnr = 28.99 (1.4 examples/sec; 0.710 sec/batch) -``` -

精度指标

- -精度：psnr: - -|gpu|npu|原论文| -|:----:|:----:|:----:| -|26.06|26.06|26.00| \ No newline at end of file diff --git a/TensorFlow/contrib/cv/DMSP_ID1290_for_Tensorflow/src/DMSPDeblur.py b/TensorFlow/contrib/cv/DMSP_ID1290_for_Tensorflow/src/DMSPDeblur.py deleted file mode 100644 index 4052bf652080f3e78c0e5cb111bb0201e1c67365..0000000000000000000000000000000000000000 --- a/TensorFlow/contrib/cv/DMSP_ID1290_for_Tensorflow/src/DMSPDeblur.py +++ /dev/null @@ -1,167 +0,0 @@ -# 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 time -import matplotlib as mpl -mpl.use('Agg') -import numpy as np -import scipy.signal as sig -import tensorflow as tf - - -def computePSNR(img1, img2, pad_y, pad_x): - """ Computes peak signal-to-noise ratio between two images. - Input: - img1: First image in range of [0, 255]. - img2: Second image in range of [0, 255]. - pad_y: Scalar radius to exclude boundaries from contributing to PSNR computation in vertical direction. - pad_x: Scalar radius to exclude boundaries from contributing to PSNR computation in horizontal direction. - - Output: PSNR """ - - img1_u = (np.clip(np.squeeze(img1), 0, 255.0)[pad_y:-pad_y,pad_x:-pad_x,:]).astype(dtype=np.uint8) - img2_u = (np.clip(np.squeeze(img2), 0, 255.0)[pad_y:-pad_y,pad_x:-pad_x,:]).astype(dtype=np.uint8) - imdiff = (img1_u).astype(dtype=np.float32) - (img2_u).astype(dtype=np.float32) - rmse = np.sqrt(np.mean(np.power(imdiff[:], 2))) - return 20.0 * np.log10(255.0 / rmse) - -def filter_image(image, kernel, mode='valid'): - """ Implements color filtering (convolution using a flipped kernel) """ - chs = [] - for d in range(image.shape[2]): - channel = sig.convolve2d(image[:,:,d], np.flipud(np.fliplr(kernel)), mode=mode) - chs.append(channel) - return np.stack(chs, axis=2) - -def convolve_image(image, kernel, mode='valid'): - """ Implements color image convolution """ - chs = [] - for d in range(image.shape[2]): - channel = sig.convolve2d(image[:,:,d], kernel, mode=mode) - chs.append(channel) - return np.stack(chs, axis=2) - - -def DMSPDeblur(degraded, kernel, sigma_d, params): - """ Implements stochastic gradient descent (SGD) Bayes risk minimization for image deblurring described in: - "Deep Mean-Shift Priors for Image Restoration" (http://home.inf.unibe.ch/~bigdeli/DMSPrior.html) - S. A. Bigdeli, M. Jin, P. Favaro, M. Zwicker, Advances in Neural Information Processing Systems (NIPS), 2017 - - Input: - degraded: Observed degraded RGB input image in range of [0, 255]. - kernel: Blur kernel (internally flipped for convolution). - sigma_d: Noise standard deviation. (set to -1 for noise-blind deblurring) - params: Set of parameters. - params.denoiser: The denoiser function hanlde. - - Optional parameters: - params.sigma_dae: The standard deviation of the denoiser training noise. default: 11 - params.num_iter: Specifies number of iterations. - params.mu: The momentum for SGD optimization. default: 0.9 - params.alpha the step length in SGD optimization. default: 0.1 - - Outputs: - res: Solution.""" - - if 'denoiser' not in params: - raise ValueError('Need a denoiser in params.denoiser!') - - if 'gt' in params: - print_iter = True - else: - print_iter = False - - if 'sigma_dae' not in params: - params['sigma_dae'] = 11.0 - - if 'num_iter' not in params: - params['num_iter'] = 10 - - if 'mu' not in params: - params['mu'] = 0.9 - - if 'alpha' not in params: - params['alpha'] = 0.1 - - pad_y = np.floor(kernel.shape[0] / 2.0).astype(np.int64) - pad_x = np.floor(kernel.shape[1] / 2.0).astype(np.int64) - res = np.pad(degraded, pad_width=((pad_y, pad_y), (pad_x, pad_x), (0, 0)), mode='edge').astype(np.float32) - - step = np.zeros(res.shape) - - if print_iter: - psnr = computePSNR(params['gt'], res, pad_y, pad_x) - print('Initialized with PSNR: ' + str(psnr)) - - for iter in range(params['num_iter']): - if print_iter: - # print('Running iteration: ' + str(iter)) - t = time.time() - - # compute prior gradient - noise = np.random.normal(0.0, params['sigma_dae'], res.shape).astype(np.float32) - - #离线推理 - input_image = res + noise - input_image.tofile("/cache/model/dmsp_input_image.bin") # 处理后的图片保存为bin文件 - - rec = params['denoiser'].denoise(res + noise,False) - prior_grad = res - rec - - # compute data gradient - map_conv = filter_image(res, kernel) - data_err = map_conv - degraded - data_grad = convolve_image(data_err, kernel, mode='full') - - relative_weight = 0.5 - if sigma_d < 0: - sigma2 = 2 * params['sigma_dae'] * params['sigma_dae'] - - data_sum = np.sum(np.power(np.int64(data_err[:]), 2)) - kernel_sum = np.sum(np.power(np.int64(kernel[:]), 2)) - lambda_ = (degraded.size) / (data_sum - + degraded.size * sigma2 * kernel_sum) - relative_weight = lambda_ / (lambda_ + 1 / params['sigma_dae'] / params['sigma_dae']) - else: - relative_weight = (1 / sigma_d / sigma_d) / ( - 1 / sigma_d / sigma_d + 1 / params['sigma_dae'] / params['sigma_dae']) - # sum the gradients - grad_joint = data_grad * relative_weight + prior_grad * (1 - relative_weight) - - # update - step = params['mu'] * step - params['alpha'] * grad_joint - res = res + step - res = np.minimum(255.0, np.maximum(0, res)).astype(np.float32) - - psnr = computePSNR(params['gt'], res, pad_y, pad_x) - if print_iter: - # print ('PSNR is: ' + str(psnr) + ', iteration finished in ' + str(time.time() - t) + ' seconds') - print('Finished psnr = %.2f (%.1f examples/sec; %.3f sec/batch)' % ( - ( psnr, 1 / (time.time() - t), (time.time() - t)))) - - - return res,psnr